Metadata-Version: 1.0
Name: scikit-weak
Version: 0.1.2b0
Summary: A package featuring utilities and algorithms for weakly supervised ML.
Home-page: https://pypi.org/project/scikit-weak/
Author: Andrea Campagner
Author-email: a.campagner@campus.unimib.it
License: LICENSE.txt
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 at the MUDI lab (DISCo dept., University of Milano-Bicocca, Milan, Italy),
         as well as some algorithms available in the literature.
        
         ## How to install
         You can install the library using the command:
        
         ```
         pip install scikit-weak
         ```
         
         ### Dependencies:
         numpy, scipy, scikit-learn, pandas
        
         ## 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: 
         ```
         sphinx-build -b html docs/source docs/build/html
         ```
         in the main folder of the project.
         
         ## References:
        
         [1] Campagner, A., Ciucci, D., Hullermeier, 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.
        
         [2] 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  
        
         [3] Campagner, A., Ciucci, D., & HÃ¼llermeier, E. (2020). Feature Reduction in Superset Learning Using Rough Sets and Evidence Theory. In International Conference on Information Processing and Management of Uncertainty in Knowledge-Based Systems (pp. 471-484). Springer, Cham. https://doi.org/10.1007/978-3-030-50146-4_35
        
        
Platform: UNKNOWN
