geometric-smote 0.2.3

Description:

geometricsmote 0.2.3

geometric-smote




Category
Tools




Development



Package



Documentation



Communication




Introduction
The package geometric-smote implements the Geometric SMOTE algorithm, a geometrically enhanced drop-in replacement for SMOTE. It
is compatible with scikit-learn and imbalanced-learn. The Geometric SMOTE algorithm can handle numerical as well as categorical
features.
Installation
For user installation, geometric-smote is currently available on the PyPi's repository, and you can
install it via pip:
pip install geometric-smote

Development installation requires cloning the repository and then using PDM to install the
project as well as the main and development dependencies:
git clone https://github.com/georgedouzas/geometric-smote.git
cd geometric-smote
pdm install

Usage
All the classes included in geometric-smote 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:
gsmote_oversampler.fit(X, y)

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

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

Douzas, G., Bacao, B. (2019). Geometric SMOTE: a geometrically enhanced
drop-in replacement for SMOTE. Information Sciences, 501, 118-135.
https://doi.org/10.1016/j.ins.2019.06.007

Publications using Geometric-SMOTE:


Fonseca, J., Douzas, G., Bacao, F. (2021). Increasing the Effectiveness of
Active Learning: Introducing Artificial Data Generation in Active Learning
for Land Use/Land Cover Classification. Remote Sensing, 13(13), 2619.
https://doi.org/10.3390/rs13132619


Douzas, G., Bacao, F., Fonseca, J., Khudinyan, M. (2019). Imbalanced
Learning in Land Cover Classification: Improving Minority Classes’
Prediction Accuracy Using the Geometric SMOTE Algorithm. Remote Sensing,
11(24), 3040. https://doi.org/10.3390/rs11243040