""" Enhanced Data Processor for Training Service with Repository Pattern Uses repository pattern for data access and dependency injection """ import pandas as pd import numpy as np from typing import Dict, List, Any, Optional, Tuple from datetime import datetime, timedelta, timezone import structlog from sklearn.preprocessing import StandardScaler from sklearn.impute import SimpleImputer from app.services.date_alignment_service import DateAlignmentService, DateRange, DataSourceType from app.repositories import ModelRepository, TrainingLogRepository from shared.database.base import create_database_manager from shared.database.transactions import transactional from shared.database.exceptions import DatabaseError from app.core.config import settings from app.ml.enhanced_features import AdvancedFeatureEngineer import holidays logger = structlog.get_logger() class EnhancedBakeryDataProcessor: """ Enhanced data processor for bakery forecasting with repository pattern. Integrates date alignment, data cleaning, feature engineering, and preparation for ML models. """ def __init__(self, database_manager=None, region: str = 'MD'): self.database_manager = database_manager or create_database_manager(settings.DATABASE_URL, "training-service") self.scalers = {} # Store scalers for each feature self.imputers = {} # Store imputers for missing value handling self.date_alignment_service = DateAlignmentService() self.feature_engineer = AdvancedFeatureEngineer() self.region = region # Region for holidays (MD=Madrid, PV=Basque, etc.) self.spain_holidays = holidays.Spain(prov=region) # Initialize holidays library def get_scalers(self) -> Dict[str, Any]: """Return the scalers/normalization parameters for use during prediction""" return self.scalers.copy() @staticmethod def _extract_numeric_from_dict(value: Any) -> Optional[float]: """ Robust extraction of numeric values from complex data structures. Handles various dict structures that might come from external APIs. Args: value: Any value that might be a dict, numeric, or other type Returns: Numeric value as float, or None if extraction fails """ # If already numeric, return it if isinstance(value, (int, float)) and not isinstance(value, bool): return float(value) # If it's a dict, try multiple extraction strategies if isinstance(value, dict): # Strategy 1: Try common keys for key in ['value', 'data', 'result', 'amount', 'count', 'number', 'val']: if key in value: extracted = value[key] # Recursively extract if nested if isinstance(extracted, dict): return EnhancedBakeryDataProcessor._extract_numeric_from_dict(extracted) elif isinstance(extracted, (int, float)) and not isinstance(extracted, bool): return float(extracted) # Strategy 2: Try to find first numeric value in dict for v in value.values(): if isinstance(v, (int, float)) and not isinstance(v, bool): return float(v) elif isinstance(v, dict): # Recursively try nested dicts result = EnhancedBakeryDataProcessor._extract_numeric_from_dict(v) if result is not None: return result # Strategy 3: Try to convert string to numeric if isinstance(value, str): try: return float(value) except (ValueError, TypeError): pass # If all strategies fail, return None (will be converted to NaN) return None async def _get_repositories(self, session): """Initialize repositories with session""" return { 'model': ModelRepository(session), 'training_log': TrainingLogRepository(session) } def _ensure_timezone_aware(self, df: pd.DataFrame, date_column: str = 'date') -> pd.DataFrame: """Ensure date column is timezone-aware to prevent conversion errors""" if date_column in df.columns: # Convert to datetime if not already df[date_column] = pd.to_datetime(df[date_column]) # If timezone-naive, localize to UTC if df[date_column].dt.tz is None: df[date_column] = df[date_column].dt.tz_localize('UTC') # If already timezone-aware but not UTC, convert to UTC elif str(df[date_column].dt.tz) != 'UTC': df[date_column] = df[date_column].dt.tz_convert('UTC') return df async def prepare_training_data(self, sales_data: pd.DataFrame, weather_data: pd.DataFrame, traffic_data: pd.DataFrame, inventory_product_id: str, tenant_id: str = None, job_id: str = None, session=None) -> pd.DataFrame: """ Prepare comprehensive training data for a specific product with repository logging. Args: sales_data: Historical sales data for the product weather_data: Weather data traffic_data: Traffic data inventory_product_id: Inventory product UUID for logging tenant_id: Optional tenant ID for tracking job_id: Optional job ID for tracking Returns: DataFrame ready for Prophet training with 'ds' and 'y' columns plus features """ try: logger.info("Preparing enhanced training data using repository pattern", inventory_product_id=inventory_product_id, tenant_id=tenant_id, job_id=job_id) # Use provided session if available, otherwise create one if session is None: logger.debug("Creating new session for data preparation", inventory_product_id=inventory_product_id) async with self.database_manager.get_session() as db_session: repos = await self._get_repositories(db_session) # Log data preparation start if we have tracking info if job_id and tenant_id: logger.debug("About to update training log progress", inventory_product_id=inventory_product_id, job_id=job_id) await repos['training_log'].update_log_progress( job_id, 15, f"preparing_data_{inventory_product_id}", "running" ) logger.debug("Updated training log progress", inventory_product_id=inventory_product_id, job_id=job_id) # Commit the created session await db_session.commit() logger.debug("Committed session after data preparation progress update", inventory_product_id=inventory_product_id) else: logger.debug("Using provided session for data preparation", inventory_product_id=inventory_product_id) # Use the provided session repos = await self._get_repositories(session) # Log data preparation start if we have tracking info if job_id and tenant_id: logger.debug("About to update training log progress with provided session", inventory_product_id=inventory_product_id, job_id=job_id) await repos['training_log'].update_log_progress( job_id, 