Metadata-Version: 2.1
Name: ITMO-FS
Version: 0.2.1
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
Description: .. -*- 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 |
        +======================================+==============================+========+
        | Spearman correlation                 | Add Del                      | MeLiF  |
        +--------------------------------------+------------------------------+--------+
        | Pearson correlation                  | Backward selection           |        |
        +--------------------------------------+------------------------------+--------+
        | Fit Criterion                        | Sequential Forward Selection |        |
        +--------------------------------------+------------------------------+--------+
        | F ratio                              |                              |        |
        +--------------------------------------+------------------------------+--------+
        | Gini index                           |                              |        |
        +--------------------------------------+------------------------------+--------+
        | Information Gain                     |                              |        |
        +--------------------------------------+------------------------------+--------+
        | Minimum Redundancy Maximum Relevance |                              |        |
        +--------------------------------------+------------------------------+--------+
        | VDM                                  |                              |        |
        +--------------------------------------+------------------------------+--------+
        | MOSNS                                |                              |        |
        +--------------------------------------+------------------------------+--------+
        | MOSS                                 |                              |        |
        +--------------------------------------+------------------------------+--------+
        
        To use basic filter:
        
        ::
        
           from sklearn.datasets import load_iris
           from filters.UnivariateFilter import * # provides you a filter class, basic measures and cutting rules
        
           data, target = load_iris(True)
           res = UnivariateFilter("SpearmanCorr", GLOB_CR["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
        
        
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
