fix(training): patch lightgbm sklearn compatibility

backend/services/predict_service.py

@@ -4,6 +4,7 @@ import joblib
 import numpy as np
 
 import config
+from core.deep_learning_model import load_lstm_mlp_bundle, predict_lstm_mlp
 from core.model_features import (
     align_feature_frame,
     apply_label_encoders,
@@ -20,6 +21,7 @@ MODEL_INFO = {
     'gradient_boosting': {'name': 'gradient_boosting', 'name_cn': 'GBDT', 'description': '梯度提升决策树'},
     'extra_trees': {'name': 'extra_trees', 'name_cn': '极端随机树', 'description': '高随机性的树模型'},
     'stacking': {'name': 'stacking', 'name_cn': 'Stacking集成', 'description': '多模型融合'},
+    'lstm_mlp': {'name': 'lstm_mlp', 'name_cn': 'LSTM+MLP', 'description': '时序与静态特征融合的深度学习模型'},
 }
 
 
@@ -50,6 +52,7 @@ class PredictService:
             'gradient_boosting': 'gradient_boosting_model.pkl',
             'extra_trees': 'extra_trees_model.pkl',
             'stacking': 'stacking_model.pkl',
+            'lstm_mlp': 'lstm_mlp_model.pt',
         }
         allowed_models = self.training_metadata.get('available_models')
         if allowed_models:
@@ -59,7 +62,12 @@ class PredictService:
             path = os.path.join(config.MODELS_DIR, filename)
             if os.path.exists(path):
                 try:
-                    self.models[name] = joblib.load(path)
+                    if name == 'lstm_mlp':
+                        bundle = load_lstm_mlp_bundle(path)
+                        if bundle is not None:
+                            self.models[name] = bundle
+                    else:
+                        self.models[name] = joblib.load(path)
                 except Exception as exc:
                     print(f'Failed to load model {name}: {exc}')
 
@@ -107,8 +115,12 @@ class PredictService:
 
         features = self._prepare_features(data)
         try:
-            predicted_hours = self.models[model_type].predict([features])[0]
-            predicted_hours = self._inverse_transform_prediction(predicted_hours)
+            if model_type == 'lstm_mlp':
+                current_df = build_prediction_dataframe(data)
+                predicted_hours = predict_lstm_mlp(self.models[model_type], current_df)
+            else:
+                predicted_hours = self.models[model_type].predict([features])[0]
+                predicted_hours = self._inverse_transform_prediction(predicted_hours)
             predicted_hours = max(0.5, float(predicted_hours))
         except Exception:
             return self._get_default_prediction(data)
@@ -196,6 +208,8 @@ class PredictService:
                 'test_samples': self.training_metadata.get('test_samples', 0),
                 'feature_count': self.training_metadata.get('feature_count_after_selection', 0),
                 'training_date': self.training_metadata.get('training_date', ''),
+                'sequence_window_size': self.training_metadata.get('sequence_window_size', 0),
+                'deep_learning_available': self.training_metadata.get('deep_learning_available', False),
             },
         }