Metadata-Version: 1.0
Name: scikit-weak
Version: 0.1.5a6
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 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: 
         ```
         sphinx-build -b html docs/source docs/build/html
         ```
         in the main folder of the project.
        
        
        
Platform: UNKNOWN
