Optimise Digital Twin Surrogate Model Hyperparameters at Scale with Optuna and MLflow

"""
Production implementation for optimizing digital twin surrogate model hyperparameters.
Utilizes Optuna for hyperparameter tuning and MLflow for tracking experiments.
"""
from typing import Dict, Any, List
import os
import logging
import optuna
import mlflow
import mlflow.sklearn

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

class Config:
    # Configuration for MLflow tracking
    mlflow_uri: str = os.getenv('MLFLOW_URI', 'http://localhost:5000')
    experiment_name: str = os.getenv('EXPERIMENT_NAME', 'optuna_experiment')

    def setup_mlflow(self):
        mlflow.set_tracking_uri(self.mlflow_uri)
        mlflow.set_experiment(self.experiment_name)

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

def normalize_data(data: List[float]) -> List[float]:
    """Normalize the input data between 0 and 1.
    
    Args:
        data: List of features to normalize
    Returns:
        Normalized list of features
    """
    min_val = min(data)
    max_val = max(data)
    return [(x - min_val) / (max_val - min_val) for x in data]

def fetch_data() -> Dict[str, List[float]]:
    """Fetch data for model training.
    
    Returns:
        Dictionary with features and target
    """
    # Placeholder for data fetching logic
    return {'features': [1.0, 2.0, 3.0], 'target': [0, 1, 0]}

def save_to_db(model):
    """Save the trained model to the database.
    
    Args:
        model: Trained model to save
    """
    # Placeholder for database save logic
    logger.info('Model saved to database.')

def handle_errors(func):
    """Decorator to handle errors gracefully.
    
    Args:
        func: Function to wrap
    """
    def wrapper(*args, **kwargs):
        try:
            return func(*args, **kwargs)
        except Exception as e:
            logger.error(f'Error occurred: {e}')
            raise
    return wrapper

class HyperparameterTuner:
    def __init__(self, config: Config):
        self.config = config
        self.config.setup_mlflow()

    @handle_errors
    def objective(self, trial: optuna.Trial) -> float:
        """Objective function for Optuna to minimize.
        
        Args:
            trial: Optuna trial object
        Returns:
            The objective value (e.g., validation loss)
        """
        # Example hyperparameters
        n_estimators = trial.suggest_int('n_estimators', 10, 100)
        max_depth = trial.suggest_int('max_depth', 1, 10)
        # Simulate model training and evaluation
        logger.info(f'Training model with n_estimators={n_estimators}, max_depth={max_depth}')
        # Here we would train the model and return the validation score
        return 0.5  # Placeholder score

    @handle_errors
    def run_optimization(self):
        """Run the optimization process.
        
        Returns:
            Best trial from Optuna
        """
        study = optuna.create_study(direction='minimize')
        study.optimize(self.objective, n_trials=10)
        logger.info('Optimization completed.')
        return study.best_trial

if __name__ == '__main__':
    # Example usage
    config = Config()
    tuner = HyperparameterTuner(config)
    data = fetch_data()  # Fetch data
    validate_input(data)  # Validate input data
    best_trial = tuner.run_optimization()  # Run optimization
    logger.info(f'Best trial: {best_trial}')  # Log best trial
    save_to_db(best_trial)  # Save the best model to database