Build Interpretable Production Yield Forecasts with Prophet and scikit-learn

                      
                     
"""
Production implementation for building interpretable production yield forecasts using Prophet and scikit-learn.
Provides secure, scalable operations with robust error handling and logging.
"""

from typing import Dict, Any, Tuple, List
import os
import logging
import pandas as pd
from sklearn.preprocessing import StandardScaler
from fbprophet import Prophet
import sqlalchemy
from sqlalchemy.orm import sessionmaker
import time

# Set 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', 'sqlite:///forecast.db')
    retry_attempts: int = int(os.getenv('RETRY_ATTEMPTS', 3))

# Create a connection pool for the database
engine = sqlalchemy.create_engine(Config.database_url)
Session = sessionmaker(bind=engine)

def validate_input(data: Dict[str, Any]) -> bool:
    """Validate request data.
    
    Args:
        data: Input to validate
    Returns:
        True if valid
    Raises:
        ValueError: If validation fails
    """
    if 'dates' not in data or 'yields' not in data:
        raise ValueError('Missing required fields: dates or yields')
    return True

def sanitize_fields(data: Dict[str, Any]) -> Dict[str, Any]:
    """Sanitize input data fields.
    
    Args:
        data: Input data to sanitize
    Returns:
        Sanitized data dictionary
    """
    # Basic sanitation: removing extra spaces
    return {k: v.strip() for k, v in data.items()}

def normalize_data(data: pd.DataFrame) -> pd.DataFrame:
    """Normalize data using standard scaling.
    
    Args:
        data: DataFrame to normalize
    Returns:
        Normalized DataFrame
    """
    scaler = StandardScaler()
    data[['yields']] = scaler.fit_transform(data[['yields']])
    return data

def transform_records(data: pd.DataFrame) -> pd.DataFrame:
    """Transform raw records for forecasting.
    
    Args:
        data: Raw data to transform
    Returns:
        Transformed DataFrame for Prophet
    """
    # Format data for Prophet
    transformed = data.rename(columns={'dates': 'ds', 'yields': 'y'})
    return transformed

def fetch_data(session: sqlalchemy.orm.Session) -> pd.DataFrame:
    """Fetch data from the database.
    
    Args:
        session: Active database session
    Returns:
        DataFrame containing the forecast data
    """
    query = "SELECT dates, yields FROM forecasts"
    return pd.read_sql(query, session)

def save_to_db(session: sqlalchemy.orm.Session, data: pd.DataFrame) -> None:
    """Save forecast data to the database.
    
    Args:
        session: Active database session
        data: DataFrame to save
    """
    data.to_sql('forecast_results', session.bind, if_exists='replace', index=False)
    logger.info('Data saved to database successfully.')

def call_api(url: str, payload: Dict[str, Any]) -> Any:
    """Call external API.
    
    Args:
        url: API endpoint
        payload: Data to send
    Returns:
        Response from the API
    Raises:
        Exception: If API call fails
    """
    import requests
    response = requests.post(url, json=payload)
    if response.status_code != 200:
        raise Exception(f'API call failed: {response.text}')
    return response.json()

class ForecastModel:
    """Main class for forecasting production yields.
    """
    def __init__(self) -> None:
        self.session = Session()  # Initialize database session

    def run_forecast(self, input_data: Dict[str, Any]) -> None:
        """Main workflow for forecasting yields.
        
        Args:
            input_data: Dictionary containing input data
        """
        try:
            validate_input(input_data)  # Validate input
            sanitized_data = sanitize_fields(input_data)  # Sanitize input
            raw_data = fetch_data(self.session)  # Fetch data
            normalized_data = normalize_data(raw_data)  # Normalize data
            transformed_data = transform_records(normalized_data)  # Transform for Prophet

            # Create and fit the Prophet model
            model = Prophet()
            model.fit(transformed_data)

            # Make future predictions
            future = model.make_future_dataframe(periods=30)  # Predict for the next 30 days
            forecast = model.predict(future)
            logger.info('Forecast generated successfully.')  # Log success

            # Save the forecast to the database
            save_to_db(self.session, forecast[['ds', 'yhat', 'yhat_lower', 'yhat_upper']])

        except ValueError as e:
            logger.error(f'Validation error: {e}')  # Log validation errors
            raise
        except Exception as e:
            logger.error(f'An error occurred: {e}')  # Log any other errors
            raise
        finally:
            self.session.close()  # Ensure session is closed

if __name__ == '__main__':
    # Example usage
    input_data = {
        'dates': ['2023-01-01', '2023-01-02'],
        'yields': [100, 200]
    }
    forecast_model = ForecastModel()
    forecast_model.run_forecast(input_data)  # Run the forecasting process