Added tests for explain_model function. gssoc-extd

tests/test_utils.py

@@ -0,0 +1,61 @@
+import pytest
+from sklearn.linear_model import LinearRegression, LogisticRegression
+from sklearn.datasets import make_classification, make_regression
+from sklearn.model_selection import train_test_split
+from explainableai.utils import explain_model, calculate_metrics
+from dotenv import load_dotenv
+import os 
+load_dotenv()
+
+def test_explain_model_regression():
+    X, y = make_regression(n_samples=100, n_features=5, noise=0.1, random_state=42)
+    X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
+    model = LinearRegression()
+    model.fit(X_train, y_train)
+    feature_names = [f"feature_{i}" for i in range(X.shape[1])]
+
+    explanation = explain_model(model, X_train, y_train, X_test, y_test, feature_names)
+
+    assert "feature_importance" in explanation
+    assert "model_type" in explanation
+    assert explanation["model_type"] == str(type(model))
+    assert len(explanation["feature_importance"]) == X.shape[1]
+
+def test_explain_model_classification():
+    X, y = make_classification(n_samples=100, n_features=5, n_classes=2, random_state=42)
+    X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
+    model = LogisticRegression()
+    model.fit(X_train, y_train)
+    feature_names = [f"feature_{i}" for i in range(X.shape[1])]
+
+    explanation = explain_model(model, X_train, y_train, X_test, y_test, feature_names)
+
+    assert "feature_importance" in explanation
+    assert "model_type" in explanation
+    assert explanation["model_type"] == str(type(model))
+    assert len(explanation["feature_importance"]) == X.shape[1]
+
+def test_calculate_metrics_regression():
+    X, y = make_regression(n_samples=100, n_features=5, noise=0.1, random_state=42)
+    X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
+    model = LinearRegression()
+    model.fit(X_train, y_train)
+
+    metrics = calculate_metrics(model, X_test, y_test)
+
+    assert "mse" in metrics
+    assert "r2" in metrics
+
+def test_calculate_metrics_classification():
+    X, y = make_classification(n_samples=100, n_features=5, n_classes=2, random_state=42)
+    X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
+    model = LogisticRegression()
+    model.fit(X_train, y_train)
+
+    metrics = calculate_metrics(model, X_test, y_test)
+
+    assert "accuracy" in metrics
+    assert "f1_score" in metrics
+
+if __name__ == "__main__":
+    pytest.main()