Metadata-Version: 2.1
Name: evofs
Version: 1.2.0
Summary: Multi-objective evolutionary feature selection.
Home-page: https://github.com/pietrobarbiero/moea-feature-selection
Maintainer: P. Barbiero
Maintainer-email: barbiero@tutanota.com
License: Apache 2.0
Download-URL: https://github.com/pietrobarbiero/moea-feature-selection.git
Description: EvoFS: Multi-objective evolutionary feature selection
        ======================================================
        
        |Build|
        |Coverage|
        
        |PyPI license|
        |PyPI-version|
        
        
        
        .. |Build| image:: https://img.shields.io/travis/pietrobarbiero/evofs?label=Master%20Build&style=for-the-badge
            :alt: Travis (.org)
            :target: https://travis-ci.org/pietrobarbiero/evofs
        
        .. |Coverage| image:: https://img.shields.io/codecov/c/gh/pietrobarbiero/evofs?label=Test%20Coverage&style=for-the-badge
            :alt: Codecov
            :target: https://codecov.io/gh/pietrobarbiero/evofs
        
        .. |PyPI license| image:: https://img.shields.io/pypi/l/evofs.svg?style=for-the-badge
           :target: https://pypi.python.org/pypi/evofs/
        
        .. |PyPI-version| image:: https://img.shields.io/pypi/v/evofs?style=for-the-badge
            :alt: PyPI
            :target: https://pypi.python.org/pypi/evofs/
        
        EvoFS is a python package providing a sklearn-like transformer
        for multi-objective evolutionary feature selection.
        
        Quick start
        -----------
        
        You can install EvoFS along with all its dependencies from
        `PyPI <https://pypi.org/project/evofs/>`__:
        
        .. code:: bash
        
            $ pip install evofs
        
        Source
        ------
        
        The source code and minimal working examples can be found on
        `GitHub <https://github.com/pietrobarbiero/moea-feature-selection>`__.
        
        
        Running tests
        -------------
        
        You can run all unittests from command line by using python:
        
        .. code:: bash
        
            $ python -m unittest discover
        
        or coverage:
        
        .. code:: bash
        
            $ coverage run -m unittest discover
        
        
        Authors
        -------
        
        `Pietro Barbiero <http://www.pietrobarbiero.eu/>`__,
        `Giovanni Squillero <https://staff.polito.it/giovanni.squillero/>`__,
        and
        `Alberto Tonda <https://www.researchgate.net/profile/Alberto_Tonda>`__.
        
        Licence
        -------
        
        Copyright 2020 Pietro Barbiero, Giovanni Squillero, and Alberto Tonda.
        
        Licensed under the Apache License, Version 2.0 (the "License"); you may
        not use this file except in compliance with the License. You may obtain
        a copy of the License at: http://www.apache.org/licenses/LICENSE-2.0.
        
        Unless required by applicable law or agreed to in writing, software
        distributed under the License is distributed on an "AS IS" BASIS,
        WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
        
        See the License for the specific language governing permissions and
        limitations under the License.
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
Description-Content-Type: text/x-rst
Provides-Extra: tests
Provides-Extra: docs
