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clusteroversampling 0.6.0

cluster-over-sampling

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Introduction
A general interface for clustering based over-sampling algorithms.
Installation
For user installation, cluster-over-sampling is currently available on the PyPi's repository, and you can
install it via pip:
pip install cluster-over-sampling

Development installation requires to clone the repository and then use PDM to install the
project as well as the main and development dependencies:
git clone https://github.com/georgedouzas/cluster-over-sampling.git
cd cluster-over-sampling
pdm install

SOM clusterer requires optional dependencies:
pip install cluster-over-sampling[som]

Usage
All the classes included in cluster-over-sampling follow the imbalanced-learn API using the functionality of the base
oversampler. Using scikit-learn convention, the data are represented as follows:

Input data X: 2D array-like or sparse matrices.
Targets y: 1D array-like.

The clustering-based oversamplers implement a fit method to learn from X and y:
clustering_based_oversampler.fit(X, y)

They also implement a fit_resample method to resample X and y:
X_resampled, y_resampled = clustering_based_oversampler.fit_resample(X, y)

References
If you use cluster-over-sampling in a scientific publication, we would appreciate citations to any of the following papers:

G. Douzas, F. Bacao, "Self-Organizing Map Oversampling (SOMO) for imbalanced data set learning", Expert Systems with
Applications, vol. 82, pp. 40-52, 2017.
G. Douzas, F. Bacao, F. Last, "Improving imbalanced learning through a heuristic oversampling method based on k-means and
SMOTE", Information Sciences, vol. 465, pp. 1-20, 2018.
G. Douzas, F. Bacao, F. Last, "G-SOMO: An oversampling approach based on self-organized maps and geometric SMOTE", Expert
Systems with Applications, vol. 183,115230, 2021.