River: machine learning for streaming data in Python

TL;DR

River is a comprehensive Python library that unifies state-of-the-art machine learning methods for streaming data and continual learning, aiming to be the primary tool for practitioners and researchers in this domain.

Contribution

It merges two leading stream learning packages into a single, improved library with a new architecture and extensive features for dynamic data stream analysis.

Findings

Provides multiple state-of-the-art algorithms

Includes data generators, transformers, and evaluation tools

Fosters a large community of users and contributors

Abstract

River is a machine learning library for dynamic data streams and continual learning. It provides multiple state-of-the-art learning methods, data generators/transformers, performance metrics and evaluators for different stream learning problems. It is the result from the merger of the two most popular packages for stream learning in Python: Creme and scikit-multiflow. River introduces a revamped architecture based on the lessons learnt from the seminal packages. River's ambition is to be the go-to library for doing machine learning on streaming data. Additionally, this open source package brings under the same umbrella a large community of practitioners and researchers. The source code is available at https://github.com/online-ml/river.