add: tests for numerical perturbation detector

tests/scan/test_numerical_perturbation_detector.py

@@ -13,3 +13,75 @@ class MockRegressionModel:
     def predict(self, dataset):
         # For simplicity, use a linear relationship plus some noise
         return (2 * dataset['feature_1'] + 3 * dataset['feature_2'] + np.random.normal(0, 5, len(dataset)))
+
+def test_numerical_perturbation_classification():
+    # Creating a simple mock classification dataset
+    df = pd.DataFrame({
+        'feature_1': [1.0, 2.0, 3.0, 4.0, 5.0],
+        'feature_2': [10.0, 20.0, 30.0, 40.0, 50.0],
+        'target': [0, 1, 1, 0, 0]
+    })
+    dataset = giskard.Dataset(df=df, target='target', column_types={'feature_1': 'numeric', 'feature_2': 'numeric'})
+
+    # Creating a mock model that always predicts 1
+    model = giskard.Model(MockClassificationModel().predict, model_type="classification", classification_labels=[0, 1])
+
+    # Running the Numerical Perturbation Detector
+    analyzer = NumericalPerturbationDetector(perturbation_fraction=0.1, threshold=0.01)
+    issues = analyzer.run(model, dataset, features=['feature_1', 'feature_2'])
+
+    assert issues  # Ensure that the detector identifies some issues
+
+
+def test_numerical_perturbation_skips_non_numerical_dtypes():
+    # Mock dataset with a text feature, but declared as numeric
+    df = pd.DataFrame({'feature': ["a", "b", "c", "d", "e"], 'target': [0, 1, 0, 1, 0]})
+    dataset = giskard.Dataset(df, target='target', column_types={'feature': 'text'})
+
+    # Creating a mock model that always predicts 1
+    model = giskard.Model(lambda df: np.ones(len(df)), model_type="classification", classification_labels=[0, 1])
+
+    # Running the Numerical Perturbation Detector
+    analyzer = NumericalPerturbationDetector(threshold=0.001, perturbation_fraction=0.5, output_sensitivity=1.0, num_samples=100)
+
+    issues = analyzer.run(model, dataset, features=['feature'])
+
+    assert not issues  # Since the feature is non-numeric, no issues should be detected
+
+
+def test_numerical_perturbation_works_with_nan_values():
+    # Mock dataset with NaN values in numeric feature
+    df = pd.DataFrame({
+        'feature': [1.0, 2.0, np.nan, 4.0, 5.0],
+        'target': [0, 1, 0, 1, 0]
+    })
+    dataset = giskard.Dataset(df, target='target', column_types={'feature': 'numeric'})
+
+    # Creating a mock model that always predicts 0
+    model = giskard.Model(lambda df: np.zeros(len(df)), model_type="classification", classification_labels=[0, 1])
+
+    # Running the Numerical Perturbation Detector
+    analyzer = NumericalPerturbationDetector(perturbation_fraction=0.1, threshold=0.01)
+    issues = analyzer.run(model, dataset, features=['feature'])
+
+    assert len(issues) == 0  # No issues should be detected if NaN values are properly handled
+
+
+@pytest.mark.memory_expensive
+def test_numerical_perturbation_on_regression():
+    # Mock regression dataset
+    df = pd.DataFrame({
+        'feature_1': [1.0, 2.0, 3.0, 4.0, 5.0],
+        'feature_2': [10.0, 20.0, 30.0, 40.0, 50.0],
+        'target': [10, 20, 30, 40, 50]
+    })
+    dataset = giskard.Dataset(df, target='target', column_types={'feature_1': 'numeric', 'feature_2': 'numeric'})
+
+    # Mock regression model that predicts a constant value
+    model = giskard.Model(MockRegressionModel().predict, model_type="regression")
+
+    # Running the Numerical Perturbation Detector
+    analyzer = NumericalPerturbationDetector(perturbation_fraction=0.8, threshold=0.01)
+    issues = analyzer.run(model, dataset, features=['feature_1', 'feature_2'])
+
+    assert issues  # Ensure that some perturbation issues are detected