Getting Started

Prerequisites

You can find the complete list of the dependencies in the following table:

Dependency	Minimum Version	Purpose
numpy	1.19.5	build, install
scipy	1.6.0	build, install
joblib	1.2.0	install
threadpoolctl	3.1.0	install
cython	3.0.10	build
meson-python	0.16.0	build
matplotlib	3.3.4	benchmark, docs, examples, tests
scikit-image	0.17.2	docs, examples, tests
pandas	1.1.5	benchmark, docs, examples, tests
seaborn	0.9.0	docs, examples
memory_profiler	0.57.0	benchmark, docs
pytest	7.1.2	tests
pytest-cov	2.9.0	tests
ruff	0.2.1	tests
black	24.3.0	tests
mypy	1.9	tests
pyamg	4.0.0	tests
polars	0.20.30	docs, tests
pyarrow	12.0.0	tests
sphinx	7.3.7	docs
sphinx-copybutton	0.5.2	docs
sphinx-gallery	0.16.0	docs
numpydoc	1.2.0	docs, tests
Pillow	7.1.2	docs
pooch	1.6.0	docs, examples, tests
sphinx-prompt	1.4.0	docs
sphinxext-opengraph	0.9.1	docs
plotly	5.14.0	docs, examples
sphinxcontrib-sass	0.3.4	docs
sphinx-remove-toctrees	1.0.0.post1	docs
sphinx-design	0.6.0	docs
pydata-sphinx-theme	0.15.3	docs
conda-lock	2.5.6	maintenance

Install

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 imbalanced-learn

The package is released also on the conda-forge repositories and you can install it with conda (or mamba):

conda install -c conda-forge imbalanced-learn

Intel optimizations via scikit-learn-intelex

Imbalanced-learn relies entirely on scikit-learn algorithms. Intel provides an optimized version of scikit-learn for Intel hardwares, called scikit-learn-intelex. Installing scikit-learn-intelex and patching scikit-learn will activate the Intel optimizations.

You can refer to the following blog post for some benchmarks.

Refer to the following documentation for instructions:

• Installation guide.

• Patching guide.

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

Test and coverage

You want to test the code before to install:

$ make test

You wish to test the coverage of your version:

$ make coverage

You can also use pytest:

$ pytest imblearn -v

Contribute

You can contribute to this code through Pull Request on GitHub. Please, make sure that your code is coming with unit tests to ensure full coverage and continuous integration in the API.