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

• 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

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{garrido2024sslearn,
  author       = {José Luis Garrido-Labrador},
  title        = {jlgarridol/sslearn},
  month        = feb,
  year         = 2024,
  publisher    = {Zenodo},
  doi          = {10.5281/zenodo.7565221},
}

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.