Metadata-Version: 2.1
Name: ITMO-FS
Version: 0.3.0
Summary: Python Feature Selection library from ITMO University.
Home-page: https://github.com/LastShekel/ITMO_FS
Maintainer: N. Pilnenskiy
Maintainer-email: somacruz@bk.ru
License: new BSD
Download-URL: https://github.com/LastShekel/ITMO_FS
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
Requires-Dist: numpy
Requires-Dist: scipy
Requires-Dist: scikit-learn
Requires-Dist: imblearn
Requires-Dist: qpsolvers
Provides-Extra: docs
Requires-Dist: sphinx ; extra == 'docs'
Requires-Dist: sphinx-gallery ; extra == 'docs'
Requires-Dist: sphinx-rtd-theme ; extra == 'docs'
Requires-Dist: numpydoc ; extra == 'docs'
Requires-Dist: matplotlib ; extra == 'docs'
Provides-Extra: tests
Requires-Dist: pytest ; extra == 'tests'
Requires-Dist: pytest-cov ; extra == 'tests'

.. -*- mode: rst -*-

ITMO_FS
=======

Feature selection library in Python

Package information: |Python 2.7| |Python 3.6| |License|

Install with

::

   pip install ITMO_FS

Current available algorithms:

+--------------------------------------+------------------------------+-----------------+----------+-----------------+
| Filters                              | Wrappers                     | Hybrid          | Embedded | Ensembles       |
+======================================+==============================+=================+==========+=================+
| Spearman correlation                 | Add Del                      | Filter Wrapper  | MOSNS    | MeLiF           |
+--------------------------------------+------------------------------+-----------------+----------+-----------------+
| Pearson correlation                  | Backward selection           |                 | MOSS     | Best goes first |
+--------------------------------------+------------------------------+-----------------+----------+-----------------+
| Fit Criterion                        | Sequential Forward Selection |                 | RFE      | Best sum        |
+--------------------------------------+------------------------------+-----------------+----------+-----------------+
| F ratio                              | QPFS                         |                 |          |                 |
+--------------------------------------+------------------------------+-----------------+----------+-----------------+
| Gini index                           | Hill climbing                |                 |          |                 |
+--------------------------------------+------------------------------+-----------------+----------+-----------------+
| Information Gain                     |                              |                 |          |                 |
+--------------------------------------+------------------------------+-----------------+----------+-----------------+
| Minimum Redundancy Maximum Relevance |                              |                 |          |                 |
+--------------------------------------+------------------------------+-----------------+----------+-----------------+
| VDM                                  |                              |                 |          |                 |
+--------------------------------------+------------------------------+-----------------+----------+-----------------+
| QPFS                                 |                              |                 |          |                 |
+--------------------------------------+------------------------------+-----------------+----------+-----------------+

To use basic filter:

::

   from sklearn.datasets import load_iris
   from ITMO_FS.filters import UnivariateFilter, spearman_corr, select_best_by_value # provides you a filter class, basic measures and cutting rules

   data, target = load_iris(True)
   res = UnivariateFilter(spearman_corr, select_best_by_value(0.9999)).run(data, target)
   print("SpearmanCorr:", data.shape, '--->', res.shape)

.. |Python 2.7| image:: https://img.shields.io/badge/python-2.7-blue.svg
.. |Python 3.6| image:: https://img.shields.io/badge/python-3.6-blue.svg
.. |License| image:: https://img.shields.io/badge/license-MIT%20License-blue.svg