Scikit-Multiflow: A Multi-output Streaming Framework

TL;DR

Scikit-multiflow is an open-source Python framework that facilitates multi-output and multi-label stream data mining, integrating state-of-the-art methods, stream generators, and evaluators to promote accessible stream learning research.

Contribution

It introduces a comprehensive, open-source Python platform for stream learning that combines multiple algorithms, generators, and evaluation tools, built on popular frameworks.

Findings

Provides a unified platform for stream learning research

Includes multiple state-of-the-art algorithms and tools

Follows open-source development best practices

Abstract

Scikit-multiflow is a multi-output/multi-label and stream data mining framework for the Python programming language. Conceived to serve as a platform to encourage democratization of stream learning research, it provides multiple state of the art methods for stream learning, stream generators and evaluators. scikit-multiflow builds upon popular open source frameworks including scikit-learn, MOA and MEKA. Development follows the FOSS principles and quality is enforced by complying with PEP8 guidelines and using continuous integration and automatic testing. The source code is publicly available at https://github.com/scikit-multiflow/scikit-multiflow.