scikit-weak 0.2.1

A package featuring utilities and algorithms for weakly supervised ML.

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.