feat: enhance logging and memory management across modules

research/model_trainer.py

@@ -28,13 +28,13 @@ class ModelTrainer:
         self.models_dir.mkdir(parents=True, exist_ok=True)
 
     def train_single_model(
-            self,
-            model_name: str,
-            model_type: str = "logistic_regression",
-            features: List[str] = None,
-            model_params: Dict[str, Any] = None,
-            tags: List[str] = None,
-            save_artifacts: bool = True,
+        self,
+        model_name: str,
+        model_type: str = "logistic_regression",
+        features: List[str] = None,
+        model_params: Dict[str, Any] = None,
+        tags: List[str] = None,
+        save_artifacts: bool = True,
     ) -> str:
         """
         Train a single model and save its artifacts.
@@ -76,10 +76,7 @@ class ModelTrainer:
         return experiment_id
 
     def train_multiple_models(
-            self,
-            base_name: str,
-            model_configs: List[Dict[str, Any]],
-            save_all: bool = True
+        self, base_name: str, model_configs: List[Dict[str, Any]], save_all: bool = True
     ) -> List[str]:
         """
         Train multiple models with different configurations.