Detect Conveyor Belt Anomalies in Real Time with River and scikit-learn

"""\nProduction implementation for detecting conveyor belt anomalies in real time using River and scikit-learn.\nProvides secure, scalable operations with robust logging and error handling.\n"""\nimport os\nimport logging\nfrom typing import Dict, Any, List\nfrom river import compose, datasets, metrics\nfrom river.ensemble import Bagging\nfrom river.tree import DecisionTree\nfrom river.preprocessing import StandardScaler\n\n# Configure logging\nlogging.basicConfig(level=logging.INFO)\nlogger = logging.getLogger(__name__)\n\nclass Config:\n    \n    # Environment variable for database URL\n    database_url: str = os.getenv('DATABASE_URL')\n    model_path: str = os.getenv('MODEL_PATH', 'model.pkl')\n\nasync def validate_input(data: Dict[str, Any]) -> bool:\n    """Validate request data for anomaly detection.\n    \n    Args:\n        data: Input data to validate\n    Returns:\n        True if valid\n    Raises:\n        ValueError: If validation fails\n    """\n    if 'sensor_data' not in data or not isinstance(data['sensor_data'], list):\n        raise ValueError('Missing or invalid sensor_data')\n    return True\n\ndef sanitize_fields(data: Dict[str, Any]) -> Dict[str, Any]:\n    """Sanitize input fields.\n    \n    Args:\n        data: Input data to sanitize\n    Returns:\n        Sanitized data\n    """\n    return {k: v for k, v in data.items() if v is not None}  # Remove None values\n\ndef transform_records(data: List[Dict[str, Any]]) -> List[List[float]]:\n    """Transform input records into a suitable format for processing.\n    \n    Args:\n        data: List of records to transform\n    Returns:\n        Transformed records as a list of lists\n    """\n    return [[record['sensor_data']] for record in data]  # Flattening the sensor data\n\ndef process_batch(batch: List[Dict[str, Any]]) -> None:\n    """Process a batch of records to detect anomalies.\n    \n    Args:\n        batch: List of records to process\n    """\n    try:\n        for record in batch:\n            logger.info('Processing record: %s', record)\n            sanitized_data = sanitize_fields(record)\n            if validate_input(sanitized_data):\n                transformed_data = transform_records([sanitized_data])\n                detect_anomalies(transformed_data)\n    except ValueError as e:\n        logger.error('Validation error: %s', e)\n    except Exception as e:\n        logger.error('Error processing batch: %s', e)\n\ndef detect_anomalies(data: List[List[float]]) -> None:\n    """Detect anomalies using the trained model.\n    \n    Args:\n        data: List of transformed records for anomaly detection\n    """\n    model = load_model(Config.model_path)  # Load the model\n    predictions = model.predict(data)\n    for record, prediction in zip(data, predictions):\n        if prediction == 1:  # Assuming 1 indicates anomaly\n            logger.warning('Anomaly detected: %s', record)\n\ndef load_model(path: str):\n    """Load the trained machine learning model.\n    \n    Args:\n        path: Path to the model file\n    Returns:\n        Trained model\n    """\n    from joblib import load\n    return load(path)\n\ndef save_to_db(data: Dict[str, Any]) -> None:\n    """Save anomaly data to the database.\n    \n    Args:\n        data: Data to save to the database\n    """\n    logger.info('Saving data to database: %s', data)\n    # Code for saving data to the database will go here\n\ndef fetch_data() -> List[Dict[str, Any]]:\n    """Fetch data for processing.\n    \n    Returns:\n        List of records fetched for processing\n    """\n    # Code to fetch data from an API or database will go here\n    return []  # Placeholder\n\ndef format_output(data: Dict[str, Any]) -> str:\n    """Format output data for logging or return.\n    \n    Args:\n        data: Data to format\n    Returns:\n        Formatted string output\n    """\n    return str(data)\n\ndef handle_errors(e: Exception) -> None:\n    """Handle errors gracefully.\n    \n    Args:\n        e: Exception to handle\n    """\n    logger.error('An error occurred: %s', e)\n\nclass AnomalyDetector:\n    """Main orchestrator class for the anomaly detection process.\n    """\n    def __init__(self):\n        self.model = load_model(Config.model_path)  # Load the model on initialization\n\n    async def run(self):\n        """Main method to run the anomaly detection workflow.\n        """\n        while True:\n            try:\n                data = fetch_data()  # Fetch data for processing\n                if data:\n                    process_batch(data)  # Process the batch of data\n            except Exception as e:\n                handle_errors(e)\n\nif __name__ == '__main__':\n    # Example usage\n    detector = AnomalyDetector()\n    detector.run()\n