Related Projects¶

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

• Fast svmlight / libsvm file loader Fast and memory-efficient svmlight / libsvm file loader for Python.

• 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-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.

Experimentation frameworks

• REP Environment for conducting data-driven research in a consistent and reproducible way

• 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.

Model inspection and visualisation

• dtreeviz A python library for decision tree visualization and model interpretation.

• eli5 A library for debugging/inspecting machine learning models and explaining their predictions.

• mlxtend Includes model visualization utilities.

• yellowbrick A suite of custom matplotlib visualizers for scikit-learn estimators to support visual feature analysis, model selection, evaluation, and diagnostics.

Model selection

• 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.

• sklearn-deap Use evolutionary

  algorithms instead of gridsearch in scikit-learn.

Model export for production

• onnxmltools Serializes many Scikit-learn pipelines to ONNX for interchange and prediction.

• 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.

• treelite Compiles tree-based ensemble models into C code for minimizing prediction latency.

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

• tslearn A machine learning library for time series that offers tools for pre-processing and feature extraction as well as dedicated models for clustering, classification and regression.

• sktime A scikit-learn compatible toolbox for machine learning with time series including time series classification/regression and (supervised/panel) forecasting.

• 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.

• 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.

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.

• scikit-multilearn Multi-label classification with focus on label space manipulation.

• seglearn Time series and sequence learning using sliding window segmentation.

• libOPF Optimal path forest classifier

• fastFM Fast factorization machine implementation compatible with scikit-learn

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.)

• 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.

• 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.