scikit-weak 0.1.0

A package featuring utilities and algorithms for weakly supervised ML.

# skweak (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 at the MUDI lab (DISCo dept., University of Milano-Bicocca, Milan, Italy), as well as some algorithms available in the literature. Some references:

Campagner, A., Ciucci, D., Hüllermeier, E. (2021). Rough set-based feature selection for weakly labeled data. International Journal of Approximate Reasoning, 136, 150-167. https://doi.org/10.1016/j.ijar.2021.06.005. Campagner, A., Ciucci, D., Svensson, C. M., Figge, M. T., & Cabitza, F. (2021). Ground truthing from multi-rater labeling with three-way decision and possibility theory. Information Sciences, 545, 771-790. https://doi.org/10.1016/j.ins.2020.09.049 Wu, J. H., & Zhang, M. L. (2019). Disambiguation enabled linear discriminant analysis for partial label dimensionality reduction. In Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining (KDD ‘19), 416-424. https://doi.org/10.1145/3292500.3330901

Dependencies: numpy, scipy, scikit-learn, pandas