A Python package for semi-supervised learning with scikit-learn
Project description
Semi-Supervised Learning Library (sslearn)
The sslearn
is a Python package for machine learning over Semi-supervised datasets. It is an extension of scikit-learn.
Installation
Dependencies
- scikit_learn = 1.1.2
- joblib = 1.1.0
- 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
How to
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)
Project details
Release history Release notifications | RSS feed
Download files
Download the file for your platform. If you're not sure which to choose, learn more about installing packages.
Source Distribution
sslearn-1.0.0.tar.gz
(38.8 kB
view hashes)
Built Distribution
sslearn-1.0.0-py3-none-any.whl
(36.0 kB
view hashes)