Awesome-OL: An Extensible Toolkit for Online Learning

TL;DR

Awesome-OL is a flexible Python toolkit designed to advance online learning research by providing a unified framework, curated datasets, and visualization tools, built on scikit-multiflow.

Contribution

It introduces an extensible, user-friendly toolkit that simplifies development, comparison, and deployment of online learning algorithms.

Findings

Provides a unified framework for online learning algorithms

Includes curated benchmark datasets for evaluation

Features multi-modal visualization tools

Abstract

In recent years, online learning has attracted increasing attention due to its adaptive capability to process streaming and non-stationary data. To facilitate algorithm development and practical deployment in this area, we introduce Awesome-OL, an extensible Python toolkit tailored for online learning research. Awesome-OL integrates state-of-the-art algorithm, which provides a unified framework for reproducible comparisons, curated benchmark datasets, and multi-modal visualization. Built upon the scikit-multiflow open-source infrastructure, Awesome-OL emphasizes user-friendly interactions without compromising research flexibility or extensibility. The source code is publicly available at: https://github.com/liuzy0708/Awesome-OL.