Update result.py

result.py

@@ -1,12 +1,74 @@
 import argparse
+import pandas as pd
+import joblib
+from sklearn.metrics import f1_score, precision_score, recall_score, classification_report, confusion_matrix
+import json
 
-def main():
-    parser = argparse.ArgumentParser()
-    parser.add_argument('--results-path', type=str, required=True, help='Path to results file')
-    args = parser.parse_args()
 
-    print(f"Analyzing results from {args.results_path}")
-    # Add analysis logic here
+def evaluate_results():
+    dataset_path = "data/processed_train_dataset.csv"
+    model_path = "model/best_model.pkl"
+    
+    # Load dataset and model
+    df = pd.read_csv(dataset_path)
+    model = joblib.load(model_path)
 
-if __name__ == '__main__':
-    main()
+    features = [
+        'product_category_1',
+        'product_category_2',
+        'user_depth',
+        'age_level',
+        'city_development_index',
+        'var_1',
+        'gender'
+    ]
+
+    # Prepare the data
+    X = df[features]
+    y = df['is_click']
+    X = pd.get_dummies(X, columns=['gender'], drop_first=True)
+
+    # Train-test split
+    _, X_test, _, y_test = train_test_split(X, y, test_size=0.3, random_state=42)
+
+    # Predict
+    y_pred = model.predict(X_test)
+
+    # Calculate metrics
+    f1 = f1_score(y_test, y_pred)
+    precision = precision_score(y_test, y_pred, zero_division=True)
+    recall = recall_score(y_test, y_pred)
+
+    # Save results
+    metrics = {
+        'f1_score': f1,
+        'precision': precision,
+        'recall': recall
+    }
+
+    with open('results/metrics.json', 'w') as f:
+        json.dump(metrics, f, indent=4)
+
+    # Classification report and confusion matrix
+    report = classification_report(y_test, y_pred)
+    matrix = confusion_matrix(y_test, y_pred)
+
+    with open('results/classification_report.txt', 'w') as f:
+        f.write(report)
+
+    with open('results/confusion_matrix.txt', 'w') as f:
+        f.write(str(matrix))
+
+    print("Results saved in the 'results' folder.")
+
+    
+# def main():
+#     parser = argparse.ArgumentParser()
+#     parser.add_argument('--results-path', type=str, required=True, help='Path to results file')
+#     args = parser.parse_args()
+
+#     print(f"Analyzing results from {args.results_path}")
+#     # Add analysis logic here
+
+# if __name__ == '__main__':
+#     main()