Add improvements

services/forecasting/app/services/prediction_service.py

@@ -195,7 +195,46 @@ class PredictionService:
 
             # Prepare features for Prophet model
             prophet_df = self._prepare_prophet_features(features)
-            
+
+            # CRITICAL FIX: Validate that model's required regressors are present
+            # Warn if using default values for features the model was trained with
+            if hasattr(model, 'extra_regressors'):
+                model_regressors = set(model.extra_regressors.keys()) if model.extra_regressors else set()
+                provided_features = set(prophet_df.columns) - {'ds'}
+
+                # Check for missing regressors
+                missing_regressors = model_regressors - provided_features
+
+                if missing_regressors:
+                    logger.warning(
+                        "Model trained with regressors that are missing in prediction",
+                        model_id=model_id,
+                        missing_regressors=list(missing_regressors)[:10],  # Log first 10
+                        total_missing=len(missing_regressors)
+                    )
+
+                # Check for default-valued critical features
+                critical_features = {
+                    'traffic_volume', 'temperature', 'precipitation',
+                    'lag_1_day', 'rolling_mean_7d'
+                }
+                using_defaults = []
+                for feature in critical_features:
+                    if feature in model_regressors:
+                        value = features.get(feature, 0)
+                        # Check if using default/fallback values
+                        if (feature == 'traffic_volume' and value == 100.0) or \
+                           (feature == 'temperature' and value == 15.0) or \
+                           (feature in ['lag_1_day', 'rolling_mean_7d'] and value == 0.0):
+                            using_defaults.append(feature)
+
+                if using_defaults:
+                    logger.warning(
+                        "Using default values for critical model features",
+                        model_id=model_id,
+                        features_with_defaults=using_defaults
+                    )
+
             # Generate prediction
             forecast = model.predict(prophet_df)
             
@@ -938,8 +977,9 @@ class PredictionService:
                 'is_month_end': int(forecast_date.day >= 28),
                 'is_payday_period': int((forecast_date.day <= 5) or (forecast_date.day >= 25)),
                 # CRITICAL FIX: Add is_payday feature to match training service
-                # Training defines: is_payday = (day == 15 OR is_month_end)
-                'is_payday': int((forecast_date.day == 15) or self._is_end_of_month(forecast_date)),
+                # Training defines: is_payday = (day == 15 OR day == 28 OR is_month_end)
+                # Spain commonly pays on 28th, 15th, or last day of month
+                'is_payday': int((forecast_date.day == 15) or (forecast_date.day == 28) or self._is_end_of_month(forecast_date)),
                 
                 # Weather-based derived features
                 'temp_squared': temperature ** 2,