River: machine learning for streaming data in Python
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.
Code (2)
Tasks
BIG-bench Machine LearningContinual LearningSimilar Papers 제목 키워드 기반
Simplifying Hyperparameter Tuning in Online Machine Learning -- The spotRiverGUI
Batch Machine Learning (BML) reaches its limits when dealing with very large amounts of streaming data. This is especially true for available memory, handling drift in data streams, and processing new, unknown data. Onli…
Anomaly DetectionHyperparameter Tuning Cookbook: A guide for scikit-learn, PyTorch, river, and spotPython
This document provides a comprehensive guide to hyperparameter tuning using spotPython for scikit-learn, PyTorch, and river. The first part introduces spotPython's surrogate model-based optimization process, while the se…
WAX-ML: A Python library for machine learning and feedback loops on streaming data
Wax is what you put on a surfboard to avoid slipping. It is an essential tool to go surfing... We introduce WAX-ML a research-oriented Python library providing tools to design powerful machine learning algorithms and fee…
BIG-bench Machine Learningreinforcement-learningReinforcement LearningReinforcement Learning (RL)+2Pathway: a fast and flexible unified stream data processing framework for analytical and Machine Learning applications
We present Pathway, a new unified data processing framework that can run workloads on both bounded and unbounded data streams. The framework was created with the original motivation of resolving challenges faced when ana…
BenchmarkingHyperStream: a Workflow Engine for Streaming Data
This paper describes HyperStream, a large-scale, flexible and robust software package, written in the Python language, for processing streaming data with workflow creation capabilities. HyperStream overcomes the limitati…
BIG-bench Machine Learning