A package featuring utilities and algorithms for weakly supervised ML.
Project description
- # scikit-weak (scikit-weakly-supervised)
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
## How to install You can install the library using the command:
` pip install scikit-weak `
### Dependencies: numpy, scipy, scikit-learn, tensorflow, keras, pytest
## Documentation 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.
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