Run Automated Multi-Model Demand Forecasting for Spare Parts with AutoGluon TimeSeries and XGBoost

"""
Production implementation for running automated multi-model demand forecasting for spare parts.
This module utilizes AutoGluon TimeSeries and XGBoost for scalable and efficient forecasting.
"""
import os
import logging
import pandas as pd
from typing import Dict, Any, List
from autogluon.timeseries import TimeSeriesPredictor
from sqlalchemy import create_engine
from sqlalchemy.engine import Engine
import time

# Logger setup with INFO level
logging.basicConfig(level=logging.INFO)
logger = logging.getLogger(__name__)

class Config:
    """Configuration class for environment variables."""
    database_url: str = os.getenv('DATABASE_URL')
    retry_attempts: int = 5
    backoff_factor: float = 2.0

# Create a connection pool for database interactions
engine: Engine = create_engine(Config.database_url)

def validate_input(data: Dict[str, Any]) -> bool:
    """Validate incoming data for forecasting.
    
    Args:
        data: Input data dictionary
    Returns:
        bool: True if data is valid
    Raises:
        ValueError: If validation fails
    """
    if not isinstance(data, dict):
        raise ValueError('Input data must be a dictionary.')
    if 'spare_parts' not in data:
        raise ValueError('Missing key: spare_parts.')
    return True

def sanitize_fields(data: Dict[str, Any]) -> Dict[str, Any]:
    """Sanitize input fields.
    
    Args:
        data: Input data dictionary
    Returns:
        Dict[str, Any]: Sanitized data
    """
    sanitized = {k: str(v).strip() for k, v in data.items()}
    return sanitized

def fetch_data(query: str) -> pd.DataFrame:
    """Fetch data from the database.
    
    Args:
        query: SQL query to execute
    Returns:
        pd.DataFrame: Data fetched from the database
    """
    try:
        with engine.connect() as connection:
            return pd.read_sql(query, connection)
    except Exception as e:
        logger.error(f'Error fetching data: {e}')
        raise

def save_to_db(data: pd.DataFrame, table_name: str) -> None:
    """Save DataFrame to the database.
    
    Args:
        data: DataFrame to save
        table_name: Target table name in the database
    """
    try:
        with engine.connect() as connection:
            data.to_sql(table_name, connection, if_exists='replace', index=False)
            logger.info(f'Data saved to {table_name}.')
    except Exception as e:
        logger.error(f'Error saving data: {e}')
        raise

def normalize_data(data: pd.DataFrame) -> pd.DataFrame:
    """Normalize the input data for forecasting.
    
    Args:
        data: Input DataFrame
    Returns:
        pd.DataFrame: Normalized DataFrame
    """
    # Normalize the data as required by the forecasting model
    return (data - data.mean()) / data.std()

def transform_records(data: pd.DataFrame) -> Dict[str, Any]:
    """Transform records into a format suitable for forecasting.
    
    Args:
        data: Input DataFrame
    Returns:
        Dict[str, Any]: Transformed records
    """
    # Transform your data appropriately
    return {'X': data.drop(columns=['target']), 'y': data['target']}

def aggregate_metrics(y_true: List[float], y_pred: List[float]) -> Dict[str, float]:
    """Aggregate forecasting metrics.
    
    Args:
        y_true: True values
        y_pred: Predicted values
    Returns:
        Dict[str, float]: Calculated metrics
    """
    # Calculate metrics like RMSE, MAE etc.
    return {'rmse': ((y_true - y_pred) ** 2).mean() ** 0.5}

class DemandForecasting:
    """Main class for orchestrating the demand forecasting process."""

    def __init__(self, model_name: str):
        self.model_name = model_name
        self.predictor = None

    def train_model(self, training_data: pd.DataFrame) -> None:
        """Train the forecasting model.
        
        Args:
            training_data: DataFrame for training
        """
        self.predictor = TimeSeriesPredictor(label='target')
        self.predictor.fit(training_data)
        logger.info('Model training completed.')

    def predict(self, data: pd.DataFrame) -> pd.DataFrame:
        """Make predictions using the trained model.
        
        Args:
            data: DataFrame for prediction
        Returns:
            pd.DataFrame: Predictions
        """
        return self.predictor.predict(data)

def main():
    """Main execution function."""
    try:
        # Sample data fetching
        query = 'SELECT * FROM spare_parts_data'
        raw_data = fetch_data(query)
        logger.info(f'Raw data fetched: {raw_data.head()}')

        # Validate input data
        if not validate_input({'spare_parts': raw_data}):
            logger.error('Data validation failed.')
            return

        # Sanitize input data
        sanitized_data = sanitize_fields({'spare_parts': raw_data})

        # Normalize data
        normalized_data = normalize_data(sanitized_data)

        # Prepare training and prediction datasets
        training_data = transform_records(normalized_data)

        # Initialize forecasting
        forecasting = DemandForecasting(model_name='XGBoost')
        forecasting.train_model(training_data)

        # Make predictions
        predictions = forecasting.predict(normalized_data)

        # Save predictions to DB
        save_to_db(predictions, 'predictions_table')
        logger.info('Predictions successfully processed and saved.')

    except Exception as e:
        logger.error(f'An error occurred: {e}')
        raise

if __name__ == '__main__':
    main()