Estimate Equipment Remaining Useful Life with Moirai and scikit-learn

                      
                     
"""
Production implementation for estimating Remaining Useful Life (RUL) of equipment using Moirai and scikit-learn.
Provides secure, scalable operations.
"""
from typing import Dict, Any, List, Tuple
import os
import logging
import numpy as np
import pandas as pd
from sklearn.ensemble import RandomForestRegressor
from sklearn.model_selection import train_test_split
from sklearn.metrics import mean_squared_error
from time import sleep

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

class Config:
    """
    Configuration class for environment variables.
    """
    database_url: str = os.getenv('DATABASE_URL', 'sqlite:///:memory:')
    max_retries: int = int(os.getenv('MAX_RETRIES', 5))
    retry_delay: int = int(os.getenv('RETRY_DELAY', 1))

def validate_input(data: Dict[str, Any]) -> bool:
    """Validate input data for RUL estimation.
    
    Args:
        data: Input data dictionary containing features
    Returns:
        True if valid
    Raises:
        ValueError: If validation fails
    """
    required_fields = ['temperature', 'pressure', 'vibration']
    for field in required_fields:
        if field not in data:
            raise ValueError(f'Missing required field: {field}')
    return True

def fetch_data() -> pd.DataFrame:
    """Fetch data from the database.
    
    Returns:
        DataFrame containing equipment data
    """
    logger.info('Fetching data from the database')
    # Here we would normally fetch data from the database
    return pd.DataFrame({
        'temperature': np.random.rand(100),
        'pressure': np.random.rand(100),
        'vibration': np.random.rand(100),
        'rul': np.random.randint(1, 100, size=100)
    })

def transform_records(data: pd.DataFrame) -> Tuple[np.ndarray, np.ndarray]:
    """Transform records into features and labels.
    
    Args:
        data: Raw DataFrame from fetch_data
    Returns:
        Tuple of features and labels
    """
    X = data[['temperature', 'pressure', 'vibration']].values
    y = data['rul'].values
    return X, y

def save_to_db(data: pd.DataFrame) -> None:
    """Save processed data to the database.
    
    Args:
        data: DataFrame to save
    """
    logger.info('Saving processed data to the database')
    # Placeholder: Save to DB logic goes here

def train_model(X: np.ndarray, y: np.ndarray) -> RandomForestRegressor:
    """Train machine learning model using Random Forest.
    
    Args:
        X: Features
        y: Labels
    Returns:
        Trained Random Forest model
    """
    logger.info('Training model')
    model = RandomForestRegressor(n_estimators=100)
    model.fit(X, y)
    return model

def estimate_rul(model: RandomForestRegressor, features: np.ndarray) -> float:
    """Estimate RUL using trained model.
    
    Args:
        model: Trained model
        features: Array of features for prediction
    Returns:
        Estimated RUL
    """
    return model.predict(features.reshape(1, -1))[0]

def process_batch(data: pd.DataFrame) -> None:
    """Process a batch of equipment data for RUL estimation.
    
    Args:
        data: DataFrame containing batch data
    """
    X, y = transform_records(data)
    model = train_model(X, y)
    for index, row in data.iterrows():
        features = row[['temperature', 'pressure', 'vibration']].values
        estimated_rul = estimate_rul(model, features)
        logger.info(f'Estimated RUL for index {index}: {estimated_rul}')

def handle_errors(func):
    """Decorator for handling errors in functions.
    
    Args:
        func: Function to wrap
    Returns:
        Wrapped function
    """
    def wrapper(*args, **kwargs):
        retries = Config.max_retries
        for attempt in range(retries):
            try:
                return func(*args, **kwargs)
            except Exception as e:
                logger.error(f'Error in {func.__name__}: {e}')
                if attempt < retries - 1:
                    sleep(Config.retry_delay)
                else:
                    logger.critical('Max retries reached')
                    raise
    return wrapper

@handle_errors
def main() -> None:
    """Main orchestration function.
    
    Returns:
        None
    """
    raw_data = fetch_data()  # Fetch data from the database
    process_batch(raw_data)  # Process the data
    save_to_db(raw_data)  # Save processed results

if __name__ == '__main__':
    # Example usage
    try:
        main()
    except Exception as e:
        logger.critical(f'Failed to execute main function: {e}')