15, f"preparing_data_{inventory_product_id}", "running" ) logger.debug("Updated training log progress with provided session", inventory_product_id=inventory_product_id, job_id=job_id) # Don't commit the provided session as the caller manages it logger.debug("Updated progress with provided session", inventory_product_id=inventory_product_id) logger.debug("Starting Step 1: Convert and validate sales data", inventory_product_id=inventory_product_id) # Step 1: Convert and validate sales data sales_clean = await self._process_sales_data(sales_data, inventory_product_id) logger.debug("Step 1 completed: Convert and validate sales data", inventory_product_id=inventory_product_id, sales_records=len(sales_clean)) logger.debug("Starting Step 2: Ensure timezone awareness", inventory_product_id=inventory_product_id) # FIX: Ensure timezone awareness before any operations sales_clean = self._ensure_timezone_aware(sales_clean) weather_data = self._ensure_timezone_aware(weather_data) if not weather_data.empty else weather_data traffic_data = self._ensure_timezone_aware(traffic_data) if not traffic_data.empty else traffic_data logger.debug("Step 2 completed: Ensure timezone awareness", inventory_product_id=inventory_product_id, weather_records=len(weather_data) if not weather_data.empty else 0, traffic_records=len(traffic_data) if not traffic_data.empty else 0) logger.debug("Starting Step 3: Apply date alignment", inventory_product_id=inventory_product_id) # Step 2: Apply date alignment if we have date constraints sales_clean = await self._apply_date_alignment(sales_clean, weather_data, traffic_data) logger.debug("Step 3 completed: Apply date alignment", inventory_product_id=inventory_product_id, sales_records=len(sales_clean)) logger.debug("Starting Step 4: Aggregate to daily level", inventory_product_id=inventory_product_id) # Step 3: Aggregate to daily level daily_sales = await self._aggregate_daily_sales(sales_clean) logger.debug("Step 4 completed: Aggregate to daily level", inventory_product_id=inventory_product_id, daily_records=len(daily_sales)) logger.debug("Starting Step 5: Add temporal features", inventory_product_id=inventory_product_id) # Step 4: Add temporal features daily_sales = self._add_temporal_features(daily_sales) logger.debug("Step 5 completed: Add temporal features", inventory_product_id=inventory_product_id, features_added=True) logger.debug("Starting Step 6: Merge external data sources", inventory_product_id=inventory_product_id) # Step 5: Merge external data sources daily_sales = self._merge_weather_features(daily_sales, weather_data) daily_sales = self._merge_traffic_features(daily_sales, traffic_data) logger.debug("Step 6 completed: Merge external data sources", inventory_product_id=inventory_product_id, merged_successfully=True) logger.debug("Starting Step 7: Engineer basic features", inventory_product_id=inventory_product_id) # Step 6: Engineer basic features daily_sales = self._engineer_features(daily_sales) logger.debug("Step 7 completed: Engineer basic features", inventory_product_id=inventory_product_id, feature_columns=len([col for col in daily_sales.columns if col not in ['date', 'quantity']])) logger.debug("Starting Step 8: Add advanced features", inventory_product_id=inventory_product_id) # Step 6b: Add advanced features (lagged, rolling, cyclical, interactions, trends) daily_sales = self._add_advanced_features(daily_sales) logger.debug("Step 8 completed: Add advanced features", inventory_product_id=inventory_product_id, total_features=len(daily_sales.columns)) logger.debug("Starting Step 9: Handle missing values", inventory_product_id=inventory_product_id) # Step 7: Handle missing values daily_sales = self._handle_missing_values(daily_sales) logger.debug("Step 9 completed: Handle missing values", inventory_product_id=inventory_product_id, missing_values_handled=True) logger.debug("Starting Step 10: Prepare for Prophet format", inventory_product_id=inventory_product_id) # Step 8: Prepare for Prophet (rename columns and validate) prophet_data = self._prepare_prophet_format(daily_sales) logger.debug("Step 10 completed: Prepare for Prophet format", inventory_product_id=inventory_product_id, prophet_records=len(prophet_data)) logger.debug("Starting Step 11: Store processing metadata", inventory_product_id=inventory_product_id) # Step 9: Store processing metadata if we have a tenant if tenant_id: await self._store_processing_metadata( repos, tenant_id, inventory_product_id, prophet_data, job_id, session ) logger.debug("Step 11 completed: Store processing metadata", inventory_product_id=inventory_product_id) logger.info("Enhanced training data prepared successfully", inventory_product_id=inventory_product_id, data_points=len(prophet_data)) return prophet_data except Exception as e: logger.error("Error preparing enhanced training data", inventory_product_id=inventory_product_id, error=str(e), exc_info=True) raise async def _store_processing_metadata(self, repos: Dict, tenant_id: str, inventory_product_id: str, processed_data: pd.DataFrame, job_id: str = None, session=None): """Store data processing metadata using repository""" try: # Create processing metadata metadata = { "inventory_product_id": inventory_product_id, "data_points": len(processed_data), "date_range": { "start": processed_data['ds'].min().isoformat(), "end": processed_data['ds'].max().isoformat() }, "features_count": len([col for col in processed_data.columns if col not in ['ds', 'y']]), "processed_at": datetime.now().isoformat() } # Log processing completion if job_id: await repos['training_log'].update_log_progress( job_id, 25, f"data_prepared_{inventory_product_id}", "running" ) # If we have a session and it's not managed elsewhere, commit it if session is not None: # Don't commit here as the caller will manage the session pass logger.debug("Data preparation metadata stored", inventory_product_id=inventory_product_id) except Exception as e: logger.warning("Failed to store processing metadata", error=str(e)) async def prepare_prediction_features(self, future_dates: pd.DatetimeIndex, weather_forecast: pd.DataFrame = None, traffic_forecast: pd.DataFrame = None, historical_data: pd.DataFrame = None) -> pd.DataFrame: """ Create features for future predictions with proper date handling. Args: future_dates: Future dates to predict weather_forecast: Weather forecast data traffic_forecast: Traffic forecast data historical_data: Historical data for creating lagged and rolling features Returns: DataFrame with features for prediction """ try: # Create base future dataframe future_df = pd.DataFrame({'ds': future_dates}) # Add temporal features future_df = self._add_temporal_features( future_df.rename(columns={'ds': 'date'}) ).rename(columns={'date': 'ds'}) # Add weather features if weather_forecast is not None and not weather_forecast.empty: weather_features = weather_forecast.copy() if 'date' in weather_features.columns: weather_features = weather_features.rename(columns={'date': 'ds'}) future_df = future_df.merge(weather_features, on='ds', how='left') # Add traffic features if traffic_forecast is not None and not traffic_forecast.empty: traffic_features = traffic_forecast.copy() if 'date' in traffic_features.columns: traffic_features = traffic_features.rename(columns={'date': 'ds'}) future_df = future_df.merge(traffic_features, on='ds', how='left') # Engineer basic features future_df = self._engineer_features(future_df.rename(columns={'ds': 'date'})) # Add advanced features if historical data is provided if historical_data is not None and not historical_data.empty: # Combine historical and future data to calculate lagged/rolling features combined_df = pd.concat([ historical_data.rename(columns={'ds': 'date'}), future_df ], ignore_index=True).sort_values('date') # Apply advanced features to combined data combined_df = self._add_advanced_features(combined_df) # Extract only the future rows future_df = combined_df[combined_df['date'].isin(future_df['date'])].copy() else: # Without historical data, add advanced features with NaN for lags logger.warning("No historical data provided, lagged features will be NaN") future_df = self._add_advanced_features(future_df) future_df = future_df.rename(columns={'date': 'ds'}) # Handle missing values in future data future_df = self._handle_missing_values_future(future_df) return future_df except Exception as e: logger.error("Error creating prediction features", error=str(e)) # Return minimal features if error return pd.DataFrame({'ds': future_dates}) async def _apply_date_alignment(self, sales_data: pd.DataFrame, weather_data: pd.DataFrame, traffic_data: pd.DataFrame) -> pd.DataFrame: """ Apply date alignment constraints to ensure data consistency across sources. """ try: if sales_data.empty: return sales_data # Create date range from sales data sales_dates = pd.to_datetime(sales_data['date']) sales_date_range = DateRange( start=sales_dates.min(), end=sales_dates.max(), source=DataSourceType.BAKERY_SALES ) # Get aligned date range considering all constraints aligned_range = self.date_alignment_service.validate_and_align_dates( user_sales_range=sales_date_range ) # Filter sales data to aligned range mask = (sales_dates >= aligned_range.start) & (sales_dates <= aligned_range.end) filtered_sales = sales_data[mask].copy() logger.info("Date alignment completed", original_records=len(sales_data), filtered_records=len(filtered_sales), date_range=f"{aligned_range.start.date()} to {aligned_range.end.date()}") if aligned_range.constraints: logger.info("Applied constraints", constraints=aligned_range.constraints) return filtered_sales except Exception as e: logger.warning("Date alignment failed, using original data", error=str(e)) return sales_data async def _process_sales_data(self, sales_data: pd.DataFrame, inventory_product_id: str) -> pd.DataFrame: """Process and clean sales data with enhanced validation""" logger.debug("Starting sales data processing", inventory_product_id=inventory_product_id, total_records=len(sales_data), columns=list(sales_data.columns)) sales_clean = sales_data.copy() logger.debug("Checking for date column existence", inventory_product_id=inventory_product_id) # Ensure date column exists and is datetime if 'date' not in sales_clean.columns: logger.error("Sales data must have a 'date' column", inventory_product_id=inventory_product_id, available_columns=list(sales_data.columns)) raise ValueError("Sales data must have a 'date' column") logger.debug("Converting date column to datetime", inventory_product_id=inventory_product_id) sales_clean['date'] = pd.to_datetime(sales_clean['date']) logger.debug("Date conversion completed", inventory_product_id=inventory_product_id) # Handle different quantity column names quantity_columns = ['quantity', 'quantity_sold', 'sales', 'units_sold'] logger.debug("Looking for quantity column", inventory_product_id=inventory_product_id, quantity_columns=quantity_columns) quantity_col = None for col in quantity_columns: if col in sales_clean.columns: quantity_col = col logger.debug("Found quantity column", inventory_product_id=inventory_product_id, quantity_column=col) break if quantity_col is None: logger.error("Sales data must have one of the expected quantity columns", inventory_product_id=inventory_product_id, expected_columns=quantity_columns, available_columns=list(sales_clean.columns)) raise ValueError(f"Sales data must have one of these columns: {quantity_columns}") # Standardize to 'quantity' if quantity_col != 'quantity': logger.debug("Mapping quantity column", inventory_product_id=inventory_product_id, from_column=quantity_col, to_column='quantity') sales_clean['quantity'] = sales_clean[quantity_col] logger.info("Mapped quantity column", from_column=quantity_col, to_column='quantity') logger.debug("Converting quantity to numeric", inventory_product_id=inventory_product_id) sales_clean['quantity'] = pd.to_numeric(sales_clean['quantity'], errors='coerce') logger.debug("Quantity conversion completed", inventory_product_id=inventory_product_id, non_numeric_count=sales_clean['quantity'].isna().sum()) # Remove rows with invalid quantities logger.debug("Removing rows with invalid quantities", inventory_product_id=inventory_product_id) sales_clean = sales_clean.dropna(subset=['quantity']) logger.debug("NaN