Monitor Time-Series Forecast Drift in Production with tsfresh and LightGBM

"""
Production implementation for monitoring time-series forecast drift in production using tsfresh and LightGBM.
Provides secure, scalable operations for data validation, transformation, and processing.
"""
from typing import Dict, Any, List
import os
import logging
import pandas as pd
import lightgbm as lgb
from tsfresh import extract_features, select_features, utilities
from sqlalchemy import create_engine
from sqlalchemy.exc import SQLAlchemyError
import time

# Set up logging for tracking application behavior
logging.basicConfig(level=logging.INFO)
logger = logging.getLogger(__name__)

class Config:
    """
    Configuration class to manage environment variables.
    """
    database_url: str = os.getenv('DATABASE_URL')
    retry_attempts: int = int(os.getenv('RETRY_ATTEMPTS', 5))
    retry_delay: float = float(os.getenv('RETRY_DELAY', 1.0))

def validate_input(data: Dict[str, Any]) -> bool:
    """Validate input data to ensure it meets required criteria.
    
    Args:
        data: Input data dictionary
    Returns:
        bool: True if valid
    Raises:
        ValueError: If validation fails
    """
    if 'time' not in data or 'value' not in data:
        raise ValueError('Missing required fields: time and value')
    return True

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

def normalize_data(df: pd.DataFrame) -> pd.DataFrame:
    """Normalize the input dataframe for processing.
    
    Args:
        df: Input dataframe
    Returns:
        pd.DataFrame: Normalized dataframe
    """
    df['value'] = (df['value'] - df['value'].mean()) / df['value'].std()
    return df

def extract_timeseries_features(df: pd.DataFrame) -> pd.DataFrame:
    """Extract features from time series data using tsfresh.
    
    Args:
        df: Input dataframe
    Returns:
        pd.DataFrame: Extracted features
    """
    features = extract_features(df, column_id='id', column_sort='time')
    return features

def fit_lightgbm_model(X_train: pd.DataFrame, y_train: pd.Series) -> lgb.Booster:
    """Fit LightGBM model to the training data.
    
    Args:
        X_train: Features for training
        y_train: Target variable
    Returns:
        lgb.Booster: Trained LightGBM model
    """
    model = lgb.LGBMRegressor()
    model.fit(X_train, y_train)
    return model

def save_to_db(df: pd.DataFrame, table_name: str) -> None:
    """Save the DataFrame to a SQL database.
    
    Args:
        df: DataFrame to save
        table_name: Name of the database table
    Raises:
        SQLAlchemyError: If database operation fails
    """
    try:
        engine = create_engine(Config.database_url)
        df.to_sql(table_name, con=engine, if_exists='replace', index=False)
        logger.info('Data saved to database successfully.')
    except SQLAlchemyError as e:
        logger.error(f'Error saving to database: {e}')
        raise

def call_api(url: str, data: Dict[str, Any]) -> None:
    """Call an external API with the provided data.
    
    Args:
        url: API endpoint
        data: Data to send to the API
    Raises:
        Exception: If API call fails
    """
    # Simulate API call
    try:
        logger.info(f'Calling API at {url} with data: {data}')
        # Here you would use requests.post() or similar
    except Exception as e:
        logger.error(f'Error calling API: {e}')
        raise

class TimeSeriesDriftMonitor:
    """Main class for monitoring time-series drift.
    
    Attributes:
        data: DataFrame containing time-series data
    """
    def __init__(self, data: pd.DataFrame) -> None:
        self.data = data
        logger.info('Initialized TimeSeriesDriftMonitor with data.')

    def process_data(self) -> None:
        """Main process for handling the data.
        
        Steps:
        1. Validate inputs
        2. Sanitize fields
        3. Normalize data
        4. Extract features
        5. Fit model
        6. Save results
        """
        try:
            validate_input(self.data.to_dict())  # Validate data
            sanitized_data = sanitize_fields(self.data.to_dict())  # Sanitize input
            df = pd.DataFrame(sanitized_data)
            normalized_df = normalize_data(df)  # Normalize data

            features = extract_timeseries_features(normalized_df)  # Extract features
            y = normalized_df['value']  # Target variable
            model = fit_lightgbm_model(features, y)  # Fit model

            # Save to database
            save_to_db(normalized_df, 'time_series_data')
            logger.info('Data processing completed successfully.')

        except ValueError as ve:
            logger.error(f'Validation error: {ve}')
        except Exception as e:
            logger.error(f'An error occurred during processing: {e}')

if __name__ == '__main__':
    # Example usage
    # Sample data for demonstration
    sample_data = {
        'id': [1, 1, 1, 2, 2, 2],
        'time': [1, 2, 3, 1, 2, 3],
        'value': [10.0, 15.0, 14.0, 20.0, 25.0, 22.0]
    }
    df = pd.DataFrame(sample_data)  # Create a DataFrame
    monitor = TimeSeriesDriftMonitor(df)  # Initialize the monitor
    monitor.process_data()  # Process the data