""" 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) # Get database session and repositories 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: await repos['training_log'].update_log_progress( job_id, 15, f"preparing_data_{inventory_product_id}", "running" ) # ✅ FIX: Commit the session to prevent deadlock with parent trainer session # The trainer has its own session, so we need to commit this update await db_session.commit() logger.debug("Committed session after data preparation progress update", 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) # 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 # Step 2: Apply date alignment if we have date constraints sales_clean = await self._apply_date_alignment(sales_clean, weather_data, traffic_data) # Step 3: Aggregate to daily level daily_sales = await self._aggregate_daily_sales(sales_clean) # Step 4: Add temporal features daily_sales = self._add_temporal_features(daily_sales) # 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) # Step 6: Engineer basic features daily_sales = self._engineer_features(daily_sales) # Step 6b: Add advanced features (lagged, rolling, cyclical, interactions, trends) daily_sales = self._add_advanced_features(daily_sales) # Step 7: Handle missing values daily_sales = self._handle_missing_values(daily_sales) # Step 8: Prepare for Prophet (rename columns and validate) prophet_data = self._prepare_prophet_format(daily_sales) # 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 ) 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)) raise async def _store_processing_metadata(self, repos: Dict, tenant_id: str, inventory_product_id: str, processed_data: pd.DataFrame, job_id: str = 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" ) 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""" sales_clean = sales_data.copy() # Ensure date column exists and is datetime if 'date' not in sales_clean.columns: raise ValueError("Sales data must have a 'date' column") sales_clean['date'] = pd.to_datetime(sales_clean['date']) # Handle different quantity column names quantity_columns = ['quantity', 'quantity_sold', 'sales', 'units_sold'] quantity_col = None for col in quantity_columns: if col in sales_clean.columns: quantity_col = col break if quantity_col is None: raise ValueError(f"Sales data must have one of these columns: {quantity_columns}") # Standardize to 'quantity' if quantity_col != 'quantity': sales_clean['quantity'] = sales_clean[quantity_col] logger.info("Mapped quantity column", from_column=quantity_col, to_column='quantity') sales_clean['quantity'] = pd.to_numeric(sales_clean['quantity'], errors='coerce') # Remove rows with invalid quantities sales_clean = sales_clean.dropna(subset=['quantity']) sales_clean = sales_clean[sales_clean['quantity'] >= 0] # No negative sales # Filter for the specific product if inventory_product_id column exists if 'inventory_product_id' in sales_clean.columns: sales_clean = sales_clean[sales_clean['inventory_product_id'] == inventory_product_id] # Remove duplicate dates (keep the one with highest quantity) sales_clean = sales_clean.sort_values(['date', 'quantity'], ascending=[True, False]) sales_clean = sales_clean.drop_duplicates(subset=['date'], keep='first') return sales_clean async def _aggregate_daily_sales(self, sales_data: pd.DataFrame) -> pd.DataFrame: """Aggregate sales to daily level with improved date handling""" if sales_data.empty: return pd.DataFrame(columns=['date', 'quantity']) # Group by date and sum quantities daily_sales = sales_data.groupby('date').agg({ 'quantity': 'sum' }).reset_index() # Ensure we have data for all dates in the range (fill gaps with 0) date_range = pd.date_range( start=daily_sales['date'].min(), end=daily_sales['date'].max(), freq='D' ) full_date_df = pd.DataFrame({'date': date_range}) daily_sales = full_date_df.merge(daily_sales, on='date', how='left') daily_sales['quantity'] = daily_sales['quantity'].fillna(0) # Fill missing days with 0 sales 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""" # 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: # Add default weather columns for feature, default_value in weather_defaults.items(): daily_sales[feature] = default_value return daily_sales try: weather_clean = weather_data.copy() # Standardize date column if 'date' not in weather_clean.columns and 'ds' in weather_clean.columns: weather_clean = weather_clean.rename(columns={'ds': 'date'}) # CRITICAL FIX: Ensure both DataFrames have compatible datetime formats weather_clean['date'] = pd.to_datetime(weather_clean['date']) 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: weather_clean['date'] = weather_clean['date'].dt.tz_convert('UTC').dt.tz_localize(None) if daily_sales['date'].dt.tz is not None: 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'] for standard_name, possible_names in weather_mapping.items(): for possible_name in possible_names: if possible_name in weather_clean.columns: # Extract numeric values using robust helper function try: # Check if column contains dict-like objects has_dicts = weather_clean[possible_name].apply(lambda x: isinstance(x, dict)).any() 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 ) else: # Direct numeric conversion for simple values weather_clean[standard_name] = pd.to_numeric(weather_clean[possible_name], errors='coerce') 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) break # Keep only the features we found weather_clean = weather_clean[weather_features].copy() # Merge with sales data merged = daily_sales.merge(weather_clean, on='date', how='left') # Fill missing weather values with Madrid-appropriate defaults for feature, default_value in weather_defaults.items(): if feature in merged.columns: # Ensure the column is numeric before filling merged[feature] = pd.to_numeric(merged[feature], errors='coerce') merged[feature] = merged[feature].fillna(default_value) return merged except Exception as e: logger.warning("Error merging weather data", error=str(e)) # Add default weather columns if merge fails for feature, default_value in weather_defaults.items(): daily_sales[feature] = default_value 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""" if traffic_data.empty: # Add default traffic column daily_sales['traffic_volume'] = 100.0 # Neutral traffic level return daily_sales try: traffic_clean = traffic_data.copy() # Standardize date column if 'date' not in traffic_clean.columns and 'ds' in traffic_clean.columns: traffic_clean = traffic_clean.rename(columns={'ds': 'date'}) # CRITICAL FIX: Ensure both DataFrames have compatible datetime formats traffic_clean['date'] = pd.to_datetime(traffic_clean['date']) 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: traffic_clean['date'] = traffic_clean['date'].dt.tz_convert('UTC').dt.tz_localize(None) if daily_sales['date'].dt.tz is not None: 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'] for standard_name, possible_names in traffic_mapping.items(): for possible_name in possible_names: if possible_name in traffic_clean.columns: # Extract numeric values using robust helper function try: # Check if column contains dict-like objects has_dicts = traffic_clean[possible_name].apply(lambda x: isinstance(x, dict)).any() 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 ) else: # Direct numeric conversion for simple values traffic_clean[standard_name] = pd.to_numeric(traffic_clean[possible_name], errors='coerce') 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) break # Keep only the features we found traffic_clean = traffic_clean[traffic_features].copy() # Merge with sales data merged = daily_sales.merge(traffic_clean, on='date', how='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 } for feature, default_value in traffic_defaults.items(): if feature in merged.columns: # Ensure the column is numeric before filling merged[feature] = pd.to_numeric(merged[feature], errors='coerce') merged[feature] = merged[feature].fillna(default_value) return merged except Exception as e: logger.warning("Error merging traffic data", error=str(e)) # Add default traffic column if merge fails daily_sales['traffic_volume'] = 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)") # Reset feature engineer to clear previous features self.feature_engineer = AdvancedFeatureEngineer() # Create all advanced features at once df = self.feature_engineer.create_all_features( df, date_column='date', include_lags=True, include_rolling=True, include_interactions=True, include_cyclical=True ) # Fill NA values from lagged and rolling features df = self.feature_engineer.fill_na_values(df, strategy='forward_backward') # 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)}