Related Projects

Projects implementing the scikit-learn estimator API are encouraged to use the scikit-learn-contrib template which facilitates best practices for testing and documenting estimators. The scikit-learn-contrib GitHub organisation also accepts high-quality contributions of repositories conforming to this template.

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.

Data formats

• sklearn_pandas bridge for scikit-learn pipelines and pandas data frame with dedicated transformers.
• sklearn_xarray provides compatibility of scikit-learn estimators with xarray data structures.

Auto-ML

• auto_ml Automated machine learning for production and analytics, built on scikit-learn and related projects. Trains a pipeline wth all the standard machine learning steps. Tuned for prediction speed and ease of transfer to production environments.
• auto-sklearn An automated machine learning toolkit and a drop-in replacement for a scikit-learn estimator
• TPOT An automated machine learning toolkit that optimizes a series of scikit-learn operators to design a machine learning pipeline, including data and feature preprocessors as well as the estimators. Works as a drop-in replacement for a scikit-learn estimator.
• scikit-optimize A library to minimize (very) expensive and noisy black-box functions. It implements several methods for sequential model-based optimization, and includes a replacement for GridSearchCV or RandomizedSearchCV to do cross-validated parameter search using any of these strategies.

Experimentation frameworks

• REP Environment for conducting data-driven research in a consistent and reproducible way
• ML Frontend provides dataset management and SVM fitting/prediction through web-based and programmatic interfaces.
• Scikit-Learn Laboratory A command-line wrapper around scikit-learn that makes it easy to run machine learning experiments with multiple learners and large feature sets.
• Xcessiv is a notebook-like application for quick, scalable, and automated hyperparameter tuning and stacked ensembling. Provides a framework for keeping track of model-hyperparameter combinations.

Model inspection and visualisation

• eli5 A library for debugging/inspecting machine learning models and explaining their predictions.
• mlxtend Includes model visualization utilities.
• scikit-plot A visualization library for quick and easy generation of common plots in data analysis and machine learning.
• yellowbrick A suite of custom matplotlib visualizers for scikit-learn estimators to support visual feature analysis, model selection, evaluation, and diagnostics.

Model export for production

• sklearn-pmml Serialization of (some) scikit-learn estimators into PMML.
• sklearn2pmml Serialization of a wide variety of scikit-learn estimators and transformers into PMML with the help of JPMML-SkLearn library.
• sklearn-porter Transpile trained scikit-learn models to C, Java, Javascript and others.
• sklearn-compiledtrees Generate a C++ implementation of the predict function for decision trees (and ensembles) trained by sklearn. Useful for latency-sensitive production environments.

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.

Structured learning

• Seqlearn Sequence classification using HMMs or structured perceptron.
• HMMLearn Implementation of hidden markov models that was previously part of scikit-learn.
• PyStruct General conditional random fields and structured prediction.
• pomegranate Probabilistic modelling for Python, with an emphasis on hidden Markov models.
• sklearn-crfsuite Linear-chain conditional random fields (CRFsuite wrapper with sklearn-like API).

Deep neural networks etc.

• pylearn2 A deep learning and neural network library build on theano with scikit-learn like interface.
• sklearn_theano scikit-learn compatible estimators, transformers, and datasets which use Theano internally
• nolearn A number of wrappers and abstractions around existing neural network libraries
• keras Deep Learning library capable of running on top of either TensorFlow or Theano.
• lasagne A lightweight library to build and train neural networks in Theano.
• skorch A scikit-learn compatible neural network library that wraps PyTorch.

Broad scope

• mlxtend Includes a number of additional estimators as well as model visualization utilities.
• sparkit-learn Scikit-learn API and functionality for PySpark’s distributed modelling.

Other regression and classification

• xgboost Optimised gradient boosted decision tree library.
• ML-Ensemble Generalized ensemble learning (stacking, blending, subsemble, deep ensembles, etc.).
• lightning Fast state-of-the-art linear model solvers (SDCA, AdaGrad, SVRG, SAG, etc…).
• py-earth Multivariate adaptive regression splines
• Kernel Regression Implementation of Nadaraya-Watson kernel regression with automatic bandwidth selection
• gplearn Genetic Programming for symbolic regression tasks.
• multiisotonic Isotonic regression on multidimensional features.
• scikit-multilearn Multi-label classification with focus on label space manipulation.
• seglearn Time series and sequence learning using sliding window segmentation.

Decomposition and clustering

• lda: Fast implementation of latent Dirichlet allocation in Cython which uses Gibbs sampling to sample from the true posterior distribution. (scikit-learn’s sklearn.decomposition.LatentDirichletAllocation implementation uses variational inference to sample from a tractable approximation of a topic model’s posterior distribution.)
• Sparse Filtering Unsupervised feature learning based on sparse-filtering
• kmodes k-modes clustering algorithm for categorical data, and several of its variations.
• hdbscan HDBSCAN and Robust Single Linkage clustering algorithms for robust variable density clustering.
• spherecluster Spherical K-means and mixture of von Mises Fisher clustering routines for data on the unit hypersphere.

Pre-processing

• categorical-encoding A library of sklearn compatible categorical variable encoders.
• imbalanced-learn Various methods to under- and over-sample datasets.

Statistical learning with Python

Other packages useful for data analysis and machine learning.

• Pandas Tools for working with heterogeneous and columnar data, relational queries, time series and basic statistics.
• theano A CPU/GPU array processing framework geared towards deep learning research.
• statsmodels Estimating and analysing statistical models. More focused on statistical tests and less on prediction than scikit-learn.
• PyMC Bayesian statistical models and fitting algorithms.
• Sacred Tool to help you configure, organize, log and reproduce experiments
• Seaborn Visualization library based on matplotlib. It provides a high-level interface for drawing attractive statistical graphics.
• Deep Learning A curated list of deep learning software libraries.

Domain specific packages

• scikit-image Image processing and computer vision in python.
• Natural language toolkit (nltk) Natural language processing and some machine learning.
• gensim A library for topic modelling, document indexing and similarity retrieval
• NiLearn Machine learning for neuro-imaging.
• AstroML Machine learning for astronomy.
• MSMBuilder Machine learning for protein conformational dynamics time series.
• scikit-surprise A scikit for building and evaluating recommender systems.

Snippets and tidbits

The wiki has more!