modAL: A modular active learning framework for Python

Tivadar Danka, Peter Horvath
5/2/2018
cs.LGstat.ML

Official code: modAL-python/modAL (2,373 stars, MIT, last commit 2024-02-26).

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.

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Code Implementations(1)

modAL-python/modALOfficial100%
MIT

A modular active learning framework for Python

2,373322Shell, PythonNov 14, 20172 years agoMIT
active-learningactive-learning-modulebayesian-optimizationmachine-learningmachine-learning-algorithms+4 more

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Cite this paper

@article{danka2018modal,
  title  = {modAL: A modular active learning framework for Python},
  author = {Tivadar Danka and Peter Horvath},
  year   = {2018},
  eprint = {1805.00979},
  archivePrefix = {arXiv},
  url    = {https://arxiv.org/abs/1805.00979}
}

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