Getting Started¶
Prerequisites¶
The imbalanced-learn package requires the following dependencies:
python (>=3.6)
numpy (>=1.13.3)
scipy (>=0.19.1)
scikit-learn (>=0.24)
keras 2 (optional)
tensorflow (optional)
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.