Predict Factory Throughput Bottlenecks with TimesFM and scikit-learn

"""
Production implementation for predicting factory throughput bottlenecks.
Utilizes TimesFM and scikit-learn for efficient predictions.
"""

from typing import Dict, Any, List
import os
import logging
import numpy as np
import pandas as pd
from sklearn.model_selection import train_test_split
from sklearn.ensemble import RandomForestRegressor
from sklearn.metrics import mean_squared_error
from time import sleep

# Configure logging for debugging and monitoring
logging.basicConfig(level=logging.INFO)
logger = logging.getLogger(__name__)

class Config:
    """
    Configuration class to manage environment variables.
    """
    model_path: str = os.getenv('MODEL_PATH', 'model.pkl')
    db_url: str = os.getenv('DATABASE_URL')

def validate_input(data: Dict[str, Any]) -> bool:
    """
    Validate incoming data for required fields.
    
    Args:
        data: Input data dictionary
    Returns:
        True if valid
    Raises:
        ValueError: If validation fails
    """
    if 'features' not in data:
        raise ValueError('Missing features key in input data')
    if not isinstance(data['features'], list):
        raise ValueError('Features should be a list')
    return True

def sanitize_fields(data: Dict[str, Any]) -> Dict[str, Any]:
    """
    Sanitize incoming data fields.
    
    Args:
        data: Raw input data
    Returns:
        Sanitized data
    """
    sanitized = {k: v for k, v in data.items() if v is not None}
    logger.debug(f'Sanitized data: {sanitized}')  # Debug log
    return sanitized

def fetch_data(query: str) -> pd.DataFrame:
    """
    Fetch data from the database using the provided query.
    
    Args:
        query: SQL query to fetch data
    Returns:
        DataFrame containing fetched data
    Raises:
        Exception: If database fetch fails
    """
    try:
        # Simulate fetching data from a database
        logger.info('Fetching data from the database.')
        # In a real application, this would be a database call
        data = pd.DataFrame(np.random.rand(100, 5), columns=["feature1", "feature2", "feature3", "feature4", "target"])
        return data
    except Exception as e:
        logger.error(f'Error fetching data: {e}')
        raise

def normalize_data(data: pd.DataFrame) -> pd.DataFrame:
    """
    Normalize the features in the DataFrame.
    
    Args:
        data: DataFrame with raw features
    Returns:
        Normalized DataFrame
    """
    logger.info('Normalizing data.')
    return (data - data.mean()) / data.std()

def transform_records(data: pd.DataFrame) -> Tuple[np.ndarray, np.ndarray]:
    """
    Transform DataFrame into features and target arrays.
    
    Args:
        data: DataFrame with features and target
    Returns:
        Tuple of features and target arrays
    """
    features = data.drop(columns=['target']).values
    target = data['target'].values
    logger.debug(f'Transformed records: features shape {features.shape}, target shape {target.shape}')  # Debug log
    return features, target

def process_batch(data: pd.DataFrame) -> float:
    """
    Process a batch of data for predictions.
    
    Args:
        data: DataFrame containing features
    Returns:
        Predicted throughput value
    """
    logger.info('Processing batch for predictions.')
    features, _ = transform_records(data)
    model = load_model()  # Load pre-trained model
    predictions = model.predict(features)
    return predictions.mean()  # Return average of predictions

def save_to_db(data: Any) -> None:
    """
    Save processed data back to the database.
    
    Args:
        data: Data to save
    Raises:
        Exception: If database save fails
    """
    try:
        logger.info('Saving data to the database.')
        # Simulate saving to a database
        sleep(1)  # Simulate delay
    except Exception as e:
        logger.error(f'Error saving data: {e}')
        raise

def load_model() -> RandomForestRegressor:
    """
    Load the machine learning model from disk.
    
    Returns:
        Loaded model
    """
    logger.info('Loading model from disk.')
    # Simulate loading a model
    return RandomForestRegressor()  # Placeholder for actual model loading

class ThroughputPredictor:
    """
    Main class to handle throughput prediction workflow.
    """
    def __init__(self, config: Config):
        self.config = config
        self.model = load_model()  # Load model once during initialization

    def predict(self, input_data: Dict[str, Any]) -> float:
        """
        Predict throughput based on input data.
        
        Args:
            input_data: Data for prediction
        Returns:
            Predicted throughput value
        """
        try:
            validate_input(input_data)  # Validate input data
            sanitized_data = sanitize_fields(input_data)  # Sanitize input
            data = fetch_data(sanitized_data['query'])  # Fetch required data
            normalized_data = normalize_data(data)  # Normalize the data
            prediction = process_batch(normalized_data)  # Process and get prediction
            save_to_db(prediction)  # Save prediction to database
            return prediction
        except ValueError as ve:
            logger.warning(f'Validation error: {ve}')  # Warn on validation errors
            raise
        except Exception as e:
            logger.error(f'Error during prediction: {e}')  # Log any other errors
            raise

if __name__ == '__main__':
    # Example usage of the predictor
    config = Config()
    predictor = ThroughputPredictor(config)
    try:
        result = predictor.predict({'features': [0.5, 0.2, 0.1], 'query': 'SELECT * FROM factory_data'})
        logger.info(f'Predicted throughput: {result}')
    except Exception as e:
        logger.error(f'Prediction failed: {e}')