Detect Manufacturing Anomalies with NeuralForecast and PyTorch

                      
                     
import os
import torch
import pandas as pd
from neuralforecast import NeuralForecast
from typing import Dict, Any

# Configuration
API_KEY = os.getenv('API_KEY')  # Environment variable for security
DATA_PATH = os.getenv('DATA_PATH', 'data.csv')  # Path to dataset

# Load dataset
def load_data(file_path: str) -> pd.DataFrame:
    try:
        data = pd.read_csv(file_path)
        return data
    except Exception as e:
        print(f'Error loading data: {e}')
        raise

# Initialize NeuralForecast model
model = NeuralForecast()

# Train model
def train_model(data: pd.DataFrame) -> None:
    try:
        model.fit(data)
    except Exception as e:
        print(f'Error during training: {e}')

# Detect anomalies
def detect_anomalies(data: pd.DataFrame) -> Dict[str, Any]:
    try:
        predictions = model.predict(data)
        anomalies = (data - predictions).abs() > 3 * predictions.std()
        return {'anomalies': anomalies}
    except Exception as e:
        print(f'Error detecting anomalies: {e}')
        return {'anomalies': None}

if __name__ == '__main__':
    data = load_data(DATA_PATH)
    train_model(data)
    results = detect_anomalies(data)
    print(results)