Getting Started#

Prerequisites#

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

Dependency	Minimum Version	Purpose
numpy	1.17.3	build, install
scipy	1.3.2	build, install
joblib	1.0.0	install
threadpoolctl	2.0.0	install
cython	0.29.24	build
matplotlib	3.1.2	benchmark, docs, examples, tests
scikit-image	0.14.5	docs, examples, tests
pandas	1.0.5	benchmark, docs, examples, tests
seaborn	0.9.0	docs, examples
memory_profiler	0.57.0	benchmark, docs
pytest	5.0.1	tests
pytest-cov	2.9.0	tests
flake8	3.8.2	tests
black	22.3.0	tests
mypy	0.770	tests
pyamg	4.0.0	tests
sphinx	4.0.1	docs
sphinx-gallery	0.7.0	docs
numpydoc	1.2.0	docs, tests
Pillow	7.1.2	docs
sphinx-prompt	1.3.0	docs
sphinxext-opengraph	0.4.2	docs

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 -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

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.

imbalanced-learn documentation

User Guide