158 lines
6.3 KiB
ReStructuredText
158 lines
6.3 KiB
ReStructuredText
.. _related_projects:
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=====================================
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Related Projects
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=====================================
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Below is a list of sister-projects, extensions and domain specific packages.
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Interoperability and framework enhancements
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-------------------------------------------
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These tools adapt scikit-learn for use with other technologies or otherwise
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enhance the functionality of scikit-learn's estimators.
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- `sklearn_pandas <https://github.com/paulgb/sklearn-pandas/>`_ bridge for
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scikit-learn pipelines and pandas data frame with dedicated transformers.
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- `Scikit-Learn Laboratory
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<https://skll.readthedocs.org/en/latest/index.html>`_ A command-line
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wrapper around scikit-learn that makes it easy to run machine learning
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experiments with multiple learners and large feature sets.
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- `auto-sklearn <https://github.com/automl/auto-sklearn/blob/master/source/index.rst>`_
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An automated machine learning toolkit and a drop-in replacement for a
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scikit-learn estimator
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- `sklearn-pmml <https://github.com/alex-pirozhenko/sklearn-pmml>`_
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Serialization of (some) scikit-learn estimators into PMML.
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- `sklearn2pmml <https://github.com/jpmml/sklearn2pmml>`_
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Serialization of a wide variety of scikit-learn estimators and transformers
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into PMML with the help of `JPMML-SkLearn <https://github.com/jpmml/jpmml-sklearn>`_
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library.
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Other estimators and tasks
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--------------------------
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Not everything belongs or is mature enough for the central scikit-learn
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project. The following are projects providing interfaces similar to
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scikit-learn for additional learning algorithms, infrastructures
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and tasks.
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- `pylearn2 <http://deeplearning.net/software/pylearn2/>`_ A deep learning and
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neural network library build on theano with scikit-learn like interface.
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- `sklearn_theano <http://sklearn-theano.github.io/>`_ scikit-learn compatible
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estimators, transformers, and datasets which use Theano internally
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- `lightning <http://www.mblondel.org/lightning/>`_ Fast state-of-the-art
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linear model solvers (SDCA, AdaGrad, SVRG, SAG, etc...).
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- `Seqlearn <https://github.com/larsmans/seqlearn>`_ Sequence classification
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using HMMs or structured perceptron.
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- `HMMLearn <https://github.com/hmmlearn/hmmlearn>`_ Implementation of hidden
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markov models that was previously part of scikit-learn.
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- `PyStruct <https://pystruct.github.io>`_ General conditional random fields
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and structured prediction.
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- `pomegranate <https://github.com/jmschrei/pomegranate>`_ Probabilistic modelling
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for Python, with an emphasis on hidden Markov models.
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- `py-earth <https://github.com/jcrudy/py-earth>`_ Multivariate adaptive
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regression splines
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- `sklearn-compiledtrees <https://github.com/ajtulloch/sklearn-compiledtrees/>`_
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Generate a C++ implementation of the predict function for decision trees (and
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ensembles) trained by sklearn. Useful for latency-sensitive production
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environments.
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- `lda <https://github.com/ariddell/lda/>`_: Fast implementation of Latent
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Dirichlet Allocation in Cython.
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- `Sparse Filtering <https://github.com/jmetzen/sparse-filtering>`_
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Unsupervised feature learning based on sparse-filtering
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- `Kernel Regression <https://github.com/jmetzen/kernel_regression>`_
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Implementation of Nadaraya-Watson kernel regression with automatic bandwidth
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selection
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- `gplearn <https://github.com/trevorstephens/gplearn>`_ Genetic Programming
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for symbolic regression tasks.
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- `nolearn <https://github.com/dnouri/nolearn>`_ A number of wrappers and
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abstractions around existing neural network libraries
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- `sparkit-learn <https://github.com/lensacom/sparkit-learn>`_ Scikit-learn functionality and API on PySpark.
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- `keras <https://github.com/fchollet/keras>`_ Theano-based Deep Learning library.
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- `mlxtend <https://github.com/rasbt/mlxtend>`_ Includes a number of additional
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estimators as well as model visualization utilities.
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- `kmodes <https://github.com/nicodv/kmodes>`_ k-modes clustering algorithm for categorical data, and
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several of its variations.
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- `hdbscan <https://github.com/lmcinnes/hdbscan>`_ HDBSCAN and Robust Single Linkage clustering algorithms
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for robust variable density clustering.
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- `lasagne <https://github.com/Lasagne/Lasagne>`_ A lightweight library to build and train neural networks in Theano.
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- `multiisotonic <https://github.com/alexfields/multiisotonic>`_ Isotonic regression on multidimensional features.
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Statistical learning with Python
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--------------------------------
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Other packages useful for data analysis and machine learning.
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- `Pandas <http://pandas.pydata.org>`_ Tools for working with heterogeneous and
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columnar data, relational queries, time series and basic statistics.
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- `theano <http://deeplearning.net/software/theano/>`_ A CPU/GPU array
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processing framework geared towards deep learning research.
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- `statsmodels <http://statsmodels.sourceforge.net/>`_ Estimating and analysing
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statistical models. More focused on statistical tests and less on prediction
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than scikit-learn.
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- `PyMC <http://pymc-devs.github.io/pymc/>`_ Bayesian statistical models and
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fitting algorithms.
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- `REP <https://github.com/yandex/REP>`_ Environment for conducting data-driven
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research in a consistent and reproducible way
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- `Sacred <https://github.com/IDSIA/Sacred>`_ Tool to help you configure,
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organize, log and reproduce experiments
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- `gensim <https://radimrehurek.com/gensim/>`_ A library for topic modelling,
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document indexing and similarity retrieval
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- `Seaborn <http://stanford.edu/~mwaskom/software/seaborn/>`_ Visualization library based on
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matplotlib. It provides a high-level interface for drawing attractive statistical graphics.
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- `Deep Learning <http://deeplearning.net/software_links/>`_ A curated list of deep learning
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software libraries.
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Domain specific packages
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~~~~~~~~~~~~~~~~~~~~~~~~
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- `scikit-image <http://scikit-image.org/>`_ Image processing and computer
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vision in python.
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- `Natural language toolkit (nltk) <http://www.nltk.org/>`_ Natural language
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processing and some machine learning.
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- `NiLearn <https://nilearn.github.io/>`_ Machine learning for neuro-imaging.
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- `AstroML <http://www.astroml.org/>`_ Machine learning for astronomy.
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- `MSMBuilder <http://msmbuilder.org/>`_ Machine learning for protein
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conformational dynamics time series.
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Snippets and tidbits
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---------------------
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The `wiki <https://github.com/scikit-learn/scikit-learn/wiki/Third-party-projects-and-code-snippets>`_ has more!
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