Model Factory Production Output with PyTorch Forecasting and GluonTS

                      
                     
"""
Production implementation for Model Factory Production Output with PyTorch Forecasting and GluonTS.
Provides secure, scalable operations for forecasting tasks using deep learning models.
"""
import os
import logging
import time
from typing import Dict, Any, List, Tuple
import torch
from gluonts.dataset.common import ListDataset
from gluonts.mx import Trainer
from gluonts.model.deepar import DeepAREstimator
from gluonts.mx import Predictor

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

class Config:
    """
    Configuration class for environment variables.
    """
    model_dir: str = os.getenv('MODEL_DIR', './models')
    data_dir: str = os.getenv('DATA_DIR', './data')
    epochs: int = int(os.getenv('EPOCHS', '50'))
    batch_size: int = int(os.getenv('BATCH_SIZE', '32'))

def validate_input(data: Dict[str, Any]) -> bool:
    """Validate the input data for the forecasting model.
    
    Args:
        data: Input dictionary containing time series data.
    Returns:
        True if valid.
    Raises:
        ValueError: If input validation fails.
    """
    if 'start' not in data or 'target' not in data:
        raise ValueError('Input must contain both start and target fields.')
    return True

def normalize_data(data: List[float]) -> List[float]:
    """Normalize time series data to a range of [0, 1].
    
    Args:
        data: List of float values representing time series data.
    Returns:
        Normalized data as a list of floats.
    """
    min_val = min(data)
    max_val = max(data)
    return [(x - min_val) / (max_val - min_val) for x in data]

def load_data(file_path: str) -> List[Dict[str, Any]]:
    """Load time series data from a CSV file.
    
    Args:
        file_path: Path to the CSV file.
    Returns:
        List of dictionaries containing time series data.
    Raises:
        FileNotFoundError: If the file does not exist.
    """
    import pandas as pd
    if not os.path.exists(file_path):
        raise FileNotFoundError(f'File not found: {file_path}')
    data = pd.read_csv(file_path)
    return data.to_dict(orient='records')

def train_model(training_data: List[Dict[str, Any]]) -> None:
    """Train the forecasting model using the provided training data.
    
    Args:
        training_data: List of dictionaries containing training time series data.
    """
    dataset = ListDataset(training_data, freq='1H')
    estimator = DeepAREstimator(
        freq='1H',
        prediction_length=24,
        trainer=Trainer(epochs=Config.epochs, batch_size=Config.batch_size)
    )
    estimator.train(training_data=dataset)
    logger.info('Model training completed.')

def save_model(model: Any, model_name: str) -> None:
    """Save the trained model to a specified directory.
    
    Args:
        model: The trained model to save.
        model_name: Name of the model file.
    """
    model_path = os.path.join(Config.model_dir, model_name)
    torch.save(model.state_dict(), model_path)
    logger.info(f'Model saved to {model_path}')

def load_model(model_name: str) -> Any:
    """Load a trained model from a specified directory.
    
    Args:
        model_name: Name of the model file to load.
    Returns:
        Loaded model.
    """
    model_path = os.path.join(Config.model_dir, model_name)
    model = DeepAREstimator()  # Placeholder for actual model loading logic
    model.load_state_dict(torch.load(model_path))
    logger.info(f'Model loaded from {model_path}')
    return model

def forecast(model: Any, input_data: Dict[str, Any]) -> List[float]:
    """Generate forecasts for the provided input data.
    
    Args:
        model: The trained model to use for forecasting.
        input_data: Input data for forecasting.
    Returns:
        List of forecasted values.
    """
    prediction = model.predict(input_data)
    return list(prediction)

def main() -> None:
    """Main function to orchestrate the model training and forecasting workflow.
    """
    try:
        # Load and validate data
        data = load_data(os.path.join(Config.data_dir, 'training_data.csv'))
        for record in data:
            validate_input(record)  # Validate each record
        # Normalize data
        normalized_data = [normalize_data(record['target']) for record in data]
        # Train model
        train_model(normalized_data)
        # Save model
        save_model(model, 'forecast_model.pth')
    except Exception as e:
        logger.error(f'An error occurred: {str(e)}')
        # Handle error gracefully

if __name__ == '__main__':
    # Example usage
    main()