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.

翻译：River是一个用于动态数据流和持续学习的机器学习图书馆,它提供多种最先进的学习方法、数据生成者/转换者、业绩衡量标准以及不同流学习问题的评价员,这是将Python最受欢迎的两个学习流包:Creme和Scikit-mult-mulflow合并在一起的结果。River根据从原始数据包中汲取的教训引进了经过改造的结构。River的雄心是要成为在流数据上进行机器学习的去图书馆。此外,这一开放源码包将大量的从业者和研究人员聚集在同一伞下。源码可在https://github.com/online-ml/river上查阅。