<commit_before># Kaggle_HomeDepot
Turing Test's Solution for Home Depot Product Search Relevance Competition on Kaggle (https://www.kaggle.com/c/home-depot-product-search-relevance)

## Submission
| Submission | CV RMSE | Public LB RMSE | Private LB RMSE | Position |
| :---: | :-------: | :--------------: | :---------------: | :------------------: |
| [Best Single Model from Chenglong](./Output/Subm/test.pred.[Feat@basic_nonlinear_201604210409]_[Learner@reg_xgb_tree]_[Id@84].[Mean0.438318]_[Std0.000786].csv) | 0.43832 | 0.43996 | 0.43811 | 9 |
| [Best Ensemble Model from Igor and Kostia](./Output/Subm/submission_kostia + igor final_ensemble (1 to 3 weights).csv) | - | 0.43819 | 0.43704 | 8 |
| [Best Ensemble Model from Chenglong](./Output/Subm/test.pred.[Feat@level2_meta_linear_201605030922]_[Learner@reg_ensemble]_[Id@1].[Mean0.436087]_[Std0.001027].csv) | 0.43609 | 0.43582 | 0.43325 | 4 |
| [Best Final Ensemble Model](./Output/Subm/reproduced_blend_0.438_0.436CV.csv) | - | 0.43465 | 0.43248 | 3 |

## FlowChart

## Documentation

See `./Doc/Kaggle_HomeDepot_Turing_Test.pdf` for documentation.

## Instruction

### Chenglong's Part
Before proceeding, one should place all the data from the [competition website](https://www.kaggle.com/c/home-depot-product-search-relevance/data) into folder `./Data`.

Note that in the following, all the commands and scripts are executed and run in directory `./Code/Chenglong`.

#### Step 1. Install Dependencies
#####1. Python
We used Python 3.5.1 and modules comes with [Anaconda 2.4.1 (64-bit)](https://repo.continuum.io/archive/Anaconda3-2.4.1-Linux-x86_64.sh). In addition, we also used the following libraries and modules:
- [gensim 0.12.4](https://github.com/piskvorky/gensim/archive/0.12.4.tar.gz)
- [hyperopt 0.0.3.dev](https://github.com/hyperopt/hyperopt)
- [keras 0.3.2](https://github.com/fchollet/keras/archive/0.3.2.tar.gz)
- [matplotlib-venn 0.11.3](https://pypi.python.org/pypi/matplotlib-venn)
- [python-Levenshtein 0.12.0](https://pypi.python.org/pypi/python-Levenshtein/0.12.0)
- [regex 2.4.85](https://pypi.python.org/pypi/regex)
- [xgboost 0.4](https://github.com/dmlc/xgboost/archive/v0.40.tar.gz)

#####2. R
We used the following packages installed via `install.packages()`:
- data.table
- Rtsne
<commit_msg>add logs for feature & update figs<commit_after># Kaggle_HomeDepot
Turing Test's Solution for Home Depot Product Search Relevance Competition on Kaggle (https://www.kaggle.com/c/home-depot-product-search-relevance)

## Submission
| Submission | CV RMSE | Public LB RMSE | Private LB RMSE | Position |
| ---: | :-------: | :--------------: | :---------------: | :------------------: |
| [Best Single Model from Chenglong](./Output/Subm/test.pred.[Feat@basic_nonlinear_201604210409]_[Learner@reg_xgb_tree]_[Id@84].[Mean0.438318]_[Std0.000786].csv) | 0.43832 | 0.43996 | 0.43811 | **9** |
| [Best Ensemble Model from Igor and Kostia](./Output/Subm/submission_kostia + igor final_ensemble (1 to 3 weights).csv) | - | 0.43819 | 0.43704 | **8** |
| [Best Ensemble Model from Chenglong](./Output/Subm/test.pred.[Feat@level2_meta_linear_201605030922]_[Learner@reg_ensemble]_[Id@1].[Mean0.436087]_[Std0.001027].csv) | 0.43609 | 0.43582 | 0.43325 | **4** |
| [Best Final Ensemble Model](./Output/Subm/reproduced_blend_0.438_0.436CV.csv) | - | 0.43465 | 0.43248 | **3** |

## FlowChart

## Documentation

See `./Doc/Kaggle_HomeDepot_Turing_Test.pdf` for documentation.

## Instruction

### Chenglong's Part
Before proceeding, one should place all the data from the [competition website](https://www.kaggle.com/c/home-depot-product-search-relevance/data) into folder `./Data`.

Note that in the following, all the commands and scripts are executed and run in directory `./Code/Chenglong`.

#### Step 1. Install Dependencies
#####1. Python
We used Python 3.5.1 and modules comes with [Anaconda 2.4.1 (64-bit)](https://repo.continuum.io/archive/Anaconda3-2.4.1-Linux-x86_64.sh). In addition, we also used the following libraries and modules:
- [gensim 0.12.4](https://github.com/piskvorky/gensim/archive/0.12.4.tar.gz)
- [hyperopt 0.0.3.dev](https://github.com/hyperopt/hyperopt)
- [keras 0.3.2](https://github.com/fchollet/keras/archive/0.3.2.tar.gz)
- [matplotlib-venn 0.11.3](https://pypi.python.org/pypi/matplotlib-venn)
- [python-Levenshtein 0.12.0](https://pypi.python.org/pypi/python-Levenshtein/0.12.0)
- [regex 2.4.85](https://pypi.python.org/pypi/regex)
- [xgboost 0.4](https://github.com/dmlc/xgboost/archive/v0.40.tar.gz)

#####2. R
We used the following packages installed via `install.packages()`:
- data.table
- Rtsne

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