<commit_before>.. _related_projects:

=====================================
Related Projects
=====================================

Below is a list of sister-projects, extensions and domain specific packages.

Interoperability and framework enhancements
-------------------------------------------

These tools adapt scikit-learn for use with other technologies or otherwise
enhance the functionality of scikit-learn's estimators.

- `sklearn_pandas <https://github.com/paulgb/sklearn-pandas/>`_ bridge for
  scikit-learn pipelines and pandas data frame with dedicated transformers.

- `Scikit-Learn Laboratory
  <https://skll.readthedocs.org/en/latest/index.html>`_  A command-line
  wrapper around scikit-learn that makes it easy to run machine learning
  experiments with multiple learners and large feature sets.

- `auto-sklearn <https://github.com/automl/auto-sklearn/blob/master/source/index.rst>`_
  An automated machine learning toolkit and a drop-in replacement for a
  scikit-learn estimator

- `sklearn-pmml <https://github.com/alex-pirozhenko/sklearn-pmml>`_
  Serialization of (some) scikit-learn estimators into PMML.

- `sklearn2pmml <https://github.com/jpmml/sklearn2pmml>`_
  Serialization of a wide variety of scikit-learn estimators and transformers
  into PMML with the help of `JPMML-SkLearn <https://github.com/jpmml/jpmml-sklearn>`_
  library.

Other estimators and tasks
--------------------------

Not everything belongs or is mature enough for the central scikit-learn
project. The following are projects providing interfaces similar to
scikit-learn for additional learning algorithms, infrastructures
and tasks.

- `pylearn2 <http://deeplearning.net/software/pylearn2/>`_ A deep learning and
  neural network library build on theano with scikit-learn like interface.

- `sklearn_theano <http://sklearn-theano.github.io/>`_ scikit-learn compatible
  estimators, transformers, and datasets which use Theano internally

- `lightning <http://www.mblondel.org/lightning/>`_ Fast state-of-the-art
  linear model solvers (SDCA, AdaGrad, SVRG, SAG, etc...).

- `Seqlearn <https://github.com/larsmans/seqlearn>`_  Sequence classification
  using HMMs or structured perceptron.

- `HMMLearn <https://github.com/hmmlearn/hmmlearn>`_ Implementation of hidden
  markov models that was previously part of scikit-learn.

- `PyStruct <https://pystruct.github.io>`_ General conditional random fields
  and structured prediction.

- `pomegranate <https://github.com/jmschrei/pomegranate>`_ Probabilistic modelling
  for Python, with an emphasis on hidden Markov models.

- `py-earth <https://github.com/jcrudy/py-earth>`_ Multivariate adaptive
  regression splines

- `sklearn-compiledtrees <https://github.com/ajtulloch/sklearn-compiledtrees/>`_
  Generate a C++ implementation of the predict function for decision trees (and
  ensembles) trained by sklearn. Useful for latency-sensitive production
  environments.

- `lda <https://github.com/ariddell/lda/>`_: Fast implementation of Latent
  Dirichlet Allocation in Cython.

- `Sparse Filtering <https://github.com/jmetzen/sparse-filtering>`_
  Unsupervised feature learning based on sparse-filtering

- `Kernel Regression <https://github.com/jmetzen/kernel_regression>`_
  Implementation of Nadaraya-Watson kernel regression with automatic bandwidth
  selection

- `gplearn <https://github.com/trevorstephens/gplearn>`_ Genetic Programming
  for symbolic regression tasks.

- `nolearn <https://github.com/dnouri/nolearn>`_ A number of wrappers and
  abstractions around existing neural network libraries

- `sparkit-learn <https://github.com/lensacom/sparkit-learn>`_ Scikit-learn functionality and API on PySpark.

- `keras <https://github.com/fchollet/keras>`_ Theano-based Deep Learning library.

- `mlxtend <https://github.com/rasbt/mlxtend>`_ Includes a number of additional
  estimators as well as model visualization utilities.

- `kmodes <https://github.com/nicodv/kmodes>`_ k-modes clustering algorithm for categorical data, and
  several of its variations.

Statistical learning with Python
--------------------------------
Other packages useful for data analysis and machine learning.

- `Pandas <http://pandas.pydata.org>`_ Tools for working with heterogeneous and
  columnar data, relational queries, time series and basic statistics.

- `theano <http://deeplearning.net/software/theano/>`_ A CPU/GPU array
  processing framework geared towards deep learning research.

- `Statsmodel <http://statsmodels.sourceforge.net/>`_ Estimating and analysing
  statistical models. More focused on statistical tests and less on prediction
  than scikit-learn.

- `PyMC <http://pymc-devs.github.io/pymc/>`_ Bayesian statistical models and
  fitting algorithms.<commit_msg>Update related_projects.rst<commit_after>.. _related_projects:

=====================================
Related Projects
=====================================

Below is a list of sister-projects, extensions and domain specific packages.

