https://github.com/jlgarridol/sslearn

The sslearn library is a Python package for machine learning over Semi-supervised datasets. It is an extension of scikit-learn.
https://github.com/jlgarridol/sslearn

classification-algorithm machine-learning scikit-learn scikit-learn-api semi-supervised semi-supervised-learning semisupervised-learning

Last synced: about 4 hours ago
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The sslearn library is a Python package for machine learning over Semi-supervised datasets. It is an extension of scikit-learn.

• Host: GitHub
• URL: https://github.com/jlgarridol/sslearn
• Owner: jlgarridol
• License: bsd-3-clause
• Created: 2020-11-30T22:54:49.000Z (about 4 years ago)
• Default Branch: main
• Last Pushed: 2025-01-08T10:25:08.000Z (5 days ago)
• Last Synced: 2025-01-12T16:31:26.463Z (about 19 hours ago)
• Topics: classification-algorithm, machine-learning, scikit-learn, scikit-learn-api, semi-supervised, semi-supervised-learning, semisupervised-learning
• Language: Python
• Homepage: https://jlgarridol.github.io/sslearn/
• Size: 3.38 MB
• Stars: 8
• Watchers: 8
• Forks: 2
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • Changelog: CHANGELOG.md
  • License: License.txt

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README

        
Semi-Supervised Learning Library (sslearn)

===



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The `sslearn` library is a Python package for machine learning over Semi-supervised datasets. It is an extension of [scikit-learn](https://github.com/scikit-learn/scikit-learn).

## Installation

### Dependencies

* joblib >= 1.2.0

* numpy >= 1.23.3

* pandas >= 1.4.3

* scikit_learn >= 1.2.0

* scipy >= 1.10.1

* statsmodels >= 0.13.2

* pytest = 7.2.0 (only for testing)

### `pip` installation

It can be installed using *Pypi*:

    pip install sslearn

## Citing 

```bibtex

@article{sslearn2025garrido,

    title = {SSLearn: A Semi-Supervised Learning library for Python},

    journal = {SoftwareX},

    volume = {29},

    pages = {102024},

    year = {2025},

    issn = {2352-7110},

    doi = {https://doi.org/10.1016/j.softx.2024.102024},

    author = {José L. Garrido-Labrador and Jesús M. Maudes-Raedo and Juan J. Rodríguez and César I. García-Osorio},

}

```

## Fundings

The research carried out for the development of this software has been partially funded by the Junta de Castilla y León (project BU055P20), by the Ministry of Science and Innovation of Spain (projects PID2020-119894GB-I00 and TED 2021-129485B-C43) and by the project AIM-LAC (EP/S023992 /1). The author has been a beneficiary of the predoctoral scholarship from the Ministry of Education of the Junta de Castilla y León EDU/875/2021.