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https://github.com/nova-ims-innovation-and-analytics-lab/cluster-over-sampling

A general interface for clustering based over-sampling algorithms.
https://github.com/nova-ims-innovation-and-analytics-lab/cluster-over-sampling

data-science imbalanced-data imbalanced-learning machine-learning oversampling python3 scikit-learn

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A general interface for clustering based over-sampling algorithms.

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[![Project Status: Inactive – The project has reached a stable, usable state but is no longer being actively developed;

support/maintenance will be provided as time

allows.](https://www.repostatus.org/badges/latest/inactive.svg)](https://www.repostatus.org/#inactive)



> **The project has been moved to [imbalanced-learn-extra](https://github.com/georgedouzas/imbalanced-learn-extra).**



# cluster-over-sampling



[![ci][ci badge]][ci] [![doc][doc badge]][doc]



| Category          | Tools    |

| ------------------| -------- |

| **Development**   | [![black][black badge]][black] [![ruff][ruff badge]][ruff] [![mypy][mypy badge]][mypy] [![docformatter][docformatter badge]][docformatter] |

| **Package**       | ![version][version badge] ![pythonversion][pythonversion badge] ![downloads][downloads badge] |

| **Documentation** | [![mkdocs][mkdocs badge]][mkdocs]|

| **Communication** | [![gitter][gitter badge]][gitter] [![discussions][discussions badge]][discussions] |



## 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`:



```bash

pip install cluster-over-sampling

```



Development installation requires to clone the repository and then use [PDM](https://github.com/pdm-project/pdm) to install the

project as well as the main and development dependencies:



```bash

git clone https://github.com/georgedouzas/cluster-over-sampling.git

cd cluster-over-sampling

pdm install

```



SOM clusterer requires optional dependencies:



```bash

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`:



```python

clustering_based_oversampler.fit(X, y)

```



They also implement a `fit_resample` method to resample `X` and `y`:



```python

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.][SOMO]

- [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.][KMeans-SMOTE]

- [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.][G-SOMO]