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Add POI feature and imporve the overall backend implementation

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This commit is contained in:
Urtzi Alfaro
2025-11-12 15:34:10 +01:00
parent e8096cd979
commit 5783c7ed05
173 changed files with 16862 additions and 9078 deletions
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19
services/training/app/ml/trainer.py
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@@ -210,9 +210,23 @@ class EnhancedBakeryMLTrainer:
# Process data for each product using enhanced processor
logger.info("Processing data using enhanced processor")
processed_data = await self._process_all_products_enhanced(
sales_df, weather_df, traffic_df, products, tenant_id, job_id, session
sales_df, weather_df, traffic_df, products, tenant_id, job_id, training_dataset.poi_features, session
)
# Validate that we have processed data
if not processed_data or len(processed_data) == 0:
error_msg = f"No products could be processed successfully. Found {len(products)} products in sales data but all failed during processing."
logger.error("Training aborted - no processed data",
tenant_id=tenant_id,
job_id=job_id,
products_found=len(products),
products_processed=0)
raise ValueError(error_msg)
logger.info(f"Successfully processed {len(processed_data)} out of {len(products)} products",
products_processed=len(processed_data),
products_found=len(products))
# Categorize all products for category-specific forecasting
logger.info("Categorizing products for optimized forecasting")
product_categories = await self._categorize_all_products(
@@ -491,6 +505,7 @@ class EnhancedBakeryMLTrainer:
products: List[str],
tenant_id: str,
job_id: str,
poi_features: Dict[str, Any],
session=None) -> Dict[str, pd.DataFrame]:
"""Process data for all products using enhanced processor with repository tracking"""
processed_data = {}
@@ -515,6 +530,7 @@ class EnhancedBakeryMLTrainer:
weather_data=weather_df,
traffic_data=traffic_df,
inventory_product_id=inventory_product_id,
poi_features=poi_features, # POI features for location-based forecasting
tenant_id=tenant_id,
job_id=job_id,
session=session # Pass the session to avoid creating new ones
@@ -1132,6 +1148,7 @@ class EnhancedBakeryMLTrainer:
weather_data=test_weather_df,
traffic_data=test_traffic_df,
inventory_product_id=inventory_product_id,
poi_features=test_dataset.poi_features, # POI features for location-based forecasting
tenant_id=tenant_id
)