85 lines
3.4 KiB
ReStructuredText
85 lines
3.4 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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Related Packages
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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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- `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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- `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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- `Statsmodel <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 fitting algorithms.
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Extensions and Algorithms
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-------------------------
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Libraries that provide a scikit-learn like interface and can be used with
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scikit-learn tools.
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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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- `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.githup.io>`_ General conditional random fields
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and structured prediction.
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- `py-earth <https://github.com/jcrudy/py-earth>`_ Multivariate adaptive 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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Domain Specific Packages
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- `scikit-image <http://scikit-image.org/>`_ Image processing and computer vision in python.
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- `Natural language toolkit (nltk) <http://www.nltk.org/>`_ Natual language 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://www.msmbuilder.org/>`_ Machine learning for protein 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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