Predict Equipment Wear Patterns with skforecast and LightGBM

"""
Production implementation for predicting equipment wear patterns using skforecast and LightGBM.
Provides secure, scalable operations for predictive maintenance.
"""
from typing import Dict, Any, List
import os
import logging
import pandas as pd
from lightgbm import LGBMRegressor
from skforecast import ForecasterAutoreg
from sqlalchemy import create_engine
from sqlalchemy.orm import sessionmaker
import time

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

class Config:
    database_url: str = os.getenv('DATABASE_URL', 'sqlite:///data.db')  # Default to SQLite for local testing

# Connection pooling setup
engine = create_engine(Config.database_url, pool_size=10, max_overflow=20)
Session = sessionmaker(bind=engine)

async def validate_input(data: Dict[str, Any]) -> bool:
    """Validate request data for equipment wear prediction.
    
    Args:
        data: Input data to validate
    Returns:
        bool: True if valid
    Raises:
        ValueError: If validation fails
    """
    if not isinstance(data, dict):
        raise ValueError('Input data must be a dictionary.')  # Ensure data is in the correct format
    if 'features' not in data or not data['features']:
        raise ValueError('Missing features for prediction.')  # Check for required fields
    return True

async def sanitize_fields(data: Dict[str, Any]) -> Dict[str, Any]:
    """Sanitize input fields to prevent injection attacks.
    
    Args:
        data: Input data to sanitize
    Returns:
        Dict[str, Any]: Sanitized data
    """
    # Example: Strip whitespace from feature names
    sanitized_data = {k.strip(): v for k, v in data.items()}
    return sanitized_data

async def normalize_data(data: pd.DataFrame) -> pd.DataFrame:
    """Normalize the input DataFrame for model ingestion.
    
    Args:
        data: Raw input DataFrame
    Returns:
        pd.DataFrame: Normalized DataFrame
    """
    # Normalize the data (example: min-max scaling)
    normalized_data = (data - data.min()) / (data.max() - data.min())
    return normalized_data

async def fetch_data(query: str) -> pd.DataFrame:
    """Fetch data from the database for prediction.
    
    Args:
        query: SQL query to execute
    Returns:
        pd.DataFrame: Data fetched from the database
    """
    with Session() as session:
        df = pd.read_sql(query, session.bind)  # Fetch data using SQLAlchemy
    return df

async def save_to_db(data: pd.DataFrame, table_name: str) -> None:
    """Save predictions to the database.
    
    Args:
        data: DataFrame containing predictions
        table_name: Name of the table to save data
    """
    with Session() as session:
        data.to_sql(table_name, session.bind, if_exists='replace', index=False)  # Replace table on save

class EquipmentWearPredictor:
    """Main orchestrator class for predicting equipment wear patterns.
    """
    def __init__(self) -> None:
        self.forecaster = None

    def train_model(self, data: pd.DataFrame) -> None:
        """Train the LightGBM model using historical data.
        
        Args:
            data: DataFrame containing historical data for training
        """
        # Create and fit the forecaster
        self.forecaster = ForecasterAutoreg(regressor=LGBMRegressor(), lag=5)
        self.forecaster.fit(data)
        logger.info('Model trained successfully.')  # Log model training success

    def predict(self, features: List[float]) -> Any:
        """Predict equipment wear based on input features.
        
        Args:
            features: List of features for prediction
        Returns:
            Any: Prediction result
        """
        if self.forecaster is None:
            raise RuntimeError('Model is not trained. Call train_model first.')  # Ensure model is trained
        prediction = self.forecaster.predict(steps=1)
        return prediction  # Return the prediction

async def main(data: Dict[str, Any]) -> None:
    """Main workflow for predicting equipment wear.
    
    Args:
        data: Input data for prediction
    """
    try:
        await validate_input(data)  # Validate input
        sanitized_data = await sanitize_fields(data)  # Sanitize input
        historical_data = await fetch_data('SELECT * FROM equipment_history')  # Fetch historical data
        normalized_data = await normalize_data(historical_data)  # Normalize data
        predictor = EquipmentWearPredictor()
        predictor.train_model(normalized_data)  # Train the model
        prediction = predictor.predict(sanitized_data['features'])  # Make prediction
        await save_to_db(pd.DataFrame({'prediction': [prediction]}), 'predictions')  # Save predictions
        logger.info('Prediction saved successfully.')  # Log save success
    except Exception as e:
        logger.error(f'Error in main workflow: {e}')  # Log any errors

if __name__ == '__main__':
    # Example usage
    example_data = {'features': [1.2, 3.4, 5.6]}  # Example input
    import asyncio
    asyncio.run(main(example_data))  # Run the main workflow in an asyncio context