bakery-ia/services/training/app/ml/data_processor.py

"""
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

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):
        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()
    
    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"""
        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"
                    )
            
                # 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 additional features
                daily_sales = self._engineer_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) -> 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
            
        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 additional features
            future_df = self._engineer_features(future_df.rename(columns={'ds': 'date'}))
            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:
                        weather_clean[standard_name] = pd.to_numeric(weather_clean[possible_name], errors='coerce')
                        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:
                    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:
                        traffic_clean[standard_name] = pd.to_numeric(traffic_clean[possible_name], errors='coerce')
                        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:
                    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:
            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:
            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:
            # 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)
        
        # 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 _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 major Spanish holiday"""
        month_day = (date.month, date.day)
        
        # Major Spanish holidays that affect bakery sales
        spanish_holidays = [
            (1, 1),   # New Year
            (1, 6),   # Epiphany (Reyes)
            (5, 1),   # Labour Day
            (8, 15),  # Assumption
            (10, 12), # National Day
            (11, 1),  # All Saints
            (12, 6),  # Constitution
            (12, 8),  # Immaculate Conception
            (12, 25), # Christmas
            (5, 15),  # San Isidro (Madrid patron saint)
            (5, 2),   # Madrid Community Day
        ]
        
        return month_day in spanish_holidays
    
    def _is_school_holiday(self, date: datetime) -> bool:
        """Check if a date is during school holidays (approximate)"""
        month = date.month
        
        # Approximate Spanish school holiday periods
        # Summer holidays (July-August)
        if month in [7, 8]:
            return True
        
        # Christmas holidays (mid December to early January)
        if month == 12 and date.day >= 20:
            return True
        if month == 1 and date.day <= 10:
            return True
        
        # Easter holidays (approximate - early April)
        if month == 4 and date.day <= 15:
            return True
        
        return False
    
    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)}


# Legacy compatibility alias
BakeryDataProcessor = EnhancedBakeryDataProcessor