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.. -*- 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|_ .. |GitHubActions| image:: https://github.com/scikit-learn-contrib/imbalanced-learn/actions/workflows/tests.yml/badge.svg .. _GitHubActions: https://github.com/scikit-learn-contrib/imbalanced-learn/actions/workflows/tests.yml .. |Codecov| image:: https://codecov.io/gh/scikit-learn-contrib/imbalanced-learn/branch/master/graph/badge.svg .. _Codecov: https://codecov.io/gh/scikit-learn-contrib/imbalanced-learn .. |CircleCI| image:: https://circleci.com/gh/scikit-learn-contrib/imbalanced-learn.svg?style=shield .. _CircleCI: https://circleci.com/gh/scikit-learn-contrib/imbalanced-learn/tree/master .. |PythonVersion| image:: https://img.shields.io/pypi/pyversions/imbalanced-learn.svg .. _PythonVersion: https://img.shields.io/pypi/pyversions/imbalanced-learn.svg .. |Pypi| image:: https://badge.fury.io/py/imbalanced-learn.svg .. _Pypi: https://badge.fury.io/py/imbalanced-learn .. |Gitter| image:: https://badges.gitter.im/scikit-learn-contrib/imbalanced-learn.svg .. _Gitter: https://gitter.im/scikit-learn-contrib/imbalanced-learn?utm_source=badge&utm_medium=badge&utm_campaign=pr-badge&utm_content=badge .. |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:: 1.25.2 .. |SciPyMinVersion| replace:: 1.11.4 .. |ScikitLearnMinVersion| replace:: 1.4.2 .. |MatplotlibMinVersion| replace:: 3.7.3 .. |PandasMinVersion| replace:: 2.0.3 .. |TensorflowMinVersion| replace:: 2.16.1 .. |KerasMinVersion| replace:: 3.3.3 .. |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 <http://scikit-learn.org/stable/developers>`_. 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:: @arti