Version 1.0.5

.github/workflows/python-package.yml

@@ -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:
@@ -27,7 +28,7 @@ jobs:
       run: |
         python -m pip install --upgrade pip
         python -m pip install flake8 pytest coverage
-        if [ -f requirements.txt ]; then pip install -r requirements.txt; fi
+        pip install -r requirements.txt
     - name: Lint with flake8
       run: |
         # stop the build if there are Python syntax errors or undefined names
@@ -37,7 +38,9 @@ jobs:
     - name: Test with pytest
       run: |
         coverage run -m pytest test
+    # Only if ubuntu-latest
     - name: Coverage
+      if: ${{ matrix.os == 'ubuntu-latest' }}
       run: |
         wget https://s3.amazonaws.com/codeclimate/test-reporter/test-reporter-latest-linux-amd64 -O reporter
         chmod +x reporter

CHANGELOG.md

@@ -4,6 +4,14 @@ 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] - 2024-05-08
+
+### Added
+- `feature_fusion` and `probability_fusion` methods for restricted in `sslearn.restricted` module. 
+
+### Fixed
+- CoForest random integer is now compatible with Windows.
+
 ## [1.0.4] - 2024-01-31
 
 ### Added
@@ -16,7 +24,7 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0
 ### Fixed
 - DeTriTraining now is vectorized and is faster than before.
 
-# [1.0.3.1] - 2023-04-01
+## [1.0.3.1] - 2023-04-01
 
 ### Changed
 - Hot fix for avoid problems with Pypi

README.md

@@ -45,14 +45,12 @@ model.score(X_unlabel, true_label)
 ## Citing 
 
 ```bibtex
-@software{jose_luis_garrido_labrador_2024_10623889,
+@software{garrido2024sslearn,
   author       = {José Luis Garrido-Labrador},
-  title        = {jlgarridol/sslearn: v1.0.4},
+  title        = {jlgarridol/sslearn},
   month        = feb,
   year         = 2024,
   publisher    = {Zenodo},
-  version      = {1.0.4},
-  doi          = {10.5281/zenodo.10623889},
-  url          = {https://doi.org/10.5281/zenodo.10623889}
+  doi          = {10.5281/zenodo.7565221},
 }
 ```

docs/sslearn.html

@@ -134,15 +134,13 @@ <h2 id="code-example">Code example</h2>
 <h2 id="citing">Citing</h2>
 
 <div class="pdoc-code codehilite">
-<pre><span></span><code><span class="nc">@software</span><span class="p">{</span><span class="nl">jose_luis_garrido_labrador_2024_10623889</span><span class="p">,</span>
+<pre><span></span><code><span class="nc">@software</span><span class="p">{</span><span class="nl">garrido2024sslearn</span><span class="p">,</span>
 <span class="w">  </span><span class="na">author</span><span class="w">       </span><span class="p">=</span><span class="w"> </span><span class="s">{José Luis Garrido-Labrador}</span><span class="p">,</span>
-<span class="w">  </span><span class="na">title</span><span class="w">        </span><span class="p">=</span><span class="w"> </span><span class="s">{jlgarridol/sslearn: v1.0.4}</span><span class="p">,</span>
+<span class="w">  </span><span class="na">title</span><span class="w">        </span><span class="p">=</span><span class="w"> </span><span class="s">{jlgarridol/sslearn}</span><span class="p">,</span>
 <span class="w">  </span><span class="na">month</span><span class="w">        </span><span class="p">=</span><span class="w"> </span><span class="nv">feb</span><span class="p">,</span>
 <span class="w">  </span><span class="na">year</span><span class="w">         </span><span class="p">=</span><span class="w"> </span><span class="m">2024</span><span class="p">,</span>
 <span class="w">  </span><span class="na">publisher</span><span class="w">    </span><span class="p">=</span><span class="w"> </span><span class="s">{Zenodo}</span><span class="p">,</span>
-<span class="w">  </span><span class="na">version</span><span class="w">      </span><span class="p">=</span><span class="w"> </span><span class="s">{1.0.4}</span><span class="p">,</span>
-<span class="w">  </span><span class="na">doi</span><span class="w">          </span><span class="p">=</span><span class="w"> </span><span class="s">{10.5281/zenodo.10623889}</span><span class="p">,</span>
-<span class="w">  </span><span class="na">url</span><span class="w">          </span><span class="p">=</span><span class="w"> </span><span class="s">{https://doi.org/10.5281/zenodo.10623889}</span>
+<span class="w">  </span><span class="na">doi</span><span class="w">          </span><span class="p">=</span><span class="w"> </span><span class="s">{10.5281/zenodo.7565221}</span><span class="p">,</span>
 <span class="p">}</span>
 </code></pre>
 </div>
@@ -164,7 +162,7 @@ <h2 id="citing">Citing</h2>
 </span><span id="L-10"><a href="#L-10"><span class="linenos">10</span></a>    <span class="vm">__doc__</span> <span class="o">=</span> <span class="s2">&quot;Semi-Supervised Learning (SSL) is a Python package that provides tools to train and evaluate semi-supervised learning models.&quot;</span>
 </span><span id="L-11"><a href="#L-11"><span class="linenos">11</span></a>
 </span><span id="L-12"><a href="#L-12"><span class="linenos">12</span></a>
-</span><span id="L-13"><a href="#L-13"><span class="linenos">13</span></a><span class="n">__version__</span><span class="o">=</span><span class="s1">&#39;1.0.4.1&#39;</span>
+</span><span id="L-13"><a href="#L-13"><span class="linenos">13</span></a><span class="n">__version__</span><span class="o">=</span><span class="s1">&#39;1.0.5&#39;</span>
 </span><span id="L-14"><a href="#L-14"><span class="linenos">14</span></a><span class="n">__AUTHOR__</span><span class="o">=</span><span class="s2">&quot;José Luis Garrido-Labrador&quot;</span>  <span class="c1"># Author of the package</span>
 </span><span id="L-15"><a href="#L-15"><span class="linenos">15</span></a><span class="n">__AUTHOR_EMAIL__</span><span class="o">=</span><span class="s2">&quot;jlgarrido@ubu.es&quot;</span>  <span class="c1"># Author&#39;s email</span>
 </span><span id="L-16"><a href="#L-16"><span class="linenos">16</span></a><span class="n">__URL__</span><span class="o">=</span><span class="s2">&quot;https://pypi.org/project/sslearn/&quot;</span>