add first unit test

.github/workflows/pytest.yml

@@ -0,0 +1,37 @@
+name: Run Python Unit Tests
+
+on:
+  pull_request:
+    branches:
+      - main
+      - develop
+
+jobs:
+  test:
+    runs-on: ubuntu-latest
+
+    steps:
+    - name: Checkout code
+      uses: actions/checkout@v3
+
+    - name: Set up Python
+      uses: actions/setup-python@v4
+      with:
+        python-version: '3.9'
+
+    - name: Install dependencies
+      run: |
+        python -m pip install --upgrade pip
+        pip install poetry
+        poetry install --with dev  # Install dependencies from pyproject.toml, including dev dependencies
+
+    - name: Run tests
+      run: |
+        poetry run pytest unit_tests/ --maxfail=5 --disable-warnings -v --tb=short  # Add verbose and short traceback options
+
+    - name: Publish test results
+      if: failure()
+      uses: actions/upload-artifact@v3
+      with:
+        name: pytest-results
+        path: ./test-results/pytest-report.xml

unit_tests/test_FuzzyART.py

@@ -0,0 +1,136 @@
+import pytest
+import numpy as np
+from unittest.mock import MagicMock
+from artlib.elementary.FuzzyART import FuzzyART
+
+# Assuming BaseART is imported and available in the current namespace
+
+@pytest.fixture
+def art_model():
+    # Fixture that sets up the model before each test
+    params = {
+        'rho': 0.5,
+        'alpha': 0.0,
+        'beta': 1.0,
+    }
+    return FuzzyART(**params)
+
+def test_initialization(art_model):
+    # Test that the ART model initializes correctly
+    assert art_model.params == {'rho': 0.5, 'alpha': 0.0, 'beta': 1.0}
+    assert art_model.sample_counter_ == 0
+    assert art_model.weight_sample_counter_ == []
+
+def test_set_get_params(art_model):
+    # Test set_params and get_params functions
+    new_params = {'rho': 0.7, 'alpha': 0.05, 'beta': 0.9}
+    art_model.set_params(**new_params)
+    assert art_model.get_params() == new_params
+    assert art_model.rho == 0.7
+    assert art_model.alpha == 0.05
+    assert art_model.beta == 0.9
+
+def test_attribute_access(art_model):
+    # Test dynamic attribute access and setting using params
+    assert art_model.rho == 0.5
+    art_model.rho = 0.8
+    assert art_model.rho == 0.8
+
+def test_invalid_attribute(art_model):
+    # Test accessing an invalid attribute
+    with pytest.raises(AttributeError):
+        art_model.non_existing_attribute
+
+def test_prepare_restore_data(art_model):
+    # Test data normalization and denormalization
+    X = np.array([[1, 2], [3, 4], [5, 6]])
+    normalized_X = art_model.prepare_data(X)
+    restored_X = art_model.restore_data(normalized_X)
+    np.testing.assert_array_almost_equal(restored_X, X)
+
+def test_check_dimensions(art_model):
+    # Test check_dimensions with valid data
+    X = np.array([[1, 2], [3, 4]])
+    art_model.check_dimensions(X)
+    assert art_model.dim_ == 2
+
+    # Test check_dimensions with invalid data (mismatch)
+    X_invalid = np.array([[1, 2, 3]])
+    with pytest.raises(AssertionError):
+        art_model.check_dimensions(X_invalid)
+
+def test_match_tracking(art_model):
+    # Test match tracking with different methods
+    cache = {'match_criterion': 0.5}
+    art_model._match_tracking(cache, epsilon=0.01, params=art_model.params, method='MT+')
+    assert art_model.rho == 0.51
+
+    art_model._match_tracking(cache, epsilon=0.01, params=art_model.params, method='MT-')
+    assert art_model.rho == 0.49
+
+    art_model._match_tracking(cache, epsilon=0.01, params=art_model.params, method='MT0')
+    assert art_model.rho == 0.5
+
+    art_model._match_tracking(cache, epsilon=0.01, params=art_model.params, method='MT~')
+    assert art_model.rho == 0.5
+
+    art_model._match_tracking(cache, epsilon=0.01, params=art_model.params, method='MT1')
+    assert np.isinf(art_model.rho)
+
+def test_step_fit(art_model):
+    # Test step_fit for creating new clusters
+    X = np.array([[0.1, 0.2], [0.3, 0.4]])
+    X = art_model.prepare_data(X)
+    art_model.W = []
+    art_model.new_weight = MagicMock(return_value=np.array([0.1, 0.2]))
+    art_model.add_weight = MagicMock()
+    art_model.update = MagicMock(return_value=np.array([0.15, 0.25]))
+    art_model.category_choice = MagicMock(return_value=(1.0, None))
+    art_model.match_criterion_bin = MagicMock(return_value=(True, {}))
+
+    label = art_model.step_fit(X[0])
+    assert label == 0
+    art_model.add_weight.assert_called_once()
+
+def test_partial_fit(art_model):
+    # Test partial_fit
+    X = np.array([[0.1, 0.2], [0.3, 0.4]])
+    X = art_model.prepare_data(X)
+    art_model.new_weight = MagicMock(return_value=np.array([0.1, 0.2]))
+    art_model.add_weight = MagicMock()
+    art_model.update = MagicMock(return_value=np.array([0.15, 0.25]))
+    art_model.category_choice = MagicMock(return_value=(1.0, None))
+    art_model.match_criterion_bin = MagicMock(return_value=(True, {}))
+
+    art_model.partial_fit(X)
+    art_model.add_weight.assert_called()
+
+def test_predict(art_model):
+    # Test predict function
+    X = np.array([[0.1, 0.2], [0.3, 0.4]])
+    X = art_model.prepare_data(X)
+    art_model.category_choice = MagicMock(return_value=(1.0, None))
+    art_model.step_pred = MagicMock(return_value=0)
+
+    labels = art_model.predict(X)
+    np.testing.assert_array_equal(labels, [0, 0])
+
+
+def test_clustering(art_model):
+    new_params = {'rho': 0.9, 'alpha': 0.05, 'beta': 1.0}
+    art_model.set_params(**new_params)
+
+    data = np.array(
+        [
+            [0.0, 0.0],
+            [0.0, 0.08],
+            [0.0, 1.0],
+            [1.0, 1.0],
+            [1.0, 0.0]
+        ]
+    )
+    data = art_model.prepare_data(data)
+    labels = art_model.fit_predict(data)
+
+    assert np.all(np.equal(labels, np.array([0, 0, 1, 2, 3])))
+