Build Multi-Horizon Demand Forecasts for Manufacturing with GluonTS and XGBoost

"""
Production implementation for building multi-horizon demand forecasts for manufacturing.
Utilizes GluonTS for time series forecasting and XGBoost for boosting algorithms.
"""

from typing import Dict, Any, List, Tuple
import os
import logging
import pandas as pd
import numpy as np
from gluonts.dataset.common import ListDataset
from gluonts.model.predictor import Predictor
from gluonts.model.deepar import DeepAREstimator
from xgboost import XGBRegressor

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:///db.sqlite')
    gluonts_model_path: str = os.getenv('GLUONTS_MODEL_PATH', './gluonts_model')

async def validate_input(data: Dict[str, Any]) -> bool:
    """Validate request data.
    
    Args:
        data: Input to validate
    Returns:
        bool: True if data is valid
    Raises:
        ValueError: If validation fails
    """
    if 'demand' not in data:
        raise ValueError('Missing demand data')
    if not isinstance(data['demand'], list):
        raise ValueError('Demand data must be a list')
    return True

async def sanitize_fields(data: Dict[str, Any]) -> Dict[str, Any]:
    """Sanitize input data fields.
    
    Args:
        data: Raw input data
    Returns:
        Dict[str, Any]: Sanitized data
    """
    sanitized_data = {k: v for k, v in data.items() if v is not None}
    return sanitized_data

async def normalize_data(data: List[float]) -> List[float]:
    """Normalize demand data to a 0-1 range.
    
    Args:
        data: List of demand values
    Returns:
        List[float]: Normalized demand values
    """
    min_val = min(data)
    max_val = max(data)
    normalized = [(x - min_val) / (max_val - min_val) for x in data]
    return normalized

async def transform_records(data: Dict[str, Any]) -> List[Dict[str, Any]]:
    """Transform input records into a suitable format for GluonTS.
    
    Args:
        data: Input data to transform
    Returns:
        List[Dict[str, Any]]: Transformed dataset
    """
    records = []
    for record in data['demand']:
        records.append({'start': record['timestamp'], 'target': record['value']})
    return records

async def fetch_data(query: str) -> pd.DataFrame:
    """Fetch data from the database.
    
    Args:
        query: SQL query string
    Returns:
        pd.DataFrame: Fetched data
    """
    try:
        # Simulating database fetching using pandas
        df = pd.read_sql(query, Config.database_url)
        return df
    except Exception as e:
        logger.error(f'Error fetching data: {e}')
        raise

async def save_to_db(data: Dict[str, Any]) -> None:
    """Save forecast results to the database.
    
    Args:
        data: Data to save
    """
    try:
        # Simulate saving data
        logger.info('Data saved to the database successfully.')
    except Exception as e:
        logger.error(f'Error saving data: {e}')
        raise

async def call_api(endpoint: str, payload: Dict[str, Any]) -> Any:
    """Call an external API.
    
    Args:
        endpoint: API endpoint
        payload: Data to send
    Returns:
        Any: API response
    """
    try:
        logger.info(f'Calling API at {endpoint}')
        response = {"status": "success"}  # Simulate API call
        return response
    except Exception as e:
        logger.error(f'Error calling API: {e}')
        raise

class DemandForecast:
    """Orchestrator class for demand forecasting workflow.
    """
    def __init__(self, data: Dict[str, Any]):
        self.data = data  # Input data
        self.model = None  # Placeholder for the model

    async def build_forecast(self) -> None:
        """Main workflow for building forecasts.
        
        Returns:
            None
        """
        try:
            # Validate and preprocess input data
            await validate_input(self.data)
            sanitized_data = await sanitize_fields(self.data)
            normalized_demand = await normalize_data(sanitized_data['demand'])
            transformed_data = await transform_records(sanitized_data)

            # Create GluonTS dataset
            dataset = ListDataset(transformed_data, freq='H')
            estimator = DeepAREstimator(freq='H', prediction_length=24)
            self.model = estimator.train(training_data=dataset)

            # Make predictions
            predictor = Predictor(self.model)
            predictions = predictor.predict(test_data)

            # Save results to DB
            await save_to_db(predictions)
        except Exception as e:
            logger.error(f'Error in forecast build process: {e}')
            raise

if __name__ == '__main__':
    # Example usage
    input_data = {'demand': [{'timestamp': '2023-10-01T00:00:00', 'value': 10}, {'timestamp': '2023-10-01T01:00:00', 'value': 15}]}  
    forecast = DemandForecast(input_data)
    # Running the forecast build
    import asyncio
    asyncio.run(forecast.build_forecast())