Metadata-Version: 2.1
Name: sslearn
Version: 1.0.4
Summary: A Python package for semi-supervised learning with scikit-learn
Home-page: https://github.com/jlgarridol/sslearn
Download-URL: https://github.com/jlgarridol/sslearn/archive/refs/tags/f1.0.4.tar.gz
Author: José Luis Garrido-Labrador
Author-email: jlgarrido@ubu.es
License: new BSD
Classifier: Development Status :: 5 - Production/Stable
Classifier: Intended Audience :: Science/Research
Classifier: Topic :: Scientific/Engineering
Classifier: License :: OSI Approved :: BSD License
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.8
Classifier: Programming Language :: Python :: 3.9
Classifier: Programming Language :: Python :: 3.10
Description-Content-Type: text/markdown
License-File: LICENSE
Requires-Dist: joblib ==1.2.0
Requires-Dist: numpy ==1.23.3
Requires-Dist: pandas ==1.4.3
Requires-Dist: scikit-learn ==1.2.0
Requires-Dist: scipy ==1.9.3
Requires-Dist: statsmodels ==0.13.2

Semi-Supervised Learning Library (sslearn)
===

![Code Climate maintainability](https://img.shields.io/codeclimate/maintainability-percentage/jlgarridol/sslearn) ![Code Climate coverage](https://img.shields.io/codeclimate/coverage/jlgarridol/sslearn) ![GitHub Workflow Status](https://img.shields.io/github/actions/workflow/status/jlgarridol/sslearn/python-package.yml) ![PyPI - Version](https://img.shields.io/pypi/v/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/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

Code example
---
```python
from sslearn.wrapper import TriTraining
from sslearn.model_selection import artificial_ssl_dataset
from sklearn.datasets import load_iris

X, y = load_iris(return_X_y=True)
X, y, X_unlabel, true_label = artificial_ssl_dataset(X, y, label_rate=0.1)

model = TriTraining().fit(X, y)
model.score(X_unlabel, true_label)
```

Citing
---
```bibtex
@software{jose_luis_garrido_labrador_2023_7781117,
  author       = {José Luis Garrido-Labrador},
  title        = {jlgarridol/sslearn: v1.0.3.1},
  month        = mar,
  year         = 2023,
  publisher    = {Zenodo},
  version      = {1.0.3.1},
  doi          = {10.5281/zenodo.7781117},
  url          = {https://doi.org/10.5281/zenodo.7781117}
}
```
