unlearning CR

Util.py

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+
+from pathlib import Path
+from architectures.Model import Model
+
+def _log_to_csv(model:Model, mode, accuracy, report_dict, strategy):
+        """Handles directory structures, file setups, and distinct CSV column formatting."""
+        arch_name = model.__class__.__name__.lower()
+        save_dir = Path(f"reports/{strategy}")
+        save_dir.mkdir(parents=True, exist_ok=True)
+        csv_path = save_dir / f"{arch_name}-{mode}.csv"
+        
+        file_exists = csv_path.exists()
+
+        headers = [
+                "accuracy", 
+                "macro_precision", "macro_recall", "macro_f1",
+                "weighted_precision", "weighted_recall", "weighted_f1"
+        ]
+        row = [
+            f"{accuracy / 100.0:.4f}",
+            f"{report_dict['macro avg']['precision']:.4f}",
+            f"{report_dict['macro avg']['recall']:.4f}",
+            f"{report_dict['macro avg']['f1-score']:.4f}",
+            f"{report_dict['weighted avg']['precision']:.4f}",
+            f"{report_dict['weighted avg']['recall']:.4f}",
+            f"{report_dict['weighted avg']['f1-score']:.4f}"
+        ]
+            
+        with open(csv_path, "a") as f:
+            if not file_exists:
+                f.write(",".join(headers) + "\n")
+            f.write(",".join(row) + "\n")
+            
+        print(f">> Direct CSV metrics appended to {csv_path}")