Predict Spare Parts Demand with Chronos-2 and XGBoost

                      
                     
"""
Production implementation for predicting spare parts demand using Chronos-2 and XGBoost.
Provides secure, scalable operations with robust error handling.
"""
from typing import Dict, Any, List
import os
import logging
import pandas as pd
import xgboost as xgb
from sqlalchemy import create_engine, text
from sqlalchemy.exc import SQLAlchemyError
import time

logging.basicConfig(level=logging.INFO)
logger = logging.getLogger(__name__)

class Config:
    """Configuration class for environment variables."""
    database_url: str = os.getenv('DATABASE_URL')
    model_path: str = os.getenv('MODEL_PATH')
    retry_attempts: int = int(os.getenv('RETRY_ATTEMPTS', 5))

async def validate_input(data: Dict[str, Any]) -> bool:
    """Validate request data.
    
    Args:
        data: Input to validate
    Returns:
        True if valid
    Raises:
        ValueError: If validation fails
    """
    if 'part_id' not in data:
        raise ValueError('Missing part_id')
    if 'date' not in data:
        raise ValueError('Missing date')
    return True

async def fetch_data(part_id: str, date: str) -> pd.DataFrame:
    """Fetch historical data for the part.
    
    Args:
        part_id: ID of the spare part
        date: Date for which to fetch the data
    Returns:
        DataFrame with historical demand
    Raises:
        SQLAlchemyError: If database fetch fails
    """
    engine = create_engine(Config.database_url)
    query = text("SELECT * FROM demand_data WHERE part_id = :part_id AND date <= :date")
    try:
        with engine.connect() as connection:
            return pd.read_sql(query, connection, params={'part_id': part_id, 'date': date})
    except SQLAlchemyError as e:
        logger.error(f"Database error: {e}")
        raise

async def transform_records(data: pd.DataFrame) -> xgb.DMatrix:
    """Transform DataFrame into XGBoost DMatrix.
    
    Args:
        data: Historical demand data
    Returns:
        DMatrix for XGBoost
    """
    return xgb.DMatrix(data)

async def save_to_db(part_id: str, date: str, prediction: float) -> None:
    """Save prediction to database.
    
    Args:
        part_id: ID of the spare part
        date: Date of prediction
        prediction: Predicted demand
    Raises:
        SQLAlchemyError: If database save fails
    """
    engine = create_engine(Config.database_url)
    query = text("INSERT INTO predictions (part_id, date, predicted_demand) VALUES (:part_id, :date, :predicted_demand)")
    try:
        with engine.connect() as connection:
            connection.execute(query, {'part_id': part_id, 'date': date, 'predicted_demand': prediction})
    except SQLAlchemyError as e:
        logger.error(f"Failed to save prediction: {e}")
        raise

async def call_api(data: Dict[str, Any]) -> Dict[str, Any]:
    """Call external API for additional data.
    
    Args:
        data: Input data for API call
    Returns:
        API response data
    Raises:
        Exception: If API call fails
    """
    # Simulate API call
    return {'additional_data': 'value'}

async def process_batch(part_id: str, date: str) -> float:
    """Process batch of data and return prediction.
    
    Args:
        part_id: ID of the spare part
        date: Date of prediction
    Returns:
        Predicted demand as float
    """
    await validate_input({'part_id': part_id, 'date': date})  # Validate input
    data = await fetch_data(part_id, date)  # Fetch historical data
    dmatrix = await transform_records(data)  # Transform data for model

    model = xgb.Booster()  # Load the model
    model.load_model(Config.model_path)  # Load the pre-trained model
    prediction = model.predict(dmatrix)  # Make prediction

    await save_to_db(part_id, date, prediction[0])  # Save prediction
    return prediction[0]  # Return prediction

async def main(part_id: str, date: str) -> None:
    """Main orchestration function.
    
    Args:
        part_id: ID of the spare part
        date: Date for prediction
    """
    attempts = 0
    while attempts < Config.retry_attempts:
        try:
            prediction = await process_batch(part_id, date)  # Process data
            logger.info(f"Prediction for {part_id} on {date}: {prediction}")  # Log prediction
            break  # Exit loop on success
        except Exception as e:
            attempts += 1
            wait_time = 2 ** attempts  # Exponential backoff
            logger.warning(f"Attempt {attempts}: {e}. Retrying in {wait_time} seconds...")
            time.sleep(wait_time)  # Wait before retrying

if __name__ == '__main__':
    # Example usage
    import asyncio
    part_id = 'part_123'
    date = '2023-10-01'
    asyncio.run(main(part_id, date))  # Run the main function asynchronously