rows removed", inventory_product_id=inventory_product_id, remaining_records=len(sales_clean)) sales_clean = sales_clean[sales_clean['quantity'] >= 0] # No negative sales logger.debug("Negative sales removed", inventory_product_id=inventory_product_id, remaining_records=len(sales_clean)) # Filter for the specific product if inventory_product_id column exists logger.debug("Checking for inventory_product_id column", inventory_product_id=inventory_product_id, has_inventory_column='inventory_product_id' in sales_clean.columns) if 'inventory_product_id' in sales_clean.columns: logger.debug("Filtering for specific product", inventory_product_id=inventory_product_id, products_in_data=sales_clean['inventory_product_id'].unique()[:5].tolist()) # Show first 5 original_count = len(sales_clean) sales_clean = sales_clean[sales_clean['inventory_product_id'] == inventory_product_id] logger.debug("Product filtering completed", inventory_product_id=inventory_product_id, original_count=original_count, filtered_count=len(sales_clean)) # Remove duplicate dates (keep the one with highest quantity) logger.debug("Removing duplicate dates", inventory_product_id=inventory_product_id, before_dedupe=len(sales_clean)) sales_clean = sales_clean.sort_values(['date', 'quantity'], ascending=[True, False]) sales_clean = sales_clean.drop_duplicates(subset=['date'], keep='first') logger.debug("Duplicate dates removed", inventory_product_id=inventory_product_id, after_dedupe=len(sales_clean)) logger.debug("Sales data processing completed", inventory_product_id=inventory_product_id, final_records=len(sales_clean)) return sales_clean async def _aggregate_daily_sales(self, sales_data: pd.DataFrame) -> pd.DataFrame: """Aggregate sales to daily level with improved date handling""" logger.debug("Starting daily sales aggregation", input_records=len(sales_data), columns=list(sales_data.columns)) if sales_data.empty: logger.debug("Sales data is empty, returning empty DataFrame") return pd.DataFrame(columns=['date', 'quantity']) logger.debug("Starting groupby aggregation", unique_dates=sales_data['date'].nunique(), date_range=(sales_data['date'].min(), sales_data['date'].max())) # Group by date and sum quantities daily_sales = sales_data.groupby('date').agg({ 'quantity': 'sum' }).reset_index() logger.debug("Groupby aggregation completed", aggregated_records=len(daily_sales)) # Ensure we have data for all dates in the range (fill gaps with 0) logger.debug("Creating full date range", start_date=daily_sales['date'].min(), end_date=daily_sales['date'].max()) date_range = pd.date_range( start=daily_sales['date'].min(), end=daily_sales['date'].max(), freq='D' ) logger.debug("Date range created", total_dates=len(date_range)) full_date_df = pd.DataFrame({'date': date_range}) logger.debug("Starting merge to fill missing dates", full_date_records=len(full_date_df), aggregated_records=len(daily_sales)) daily_sales = full_date_df.merge(daily_sales, on='date', how='left') logger.debug("Missing date filling merge completed", final_records=len(daily_sales)) daily_sales['quantity'] = daily_sales['quantity'].fillna(0) # Fill missing days with 0 sales logger.debug("NaN filling completed", remaining_nan_count=daily_sales['quantity'].isna().sum(), zero_filled_count=(daily_sales['quantity'] == 0).sum()) logger.debug("Daily sales aggregation completed", final_records=len(daily_sales), final_columns=len(daily_sales.columns)) return daily_sales def _add_temporal_features(self, df: pd.DataFrame) -> pd.DataFrame: """Add comprehensive temporal features for bakery demand patterns""" df = df.copy() # Ensure we have a date column if 'date' not in df.columns: raise ValueError("DataFrame must have a 'date' column") df['date'] = pd.to_datetime(df['date']) # Basic temporal features df['day_of_week'] = df['date'].dt.dayofweek # 0=Monday, 6=Sunday df['day_of_month'] = df['date'].dt.day df['month'] = df['date'].dt.month df['quarter'] = df['date'].dt.quarter df['week_of_year'] = df['date'].dt.isocalendar().week # Bakery-specific features df['is_weekend'] = df['day_of_week'].isin([5, 6]).astype(int) df['is_monday'] = (df['day_of_week'] == 0).astype(int) # Monday often has different patterns df['is_friday'] = (df['day_of_week'] == 4).astype(int) # Friday often busy # Season mapping for Madrid df['season'] = df['month'].apply(self._get_season) df['is_summer'] = (df['season'] == 3).astype(int) # Summer seasonality df['is_winter'] = (df['season'] == 1).astype(int) # Winter seasonality # Holiday and special day indicators df['is_holiday'] = df['date'].apply(self._is_spanish_holiday).astype(int) df['is_school_holiday'] = df['date'].apply(self._is_school_holiday).astype(int) df['is_month_start'] = (df['day_of_month'] <= 3).astype(int) df['is_month_end'] = (df['day_of_month'] >= 28).astype(int) # Payday patterns (common in Spain: end/beginning of month) df['is_payday_period'] = ((df['day_of_month'] <= 5) | (df['day_of_month'] >= 25)).astype(int) return df def _merge_weather_features(self, daily_sales: pd.DataFrame, weather_data: pd.DataFrame) -> pd.DataFrame: """Merge weather features with enhanced Madrid-specific handling""" logger.debug("Starting weather features merge", daily_sales_records=len(daily_sales), weather_data_records=len(weather_data) if not weather_data.empty else 0, weather_columns=list(weather_data.columns) if not weather_data.empty else []) # Define weather_defaults OUTSIDE try block to fix scope error weather_defaults = { 'temperature': 15.0, 'precipitation': 0.0, 'humidity': 60.0, 'wind_speed': 5.0, 'pressure': 1013.0 } if weather_data.empty: logger.debug("Weather data is empty, adding default columns") # Add default weather columns for feature, default_value in weather_defaults.items(): daily_sales[feature] = default_value logger.debug("Default weather columns added", features_added=list(weather_defaults.keys())) return daily_sales try: weather_clean = weather_data.copy() logger.debug("Weather data copied", records=len(weather_clean), columns=list(weather_clean.columns)) # Standardize date column if 'date' not in weather_clean.columns and 'ds' in weather_clean.columns: