REFACTOR data service

services/training/app/ml/data_processor.py

@@ -31,6 +31,10 @@ class EnhancedBakeryDataProcessor:
         self.scalers = {}  # Store scalers for each feature
         self.imputers = {}  # Store imputers for missing value handling
         self.date_alignment_service = DateAlignmentService()
+    
+    def get_scalers(self) -> Dict[str, Any]:
+        """Return the scalers/normalization parameters for use during prediction"""
+        return self.scalers.copy()
         
     async def _get_repositories(self, session):
         """Initialize repositories with session"""
@@ -558,9 +562,19 @@ class EnhancedBakeryDataProcessor:
             
             if traffic_std > 0 and not pd.isna(traffic_std) and not pd.isna(traffic_mean):
                 df['traffic_normalized'] = (df['traffic_volume'] - traffic_mean) / traffic_std
+                
+                # Store normalization parameters for later use in predictions
+                self.scalers['traffic_mean'] = float(traffic_mean)
+                self.scalers['traffic_std'] = float(traffic_std)
+                
+                logger.info(f"Traffic normalization parameters: mean={traffic_mean:.2f}, std={traffic_std:.2f}")
             else:
                 logger.warning("Traffic volume has zero standard deviation, using zeros for normalized values")
                 df['traffic_normalized'] = 0.0
+                
+                # Store default parameters for consistency
+                self.scalers['traffic_mean'] = 100.0  # Default traffic level used during training
+                self.scalers['traffic_std'] = 50.0   # Reasonable std for traffic normalization
             
             # Fill any remaining NaN values
             df['traffic_normalized'] = df['traffic_normalized'].fillna(0.0)

services/training/app/ml/trainer.py

@@ -349,6 +349,7 @@ class EnhancedBakeryMLTrainer:
                 "training_samples": len(processed_data),
                 "hyperparameters": model_info.get("hyperparameters"),
                 "features_used": list(processed_data.columns),
+                "normalization_params": self.enhanced_data_processor.get_scalers(),  # Include scalers for prediction consistency
                 "is_active": True,
                 "is_production": True,
                 "data_quality_score": model_info.get("data_quality_score", 100.0)