ColGenEstimator


NameColGenEstimator JSON
Version 0.1.1 PyPI version JSON
download
home_pagehttps://github.com/krooonal/col_gen_estimator
SummaryClassifiers using column generation
upload_time2023-06-27 14:30:32
maintainerK. Patel
docs_urlNone
author
requires_python
licensenew BSD
keywords
VCS
bugtrack_url
requirements numpy scipy pandas scikit-learn ortools bitarray
Travis-CI
coveralls test coverage
            .. -*- 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!*


            

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