modAL: A modular active learning framework for Python
This provides a practical tool for researchers and practitioners in machine learning to streamline active learning workflows, though it is incremental as it builds on existing methods.
The authors tackled the complexity of implementing active learning by introducing modAL, a modular Python framework that simplifies research and practice, resulting in a tool with clear design, scikit-learn compatibility, and extensive documentation.
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.