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.
@article{arxiv.2012.04740,
title = {River: machine learning for streaming data in Python},
author = {Jacob Montiel and Max Halford and Saulo Martiello Mastelini and Geoffrey Bolmier and Raphael Sourty and Robin Vaysse and Adil Zouitine and Heitor Murilo Gomes and Jesse Read and Talel Abdessalem and Albert Bifet},
journal= {arXiv preprint arXiv:2012.04740},
year = {2020}
}