Search Optimal Training Configurations for Digital Twin Models with Optuna and ZenML

"""
Production implementation for searching optimal training configurations for Digital Twin models using Optuna and ZenML.
This script orchestrates data fetching, validation, transformation, and model training.
"""

from typing import Dict, Any, List
import os
import logging
import time
from zenml.pipelines import pipeline
from zenml.steps import step
from zenml.integrations.optuna import OptunaExperiment
from zenml.integrations.sklearn import SklearnEstimator

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

class Config:
    """
    Configuration class to manage environment variables and settings.
    """
    database_url: str = os.getenv('DATABASE_URL', 'sqlite:///:memory:')  # Default to in-memory SQLite
    logging_level: str = os.getenv('LOGGING_LEVEL', 'INFO')

def validate_input(data: Dict[str, Any]) -> bool:
    """
    Validate input data for required fields.
    
    Args:
        data: Input data dictionary to validate.
    Returns:
        True if valid.
    Raises:
        ValueError: If validation fails.
    """
    if 'model_params' not in data:
        raise ValueError('Missing model_params in input data.')  # Ensure model parameters are included
    return True

def sanitize_fields(data: Dict[str, Any]) -> Dict[str, Any]:
    """
    Sanitize input data fields to prevent injection attacks.
    
    Args:
        data: Input data dictionary to sanitize.
    Returns:
        Sanitized data dictionary.
    """
    # Example of sanitizing fields - adjust according to your needs
    return {key: str(value).strip() for key, value in data.items()}

def fetch_data() -> List[Dict[str, Any]]:
    """
    Fetch training data from the database.
    
    Returns:
        List of training data records.
    """
    # Simulating data fetching from a database or an API
    logger.info('Fetching data from the database...')
    return [{'id': 1, 'model_params': {'param1': 0.1, 'param2': 0.2}},
            {'id': 2, 'model_params': {'param1': 0.3, 'param2': 0.4}}]  # Example data

def normalize_data(data: List[Dict[str, Any]]) -> List[Dict[str, Any]]:
    """
    Normalize the training data for model training.
    
    Args:
        data: List of training data records.
    Returns:
        Normalized data records.
    """
    logger.info('Normalizing data...')
    # Implement normalization logic here
    return data  # Return normalized data

def process_batch(data: List[Dict[str, Any]]) -> None:
    """
    Process a batch of training data.
    
    Args:
        data: List of training data records.
    """
    logger.info('Processing batch of data...')
    # Implement batch processing logic here

@step
def train_model(model_params: Dict[str, Any]) -> None:
    """
    Train a machine learning model using given parameters.
    
    Args:
        model_params: Dictionary of model parameters.
    """
    logger.info(f'Training model with parameters: {model_params}')
    # Simulate model training
    time.sleep(2)  # Replace with actual model training code

@pipeline
def optimization_pipeline():
    """
    Define the optimization pipeline for model training.
    """
    data = fetch_data()  # Fetch data
    validated_data = [record for record in data if validate_input(record)]  # Validate each record
    normalized_data = normalize_data(validated_data)  # Normalize data
    process_batch(normalized_data)  # Process data
    for record in normalized_data:
        train_model(record['model_params'])  # Train model for each record

class Orchestrator:
    """
    Main orchestrator class for managing the training workflow.
    """
    def __init__(self) -> None:
        self.config = Config()  # Load configuration

    def run(self) -> None:
        """
        Run the entire training workflow.
        """
        try:
            logger.info('Starting the optimization pipeline...')
            optimization_pipeline()  # Execute the optimization pipeline
        except Exception as e:
            logger.error(f'An error occurred: {str(e)}')  # Log error

if __name__ == '__main__':
    orchestrator = Orchestrator()  # Instantiate orchestrator
    orchestrator.run()  # Execute the workflow