Forecast Industrial Demand Distributions Across SKU Hierarchies with Aeon and XGBoost

"""
Production implementation for forecasting industrial demand distributions across SKU hierarchies using Aeon and XGBoost.
Provides secure, scalable operations.
"""
import os
import logging
import pandas as pd
import xgboost as xgb
from typing import Dict, Any, List, Tuple
from sqlalchemy import create_engine, text

# Logger setup
logging.basicConfig(level=logging.INFO)
logger = logging.getLogger(__name__)

# Configuration class
class Config:
    """
    Configuration for the application.
    Loads environment variables.
    """
    database_url: str = os.getenv('DATABASE_URL')
    retry_attempts: int = int(os.getenv('RETRY_ATTEMPTS', 5))
    backoff_factor: float = float(os.getenv('BACKOFF_FACTOR', 1.5))

# Validation function
async 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 'sku' not in data:
        raise ValueError('Missing SKU in input data')
    return True

# Sanitize fields
async def sanitize_fields(data: Dict[str, Any]) -> Dict[str, Any]:
    """Sanitize input data fields.
    Args:
        data: Input data
    Returns:
        Sanitized data
    """
    return {k: str(v).strip() for k, v in data.items()}

# Fetch data from the database
async def fetch_data(sku: str) -> pd.DataFrame:
    """Fetch SKU data from the database.
    Args:
        sku: SKU identifier
    Returns:
        DataFrame with SKU data
    Raises:
        ConnectionError: If DB connection fails
    """
    try:
        engine = create_engine(Config.database_url)
        with engine.connect() as connection:
            query = text(f'SELECT * FROM demand_data WHERE sku = :sku')
            return pd.read_sql(query, connection, params={'sku': sku})
    except Exception as e:
        logger.error('Database connection failed', exc_info=e)
        raise ConnectionError('Could not connect to database')

# Normalize data
def normalize_data(df: pd.DataFrame) -> pd.DataFrame:
    """Normalize DataFrame for model input.
    Args:
        df: Input DataFrame
    Returns:
        Normalized DataFrame
    """
    return (df - df.mean()) / df.std()

# Train XGBoost model
async def train_model(X: pd.DataFrame, y: pd.Series) -> xgb.Booster:
    """Train the XGBoost model.
    Args:
        X: Feature DataFrame
        y: Target Series
    Returns:
        Trained XGBoost model
    """
    dtrain = xgb.DMatrix(X, label=y)
    params = {'objective': 'reg:squarederror', 'eval_metric': 'rmse'}
    model = xgb.train(params, dtrain)
    return model

# Predict demand using the trained model
async def predict_demand(model: xgb.Booster, X: pd.DataFrame) -> List[float]:
    """Make demand predictions.
    Args:
        model: Trained XGBoost model
        X: Feature DataFrame for prediction
    Returns:
        List of predictions
    """
    dmatrix = xgb.DMatrix(X)
    return model.predict(dmatrix).tolist()

# Save predictions to the database
async def save_predictions(sku: str, predictions: List[float]) -> None:
    """Save predictions to the database.
    Args:
        sku: SKU identifier
        predictions: List of predictions
    Raises:
        RuntimeError: If saving fails
    """
    try:
        engine = create_engine(Config.database_url)
        with engine.connect() as connection:
            for prediction in predictions:
                connection.execute(text('INSERT INTO predictions (sku, prediction) VALUES (:sku, :prediction)'),
                                   {'sku': sku, 'prediction': prediction})
    except Exception as e:
        logger.error('Failed to save predictions', exc_info=e)
        raise RuntimeError('Could not save predictions to database')

# Orchestrator class
class DemandForecast:
    """Orchestrates the demand forecasting workflow.
    """
    def __init__(self, sku: str):
        self.sku = sku
        self.logger = logger

    async def execute(self) -> None:
        """Execute the demand forecasting process.
        """
        try:
            # Fetch and validate data
            raw_data = await fetch_data(self.sku)
            await validate_input(raw_data.to_dict())
            sanitized_data = await sanitize_fields(raw_data.to_dict())

            # Prepare data for modeling
            df = normalize_data(pd.DataFrame(sanitized_data))
            X = df.drop('target', axis=1)
            y = df['target']

            # Train model
            model = await train_model(X, y)

            # Make predictions
            predictions = await predict_demand(model, X)

            # Save predictions
            await save_predictions(self.sku, predictions)
        except Exception as e:
            self.logger.error('Error in demand forecasting process', exc_info=e)

if __name__ == '__main__':
    sku_input = {'sku': 'SKU12345'}  # Example SKU input
    forecast = DemandForecast(sku_input['sku'])
    await forecast.execute()  # Run the prediction workflow