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https://github.com/scikit-learn-contrib/imbalanced-learn

A Python Package to Tackle the Curse of Imbalanced Datasets in Machine Learning
https://github.com/scikit-learn-contrib/imbalanced-learn

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A Python Package to Tackle the Curse of Imbalanced Datasets in Machine Learning

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README 

        
.. -*- mode: rst -*-



.. _scikit-learn: http://scikit-learn.org/stable/



.. _scikit-learn-contrib: https://github.com/scikit-learn-contrib



|GitHubActions|_ |Codecov|_ |CircleCI|_ |PythonVersion|_ |Pypi|_ |Gitter|_ |Black|_



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.. _CircleCI: https://circleci.com/gh/scikit-learn-contrib/imbalanced-learn/tree/master



.. |PythonVersion| image:: https://img.shields.io/pypi/pyversions/imbalanced-learn.svg

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.. |Black| image:: https://img.shields.io/badge/code%20style-black-000000.svg

.. _Black: :target: https://github.com/psf/black



.. |PythonMinVersion| replace:: 3.10

.. |NumPyMinVersion| replace:: 1.24.3

.. |SciPyMinVersion| replace:: 1.10.1

.. |ScikitLearnMinVersion| replace:: 1.3.2

.. |MatplotlibMinVersion| replace:: 3.7.3

.. |PandasMinVersion| replace:: 1.5.3

.. |TensorflowMinVersion| replace:: 2.13.1

.. |KerasMinVersion| replace:: 3.0.5

.. |SeabornMinVersion| replace:: 0.12.2

.. |PytestMinVersion| replace:: 7.2.2



imbalanced-learn

================



imbalanced-learn is a python package offering a number of re-sampling techniques

commonly used in datasets showing strong between-class imbalance.

It is compatible with scikit-learn_ and is part of scikit-learn-contrib_

projects.



Documentation

-------------



Installation documentation, API documentation, and examples can be found on the

documentation_.



.. _documentation: https://imbalanced-learn.org/stable/



Installation

------------



Dependencies

~~~~~~~~~~~~



`imbalanced-learn` requires the following dependencies:



- Python (>= |PythonMinVersion|)

- NumPy (>= |NumPyMinVersion|)

- SciPy (>= |SciPyMinVersion|)

- Scikit-learn (>= |ScikitLearnMinVersion|)

- Pytest (>= |PytestMinVersion|)



Additionally, `imbalanced-learn` requires the following optional dependencies:



- Pandas (>= |PandasMinVersion|) for dealing with dataframes

- Tensorflow (>= |TensorflowMinVersion|) for dealing with TensorFlow models

- Keras (>= |KerasMinVersion|) for dealing with Keras models



The examples will requires the following additional dependencies:



- Matplotlib (>= |MatplotlibMinVersion|)

- Seaborn (>= |SeabornMinVersion|)



Installation

~~~~~~~~~~~~



From PyPi or conda-forge repositories

.....................................



imbalanced-learn is currently available on the PyPi's repositories and you can

install it via `pip`::



  pip install -U imbalanced-learn



The package is release also in Anaconda Cloud platform::



  conda install -c conda-forge imbalanced-learn



From source available on GitHub

...............................



If you prefer, you can clone it and run the setup.py file. Use the following

commands to get a copy from Github and install all dependencies::



  git clone https://github.com/scikit-learn-contrib/imbalanced-learn.git

  cd imbalanced-learn

  pip install .



Be aware that you can install in developer mode with::



  pip install --no-build-isolation --editable .



If you wish to make pull-requests on GitHub, we advise you to install

pre-commit::



  pip install pre-commit

  pre-commit install



Testing

~~~~~~~



After installation, you can use `pytest` to run the test suite::



  make coverage



Development

-----------



The development of this scikit-learn-contrib is in line with the one

of the scikit-learn community. Therefore, you can refer to their

`Development Guide

`_.



Endorsement of the Scientific Python Specification

--------------------------------------------------



We endorse good practices from the Scientific Python Ecosystem Coordination (SPEC).

The full list of recommendations is available `here`_.



See below the list of recommendations that we endorse for the imbalanced-learn project.



|SPEC 0 — Minimum Supported Dependencies|



.. |SPEC 0 — Minimum Supported Dependencies| image:: https://img.shields.io/badge/SPEC-0-green?labelColor=%23004811&color=%235CA038

   :target: https://scientific-python.org/specs/spec-0000/



.. _here: https://scientific-python.org/specs/



About

-----



If you use imbalanced-learn in a scientific publication, we would appreciate

citations to the following paper::



  @article{JMLR:v18:16-365,

  author  = {Guillaume  Lema{{\^i}}tre and Fernando Nogueira and Christos K. Aridas},

  title   = {Imbalanced-learn: A Python Toolbox to Tackle the Curse of Imbalanced Datasets in Machine Learning},

  journal = {Journal of Machine Learning Research},

  year    = {2017},

  volume  = {18},

  number  = {17},

  pages   = {1-5},

  url     = {http://jmlr.org/papers/v18/16-365}

  }



Most classification algorithms will only perform optimally when the number of

samples of each class is roughly the same. Highly skewed datasets, where the

minority is heavily outnumbered by one or more classes, have proven to be a

challenge while at the same time becoming more and more common.



One way of addressing this issue is by re-sampling the dataset as to offset this

imbalance with the hope of arriving at a more robust and fair decision boundary

than you would otherwise.



You can refer to the `imbalanced-learn`_ documentation to find details about

the implemented algorithms.



.. _imbalanced-learn: https://imbalanced-learn.org/stable/user_guide.html