Score Multivariate Sensor Anomalies with dtaianomaly and scikit-learn

"""
Production implementation for scoring multivariate sensor anomalies.
This module integrates dtaianomaly and scikit-learn for anomaly detection.
"""
from typing import Dict, Any, List, Tuple
import os
import logging
import numpy as np
import pandas as pd
from dtaidistance import dtw
from sklearn.ensemble import IsolationForest
from sklearn.preprocessing import StandardScaler

# Set up logging configuration
logging.basicConfig(level=logging.INFO)
logger = logging.getLogger(__name__)

class Config:
    """Configuration class for environment variables."""
    dtaidistance_model: str = os.getenv('DTAIDISTANCE_MODEL', 'default_model')
    threshold: float = float(os.getenv('ANOMALY_THRESHOLD', 0.5))

def validate_input(data: Dict[str, Any]) -> bool:
    """Validate input data structure.
    Args:
        data: Input data dictionary to validate
    Returns:
        True if valid
    Raises:
        ValueError: If validation fails
    """
    if not isinstance(data, dict):
        raise ValueError('Input must be a dictionary.')
    if 'sensor_data' not in data:
        raise ValueError('Missing key: sensor_data')
    if not isinstance(data['sensor_data'], (list, pd.DataFrame)):
        raise ValueError('sensor_data must be a list or DataFrame.')
    return True

def sanitize_fields(data: Dict[str, Any]) -> Dict[str, Any]:
    """Sanitize input data fields.
    Args:
        data: Input data dictionary
    Returns:
        Sanitized data dictionary
    """
    data['sensor_data'] = pd.DataFrame(data['sensor_data']).fillna(0)
    return data

def normalize_data(data: pd.DataFrame) -> pd.DataFrame:
    """Normalize data using StandardScaler.
    Args:
        data: DataFrame to normalize
    Returns:
        Normalized DataFrame
    """
    scaler = StandardScaler()
    normalized = scaler.fit_transform(data)
    return pd.DataFrame(normalized, columns=data.columns)

def fetch_data(source: str) -> List[Dict[str, Any]]:
    """Fetch data from a specified source.
    Args:
        source: Source URL or file path
    Returns:
        List of data records
    Raises:
        IOError: If fetching data fails
    """
    try:
        # Placeholder for actual data fetching logic
        logger.info('Fetching data from source.')
        # Simulated data for demonstration
        return [{'sensor_data': [[1, 2, 3], [4, 5, 6]]}]
    except Exception as e:
        logger.error(f'Error fetching data: {e}')
        raise IOError('Failed to fetch data.')

def process_batch(sensor_data: pd.DataFrame) -> Tuple[np.ndarray, List[int]]:
    """Process a batch of sensor data for anomaly detection.
    Args:
        sensor_data: DataFrame containing sensor readings
    Returns:
        Tuple of anomaly scores and indices
    """
    model = IsolationForest(contamination=Config.threshold)
    model.fit(sensor_data)
    scores = model.decision_function(sensor_data)
    anomalies = np.where(scores < 0)[0].tolist()
    return scores, anomalies

def aggregate_metrics(scores: np.ndarray, anomalies: List[int]) -> Dict[str, Any]:
    """Aggregate metrics for reporting.
    Args:
        scores: Anomaly scores
        anomalies: Indices of detected anomalies
    Returns:
        Dictionary containing aggregated metrics
    """
    return {
        'total_scores': len(scores),
        'anomaly_count': len(anomalies),
        'anomaly_indices': anomalies
    }

def save_to_db(data: Dict[str, Any]) -> None:
    """Save processed data to the database.
    Args:
        data: Data to save
    Raises:
        Exception: If saving fails
    """
    try:
        # Placeholder for actual database saving logic
        logger.info('Saving data to the database.')
        pass  # Replace with actual DB logic
    except Exception as e:
        logger.error(f'Error saving data: {e}')
        raise Exception('Failed to save data.')

class AnomalyScorer:
    """Main class for scoring anomalies in sensor data."""
    def __init__(self, config: Config):
        self.config = config

    def run(self, source: str) -> None:
        """Run the anomaly scoring process.
        Args:
            source: Data source for sensor readings
        """
        try:
            raw_data = fetch_data(source)
            for record in raw_data:
                validated_data = validate_input(record)
                sanitized_data = sanitize_fields(validated_data)
                normalized_data = normalize_data(sanitized_data['sensor_data'])
                scores, anomalies = process_batch(normalized_data)
                metrics = aggregate_metrics(scores, anomalies)
                save_to_db(metrics)
                logger.info(f'Processed metrics: {metrics}')
        except Exception as e:
            logger.error(f'An error occurred during processing: {e}')

if __name__ == '__main__':
    # Example usage of the anomaly scorer
    scorer = AnomalyScorer(Config())
    scorer.run(source='sensor_data_source')