kmeans-smote 0.1.2

Dependencies

The implementation is tested under python 3.6 and works with the latest release of the imbalanced-learn framework:

• imbalanced-learn (>=0.4.0, <0.5)

• numpy (numpy>=1.13, <1.16)

• scikit-learn (>=0.19.0, <0.21)

Installation

pip install kmeans-smote

git clone https://github.com/felix-last/kmeans_smote.git
cd kmeans-smote
pip install .

Example Usage

import numpy as np
from imblearn.datasets import fetch_datasets
from kmeans_smote import KMeansSMOTE

datasets = fetch_datasets(filter_data=['oil'])
X, y = datasets['oil']['data'], datasets['oil']['target']

[print('Class {} has {} instances'.format(label, count))
 for label, count in zip(*np.unique(y, return_counts=True))]

kmeans_smote = KMeansSMOTE(
    kmeans_args={
        'n_clusters': 100
    },
    smote_args={
        'k_neighbors': 10
    }
)
X_resampled, y_resampled = kmeans_smote.fit_sample(X, y)

[print('Class {} has {} instances after oversampling'.format(label, count))
 for label, count in zip(*np.unique(y_resampled, return_counts=True))]

Expected Output:

Class -1 has 896 instances
Class 1 has 41 instances
Class -1 has 896 instances after oversampling
Class 1 has 896 instances after oversampling

Take a look at imbalanced-learn pipelines for efficient usage with cross-validation.

Contributing

Please feel free to submit an issue if things work differently than expected. Pull requests are also welcome - just make sure that tests are green by running pytest before submitting.

Citation

If you use k-means SMOTE in a scientific publication, we would appreciate citations to the following paper:

@article{kmeans_smote,
    title = {Oversampling for Imbalanced Learning Based on K-Means and SMOTE},
    author = {Last, Felix and Douzas, Georgios and Bacao, Fernando},
    year = {2017},
    archivePrefix = "arXiv",
    eprint = "1711.00837",
    primaryClass = "cs.LG"
}

References

[1] MacQueen, J. “Some Methods for Classification and Analysis of Multivariate Observations.” Proceedings of the Fifth Berkeley Symposium on Mathematical Statistics and Probability, 1967, p. 281-297.

[2] Chawla, Nitesh V., et al. “SMOTE: Synthetic Minority over-Sampling Technique.” Journal of Artificial Intelligence Research, vol. 16, Jan. 2002, p. 321357, doi:10.1613/jair.953.