【推荐】Python机器学习生态圈(Scikit-Learn相关项目)

2017 年 8 月 23 日 机器学习研究会

点击上方 “机器学习研究会”可以订阅

摘要

转自：爱可可-爱生活

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.

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.

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 programmaticinterfaces.
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.

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.
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.

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.