[scikit-learn]: <http://scikit-learn.org/stable/>
[imbalanced-learn]: <http://imbalanced-learn.org/stable/>
[SOMO]: <https://www.sciencedirect.com/science/article/abs/pii/S0957417417302324>
[KMeans-SMOTE]: <https://www.sciencedirect.com/science/article/abs/pii/S0020025518304997>
[G-SOMO]: <https://www.sciencedirect.com/science/article/abs/pii/S095741742100662X>
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[ruff]: <https://github.com/charliermarsh/ruff>
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# imbalanced-learn-extra
[![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
`imbalanced-learn-extra` is a Python package that extends [imbalanced-learn]. It implements algorithms that are not included in
[imbalanced-learn] due to their novelty or lower citation number. The current version includes the following:
- A general interface for clustering-based oversampling algorithms.
- The Geometric SMOTE algorithm. It is a geometrically enhanced drop-in replacement for SMOTE, that handles numerical as well as
categorical features.
## Installation
For user installation, `imbalanced-learn-extra` is currently available on the PyPi's repository, and you can
install it via `pip`:
```bash
pip install imbalanced-learn-extra
```
Development installation requires cloning the repository and then using [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/imbalanced-learn-extra.git
cd imbalanced-learn-extra
pdm install
```
SOM clusterer requires optional dependencies:
```bash
pip install imbalanced-learn-extra[som]
```
## Usage
All the classes included in `imbalanced-learn-extra` 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 oversamplers implement a `fit` method to learn from `X` and `y`:
```python
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)
```
## Citing `imbalanced-learn-extra`
Publications using clustering-based oversampling:
- [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]
Publications using Geometric-SMOTE:
- 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>
- 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>
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It implements algorithms that are not included in\n[imbalanced-learn] due to their novelty or lower citation number. The current version includes the following:\n\n- A general interface for clustering-based oversampling algorithms.\n\n- The Geometric SMOTE algorithm. It is a geometrically enhanced drop-in replacement for SMOTE, that handles numerical as well as\ncategorical features.\n\n## Installation\n\nFor user installation, `imbalanced-learn-extra` is currently available on the PyPi's repository, and you can\ninstall it via `pip`:\n\n```bash\npip install imbalanced-learn-extra\n```\n\nDevelopment installation requires cloning the repository and then using [PDM](https://github.com/pdm-project/pdm) to install the\nproject as well as the main and development dependencies:\n\n```bash\ngit clone https://github.com/georgedouzas/imbalanced-learn-extra.git\ncd imbalanced-learn-extra\npdm install\n```\n\nSOM clusterer requires optional dependencies:\n\n```bash\npip install imbalanced-learn-extra[som]\n```\n\n## Usage\n\nAll the classes included in `imbalanced-learn-extra` follow the [imbalanced-learn] API using the functionality of the base\noversampler. Using [scikit-learn] convention, the data are represented as follows:\n\n- Input data `X`: 2D array-like or sparse matrices.\n- Targets `y`: 1D array-like.\n\nThe oversamplers implement a `fit` method to learn from `X` and `y`:\n\n```python\noversampler.fit(X, y)\n```\n\nThey also implement a `fit_resample` method to resample `X` and `y`:\n\n```python\nX_resampled, y_resampled = clustering_based_oversampler.fit_resample(X, y)\n```\n\n## Citing `imbalanced-learn-extra`\n\nPublications using clustering-based oversampling:\n\n- [G. Douzas, F. Bacao, \"Self-Organizing Map Oversampling (SOMO) for imbalanced data set learning\", Expert Systems with\n Applications, vol. 82, pp. 40-52, 2017.][SOMO]\n- [G. Douzas, F. Bacao, F. Last, \"Improving imbalanced learning through a heuristic oversampling method based on k-means and\n SMOTE\", Information Sciences, vol. 465, pp. 1-20, 2018.][KMeans-SMOTE]\n- [G. Douzas, F. Bacao, F. 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