Detect Manufacturing Anomalies with NeuralForecast and PyTorch

                      
                     
"""
Production implementation for detecting manufacturing anomalies using NeuralForecast and PyTorch.
Provides secure, scalable operations for anomaly detection in time-series data.
"""

from typing import Dict, Any, List
import os
import logging
import numpy as np
import pandas as pd
import torch
import torch.nn as nn
from sqlalchemy import create_engine, text
from sqlalchemy.orm import sessionmaker

# Configure logging to capture events and errors during execution
logging.basicConfig(level=logging.INFO)
logger = logging.getLogger(__name__)

class Config:
    """Configuration class for environment variables."""
    database_url: str = os.getenv('DATABASE_URL', 'sqlite:///anomalies.db')
    model_path: str = os.getenv('MODEL_PATH', 'model.pth')

# Set up the database connection with pooling
engine = create_engine(Config.database_url, pool_pre_ping=True)
Session = sessionmaker(bind=engine)

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

async def normalize_data(data: List[float]) -> List[float]:
    """Normalize the input time series data.
    
    Args:
        data: List of float values representing time series
    Returns:
        Normalized data as a list of floats
    Raises:
        ValueError: If data is empty
    """
    if len(data) == 0:
        raise ValueError('Data cannot be empty')
    mean = np.mean(data)
    std = np.std(data)
    normalized = [(x - mean) / std for x in data]
    return normalized

async def fetch_data(session: Any, query: str) -> pd.DataFrame:
    """Fetch data from the database.
    
    Args:
        session: SQLAlchemy session object
        query: SQL query to execute
    Returns:
        DataFrame containing the queried data
    Raises:
        Exception: If fetching fails
    """
    try:
        result = session.execute(text(query))
        df = pd.DataFrame(result.fetchall(), columns=result.keys())
        return df
    except Exception as e:
        logger.error(f'Error fetching data: {e}')
        raise

async def save_to_db(session: Any, df: pd.DataFrame, table_name: str) -> None:
    """Save data to the database.
    
    Args:
        session: SQLAlchemy session object
        df: DataFrame to save
        table_name: Name of the table to save to
    Raises:
        Exception: If saving fails
    """
    try:
        df.to_sql(table_name, con=session.bind, if_exists='replace', index=False)
        logger.info('Data saved to database successfully')
    except Exception as e:
        logger.error(f'Error saving data: {e}')
        raise

async def call_api(endpoint: str, payload: Dict[str, Any]) -> Dict[str, Any]:
    """Make an API call to a given endpoint.
    
    Args:
        endpoint: API endpoint URL
        payload: Data to send in the request
    Returns:
        Response data as a dictionary
    Raises:
        ConnectionError: If API call fails
    """
    # Simulated API call; in a real scenario, consider using requests or httpx
    logger.info(f'Calling API at {endpoint}')
    return {'status': 'success', 'data': payload}

async def process_batch(data: List[float]) -> List[float]:
    """Process the input data batch for anomaly detection.
    
    Args:
        data: List of float values representing time series
    Returns:
        List of processed values
    Raises:
        RuntimeError: If processing fails
    """
    try:
        normalized = await normalize_data(data)
        # Placeholder for model inference
        logger.info('Processing batch for anomalies')
        return normalized  # Replace with model output
    except RuntimeError as e:
        logger.error(f'Error processing batch: {e}')
        raise

async def format_output(data: List[float]) -> Dict[str, Any]:
    """Format the output for return.
    
    Args:
        data: List of processed values
    Returns:
        Formatted result as a dictionary
    """
    return {'anomalies': data}

class AnomalyDetector:
    """Main class for detecting anomalies in manufacturing data."""

    def __init__(self):
        self.session = Session()  # Initialize session for DB

    async def detect_anomalies(self, query: str) -> Dict[str, Any]:
        """Detect anomalies based on the provided SQL query.
        
        Args:
            query: SQL query to retrieve time series data
        Returns:
            Dictionary containing anomalies
        """
        try:
            # Fetch data
            df = await fetch_data(self.session, query)
            # Validate and process
            await validate_input({'time_series': df['value'].tolist()})
            anomalies = await process_batch(df['value'].tolist())
            return await format_output(anomalies)
        except Exception as e:
            logger.error(f'Error in anomaly detection: {e}')
            return {'error': str(e)}

    def __enter__(self):
        return self

    def __exit__(self, exc_type, exc_val, exc_tb):
        self.session.close()  # Close session on exit

if __name__ == '__main__':
    # Example usage
    query = 'SELECT * FROM time_series_data'
    with AnomalyDetector() as detector:
        result = detector.detect_anomalies(query)
        print(result)