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Now with windows and mac-os test
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jlgarridol committed May 6, 2024
1 parent 993598f commit 15e849a
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3 changes: 2 additions & 1 deletion .github/workflows/python-package.yml
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Expand Up @@ -11,10 +11,11 @@ on:

jobs:
build:
runs-on: ubuntu-latest
runs-on: ${{ matrix.os }}
strategy:
fail-fast: false
matrix:
os: [ubuntu-latest, macos-latest, windows-latest]
python-version: ["3.8", "3.9", "3.10", "3.11", "3.12"]

steps:
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5 changes: 5 additions & 0 deletions CHANGELOG.md
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Expand Up @@ -4,6 +4,11 @@ All notable changes to this project will be documented in this file.
The format is based on [Keep a Changelog](https://keepachangelog.com/en/1.0.0/),
and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0.html).

## [1.0.5] - XXXX-XX-XX

### Fixed
- CoForest random integer is now compatible with Windows.

## [1.0.4] - 2024-01-31

### Added
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10 changes: 5 additions & 5 deletions README.md
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Expand Up @@ -45,14 +45,14 @@ model.score(X_unlabel, true_label)
## Citing

```bibtex
@software{jose_luis_garrido_labrador_2024_10623889,
@software{jose_luis_garrido_labrador_2024_10624401,
author = {José Luis Garrido-Labrador},
title = {jlgarridol/sslearn: v1.0.4},
title = {jlgarridol/sslearn: v1.0.4.1},
month = feb,
year = 2024,
publisher = {Zenodo},
version = {1.0.4},
doi = {10.5281/zenodo.10623889},
url = {https://doi.org/10.5281/zenodo.10623889}
version = {1.0.4.1},
doi = {10.5281/zenodo.10624401},
url = {https://doi.org/10.5281/zenodo.10624401}
}
```
2 changes: 1 addition & 1 deletion sslearn/__init__.py
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Expand Up @@ -10,7 +10,7 @@
__doc__ = "Semi-Supervised Learning (SSL) is a Python package that provides tools to train and evaluate semi-supervised learning models."


__version__='1.0.4.1'
__version__='1.0.5'
__AUTHOR__="José Luis Garrido-Labrador" # Author of the package
__AUTHOR_EMAIL__="[email protected]" # Author's email
__URL__="https://pypi.org/project/sslearn/"
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2 changes: 1 addition & 1 deletion sslearn/wrapper/_co.py
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Expand Up @@ -1467,7 +1467,7 @@ def fit(self, X, y, **kwards):
for i in range(self.n_estimators):
self.hypotheses.append(skclone(self.base_estimator if type(self.base_estimator) is not list else self.base_estimator[i]))
if "random_state" in dir(self.hypotheses[-1]):
self.hypotheses[-1].set_params(random_state=random_state.randint(0, 2 ** 32 - 1))
self.hypotheses[-1].set_params(random_state=random_state.randint(0, 2147483647))
errors.append(0.5)

self.hypotheses = Parallel(n_jobs=n_jobs)(
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