A Python package for semi-supervised learning with scikit-learn
Project description
Semi-Supervised Learning Library (sslearn)
The sslearn
library is a Python package for machine learning over Semi-supervised datasets. It is an extension of scikit-learn.
Installation
Dependencies
- scikit_learn = 1.2.0
- joblib = 1.2.0
- numpy = 1.23.3
- pandas = 1.4.3
- scipy = 1.9.3
- statsmodels = 0.13.2
- pytest = 7.2.0 (only for testing)
pip
installation
It can be installed using Pypi:
pip install sslearn
Code example
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
@software{jose_luis_garrido_labrador_2023_7565222,
author = {José Luis Garrido-Labrador and
César García-Osorio and
Juan J. Rodríguez and
Jesus Maudes},
title = {jlgarridol/sslearn: V1.0.2},
month = feb,
year = 2023,
publisher = {Zenodo},
version = {1.0.2},
doi = {10.5281/zenodo.7650049},
url = {https://doi.org/10.5281/zenodo.7650049}
}
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