Forecast Production Line Output Variance with statsforecast and Darts

"""
Production implementation for forecasting production line output variance.
Utilizes statsforecast and Darts for statistical analysis and forecasting.
"""
from typing import Dict, Any, List, Tuple
import os
import logging
import pandas as pd
import numpy as np
from statsforecast import StatsForecast
from darts import TimeSeries
from darts.models import ExponentialSmoothing
from sqlalchemy import create_engine, text
from sqlalchemy.exc import SQLAlchemyError
import time

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

class Config:
    """
    Configuration class for environment variables.
    """
    database_url: str = os.getenv('DATABASE_URL')

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

def sanitize_fields(data: Dict[str, Any]) -> Dict[str, Any]:
    """Sanitize input fields to prevent injection.
    
    Args:
        data: Input data to sanitize
    Returns:
        Sanitized data
    """
    sanitized_data = {k: str(v).strip() for k, v in data.items()}
    logger.info('Sanitized input data fields.')
    return sanitized_data

def fetch_data(engine: Any, query: str) -> pd.DataFrame:
    """Fetch data from the database.
    
    Args:
        engine: SQLAlchemy engine
        query: SQL query to execute
    Returns:
        DataFrame with fetched data
    Raises:
        SQLAlchemyError: If the query execution fails
    """
    try:
        with engine.connect() as connection:
            return pd.read_sql(text(query), connection)
    except SQLAlchemyError as e:
        logger.error(f'Error fetching data: {e}')
        raise

def transform_records(data: pd.DataFrame) -> TimeSeries:
    """Transform DataFrame records into Darts TimeSeries.
    
    Args:
        data: DataFrame with production data
    Returns:
        Darts TimeSeries object
    """
    ts = TimeSeries.from_dataframe(data, time_col='timestamp', value_cols='output')
    logger.info('Transformed DataFrame to TimeSeries.')
    return ts

def forecast_variance(ts: TimeSeries) -> List[float]:
    """Forecast future variance using statsforecast.
    
    Args:
        ts: Darts TimeSeries object
    Returns:
        List of forecasted variance values
    """
    model = ExponentialSmoothing()
    model.fit(ts)
    forecast = model.predict(steps=12)
    logger.info('Forecasted future variance.')
    return forecast.values()

def save_to_db(engine: Any, data: List[float]) -> None:
    """Save forecasted data to the database.
    
    Args:
        engine: SQLAlchemy engine
        data: List of forecasted values to save
    Raises:
        SQLAlchemyError: If the save operation fails
    """
    try:
        with engine.connect() as connection:
            for value in data:
                connection.execute(text('INSERT INTO forecasts (value) VALUES (:value)'), {'value': value})
        logger.info('Saved forecasted data to database.')
    except SQLAlchemyError as e:
        logger.error(f'Error saving data: {e}')
        raise

class ForecastOrchestrator:
    """Main orchestrator class for forecasting workflow.
    """
    def __init__(self, config: Config) -> None:
        self.config = config
        self.engine = create_engine(config.database_url)

    def run(self, input_data: Dict[str, Any]) -> None:
        """Run the forecasting workflow.
        
        Args:
            input_data: Input data for the workflow
        """
        validate_input(input_data)  # Validate input data
        sanitized_data = sanitize_fields(input_data)  # Sanitize input fields
        query = "SELECT timestamp, output FROM production WHERE id = :id"  # SQL query
        data = fetch_data(self.engine, query)  # Fetch data from DB
        ts = transform_records(data)  # Transform to TimeSeries
        forecast = forecast_variance(ts)  # Forecast variance
        save_to_db(self.engine, forecast)  # Save forecasted data

if __name__ == '__main__':
    config = Config()  # Load configuration
    orchestrator = ForecastOrchestrator(config)  # Create orchestrator
    input_data = {'production_data': {'id': 1}}  # Example input
    try:
        orchestrator.run(input_data)  # Execute forecasting workflow
    except Exception as e:
        logger.error(f'Error in forecasting workflow: {e}')  # Handle errors