formatting

Author: SamoraHunter

ml_grid/pipeline/grid_search_cross_validate.py

@@ -64,6 +64,7 @@
     H2OStackedEnsembleClassifier,
 )
 
+
 class grid_search_crossvalidate:
 
     def __init__(
@@ -441,10 +442,10 @@ def __init__(
             "train_score": np.array([0.5]),
             "test_recall": np.array([0.5]),
         }
-        
+
         failed = False
         scores = None
-        
+
         # Initialize start_time early
         start_time = time.time()
 

ml_grid/pipeline/main.py

@@ -24,7 +24,9 @@ def time_limit(seconds):
     try:
         seconds_int = int(seconds)
     except (ValueError, TypeError):
-        logging.getLogger("ml_grid").warning(f"Invalid timeout value: {seconds}. Timeout disabled.")
+        logging.getLogger("ml_grid").warning(
+            f"Invalid timeout value: {seconds}. Timeout disabled."
+        )
         yield
         return
 
@@ -33,9 +35,12 @@ def time_limit(seconds):
         return
 
     if not hasattr(signal, "SIGALRM"):
-        logging.getLogger("ml_grid").warning("Timeout not supported on this platform (SIGALRM missing).")
+        logging.getLogger("ml_grid").warning(
+            "Timeout not supported on this platform (SIGALRM missing)."
+        )
         yield
         return
+
     def signal_handler(signum, frame):
         raise TimeoutError(f"Timeout of {seconds}s reached")
 
@@ -65,6 +70,7 @@ def signal_handler(signum, frame):
             remaining_outer = max(1, int(previous_remaining - elapsed))
             signal.alarm(remaining_outer)
 
+
 class run:
     """Orchestrates the hyperparameter search for a list of models."""
 
@@ -294,16 +300,18 @@ def execute_single_model(self, args: Tuple) -> float:
         """
         try:
             self.logger.info(f"Starting grid search for {args[2]}...")
-            
+
             # Retrieve timeout from local_param_dict via ml_grid_object (args[3])
             timeout = args[3].local_param_dict.get("model_eval_time_limit")
             if timeout is None:
                 timeout = args[3].global_params.model_eval_time_limit
-            
+
             with time_limit(timeout):
-                gscv_instance = grid_search_cross_validate.grid_search_crossvalidate(*args)
+                gscv_instance = grid_search_cross_validate.grid_search_crossvalidate(
+                    *args
+                )
                 score = gscv_instance.grid_search_cross_validate_score_result
-            
+
             self.logger.info(f"Score for {args[2]}: {score:.4f}")
             return score
 
@@ -364,7 +372,7 @@ def multi_run_wrapper(args: Tuple) -> Any:
                     self.logger.info(
                         f"Starting grid search for {self.arg_list[k][2]}..."
                     )
-                    
+
                     timeout = self.local_param_dict.get("model_eval_time_limit")
                     if timeout is None:
                         timeout = self.global_params.model_eval_time_limit
@@ -383,7 +391,9 @@ def multi_run_wrapper(args: Tuple) -> Any:
                     self.logger.info(f"Current highest score: {self.highest_score:.4f}")
 
                 except TimeoutError as e:
-                    self.logger.warning(f"Timeout occurred for {self.arg_list[k][2]}: {e}")
+                    self.logger.warning(
+                        f"Timeout occurred for {self.arg_list[k][2]}: {e}"
+                    )
                     self.model_error_list.append(
                         [self.arg_list[k][0], e, traceback.format_exc()]
                     )

ml_grid/util/project_score_save.py

@@ -190,7 +190,11 @@ def update_score_log(
             # --- OPTIMIZATION: Pre-process targets for faster metric calculation ---
             # Convert to numpy arrays to avoid pandas overhead in sklearn metrics
             y_test_np = y_test.values if hasattr(y_test, "values") else y_test
-            best_pred_np = best_pred_orig.values if hasattr(best_pred_orig, "values") else best_pred_orig
+            best_pred_np = (
+                best_pred_orig.values
+                if hasattr(best_pred_orig, "values")
+                else best_pred_orig
+            )
 
             # Ensure 1D arrays to prevent shape mismatch errors
             y_test_np = np.ravel(y_test_np)