Interoperability and framework enhancements
-------------------------------------------

These tools adapt scikit-learn for use with other technologies or otherwise
enhance the functionality of scikit-learn's estimators.

- `sklearn_pandas <https://github.com/paulgb/sklearn-pandas/>`_ bridge for
  scikit-learn pipelines and pandas data frame with dedicated transformers.

- `Scikit-Learn Laboratory
  <https://skll.readthedocs.org/en/latest/index.html>`_  A command-line
  wrapper around scikit-learn that makes it easy to run machine learning
  experiments with multiple learners and large feature sets.

- `auto-sklearn <https://github.com/automl/auto-sklearn/blob/master/source/index.rst>`_
  An automated machine learning toolkit and a drop-in replacement for a
  scikit-learn estimator

- `sklearn-pmml <https://github.com/alex-pirozhenko/sklearn-pmml>`_
  Serialization of (some) scikit-learn estimators into PMML.

- `sklearn2pmml <https://github.com/jpmml/sklearn2pmml>`_
  Serialization of a wide variety of scikit-learn estimators and transformers
  into PMML with the help of `JPMML-SkLearn <https://github.com/jpmml/jpmml-sklearn>`_
  library.

Other estimators and tasks
--------------------------

Not everything belongs or is mature enough for the central scikit-learn
project. The following are projects providing interfaces similar to
scikit-learn for additional learning algorithms, infrastructures
and tasks.

- `pylearn2 <http://deeplearning.net/software/pylearn2/>`_ A deep learning and
  neural network library build on theano with scikit-learn like interface.

- `sklearn_theano <http://sklearn-theano.github.io/>`_ scikit-learn compatible
  estimators, transformers, and datasets which use Theano internally

- `lightning <http://www.mblondel.org/lightning/>`_ Fast state-of-the-art
  linear model solvers (SDCA, AdaGrad, SVRG, SAG, etc...).

- `Seqlearn <https://github.com/larsmans/seqlearn>`_  Sequence classification
  using HMMs or structured perceptron.

- `HMMLearn <https://github.com/hmmlearn/hmmlearn>`_ Implementation of hidden
  markov models that was previously part of scikit-learn.

- `PyStruct <https://pystruct.github.io>`_ General conditional random fields
  and structured prediction.

- `pomegranate <https://github.com/jmschrei/pomegranate>`_ Probabilistic modelling
  for Python, with an emphasis on hidden Markov models.

- `py-earth <https://github.com/jcrudy/py-earth>`_ Multivariate adaptive
  regression splines

- `sklearn-compiledtrees <https://github.com/ajtulloch/sklearn-compiledtrees/>`_
  Generate a C++ implementation of the predict function for decision trees (and
  ensembles) trained by sklearn. Useful for latency-sensitive production
  environments.

- `lda <https://github.com/ariddell/lda/>`_: Fast implementation of Latent
  Dirichlet Allocation in Cython.

- `Sparse Filtering <https://github.com/jmetzen/sparse-filtering>`_
  Unsupervised feature learning based on sparse-filtering

- `Kernel Regression <https://github.com/jmetzen/kernel_regression>`_
  Implementation of Nadaraya-Watson kernel regression with automatic bandwidth
  selection

- `gplearn <https://github.com/trevorstephens/gplearn>`_ Genetic Programming
  for symbolic regression tasks.

- `nolearn <https://github.com/dnouri/nolearn>`_ A number of wrappers and
  abstractions around existing neural network libraries

- `sparkit-learn <https://github.com/lensacom/sparkit-learn>`_ Scikit-learn functionality and API on PySpark.

- `keras <https://github.com/fchollet/keras>`_ Theano-based Deep Learning library.

- `mlxtend <https://github.com/rasbt/mlxtend>`_ Includes a number of additional
  estimators as well as model visualization utilities.

- `kmodes <https://github.com/nicodv/kmodes>`_ k-modes clustering algorithm for categorical data, and
  several of its variations.
  
- `hdbscan <https://github.com/lmcinnes/hdbscan>`_ HDBSCAN and Robust Single Linkage clustering algorithms 
  for robust variable density clustering. 

Statistical learning with Python
--------------------------------
Other packages useful for data analysis and machine learning.

- `Pandas <http://pandas.pydata.org>`_ Tools for working with heterogeneous and
  columnar data, relational queries, time series and basic statistics.

- `theano <http://deeplearning.net/software/theano/>`_ A CPU/GPU array
  processing framework geared towards deep learning research.

- `Statsmodel <http://statsmodels.sourceforge.net/>`_ Estimating and analysing
  statistical models. More focused on statistical tests and less on prediction
  than scikit-learn.

- `PyMC <http://pymc-devs.github.io/pymc/>`_ Bayesian statistical models and
  fitting algorithms.
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