Build Conditional Digital Twin Retraining Pipelines with Metaflow and MLflow

"""
Production implementation for building conditional digital twin retraining pipelines.
Integrates Metaflow for orchestration and MLflow for tracking experiments.
"""
from typing import Dict, Any, List
import os
import logging
import time
import random

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

class Config:
    """
    Configuration class to manage environment variables.
    """
    mlflow_uri: str = os.getenv('MLFLOW_URI')
    metaflow_environment: str = os.getenv('METAFLOW_ENV')
    retraining_threshold: float = float(os.getenv('RETRAINING_THRESHOLD', 0.8))

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

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

def fetch_data(model_id: str) -> Dict[str, Any]:
    """Simulate fetching model data from a database.
    
    Args:
        model_id: The ID of the model to fetch
    Returns:
        Model data
    """
    logger.info(f'Fetching data for model_id: {model_id}')
    # Simulating a database call with random data
    return {'model_id': model_id, 'metrics': {'accuracy': random.uniform(0.5, 1.0)}}

def normalize_data(data: Dict[str, Any]) -> Dict[str, Any]:
    """Normalize metrics for processing.
    
    Args:
        data: Metrics data
    Returns:
        Normalized metrics
    """
    normalized = {k: v / 100 for k, v in data.items()}
    return normalized

def aggregate_metrics(metrics: List[Dict[str, Any]]) -> Dict[str, float]:
    """Aggregate metrics from multiple experiments.
    
    Args:
        metrics: List of metrics dictionaries
    Returns:
        Aggregated metrics
    """
    aggregate = {'accuracy': sum(m['metrics']['accuracy'] for m in metrics) / len(metrics)}
    return aggregate

def save_to_db(data: Dict[str, Any]) -> None:
    """Simulate saving the retrained model to a database.
    
    Args:
        data: The data to save
    """
    logger.info(f'Saving model data: {data}')
    # Simulating a DB save with a sleep
    time.sleep(1)

def call_api(endpoint: str, payload: Dict[str, Any]) -> None:
    """Simulate calling an external API.
    
    Args:
        endpoint: The API endpoint to call
        payload: Data payload for the API
    """
    logger.info(f'Calling API at {endpoint} with payload: {payload}')
    # Simulating an API call with a random delay
    time.sleep(random.uniform(0.5, 2.0))

class RetrainingPipeline:
    """Main class for orchestrating the retraining pipeline.
    """
    def __init__(self, config: Config) -> None:
        self.config = config

    def run(self, input_data: Dict[str, Any]) -> None:
        """Run the retraining pipeline.
        
        Args:
            input_data: Input data for the pipeline
        """
        try:
            # Validate and sanitize input data
            validate_input(input_data)
            sanitized_data = sanitize_fields(input_data)
            model_data = fetch_data(sanitized_data['model_id'])
            normalized_metrics = normalize_data(model_data['metrics'])

            # Check if retraining is needed
            if normalized_metrics['accuracy'] < self.config.retraining_threshold:
                logger.info('Retraining model...')
                # Simulate retraining process
                time.sleep(2)
                logger.info('Model retrained successfully.')
                save_to_db(model_data)
                call_api('http://example.com/api/update', model_data)
            else:
                logger.info('No retraining needed.')
        except ValueError as e:
            logger.error(f'Validation error: {e}')
        except Exception as e:
            logger.error(f'An error occurred: {e}')

if __name__ == '__main__':
    # Example usage
    config = Config()
    pipeline = RetrainingPipeline(config)
    input_data = {'model_id': '12345', 'metrics': {'accuracy': 0.75}}
    pipeline.run(input_data)