A package featuring utilities and algorithms for weakly supervised ML. Should be (more-or-less) compatible with scikit-learn! It collects original algorithms and methods developed by the contributors, as well as some algorithms available in the literature.
Current contributors:
- Andrea Campagner, MUDI Lab, University of Milano-Bicocca
- Julian Lienen, Paderborn University
You can install the library using the command:
pip install scikit-weak
numpy, scipy, scikit-learn, tensorflow, keras, pytest
The documentation is generated using Sphinx (https://www.sphinx-doc.org/). If you download the source code from this repository you can generate the documentation in html format by typing:
pip install sphinx-rtd-theme
sphinx-build -b html docs/source docs/build/html
in the main folder of the project.