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.