logger.debug("Renaming ds column to date") weather_clean = weather_clean.rename(columns={'ds': 'date'}) # CRITICAL FIX: Ensure both DataFrames have compatible datetime formats logger.debug("Converting weather data date column to datetime") weather_clean['date'] = pd.to_datetime(weather_clean['date']) logger.debug("Converting daily sales date column to datetime") daily_sales['date'] = pd.to_datetime(daily_sales['date']) # NEW FIX: Normalize both to timezone-naive datetime for merge compatibility if weather_clean['date'].dt.tz is not None: logger.debug("Removing timezone from weather data") weather_clean['date'] = weather_clean['date'].dt.tz_convert('UTC').dt.tz_localize(None) if daily_sales['date'].dt.tz is not None: logger.debug("Removing timezone from daily sales data") daily_sales['date'] = daily_sales['date'].dt.tz_convert('UTC').dt.tz_localize(None) # Map weather columns to standard names weather_mapping = { 'temperature': ['temperature', 'temp', 'temperatura'], 'precipitation': ['precipitation', 'precip', 'rain', 'lluvia'], 'humidity': ['humidity', 'humedad', 'relative_humidity'], 'wind_speed': ['wind_speed', 'viento', 'wind'], 'pressure': ['pressure', 'presion', 'atmospheric_pressure'] } weather_features = ['date'] logger.debug("Mapping weather columns", mapping_attempts=list(weather_mapping.keys())) for standard_name, possible_names in weather_mapping.items(): for possible_name in possible_names: if possible_name in weather_clean.columns: logger.debug("Processing weather column", standard_name=standard_name, possible_name=possible_name, records=len(weather_clean)) # Extract numeric values using robust helper function try: # Check if column contains dict-like objects logger.debug("Checking for dict objects in weather column") has_dicts = weather_clean[possible_name].apply(lambda x: isinstance(x, dict)).any() logger.debug("Dict object check completed", has_dicts=has_dicts) if has_dicts: logger.warning(f"Weather column {possible_name} contains dict objects, extracting numeric values") # Use robust extraction for all values weather_clean[standard_name] = weather_clean[possible_name].apply( self._extract_numeric_from_dict ) logger.debug("Dict extraction completed for weather column", extracted_column=standard_name, extracted_count=weather_clean[standard_name].notna().sum()) else: # Direct numeric conversion for simple values logger.debug("Performing direct numeric conversion") weather_clean[standard_name] = pd.to_numeric(weather_clean[possible_name], errors='coerce') logger.debug("Direct numeric conversion completed") except Exception as e: logger.warning(f"Error converting weather column {possible_name}: {e}") # Fallback: try to extract from each value weather_clean[standard_name] = weather_clean[possible_name].apply( self._extract_numeric_from_dict ) weather_features.append(standard_name) logger.debug("Added weather feature to list", feature=standard_name) break # Keep only the features we found logger.debug("Selecting weather features", selected_features=weather_features) weather_clean = weather_clean[weather_features].copy() # Merge with sales data logger.debug("Starting merge operation", daily_sales_rows=len(daily_sales), weather_rows=len(weather_clean), date_range_sales=(daily_sales['date'].min(), daily_sales['date'].max()) if len(daily_sales) > 0 else None, date_range_weather=(weather_clean['date'].min(), weather_clean['date'].max()) if len(weather_clean) > 0 else None) merged = daily_sales.merge(weather_clean, on='date', how='left') logger.debug("Merge completed", merged_rows=len(merged), merge_type='left') # Fill missing weather values with Madrid-appropriate defaults logger.debug("Filling missing weather values", features_to_fill=list(weather_defaults.keys())) for feature, default_value in weather_defaults.items(): if feature in merged.columns: logger.debug("Processing feature for NaN fill", feature=feature, nan_count=merged[feature].isna().sum()) # Ensure the column is numeric before filling merged[feature] = pd.to_numeric(merged[feature], errors='coerce') merged[feature] = merged[feature].fillna(default_value) logger.debug("NaN fill completed for feature", feature=feature, final_nan_count=merged[feature].isna().sum()) logger.debug("Weather features merge completed", final_rows=len(merged), final_columns=len(merged.columns)) return merged except Exception as e: logger.warning("Error merging weather data", error=str(e), exc_info=True) # Add default weather columns if merge fails for feature, default_value in weather_defaults.items(): daily_sales[feature] = default_value logger.debug("Default weather columns added after merge failure", features_added=list(weather_defaults.keys())) return daily_sales def _merge_traffic_features(self, daily_sales: pd.DataFrame, traffic_data: pd.DataFrame) -> pd.DataFrame: """Merge traffic features with enhanced Madrid-specific handling""" logger.debug("Starting traffic features merge", daily_sales_records=len(daily_sales), traffic_data_records=len(traffic_data) if not traffic_data.empty else 0, traffic_columns=list(traffic_data.columns) if not traffic_data.empty else []) if traffic_data.empty: logger.debug("Traffic data is empty, adding default column") # Add default traffic column daily_sales['traffic_volume'] = 100.0 # Neutral traffic level logger.debug("Default traffic column added", default_value=100.0) return daily_sales try: traffic_clean = traffic_data.copy() logger.debug("Traffic data copied", records=len(traffic_clean), columns=list(traffic_clean.columns)) # Standardize date column if 'date' not in traffic_clean.columns and 'ds' in traffic_clean.columns: logger.debug("Renaming ds column to date") traffic_clean = traffic_clean.rename(columns={'ds': 'date'}) # CRITICAL FIX: Ensure both DataFrames have compatible datetime formats logger.debug("Converting traffic data date column to datetime") traffic_clean['date'] = pd.to_datetime(traffic_clean['date']) logger.debug("Converting daily sales date column to datetime") daily_sales['date'] = pd.to_datetime(daily_sales['date']) # NEW FIX: Normalize both to timezone-naive datetime for merge compatibility if traffic_clean['date'].dt.tz