Forecast Equipment Maintenance Windows with TimesFM and XGBoost

                      
                     
"""
Production implementation for forecasting equipment maintenance windows using TimesFM and XGBoost.
Provides secure, scalable operations with extensive logging, error handling, and input validation.
"""

from typing import Dict, Any, List, Tuple
import os
import logging
import pandas as pd
from sqlalchemy import create_engine, text
from sklearn.model_selection import train_test_split
from xgboost import XGBRegressor

# Set up logging with INFO level
logging.basicConfig(level=logging.INFO)
logger = logging.getLogger(__name__)

class Config:
    database_url: str = os.getenv('DATABASE_URL', 'sqlite:///default.db')  # Example default

def validate_input(data: Dict[str, Any]) -> bool:
    """Validate input data for forecasting.
    
    Args:
        data: Input data to validate
    Returns:
        True if valid
    Raises:
        ValueError: If validation fails
    """
    if 'equipment_id' not in data or 'start_date' not in data:
        raise ValueError('Missing required fields: equipment_id, start_date')
    return True

def sanitize_fields(data: Dict[str, Any]) -> Dict[str, Any]:
    """Sanitize input fields for security.
    
    Args:
        data: Input data to sanitize
    Returns:
        Sanitized data
    """
    sanitized_data = {k: str(v).strip() for k, v in data.items()}
    logger.debug(f'Sanitized data: {sanitized_data}')  # Log sanitized data
    return sanitized_data

def fetch_data(equipment_id: str, start_date: str) -> pd.DataFrame:
    """Fetch historical maintenance data from the database.
    
    Args:
        equipment_id: Equipment ID to fetch data for
        start_date: Start date for the data query
    Returns:
        DataFrame containing the historical maintenance data
    """
    try:
        engine = create_engine(Config.database_url)
        query = text("SELECT * FROM maintenance_history WHERE equipment_id = :equipment_id AND date >= :start_date")
        with engine.connect() as conn:
            result = pd.read_sql(query, conn, params={'equipment_id': equipment_id, 'start_date': start_date})
        logger.info(f'Fetched {len(result)} records for equipment_id={equipment_id}')  # Log fetch count
        return result
    except Exception as e:
        logger.error('Error fetching data from the database', exc_info=True)
        raise RuntimeError('Database fetch error') from e

def transform_records(records: pd.DataFrame) -> Tuple[pd.DataFrame, pd.Series]:
    """Transform records into features and labels for modeling.
    
    Args:
        records: DataFrame of historical maintenance records
    Returns:
        Tuple of features DataFrame and target Series
    """
    records['date'] = pd.to_datetime(records['date'])
    records.set_index('date', inplace=True)
    features = records.drop(columns=['maintenance_duration'])  # Drop target column
    target = records['maintenance_duration']
    logger.debug('Transformed records into features and target')  # Log transformation
    return features, target

def process_batch(features: pd.DataFrame, target: pd.Series) -> XGBRegressor:
    """Train the XGBoost model on the provided features and target.
    
    Args:
        features: Features DataFrame
        target: Target Series
    Returns:
        Trained XGBoost model
    """
    model = XGBRegressor(objective='reg:squarederror', n_estimators=100)
    X_train, X_test, y_train, y_test = train_test_split(features, target, test_size=0.2, random_state=42)
    model.fit(X_train, y_train)  # Train the model
    logger.info('Model training completed')  # Log training completion
    return model

def save_to_db(equipment_id: str, predictions: List[float]) -> None:
    """Save predictions to the database.
    
    Args:
        equipment_id: Equipment ID for which predictions are made
        predictions: List of predicted maintenance durations
    Raises:
        RuntimeError: If database save fails
    """
    try:
        engine = create_engine(Config.database_url)
        with engine.connect() as conn:
            for pred in predictions:
                conn.execute(text("INSERT INTO maintenance_predictions (equipment_id, predicted_duration) VALUES (:equipment_id, :predicted_duration)"),
                             {'equipment_id': equipment_id, 'predicted_duration': pred})
        logger.info('Predictions saved to database')  # Log save action
    except Exception as e:
        logger.error('Error saving predictions to the database', exc_info=True)
        raise RuntimeError('Database save error') from e

class MaintenanceForecaster:
    """Main class to orchestrate the forecasting process."""

    def __init__(self, equipment_id: str, start_date: str):
        self.equipment_id = equipment_id
        self.start_date = start_date

    def run(self) -> None:
        """Run the forecasting workflow.
        
        Raises:
            RuntimeError: If any step fails within the workflow
        """
        try:
            # Step 1: Validate input
            data = {'equipment_id': self.equipment_id, 'start_date': self.start_date}
            validate_input(data)  # Validate input data
            sanitized_data = sanitize_fields(data)  # Sanitize input fields
            # Step 2: Fetch data
            records = fetch_data(sanitized_data['equipment_id'], sanitized_data['start_date'])  # Fetch records
            # Step 3: Transform records
            features, target = transform_records(records)  # Transform records
            # Step 4: Process batch
            model = process_batch(features, target)  # Train the model
            # Step 5: Make predictions
            predictions = model.predict(features)  # Predict maintenance durations
            # Step 6: Save predictions
            save_to_db(self.equipment_id, predictions.tolist())  # Save predictions
            logger.info('Forecasting process completed successfully')  # Log end of process
        except Exception as e:
            logger.error('Error in maintenance forecasting process', exc_info=True)
            raise RuntimeError('Forecasting process failed') from e

if __name__ == '__main__':
    # Example usage of the MaintenanceForecaster
    forecaster = MaintenanceForecaster(equipment_id='EQUIP123', start_date='2023-01-01')
    forecaster.run()  # Run the forecasting process