Update initialize_system.py

initialize_system.py

@@ -332,12 +332,38 @@ def run_initial_training():
         test_acc_mean = cv_data['test_scores']['accuracy']['mean']
         test_acc_std = cv_data['test_scores']['accuracy']['std']
         
-        cv_data['performance_indicators'] = {
-            'overfitting_score': float(train_acc_mean - test_acc_mean),
-            'stability_score': float(1 - (test_acc_std / test_acc_mean)) if test_acc_mean > 0 else 0
+        cv_data = {
+            "methodology": {
+                "n_splits": 3,
+                "cv_type": "StratifiedKFold",
+                "random_state": 42
+            },
+            "test_scores": {
+                "accuracy": {
+                    "mean": test_acc_mean,
+                    "std": test_acc_std,
+                    "scores": cv_results['test_accuracy'].tolist()
+                },
+                "f1": {
+                    "mean": float(cv_results['test_f1_weighted'].mean()),
+                    "std": float(cv_results['test_f1_weighted'].std()),
+                    "scores": cv_results['test_f1_weighted'].tolist()
+                }
+            },
+            "train_scores": {
+                "accuracy": {
+                    "mean": train_acc_mean,
+                    "std": float(cv_results['train_accuracy'].std()),
+                    "scores": cv_results['train_accuracy'].tolist()
+                }
+            },
+            "performance_indicators": {
+                "overfitting_score": overfitting_score,
+                "stability_score": stability_score
+            }
         }
         
-        # Save CV results to file
+        # Save CV results
         cv_results_path = path_manager.get_logs_path("cv_results.json")
         with open(cv_results_path, 'w') as f:
             json.dump(cv_data, f, indent=2)