Classify Machine Degradation Stages from Vibration Motif Patterns with STUMPY and LightGBM

"""
Production implementation for classifying machine degradation stages from vibration motif patterns.
Utilizes STUMPY for motif discovery and LightGBM for classification.
"""
from typing import Dict, Any, List
import os
import logging
import numpy as np
import pandas as pd
import stumpy
import lightgbm as lgb
from sklearn.model_selection import train_test_split
from sklearn.metrics import accuracy_score

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

class Config:
    """
    Configuration class to manage environment variables.
    """
    database_url: str = os.getenv('DATABASE_URL', 'sqlite:///default.db')
    model_path: str = os.getenv('MODEL_PATH', 'model.txt')

def validate_input(data: Dict[str, Any]) -> bool:
    """Validate input data for classification.
    
    Args:
        data: Input to validate
    Returns:
        True if valid
    Raises:
        ValueError: If validation fails
    """
    if 'vibration_data' not in data or not isinstance(data['vibration_data'], list):
        raise ValueError('Missing or invalid vibration_data')
    return True

def sanitize_fields(data: Dict[str, Any]) -> Dict[str, Any]:
    """Sanitize input fields for processing.
    
    Args:
        data: Input data to sanitize
    Returns:
        Sanitized data
    """
    sanitized = {key: str(value).strip() for key, value in data.items()}
    return sanitized

def fetch_data() -> pd.DataFrame:
    """Fetch the vibration data from the database.
    
    Returns:
        DataFrame containing vibration data
    Raises:
        Exception: If database connection fails
    """
    try:
        # Simulate fetching data from a database
        logger.info('Fetching data from the database')
        data = pd.DataFrame({
            'id': [1, 2, 3],
            'vibration': [np.random.rand(100), np.random.rand(100), np.random.rand(100)],
            'label': ['normal', 'fault', 'fault']
        })
        return data
    except Exception as e:
        logger.error(f'Error fetching data: {e}')
        raise

def transform_records(data: pd.DataFrame) -> np.ndarray:
    """Transform records into appropriate format for STUMPY.
    
    Args:
        data: DataFrame containing raw vibration data
    Returns:
        Numpy array of transformed records
    """
    logger.info('Transforming records for STUMPY')
    transformed_data = np.array([row for row in data['vibration']])
    return transformed_data

def process_batch(vibration_data: np.ndarray) -> List[float]:
    """Process a batch of vibration data for classification.
    
    Args:
        vibration_data: Numpy array of vibration data
    Returns:
        List of classification probabilities
    """
    logger.info('Processing batch of vibration data')
    motifs = stumpy.stump(time_series_a=vibration_data, m=20)
    # Using LightGBM for classification
    model = lgb.Booster(model_file=Config.model_path)
    predictions = model.predict(motifs)
    return predictions.tolist()

def save_to_db(results: List[float]) -> None:
    """Save classification results to the database.
    
    Args:
        results: List of classification results to save
    Raises:
        Exception: If database save fails
    """
    try:
        logger.info('Saving results to the database')
        # Simulate saving to database
        for result in results:
            logger.debug(f'Saving result: {result}')  # Log each result
    except Exception as e:
        logger.error(f'Error saving results: {e}')
        raise

class VibrationClassifier:
    """Main class for managing vibration classification workflow."""
    def __init__(self) -> None:
        self.config = Config()  # Load configuration

    def classify(self, data: Dict[str, Any]) -> List[float]:
        """Classify vibration data.
        
        Args:
            data: Input data for classification
        Returns:
            List of classification probabilities
        """
        try:
            validate_input(data)  # Validate input
            sanitized_data = sanitize_fields(data)  # Sanitize data
            raw_data = fetch_data()  # Fetch data
            transformed_data = transform_records(raw_data)  # Transform data
            results = process_batch(transformed_data)  # Process data
            save_to_db(results)  # Save results
            return results
        except ValueError as ve:
            logger.warning(f'Validation error: {ve}')  # Log validation errors
            return []
        except Exception as e:
            logger.error(f'Error during classification: {e}')  # Log any other errors
            return []

if __name__ == '__main__':
    # Example usage
    classifier = VibrationClassifier()  # Create classifier instance
    sample_data = {'vibration_data': [np.random.rand(100)]}
    results = classifier.classify(sample_data)  # Classify sample data
    print(results)  # Print classification results