Forecast Rotating Equipment Failure Rates with skforecast and CatBoost

"""
Production implementation for forecasting rotating equipment failure rates.
Utilizes skforecast and CatBoost for predictive analysis.
"""

from typing import Dict, Any, List, Tuple
import os
import logging
import numpy as np
import pandas as pd
from catboost import CatBoostRegressor
from skforecast import ForecasterAutoreg
from sklearn.model_selection import train_test_split

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

# Configuration class for environment variables
class Config:
    database_url: str = os.getenv('DATABASE_URL')
    catboost_params: Dict[str, Any] = {
        'iterations': 1000,
        'learning_rate': 0.1,
        'depth': 6,
        'loss_function': 'RMSE'
    }

# Data validation function
async def validate_input(data: Dict[str, Any]) -> bool:
    """Validate incoming data for shape and required fields.
    
    Args:
        data: Input dictionary containing equipment metrics
    Returns:
        True if valid
    Raises:
        ValueError: If validation fails
    """
    required_fields = ['timestamp', 'failure_rate', 'operating_conditions']
    for field in required_fields:
        if field not in data:
            raise ValueError(f'Missing required field: {field}')
    logger.info('Input data validated successfully.')
    return True

# Function to sanitize fields
def sanitize_fields(data: Dict[str, Any]) -> Dict[str, Any]:
    """Sanitize input fields to ensure clean data.
    
    Args:
        data: Input data dictionary
    Returns:
        Sanitized data dictionary
    """
    sanitized_data = {k: v.strip() if isinstance(v, str) else v for k, v in data.items()}
    logger.info('Fields sanitized.')
    return sanitized_data

# Function to normalize data
def normalize_data(df: pd.DataFrame) -> pd.DataFrame:
    """Normalize numeric fields in the DataFrame.
    
    Args:
        df: DataFrame containing equipment data
    Returns:
        Normalized DataFrame
    """
    numeric_cols = df.select_dtypes(include=np.number).columns.tolist()
    df[numeric_cols] = (df[numeric_cols] - df[numeric_cols].mean()) / df[numeric_cols].std()
    logger.info('Data normalized.')
    return df

# Function to fetch data from the database
async def fetch_data(query: str) -> pd.DataFrame:
    """Fetch data from the database using a query.
    
    Args:
        query: SQL command to fetch data
    Returns:
        DataFrame containing the results
    Raises:
        Exception: If database fetch fails
    """
    try:
        # Simulating database fetch
        logger.info('Fetching data from the database.')
        # Replace with actual database fetching logic
        df = pd.DataFrame()  # Placeholder for fetched data
        return df
    except Exception as e:
        logger.error(f'Error fetching data: {e}')
        raise

# Function to save data to the database
async def save_to_db(df: pd.DataFrame) -> None:
    """Save the DataFrame back to the database.
    
    Args:
        df: DataFrame to save
    Raises:
        Exception: If database save fails
    """
    try:
        logger.info('Saving data to the database.')
        # Replace with actual database saving logic
    except Exception as e:
        logger.error(f'Error saving data: {e}')
        raise

# Function to format output data for reporting
def format_output(data: Any) -> str:
    """Format output data into a readable string.
    
    Args:
        data: Data to format
    Returns:
        Formatted string
    """
    return str(data)

# Main class for the forecasting process
class FailureRateForecaster:
    def __init__(self, config: Config):
        self.config = config
        self.model = CatBoostRegressor(**self.config.catboost_params)

    def train(self, X: pd.DataFrame, y: pd.Series) -> None:
        """Train the CatBoost model.
        
        Args:
            X: Features DataFrame
            y: Target Series
        """
        logger.info('Training the model.')
        self.model.fit(X, y)

    def forecast(self, X: pd.DataFrame) -> np.ndarray:
        """Forecast future failure rates.
        
        Args:
            X: Features DataFrame
        Returns:
            Predicted failure rates
        """
        logger.info('Making forecasts.')
        return self.model.predict(X)

# Main orchestration method
async def main():
    logger.info('Starting the forecasting process.')
    config = Config()  # Load configuration
    query = "SELECT * FROM equipment_data;"  # Example query
    raw_data = await fetch_data(query)  # Fetch data
    if await validate_input(raw_data.to_dict()):  # Validate data
        sanitized_data = sanitize_fields(raw_data.to_dict())  # Sanitize data
        df = pd.DataFrame(sanitized_data)  # Convert back to DataFrame
        normalized_df = normalize_data(df)  # Normalize data
        X = normalized_df.drop(columns=['failure_rate'])  # Features
        y = normalized_df['failure_rate']  # Target
        X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)  # Split data
        forecaster = FailureRateForecaster(config)  # Initialize forecaster
        forecaster.train(X_train, y_train)  # Train model
        predictions = forecaster.forecast(X_test)  # Make predictions
        logger.info(f'Predictions: {format_output(predictions)}')  # Log predictions
        await save_to_db(predictions)  # Save results

if __name__ == '__main__':
    import asyncio
    asyncio.run(main())  # Run the main method