is not None: logger.debug("Removing timezone from traffic data") traffic_clean['date'] = traffic_clean['date'].dt.tz_convert('UTC').dt.tz_localize(None) if daily_sales['date'].dt.tz is not None: logger.debug("Removing timezone from daily sales data") daily_sales['date'] = daily_sales['date'].dt.tz_convert('UTC').dt.tz_localize(None) # Map traffic columns to standard names traffic_mapping = { 'traffic_volume': ['traffic_volume', 'traffic_intensity', 'trafico', 'intensidad', 'volume'], 'pedestrian_count': ['pedestrian_count', 'peatones', 'pedestrians'], 'congestion_level': ['congestion_level', 'congestion', 'nivel_congestion'], 'average_speed': ['average_speed', 'speed', 'velocidad_media', 'avg_speed'] } traffic_features = ['date'] logger.debug("Mapping traffic columns", mapping_attempts=list(traffic_mapping.keys())) for standard_name, possible_names in traffic_mapping.items(): for possible_name in possible_names: if possible_name in traffic_clean.columns: logger.debug("Processing traffic column", standard_name=standard_name, possible_name=possible_name, records=len(traffic_clean)) # Extract numeric values using robust helper function try: # Check if column contains dict-like objects logger.debug("Checking for dict objects in traffic column") has_dicts = traffic_clean[possible_name].apply(lambda x: isinstance(x, dict)).any() logger.debug("Dict object check completed", has_dicts=has_dicts) if has_dicts: logger.warning(f"Traffic column {possible_name} contains dict objects, extracting numeric values") # Use robust extraction for all values traffic_clean[standard_name] = traffic_clean[possible_name].apply( self._extract_numeric_from_dict ) logger.debug("Dict extraction completed for traffic column", extracted_column=standard_name, extracted_count=traffic_clean[standard_name].notna().sum()) else: # Direct numeric conversion for simple values logger.debug("Performing direct numeric conversion") traffic_clean[standard_name] = pd.to_numeric(traffic_clean[possible_name], errors='coerce') logger.debug("Direct numeric conversion completed") except Exception as e: logger.warning(f"Error converting traffic column {possible_name}: {e}") # Fallback: try to extract from each value traffic_clean[standard_name] = traffic_clean[possible_name].apply( self._extract_numeric_from_dict ) traffic_features.append(standard_name) logger.debug("Added traffic feature to list", feature=standard_name) break # Keep only the features we found logger.debug("Selecting traffic features", selected_features=traffic_features) traffic_clean = traffic_clean[traffic_features].copy() # Merge with sales data logger.debug("Starting traffic merge operation", daily_sales_rows=len(daily_sales), traffic_rows=len(traffic_clean), date_range_sales=(daily_sales['date'].min(), daily_sales['date'].max()) if len(daily_sales) > 0 else None, date_range_traffic=(traffic_clean['date'].min(), traffic_clean['date'].max()) if len(traffic_clean) > 0 else None) merged = daily_sales.merge(traffic_clean, on='date', how='left') logger.debug("Traffic merge completed", merged_rows=len(merged), merge_type='left') # Fill missing traffic values with reasonable defaults traffic_defaults = { 'traffic_volume': 100.0, 'pedestrian_count': 50.0, 'congestion_level': 1.0, # Low congestion 'average_speed': 30.0 # km/h typical for Madrid } logger.debug("Filling missing traffic values", features_to_fill=list(traffic_defaults.keys())) for feature, default_value in traffic_defaults.items(): if feature in merged.columns: logger.debug("Processing traffic feature for NaN fill", feature=feature, nan_count=merged[feature].isna().sum()) # Ensure the column is numeric before filling merged[feature] = pd.to_numeric(merged[feature], errors='coerce') merged[feature] = merged[feature].fillna(default_value) logger.debug("NaN fill completed for traffic feature", feature=feature, final_nan_count=merged[feature].isna().sum()) logger.debug("Traffic features merge completed", final_rows=len(merged), final_columns=len(merged.columns)) return merged except Exception as e: logger.warning("Error merging traffic data", error=str(e), exc_info=True) # Add default traffic column if merge fails daily_sales['traffic_volume'] = 100.0 logger.debug("Default traffic column added after merge failure", default_value=100.0) return daily_sales def _engineer_features(self, df: pd.DataFrame) -> pd.DataFrame: """Engineer additional features from existing data with bakery-specific insights""" df = df.copy() # Weather-based features if 'temperature' in df.columns: # Ensure temperature is numeric (defensive check) df['temperature'] = pd.to_numeric(df['temperature'], errors='coerce').fillna(15.0) df['temp_squared'] = df['temperature'] ** 2 df['is_hot_day'] = (df['temperature'] > 25).astype(int) # Hot days in Madrid df['is_cold_day'] = (df['temperature'] < 10).astype(int) # Cold days df['is_pleasant_day'] = ((df['temperature'] >= 18) & (df['temperature'] <= 25)).astype(int) # Temperature categories for bakery products df['temp_category'] = pd.cut(df['temperature'], bins=[-np.inf, 5, 15, 25, np.inf], labels=[0, 1, 2, 3]).astype(int) if 'precipitation' in df.columns: # Ensure precipitation is numeric (defensive check) df['precipitation'] = pd.to_numeric(df['precipitation'], errors='coerce').fillna(0.0) df['is_rainy_day'] = (df['precipitation'] > 0.1).astype(int) df['is_heavy_rain'] = (df['precipitation'] > 10).astype(int) df['rain_intensity'] = pd.cut(df['precipitation'], bins=[-0.1, 0, 2, 10, np.inf], labels=[0, 1, 2, 3]).astype(int) # Traffic-based features with NaN protection if 'traffic_volume' in df.columns: # Ensure traffic_volume is numeric (defensive check) df['traffic_volume'] = pd.to_numeric(df['traffic_volume'], errors='coerce').fillna(100.0) # Calculate traffic quantiles for relative measures q75 = df['traffic_volume'].quantile(0.75) q25 = df['traffic_volume'].quantile(0.25) df['high_traffic'] = (df['traffic_volume'] > q75).astype(int) df['low_traffic'] = (df['traffic_volume'] < q25).astype(int) # Safe normalization with NaN protection traffic_std = df['traffic_volume'].std() traffic_mean = df['traffic_volume'].mean() 