georgedouzas / geometric-smote

geometric-smote

Implementation of the Geometric SMOTE algorithm [1], a geometrically enhanced drop-in replacement for SMOTE. It is compatible with scikit-learn and imbalanced-learn.

Documentation

Installation documentation, API documentation, and examples can be found on the documentation.

Dependencies

geometric-smote is tested to work under Python 3.6+. The dependencies are the following:

• numpy(>=1.1)
• scikit-learn(>=0.21)
• imbalanced-learn(>=0.4.3)

Additionally, to run the examples, you need matplotlib(>=2.0.0) and pandas(>=0.22).

Installation

geometric-smote is currently available on the PyPi's repository and you can install it via pip:

pip install -U geometric-smote

The package is released also in Anaconda Cloud platform:

conda install -c algowit geometric-smote

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/AlgoWit/geometric-smote.git
cd geometric-smote
pip install .

Or install using pip and GitHub:

pip install -U git+https://github.com/AlgoWit/geometric-smote.git

Testing

After installation, you can use pytest to run the test suite:

make test

About

If you use geometric-smote in a scientific publication, we would appreciate citations to the following paper:

@article{Douzas2019,
  doi = {10.1016/j.ins.2019.06.007},
  url = {https://doi.org/10.1016/j.ins.2019.06.007},
  year = {2019},
  month = oct,
  publisher = {Elsevier {BV}},
  volume = {501},
  pages = {118--135},
  author = {Georgios Douzas and Fernando Bacao},
  title = {Geometric {SMOTE} a geometrically enhanced drop-in replacement for {SMOTE}},
  journal = {Information Sciences}
}

Classification of imbalanced datasets is a challenging task for standard algorithms. Although many methods exist to address this problem in different ways, generating artificial data for the minority class is a more general approach compared to algorithmic modifications. SMOTE algorithm [2], as well as any other oversampling method based on the SMOTE mechanism, generates synthetic samples along line segments that join minority class instances. Geometric SMOTE (G-SMOTE) is an enhancement of the SMOTE data generation mechanism. G-SMOTE generates synthetic samples in a geometric region of the input space, around each selected minority instance.

References: