fix:评估对齐

backend/core/deep_learning_model.py

@@ -506,7 +506,7 @@ def train_lstm_mlp(
     val_seq_num, val_seq_cat, val_static_num, val_static_cat, y_val = _build_sequence_arrays(
         validation_df, feature_layout, category_maps, target_transform
     )
-    test_seq_num, test_seq_cat, test_static_num, test_static_cat, _ = _build_sequence_arrays(
+    test_seq_num, test_seq_cat, test_static_num, test_static_cat, y_test_aligned = _build_sequence_arrays(
         test_df, feature_layout, category_maps, target_transform
     )
 
@@ -620,9 +620,10 @@ def train_lstm_mlp(
 
     if target_transform == 'log1p':
         y_pred = np.expm1(predictions)
+        y_true = np.expm1(y_test_aligned)
     else:
         y_pred = predictions
-    y_true = test_df[config.TARGET_COLUMN].astype(float).to_numpy(dtype=np.float32)
+        y_true = y_test_aligned
     y_pred = np.clip(y_pred, a_min=0, a_max=None)
     mse = mean_squared_error(y_true, y_pred)
 

backend/core/train_model.py

@@ -56,11 +56,15 @@ def patch_lightgbm_sklearn_compatibility():
         return
 
     params = inspect.signature(check_X_y).parameters
-    if 'force_all_finite' in params or 'ensure_all_finite' not in params:
+    if 'force_all_finite' in params:
         return
 
     def wrapped_check_X_y(*args, force_all_finite=None, **kwargs):
-        if force_all_finite is not None and 'ensure_all_finite' not in kwargs:
+        if (
+            force_all_finite is not None
+            and 'ensure_all_finite' in params
+            and 'ensure_all_finite' not in kwargs
+        ):
             kwargs['ensure_all_finite'] = force_all_finite
         return check_X_y(*args, **kwargs)