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) # Ensure other weather features are numeric if they exist for weather_col in ['humidity', 'wind_speed', 'pressure', 'pedestrian_count', 'congestion_level', 'average_speed']: if weather_col in df.columns: df[weather_col] = pd.to_numeric(df[weather_col], errors='coerce').fillna( {'humidity': 60.0, 'wind_speed': 5.0, 'pressure': 1013.0, 'pedestrian_count': 50.0, 'congestion_level': 1.0, 'average_speed': 30.0}.get(weather_col, 0.0) ) # Interaction features - bakery specific if 'is_weekend' in df.columns and 'temperature' in df.columns: df['weekend_temp_interaction'] = df['is_weekend'] * df['temperature'] df['weekend_pleasant_weather'] = df['is_weekend'] * df.get('is_pleasant_day', 0) if 'is_rainy_day' in df.columns and 'traffic_volume' in df.columns: df['rain_traffic_interaction'] = df['is_rainy_day'] * df['traffic_volume'] if 'is_holiday' in df.columns and 'temperature' in df.columns: df['holiday_temp_interaction'] = df['is_holiday'] * df['temperature'] # Seasonal interactions if 'season' in df.columns and 'temperature' in df.columns: df['season_temp_interaction'] = df['season'] * df['temperature'] # Day-of-week specific features if 'day_of_week' in df.columns: # Working days vs weekends df['is_working_day'] = (~df['day_of_week'].isin([5, 6])).astype(int) # Peak bakery days (Friday, Saturday, Sunday often busy) df['is_peak_bakery_day'] = df['day_of_week'].isin([4, 5, 6]).astype(int) # Month-specific features for bakery seasonality if 'month' in df.columns: # High-demand months (holidays, summer) df['is_high_demand_month'] = df['month'].isin([6, 7, 8, 12]).astype(int) # Spring/summer months df['is_warm_season'] = df['month'].isin([4, 5, 6, 7, 8, 9]).astype(int) # FINAL SAFETY CHECK: Remove any remaining NaN values numeric_columns = df.select_dtypes(include=[np.number]).columns for col in numeric_columns: if df[col].isna().any(): nan_count = df[col].isna().sum() logger.warning("Found NaN values in column, filling with 0", column=col, nan_count=nan_count) df[col] = df[col].fillna(0.0) return df def _add_advanced_features(self, df: pd.DataFrame) -> pd.DataFrame: """ Add advanced features using AdvancedFeatureEngineer. Includes lagged features, rolling statistics, cyclical encoding, and trend features. """ df = df.copy() logger.info("Adding advanced features (lagged, rolling, cyclical, trends)", input_rows=len(df), input_columns=len(df.columns)) # Log column dtypes to identify potential issues logger.debug("Input dataframe dtypes", dtypes={col: str(dtype) for col, dtype in df.dtypes.items()}, date_column_exists='date' in df.columns) # Reset feature engineer to clear previous features logger.debug("Initializing AdvancedFeatureEngineer") self.feature_engineer = AdvancedFeatureEngineer() # Create all advanced features at once logger.debug("Starting creation of advanced features", include_lags=True, include_rolling=True, include_interactions=True, include_cyclical=True) df = self.feature_engineer.create_all_features( df, date_column='date', include_lags=True, include_rolling=True, include_interactions=True, include_cyclical=True ) logger.debug("Advanced features creation completed", output_rows=len(df), output_columns=len(df.columns)) # Fill NA values from lagged and rolling features logger.debug("Starting NA value filling", na_counts={col: df[col].isna().sum() for col in df.columns if df[col].isna().any()}) df = self.feature_engineer.fill_na_values(df, strategy='forward_backward') logger.debug("NA value filling completed", remaining_na_counts={col: df[col].isna().sum() for col in df.columns if df[col].isna().any()}) # Store created feature columns for later reference created_features = self.feature_engineer.get_feature_columns() logger.info(f"Added {len(created_features)} advanced features", features=created_features[:10]) # Log first 10 for brevity return df def _handle_missing_values(self, df: pd.DataFrame) -> pd.DataFrame: """Handle missing values in the dataset with improved strategies""" df = df.copy() # For numeric columns, use appropriate imputation strategies numeric_columns = df.select_dtypes(include=[np.number]).columns for col in numeric_columns: if col != 'quantity' and df[col].isna().any(): # Use different strategies based on column type if 'temperature' in col: df[col] = df[col].fillna(15.0) # Madrid average elif 'precipitation' in col or 'rain' in col: df[col] = df[col].fillna(0.0) # Default no rain elif 'humidity' in col: df[col] = df[col].fillna(60.0) # Moderate humidity elif 'traffic' in col: df[col] = df[col].fillna(df[col].median()) # Use median for traffic elif 'wind' in col: df[col] = df[col].fillna(5.0) # Light wind elif 'pressure' in col: df[col] = df[col].fillna(1013.0) # Standard atmospheric pressure else: # For other columns, use median or forward fill if df[col].count() > 0: df[col] = df[col].fillna(df[col].median()) else: df[col] = df[col].fillna(0) return df def _handle_missing_values_future(self, df: pd.DataFrame) -> pd.DataFrame: """Handle missing values in future prediction data""" numeric_columns = df.select_dtypes(include=[np.number]).columns madrid_defaults = { 'temperature': 15.0, 'precipitation': 0.0, 'humidity': 60.0, 'wind_speed': 5.0, 'traffic_volume': 100.0, 'pedestrian_count': 50.0, 'pressure': 1013.0 } for col in numeric_columns: if df[col].isna().any(): # Find appropriate default value default_value = 0 for key, value in madrid_defaults.items(): if key in col.lower(): default_value = value break df[col] = df[col].fillna(default_value) return df def _prepare_prophet_format(self, df: pd.DataFrame) -> pd.DataFrame: """Prepare data in Prophet format with enhanced validation""" prophet_df = df.copy() # Rename columns for Prophet if 'date' in prophet_df.columns: prophet_df = prophet_df.rename(columns={'date': 'ds'}) if 'quantity' in prophet_df.columns: prophet_df = prophet_df.rename(columns={'quantity': 'y'}) # Ensure ds is datetime and remove timezone info if 'ds' in prophet_df.columns: prophet_df['ds'] = pd.to_datetime(prophet_df['ds']) if prophet_df['ds'].dt.tz is not None: prophet_df['ds'] = prophet_df['ds'].dt.tz_localize(None) # Validate