Metadata-Version: 2.1
Name: ColGenEstimator
Version: 0.1.1
Summary: Classifiers using column generation
Home-page: https://github.com/krooonal/col_gen_estimator
Maintainer: K. Patel
Maintainer-email: krunalkachadia@gmail.com
License: new BSD
Download-URL: https://github.com/krooonal/col_gen_estimator
Description: .. -*- mode: rst -*-
        
        |AppVeyor|_ |Codecov|_ |CircleCI|_
        
        .. |AppVeyor| image:: https://ci.appveyor.com/api/projects/status/6eo2m9ydofn1nvb6?svg=true
        .. _AppVeyor: https://ci.appveyor.com/api/projects/status/6eo2m9ydofn1nvb6
        
        .. |Codecov| image:: https://codecov.io/gh/krooonal/col_gen_estimator/branch/master/graph/badge.svg?token=ZR8HME2LGV
        .. _Codecov: https://codecov.io/gh/krooonal/col_gen_estimator
        
        .. |CircleCI| image:: https://circleci.com/gh/krooonal/col_gen_estimator/tree/master.svg?style=svg
        .. _CircleCI: https://circleci.com/gh/krooonal/col_gen_estimator/tree/master
        
        
        col_gen_estimator - A template for scikit-learn compatible column generation 
        based estimators contributions
        ============================================================
        
        **col_gen_estimator** is a template project for scikit-learn compatible
        column generation based estimators.
        
        This project is built using the sklearn template. 
        
        It aids development of estimators that can be used in scikit-learn pipelines
        and (hyper)parameter search, while facilitating testing (including some API
        compliance), documentation, open source development, packaging, and continuous
        integration.
        
        Following example extensions of a column generation based binary classifiers 
        are included.
        - Boolean Decision Rule Generation by S. Dash et. al. 2018 
        - Decision Tree classifiers by Firat et. al. 2020 (modified)  
        
        The developer needs to extend the master and subproblem classes and implement 
        the required methods. The coumn generation part is taken care of by the 
        template fit method.
        
        The Decision Tree Classifier experiments can be launched from the example 
        directory. See the README file in the examples directory for details.
        
        To reproduce the results submitted in the Decision tree paper, use the branch
        'dtreedev'.
        
        *Thank you for cleanly contributing to the estimator template!*
Platform: UNKNOWN
Classifier: Intended Audience :: Science/Research
Classifier: Intended Audience :: Developers
Classifier: License :: OSI Approved
Classifier: Programming Language :: Python
Classifier: Topic :: Software Development
Classifier: Topic :: Scientific/Engineering
Classifier: Operating System :: Microsoft :: Windows
Classifier: Operating System :: POSIX
Classifier: Operating System :: Unix
Classifier: Operating System :: MacOS
Classifier: Programming Language :: Python :: 2.7
Classifier: Programming Language :: Python :: 3.5
Classifier: Programming Language :: Python :: 3.6
Classifier: Programming Language :: Python :: 3.7
Provides-Extra: tests
Provides-Extra: docs
