Comprehensive comparison for AI technology in ML Framework applications

import h2o
from h2o.automl import H2OAutoML
from h2o.estimators import H2OGradientBoostingEstimator
import pandas as pd
import logging
from typing import Dict, Any

# Configure logging for production monitoring
logging.basicConfig(level=logging.INFO)
logger = logging.getLogger(__name__)

class CreditRiskPredictor:
    """Production ML service for credit risk assessment using H2O.ai"""
    
    def __init__(self, model_path: str = None):
        self.model = None
        self.model_path = model_path
        try:
            h2o.init(max_mem_size="4G", nthreads=-1)
            logger.info("H2O cluster initialized successfully")
            if model_path:
                self.load_model(model_path)
        except Exception as e:
            logger.error(f"Failed to initialize H2O: {str(e)}")
            raise
    
    def train_model(self, training_data: pd.DataFrame, target_col: str, max_runtime_secs: int = 300):
        """Train AutoML model with best practices for production"""
        try:
            # Convert pandas DataFrame to H2OFrame
            h2o_df = h2o.H2OFrame(training_data)
            
            # Split data for validation
            train, valid = h2o_df.split_frame(ratios=[0.8], seed=42)
            
            # Set predictor and response columns
            x = h2o_df.columns
            x.remove(target_col)
            y = target_col
            
            # Convert target to factor for classification
            train[y] = train[y].asfactor()
            valid[y] = valid[y].asfactor()
            
            # Initialize AutoML with production settings
            aml = H2OAutoML(
                max_runtime_secs=max_runtime_secs,
                max_models=20,
                seed=42,
                balance_classes=True,
                stopping_metric="AUC",
                sort_metric="AUC",
                nfolds=5,
                keep_cross_validation_predictions=True
            )
            
            logger.info("Starting AutoML training...")
            aml.train(x=x, y=y, training_frame=train, validation_frame=valid)
            
            # Get the best model
            self.model = aml.leader
            logger.info(f"Best model: {self.model.model_id}")
            logger.info(f"Model AUC: {self.model.auc(valid=True)}")
            
            return aml.leaderboard
            
        except Exception as e:
            logger.error(f"Training failed: {str(e)}")
            raise
    
    def predict(self, input_data: pd.DataFrame) -> Dict[str, Any]:
        """Make predictions with error handling"""
        if self.model is None:
            raise ValueError("Model not trained or loaded")
        
        try:
            # Convert to H2OFrame
            h2o_input = h2o.H2OFrame(input_data)
            
            # Make predictions
            predictions = self.model.predict(h2o_input)
            
            # Convert to pandas for API response
            pred_df = predictions.as_data_frame()
            
            return {
                "predictions": pred_df['predict'].tolist(),
                "probabilities": pred_df.iloc[:, 1:].to_dict('records'),
                "model_id": self.model.model_id,
                "status": "success"
            }
            
        except Exception as e:
            logger.error(f"Prediction failed: {str(e)}")
            return {
                "predictions": None,
                "error": str(e),
                "status": "failed"
            }
    
    def save_model(self, path: str):
        """Save model for production deployment"""
        if self.model is None:
            raise ValueError("No model to save")
        try:
            model_path = h2o.save_model(model=self.model, path=path, force=True)
            logger.info(f"Model saved to {model_path}")
            return model_path
        except Exception as e:
            logger.error(f"Failed to save model: {str(e)}")
            raise
    
    def load_model(self, path: str):
        """Load pre-trained model"""
        try:
            self.model = h2o.load_model(path)
            logger.info(f"Model loaded from {path}")
        except Exception as e:
            logger.error(f"Failed to load model: {str(e)}")
            raise
    
    def shutdown(self):
        """Cleanup H2O cluster resources"""
        try:
            h2o.cluster().shutdown()
            logger.info("H2O cluster shut down successfully")
        except Exception as e:
            logger.warning(f"Error during shutdown: {str(e)}")

# Example usage in production API endpoint
if __name__ == "__main__":
    # Sample training data
    data = pd.DataFrame({
        'credit_score': [720, 650, 800, 590, 710],
        'income': [75000, 45000, 120000, 35000, 68000],
        'debt_ratio': [0.3, 0.5, 0.2, 0.7, 0.4],
        'default': [0, 1, 0, 1, 0]
    })
    
    predictor = CreditRiskPredictor()
    predictor.train_model(data, 'default', max_runtime_secs=60)
    
    # Make predictions
    new_data = pd.DataFrame({
        'credit_score': [700],
        'income': [60000],
        'debt_ratio': [0.35]
    })
    
    result = predictor.predict(new_data)
    print(result)
    
    predictor.shutdown()