required columns if 'ds' not in prophet_df.columns or 'y' not in prophet_df.columns: raise ValueError("Prophet data must have 'ds' and 'y' columns") # Clean target values prophet_df = prophet_df.dropna(subset=['y']) prophet_df['y'] = prophet_df['y'].clip(lower=0) # No negative sales # Remove any duplicate dates (keep last occurrence) prophet_df = prophet_df.drop_duplicates(subset=['ds'], keep='last') # Sort by date prophet_df = prophet_df.sort_values('ds').reset_index(drop=True) # Final validation if len(prophet_df) == 0: raise ValueError("No valid data points after cleaning") logger.info("Prophet data prepared", rows=len(prophet_df), date_range=f"{prophet_df['ds'].min()} to {prophet_df['ds'].max()}") return prophet_df def _get_season(self, month: int) -> int: """Get season from month (1-4 for Winter, Spring, Summer, Autumn)""" if month in [12, 1, 2]: return 1 # Winter elif month in [3, 4, 5]: return 2 # Spring elif month in [6, 7, 8]: return 3 # Summer else: return 4 # Autumn def _is_spanish_holiday(self, date: datetime) -> bool: """ Check if a date is a Spanish holiday using holidays library. Supports dynamic Easter calculation and regional holidays. """ try: # Convert to date if datetime if isinstance(date, datetime): date = date.date() elif isinstance(date, pd.Timestamp): date = date.date() # Check if date is in holidays return date in self.spain_holidays except Exception as e: logger.warning(f"Error checking holiday status for {date}: {e}") # Fallback to checking basic holidays month_day = (date.month, date.day) basic_holidays = [ (1, 1), (1, 6), (5, 1), (8, 15), (10, 12), (11, 1), (12, 6), (12, 8), (12, 25) ] return month_day in basic_holidays def _is_school_holiday(self, date: datetime) -> bool: """ Check if a date is during school holidays in Spain. Uses dynamic Easter calculation and standard Spanish school calendar. """ try: from datetime import timedelta import holidays as hol # Convert to date if datetime if isinstance(date, datetime): check_date = date.date() elif isinstance(date, pd.Timestamp): check_date = date.date() else: check_date = date month = check_date.month day = check_date.day # Summer holidays (July 1 - August 31) if month in [7, 8]: return True # Christmas holidays (December 23 - January 7) if (month == 12 and day >= 23) or (month == 1 and day <= 7): return True # Easter/Spring break (Semana Santa) # Calculate Easter for this year year = check_date.year spain_hol = hol.Spain(years=year, prov=self.region) # Find Easter dates (Viernes Santo - Good Friday, and nearby days) # Easter break typically spans 1 week before and after Easter Sunday for holiday_date, holiday_name in spain_hol.items(): if 'viernes santo' in holiday_name.lower() or 'easter' in holiday_name.lower(): # Easter break: 7 days before and 7 days after easter_start = holiday_date - timedelta(days=7) easter_end = holiday_date + timedelta(days=7) if easter_start <= check_date <= easter_end: return True return False except Exception as e: logger.warning(f"Error checking school holiday for {date}: {e}") # Fallback to simple approximation month = date.month if hasattr(date, 'month') else date.month day = date.day if hasattr(date, 'day') else date.day return (month in [7, 8] or (month == 12 and day >= 23) or (month == 1 and day <= 7) or (month == 4 and 1 <= day <= 15)) # Approximate Easter async def calculate_feature_importance(self, model_data: pd.DataFrame, target_column: str = 'y') -> Dict[str, float]: """ Calculate feature importance for the model using correlation analysis with repository logging. """ try: # Get numeric features numeric_features = model_data.select_dtypes(include=[np.number]).columns numeric_features = [col for col in numeric_features if col != target_column] importance_scores = {} if target_column not in model_data.columns: logger.warning("Target column not found", target_column=target_column) return {} for feature in numeric_features: if feature in model_data.columns: correlation = model_data[feature].corr(model_data[target_column]) if not pd.isna(correlation) and not np.isinf(correlation): importance_scores[feature] = abs(correlation) # Sort by importance importance_scores = dict(sorted(importance_scores.items(), key=lambda x: x[1], reverse=True)) logger.info("Calculated feature importance", features_count=len(importance_scores)) return importance_scores except Exception as e: logger.error("Error calculating feature importance", error=str(e)) return {} async def get_data_quality_report(self, df: pd.DataFrame) -> Dict[str, Any]: """ Generate a comprehensive data quality report with repository integration. """ try: report = { "total_records": len(df), "date_range": { "start": df['ds'].min().isoformat() if 'ds' in df.columns else None, "end": df['ds'].max().isoformat() if 'ds' in df.columns else None, "duration_days": (df['ds'].max() - df['ds'].min()).days if 'ds' in df.columns else 0 }, "missing_values": {}, "data_completeness": 0.0, "target_statistics": {}, "feature_count": 0 } # Calculate missing values missing_counts = df.isnull().sum() total_cells = len(df) for col in df.columns: missing_count = missing_counts[col] report["missing_values"][col] = { "count": int(missing_count), "percentage": round((missing_count / total_cells) * 100, 2) } # Overall completeness total_missing = missing_counts.sum() total_possible = len(df) * len(df.columns) report["data_completeness"] = round(((total_possible - total_missing) / total_possible) * 100, 2) # Target variable statistics if 'y' in df.columns: y_col = df['y'] report["target_statistics"] = { "mean": round(y_col.mean(), 2), "median": round(y_col.median(), 2), "std": round(y_col.std(), 2), "min": round(y_col.min(), 2), "max": round(y_col.max(), 2), "zero_count": int((y_col == 0).sum()), "zero_percentage": round(((y_col == 0).sum() / len(y_col)) * 100, 2) } # Feature count numeric_features = df.select_dtypes(include=[np.number]).columns report["feature_count"] = len([col for col in numeric_features if col not in ['y', 'ds']]) return report except Exception as e: logger.error("Error generating data quality report", error=str(e)) return {"error": str(e)}