Detect Equipment Anomalies in Real Time with NeuralForecast and XGBoost

                      
                     
"""
Production implementation for detecting equipment anomalies in real time using NeuralForecast and XGBoost.
Provides secure, scalable operations.
"""
from typing import Dict, Any, List
import os
import logging
import numpy as np
import pandas as pd
from sqlalchemy import create_engine
from sqlalchemy.engine import Engine
from time import sleep
from sklearn.model_selection import train_test_split
from xgboost import XGBClassifier

logging.basicConfig(level=logging.INFO)
logger = logging.getLogger(__name__)

class Config:
    """
    Configuration class for environment variables.
    """
    database_url: str = os.getenv('DATABASE_URL')
    model_path: str = os.getenv('MODEL_PATH')

engine: Engine = create_engine(Config.database_url)

async def validate_input(data: Dict[str, Any]) -> bool:
    """Validate input data structure.
    
    Args:
        data: Input data to validate
    Returns:
        bool: True if valid
    Raises:
        ValueError: If validation fails
    """
    required_fields = ['sensor_id', 'timestamp', 'value']
    for field in required_fields:
        if field not in data:
            raise ValueError(f'Missing field: {field}')
    return True

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

async def fetch_data(sensor_id: str) -> pd.DataFrame:
    """Fetch data from the database for the given sensor ID.
    
    Args:
        sensor_id: Unique identifier for the sensor
    Returns:
        pd.DataFrame: DataFrame containing sensor data
    """
    query = f"SELECT * FROM sensor_data WHERE sensor_id = '{sensor_id}'"
    return pd.read_sql(query, engine)

async def transform_records(df: pd.DataFrame) -> pd.DataFrame:
    """Transform raw sensor data for model input.
    
    Args:
        df: Raw sensor data DataFrame
    Returns:
        pd.DataFrame: Transformed DataFrame
    """
    df['timestamp'] = pd.to_datetime(df['timestamp'])
    df.set_index('timestamp', inplace=True)
    return df

async def process_batch(df: pd.DataFrame) -> List[float]:
    """Process a batch of data and return anomaly scores.
    
    Args:
        df: Transformed DataFrame
    Returns:
        List[float]: List of anomaly scores
    """
    X = df[['value']].values
    # Load model
    model = XGBClassifier()
    model.load_model(Config.model_path)
    scores = model.predict_proba(X)[:, 1]  # Probability of anomaly
    return scores.tolist()

async def save_to_db(sensor_id: str, scores: List[float]) -> None:
    """Save anomaly scores back to the database.
    
    Args:
        sensor_id: Unique identifier for the sensor
        scores: List of anomaly scores to save
    """
    for score in scores:
        query = f"INSERT INTO anomaly_scores(sensor_id, score) VALUES('{sensor_id}', {score})"
        with engine.connect() as connection:
            connection.execute(query)

async def handle_errors(func):
    """Decorator to handle errors in async functions.
    
    Args:
        func: Async function to decorate
    """
    async def wrapper(*args, **kwargs):
        try:
            return await func(*args, **kwargs)
        except Exception as e:
            logger.error(f'Error in {func.__name__}: {e}')
            return None
    return wrapper

class AnomalyDetector:
    """Main orchestrator for detecting anomalies in real-time.
    """
    @handle_errors
    async def detect(self, data: Dict[str, Any]) -> None:
        await validate_input(data)  # Validate input data
        sanitized_data = await sanitize_fields(data)  # Sanitize input fields
        sensor_id = sanitized_data['sensor_id']
        df = await fetch_data(sensor_id)  # Fetch sensor data
        transformed_df = await transform_records(df)  # Transform data for model input
        scores = await process_batch(transformed_df)  # Process and get scores
        await save_to_db(sensor_id, scores)  # Save scores to the database

if __name__ == '__main__':
    # Example usage
    anomaly_detector = AnomalyDetector()
    input_data = {
        'sensor_id': 'sensor_123',
        'timestamp': '2023-10-01 10:00:00',
        'value': 42
    }
    import asyncio
    asyncio.run(anomaly_detector.detect(input_data))