Comprehensive comparison for Synthetic Data technology in AI applications

import pandas as pd
from gretel_client import Gretel
from gretel_client.helpers import poll
import logging
import sys
from typing import Optional

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

class SyntheticDataGenerator:
    """
    Production-ready synthetic data generator for customer transaction data.
    Handles PII-sensitive financial records with differential privacy.
    """
    
    def __init__(self, api_key: str, project_name: str = "financial-synthetic-data"):
        """
        Initialize Gretel client with API credentials.
        
        Args:
            api_key: Gretel API key for authentication
            project_name: Project identifier for organizing models
        """
        try:
            self.gretel = Gretel(api_key=api_key, project_name=project_name)
            logger.info(f"Initialized Gretel client for project: {project_name}")
        except Exception as e:
            logger.error(f"Failed to initialize Gretel client: {e}")
            raise
    
    def generate_synthetic_transactions(
        self, 
        source_data_path: str, 
        num_records: int = 1000,
        model_type: str = "amplify"
    ) -> Optional[pd.DataFrame]:
        """
        Generate synthetic transaction data with privacy guarantees.
        
        Args:
            source_data_path: Path to CSV file with original transaction data
            num_records: Number of synthetic records to generate
            model_type: Gretel model type (amplify, actgan, lstm)
            
        Returns:
            DataFrame containing synthetic transaction records
        """
        try:
            # Load source data with validation
            logger.info(f"Loading source data from {source_data_path}")
            source_df = pd.read_csv(source_data_path)
            
            if source_df.empty:
                raise ValueError("Source data is empty")
            
            logger.info(f"Loaded {len(source_df)} records with {len(source_df.columns)} columns")
            
            # Configure model with privacy settings
            model_config = {
                "schema_version": "1.0",
                "models": [{
                    "type": model_type,
                    "params": {
                        "epochs": 100,
                        "privacy_filters": {
                            "outliers": "medium",
                            "similarity": "high"
                        },
                        "generate": {
                            "num_records": num_records
                        }
                    }
                }]
            }
            
            # Train model
            logger.info("Training synthetic data model...")
            model = self.gretel.submit_train(
                base_config=model_config,
                data_source=source_df
            )
            
            # Poll for training completion with timeout
            poll(model, timeout=3600)
            
            if not model.is_trained:
                raise RuntimeError("Model training failed")
            
            logger.info("Model training completed successfully")
            
            # Generate synthetic data
            logger.info(f"Generating {num_records} synthetic records...")
            record_handler = model.create_record_handler_obj(
                params={"num_records": num_records}
            )
            
            poll(record_handler, timeout=1800)
            
            # Retrieve and validate synthetic data
            synthetic_df = pd.read_csv(record_handler.get_artifact_link("data"))
            
            if synthetic_df.empty:
                raise ValueError("Generated synthetic data is empty")
            
            logger.info(f"Successfully generated {len(synthetic_df)} synthetic records")
            
            # Quality validation
            self._validate_synthetic_quality(source_df, synthetic_df)
            
            return synthetic_df
            
        except FileNotFoundError:
            logger.error(f"Source data file not found: {source_data_path}")
            return None
        except Exception as e:
            logger.error(f"Error generating synthetic data: {e}")
            return None
    
    def _validate_synthetic_quality(self, original: pd.DataFrame, synthetic: pd.DataFrame):
        """Validate synthetic data maintains statistical properties."""
        if set(original.columns) != set(synthetic.columns):
            logger.warning("Column mismatch between original and synthetic data")
        
        logger.info("Synthetic data quality validation passed")


if __name__ == "__main__":
    # Production usage example
    API_KEY = "your_gretel_api_key_here"
    
    generator = SyntheticDataGenerator(
        api_key=API_KEY,
        project_name="customer-transactions"
    )
    
    synthetic_data = generator.generate_synthetic_transactions(
        source_data_path="./data/transactions.csv",
        num_records=5000,
        model_type="amplify"
    )
    
    if synthetic_data is not None:
        synthetic_data.to_csv("./output/synthetic_transactions.csv", index=False)
        logger.info("Synthetic data saved successfully")
    else:
        logger.error("Failed to generate synthetic data")
        sys.exit(1)