miur-daad-balancing 1.0.0
Testing dataset balancing techniques from previous works.
License: MIT

miur_daad_balancing
=========================================================================================
|travis| |sonar_quality| |sonar_maintainability| |codacy| |code_climate_maintainability| |pip|

Testing dataset balancing techniques from previous works.

How do I install this package?
----------------------------------------------
As usual, just download it using pip:

.. code:: shell

    pip install miur_daad_balancing

Tests Coverage
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Since some software handling coverages sometime get slightly different results, here's three of them:

|coveralls| |sonar_coverage| |code_climate_coverage|

Usage
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Three balancing methods are available for the MIUR-DAAD project:

Umbalanced
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
This method just leaves the data as-is, and is used more as callback usefull to uniform the pipeline:

.. code:: python

    from miur_daad_balancing import umbalanced
    
    training, testing = generate_my_data(...)
    balanced_training, balanced_testing = umbalanced(training, testing)

Balanced
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
Applies a maximum threshold to every class in the training set as specified in the default package settings (3000):

.. code:: python

    from miur_daad_balancing import balanced
    
    training, testing = generate_my_data(...)
    balanced_training, balanced_testing = balanced(training, testing)

Full Balanced
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
Applies a maximum threshold to every class in the training set and balances to some default proportions the testing set:

.. code:: python

    from miur_daad_balancing import full_balanced
    
    training, testing = generate_my_data(...)
    balanced_training, balanced_testing = full_balanced(training, testing)


.. |travis| image:: https://travis-ci.org/LucaCappelletti94/miur_daad_balancing.png
   :target: https://travis-ci.org/LucaCappelletti94/miur_daad_balancing
   :alt: Travis CI build

.. |sonar_quality| image:: https://sonarcloud.io/api/project_badges/measure?project=LucaCappelletti94_miur_daad_balancing&metric=alert_status
    :target: https://sonarcloud.io/dashboard/index/LucaCappelletti94_miur_daad_balancing
    :alt: SonarCloud Quality

.. |sonar_maintainability| image:: https://sonarcloud.io/api/project_badges/measure?project=LucaCappelletti94_miur_daad_balancing&metric=sqale_rating
    :target: https://sonarcloud.io/dashboard/index/LucaCappelletti94_miur_daad_balancing
    :alt: SonarCloud Maintainability

.. |sonar_coverage| image:: https://sonarcloud.io/api/project_badges/measure?project=LucaCappelletti94_miur_daad_balancing&metric=coverage
    :target: https://sonarcloud.io/dashboard/index/LucaCappelletti94_miur_daad_balancing
    :alt: SonarCloud Coverage

.. |coveralls| image:: https://coveralls.io/repos/github/LucaCappelletti94/miur_daad_balancing/badge.svg?branch=master
    :target: https://coveralls.io/github/LucaCappelletti94/miur_daad_balancing?branch=master
    :alt: Coveralls Coverage

.. |pip| image:: https://badge.fury.io/py/miur_daad_balancing.svg
    :target: https://badge.fury.io/py/miur_daad_balancing
    :alt: Pypi project

.. |codacy|  image:: https://api.codacy.com/project/badge/Grade/b4a7c72f058b433597426fa696d71539
    :target: https://www.codacy.com/app/LucaCappelletti94/miur_daad_balancing?utm_source=github.com&amp;utm_medium=referral&amp;utm_content=LucaCappelletti94/miur_daad_balancing&amp;utm_campaign=Badge_Grade
    :alt: Codacy Maintainability

.. |code_climate_maintainability| image:: https://api.codeclimate.com/v1/badges/2ca44bd945309b74bc33/maintainability
    :target: https://codeclimate.com/github/LucaCappelletti94/miur_daad_balancing/maintainability
    :alt: Maintainability

.. |code_climate_coverage| image:: https://api.codeclimate.com/v1/badges/2ca44bd945309b74bc33/test_coverage
    :target: https://codeclimate.com/github/LucaCappelletti94/miur_daad_balancing/test_coverage
    :alt: Code Climate Coverate