Benchmark Probabilistic Failure Forecasts Across Models with Sktime and Darts

"""
Production implementation for benchmarking probabilistic failure forecasts
across multiple models using Sktime and Darts libraries.
Provides secure, scalable operations with robust logging and error handling.
"""

from typing import Dict, Any, List, Tuple, Optional
import os
import logging
import pandas as pd
import numpy as np
from darts import TimeSeries
from darts.models import ExponentialSmoothing, Theta, ARIMA
from darts.metrics import mape
from darts.utils import timeseries_generation as tg
from sklearn.model_selection import train_test_split
from sqlalchemy import create_engine
from sqlalchemy.exc import SQLAlchemyError

# Logger setup for monitoring 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', 'sqlite:///forecasts.db')  # Default to SQLite

def validate_input(data: Dict[str, Any]) -> bool:
    """Validate request data for forecasting.
    
    Args:
        data: Input dictionary containing time series data
    Returns:
        True if valid
    Raises:
        ValueError: If validation fails
    """
    if 'series' not in data:
        raise ValueError('Missing time series data')
    return True

def sanitize_fields(data: Dict[str, Any]) -> Dict[str, Any]:
    """Sanitize input data fields for processing.
    
    Args:
        data: Input dictionary
    Returns:
        Sanitized dictionary
    """
    # Assume we want to strip whitespace from all string fields
    return {k: v.strip() if isinstance(v, str) else v for k, v in data.items()}

def fetch_data() -> pd.DataFrame:
    """Fetch time series data from the database.
    
    Returns:
        DataFrame containing time series data
    Raises:
        SQLAlchemyError: If database connection fails
    """
    try:
        engine = create_engine(Config.database_url)
        with engine.connect() as connection:
            return pd.read_sql('SELECT * FROM time_series_data', connection)
    except SQLAlchemyError as e:
        logger.error('Database connection failed: %s', e)
        raise

def transform_records(data: pd.DataFrame) -> List[TimeSeries]:
    """Transform DataFrame records into Darts TimeSeries objects.
    
    Args:
        data: DataFrame containing time series data
    Returns:
        List of TimeSeries objects
    """
    series_list = []
    for _, row in data.iterrows():
        ts = TimeSeries.from_dataframe(row)
        series_list.append(ts)
    return series_list

def process_batch(series_list: List[TimeSeries]) -> Dict[str, float]:
    """Process a batch of time series and calculate metrics.
    
    Args:
        series_list: List of TimeSeries objects to process
    Returns:
        Dictionary of metrics indicating performance
    """
    results = {}
    for series in series_list:
        train, test = train_test_split(series, train_size=0.8)
        model = ExponentialSmoothing()
        model.fit(train)
        forecast = model.predict(len(test))
        results[series.name] = mape(test, forecast)  # Mean Absolute Percentage Error
    return results

def save_to_db(results: Dict[str, float]) -> None:
    """Save processed results back to the database.
    
    Args:
        results: Dictionary of metrics to save
    Raises:
        SQLAlchemyError: If database save fails
    """
    try:
        engine = create_engine(Config.database_url)
        with engine.connect() as connection:
            for model_name, error in results.items():
                connection.execute(f"INSERT INTO forecast_results (model, error) VALUES ('{model_name}', {error})")
    except SQLAlchemyError as e:
        logger.error('Failed to save results: %s', e)
        raise

def format_output(results: Dict[str, float]) -> str:
    """Format results into a string for display.
    
    Args:
        results: Dictionary of metrics
    Returns:
        Formatted string
    """
    return '\n'.join([f'{model}: {error:.2f}' for model, error in results.items()])

def handle_errors(func):
    """Decorator to handle errors in the functions.
    
    Args:
        func: Function to wrap
    Returns:
        Wrapped function with error handling
    """
    def wrapper(*args, **kwargs):
        try:
            return func(*args, **kwargs)
        except Exception as e:
            logger.error('Error occurred in %s: %s', func.__name__, e)
            raise
    return wrapper

@handle_errors
def benchmark_forecasts() -> None:
    """Main orchestration function to benchmark forecasts.
    
    Returns:
        None
    """
    data = fetch_data()  # Fetching data from the database
    if validate_input(data):  # Validating input
        data = sanitize_fields(data)  # Sanitizing fields
        series_list = transform_records(data)  # Transforming records
        results = process_batch(series_list)  # Processing batch
        save_to_db(results)  # Saving results back to the database
        logger.info('Forecasting completed with results: %s', format_output(results))

if __name__ == '__main__':
    benchmark_forecasts()  # Example usage to run the forecasting process