Build Composable Probabilistic Production Forecasting Pipelines with Sktime and XGBoost

"""
Production implementation for building composable probabilistic forecasting pipelines.
Utilizes Sktime for time series analysis and XGBoost for predictions.
"""

from typing import Dict, Any, List
import os
import logging
import pandas as pd
from sklearn.model_selection import train_test_split
from sktime.forecasting.compose import make_reduction
from xgboost import XGBRegressor

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

class Config:
    """
    Configuration class for environment variables.
    """
    DATABASE_URL: str = os.getenv('DATABASE_URL')
    FORECAST_HORIZON: int = int(os.getenv('FORECAST_HORIZON', 5))

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

def sanitize_fields(data: Dict[str, Any]) -> Dict[str, Any]:
    """
    Sanitize input fields to prevent injection attacks.
    
    Args:
        data: Input data
    Returns:
        Sanitized data
    """
    # Example of sanitation: stripping whitespace
    return {k: str(v).strip() for k, v in data.items()}

def normalize_data(df: pd.DataFrame) -> pd.DataFrame:
    """
    Normalize the given DataFrame.
    
    Args:
        df: Input DataFrame
    Returns:
        Normalized DataFrame
    """
    return (df - df.min()) / (df.max() - df.min())

def transform_records(data: List[Dict[str, Any]]) -> pd.DataFrame:
    """
    Transform list of records into a DataFrame.
    
    Args:
        data: List of records
    Returns:
        DataFrame
    """
    df = pd.DataFrame(data)
    return normalize_data(df)

def fetch_data() -> List[Dict[str, Any]]:
    """
    Mock function to fetch data from a source.
    
    Returns:
        List of records
    """
    # In production, implement actual data fetching logic
    return [{'time_series': [1, 2, 3, 4, 5]}]  # Example data

def save_to_db(data: pd.DataFrame) -> None:
    """
    Save DataFrame to the database.
    
    Args:
        data: DataFrame to save
    """
    logger.info('Saving data to the database')
    # Implement saving logic here

def aggregate_metrics(y_true: List[float], y_pred: List[float]) -> Dict[str, float]:
    """
    Aggregate metrics for model evaluation.
    
    Args:
        y_true: Actual values
        y_pred: Predicted values
    Returns:
        Dictionary of metrics
    """
    mse = ((y_true - y_pred) ** 2).mean()
    return {'mse': mse}

class ForecastPipeline:
    """
    Main class for handling the forecasting pipeline.
    """
    def __init__(self, config: Config):
        self.config = config
        self.model = XGBRegressor()

    def fit(self, X: pd.DataFrame, y: pd.Series) -> None:
        """
        Fit the forecasting model.
        
        Args:
            X: Features DataFrame
            y: Target Series
        """
        logger.info('Fitting model')
        self.model.fit(X, y)

    def predict(self, X: pd.DataFrame) -> List[float]:
        """
        Make predictions using the fitted model.
        
        Args:
            X: Features DataFrame
        Returns:
            List of predictions
        """
        logger.info('Making predictions')
        return self.model.predict(X).tolist()

    def run_pipeline(self) -> None:
        """
        Orchestrate the complete forecasting pipeline.
        """
        try:
            raw_data = fetch_data()  # Step 1: Fetch data
            validated_data = validate_input(raw_data)  # Step 2: Validate input
            if validated_data:
                df = transform_records(raw_data)  # Step 3: Transform data
                y = df.pop('time_series')  # Target variable
                X = df  # Features
                self.fit(X, y)  # Step 4: Fit model
                predictions = self.predict(X)  # Step 5: Predict
                metrics = aggregate_metrics(y.tolist(), predictions)  # Step 6: Aggregate metrics
                logger.info(f'Metrics: {metrics}')
                save_to_db(df)  # Step 7: Save to DB
        except Exception as e:
            logger.error(f'Error in pipeline: {str(e)}')

if __name__ == '__main__':
    config = Config()  # Load configuration
    pipeline = ForecastPipeline(config)  # Create pipeline instance
    pipeline.run_pipeline()  # Run the pipeline