modAL: A modular active learning framework for Python

TL;DR

modAL is a flexible, modular active learning framework in Python that integrates seamlessly with scikit-learn, facilitating rapid prototyping, easy extension, and development of active learning algorithms and pipelines.

Contribution

It introduces a modular, object-oriented design for active learning in Python, enhancing usability, extensibility, and compatibility with scikit-learn workflows.

Findings

Supports fast prototyping of active learning algorithms

Ensures high code quality with extensive testing and CI

Provides comprehensive documentation and tutorials

Abstract

modAL is a modular active learning framework for Python, aimed to make active learning research and practice simpler. Its distinguishing features are (i) clear and modular object oriented design (ii) full compatibility with scikit-learn models and workflows. These features make fast prototyping and easy extensibility possible, aiding the development of real-life active learning pipelines and novel algorithms as well. modAL is fully open source, hosted on GitHub at https://github.com/cosmic-cortex/modAL. To assure code quality, extensive unit tests are provided and continuous integration is applied. In addition, a detailed documentation with several tutorials are also available for ease of use. The framework is available in PyPI and distributed under the MIT license.