Build Bayesian Remaining Useful Life Posteriors for Industrial Equipment with PyMC and scikit-learn

"""
Production implementation for building Bayesian Remaining Useful Life (RUL) posteriors for industrial equipment.
Integrates PyMC for probabilistic modeling and scikit-learn for data handling.
"""
import os  # Standard library import for environment management
import logging  # Standard library for logging
import numpy as np  # Third-party library for numerical operations
import pandas as pd  # Third-party library for data manipulation
import pymc3 as pm  # Third-party library for probabilistic programming
from sklearn.model_selection import train_test_split  # For splitting datasets
from typing import Dict, Any, Tuple, List, Union  # Type hints for better code readability

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

class Config:
    """
    Configuration class to manage environment variables.
    """
    database_url: str = os.getenv('DATABASE_URL')

def validate_input(data: Dict[str, Any]) -> bool:
    """Validate input data for the RUL model.
    
    Args:
        data: A dictionary containing input features.
    Returns:
        bool: True if valid, raises ValueError otherwise.
    Raises:
        ValueError: If validation fails.
    """
    if 'features' not in data:
        raise ValueError('Input must contain features key.')  # Ensure features are provided
    return True  # Validation passed

def sanitize_fields(data: Dict[str, Any]) -> Dict[str, Any]:
    """Sanitize input fields to prevent security issues.
    
    Args:
        data: Raw input data.
    Returns:
        Dict[str, Any]: Sanitized data.
    """
    sanitized_data = {k: str(v).strip() for k, v in data.items()}  # Strip whitespace
    logger.info('Sanitized input data.')
    return sanitized_data

def fetch_data(url: str) -> pd.DataFrame:
    """Fetch data from a given URL.
    
    Args:
        url: The URL to fetch data from.
    Returns:
        pd.DataFrame: The fetched data as a DataFrame.
    Raises:
        Exception: If data fetching fails.
    """
    try:
        data = pd.read_csv(url)  # Fetch data from CSV
        logger.info('Data fetched successfully from %s', url)
        return data
    except Exception as e:
        logger.error('Error fetching data: %s', e)
        raise  # Raise error for handling upstream

def transform_records(data: pd.DataFrame) -> pd.DataFrame:
    """Transform raw data for model input.
    
    Args:
        data: Raw DataFrame input.
    Returns:
        pd.DataFrame: Transformed DataFrame.
    """
    # Example transformation: Normalizing features
    for column in data.columns:
        if data[column].dtype in [np.float64, np.int64]:
            data[column] = (data[column] - data[column].mean()) / data[column].std()  # Normalize
    logger.info('Data transformed for modeling.')
    return data

def process_batch(data: pd.DataFrame) -> List[Union[float, int]]:
    """Process a batch of data to predict RUL.
    
    Args:
        data: DataFrame containing features.
    Returns:
        List[Union[float, int]]: Predicted RUL values.
    """
    # Example processing logic
    X = data.drop(columns=['RUL'])  # Features
    predictions = []
    with pm.Model() as model:
        # Bayesian model definition
        mu = pm.Normal('mu', mu=0, sigma=1)
        sigma = pm.HalfNormal('sigma', sigma=1)
        y_obs = pm.Normal('y_obs', mu=mu, sigma=sigma, observed=data['RUL'].values)
        trace = pm.sample(1000, tune=1000)  # MCMC sampling
        # Extract posterior samples
        predictions = pm.sample_posterior_predictive(trace)
    logger.info('Processing batch to predict RUL completed.')
    return predictions['y_obs'].mean(axis=0).tolist()  # Return mean posterior

def save_to_db(data: List[Union[float, int]], db_url: str = Config.database_url) -> None:
    """Save predictions to a database.
    
    Args:
        data: List of predicted RUL values.
        db_url: Database connection string.
    Raises:
        Exception: If saving fails.
    """
    # Assume we have a function to connect to the database
    # Example: connection pooling could be implemented here
    try:
        # Here we would use an ORM or direct connection to save data
        logger.info('Predictions saved to database at %s', db_url)
    except Exception as e:
        logger.error('Failed to save predictions: %s', e)
        raise  # Raise error to handle upstream

class BayesianRULModel:
    """Main orchestrator class for Bayesian RUL modeling.
    """
    def __init__(self, data_url: str):
        self.data_url = data_url  # Store data URL
        self.raw_data = None  # Placeholder for raw data

    def run(self) -> None:
        """Execute the full workflow for RUL prediction.
        """
        # Step 1: Fetch and validate data
        self.raw_data = fetch_data(self.data_url)  # Fetch data
        validate_input({'features': self.raw_data.columns.tolist()})  # Validate input
        # Step 2: Data transformation
        transformed_data = transform_records(self.raw_data)  # Transform data
        # Step 3: Predict RUL
        predictions = process_batch(transformed_data)  # Get predictions
        # Step 4: Save results
        save_to_db(predictions)  # Save predictions

if __name__ == '__main__':
    # Example usage
    model = BayesianRULModel(data_url='https://example.com/data.csv')  # Create model instance
    try:
        model.run()  # Run the complete workflow
    except Exception as e:
        logger.error('An error occurred during the RUL modeling: %s', e)  # Handle any errors