tabularforge-sgk 0.1.0

Privacy-Preserving Synthetic Tabular Data Generation

🔨 TabularForge

Privacy-Preserving Synthetic Tabular Data Generation

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🎯 What is TabularForge?

TabularForge is a unified, production-ready Python library for generating high-quality synthetic tabular data with built-in privacy guarantees. It combines multiple state-of-the-art approaches (GANs, VAEs, Copulas) into a simple, one-line API.

Why Synthetic Data?

Organizations have valuable tabular data (patient records, financial transactions, customer data) but often can't share it due to:

• Privacy regulations (GDPR, HIPAA, CCPA)
• Competitive sensitivity
• Data scarcity for ML development

Synthetic data solves this by generating realistic, statistically similar data that protects individual privacy while preserving analytical utility.

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✨ Key Features

Feature	Description
🤖 Multiple Generators	CTGAN, TVAE, Gaussian Copula, and more
🔒 Differential Privacy	Formal privacy guarantees with configurable epsilon
📊 Quality Metrics	Statistical similarity, ML utility, privacy leakage tests
🔧 Auto Preprocessing	Handles mixed types, missing values, imbalanced data
⚡ One-Line API	Generate synthetic data in a single line of code
📈 Benchmarking	Compare generators on your specific data

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🚀 Quick Start

Installation

# Install from PyPI
pip install tabularforge

# Or install from source
git clone https://github.com/ganeshreddy28/tabularforge.git
cd tabularforge
pip install -e .

Basic Usage

from tabularforge import TabularForge
import pandas as pd

# Load your real data
real_data = pd.read_csv("your_data.csv")

# Generate synthetic data in ONE line!
forge = TabularForge(real_data)
synthetic_data = forge.generate(n_samples=1000)

# That's it! synthetic_data is a pandas DataFrame
print(synthetic_data.head())

With Privacy Guarantees

from tabularforge import TabularForge

# Generate with differential privacy (epsilon=1.0)
forge = TabularForge(real_data, privacy_epsilon=1.0)
private_synthetic = forge.generate(n_samples=1000)

# Check privacy metrics
privacy_report = forge.evaluate_privacy()
print(privacy_report)

Compare Different Generators

from tabularforge import TabularForge

# Benchmark all available generators
forge = TabularForge(real_data)
benchmark_results = forge.benchmark(generators=['ctgan', 'tvae', 'copula'])

# See which generator works best for your data
print(benchmark_results)

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📖 Detailed Usage

Choosing a Generator

TabularForge supports multiple synthetic data generators:

Generator	Best For	Speed	Quality
copula	Simple distributions, fast generation	⚡⚡⚡	⭐⭐⭐
ctgan	Complex relationships, mixed types	⚡⚡	⭐⭐⭐⭐
tvae	High-dimensional data	⚡⚡	⭐⭐⭐⭐

# Specify a generator
forge = TabularForge(real_data, generator='ctgan')
synthetic = forge.generate(n_samples=500)

Handling Different Data Types

TabularForge automatically detects and handles:

• Numerical columns (continuous and discrete)
• Categorical columns (including high-cardinality)
• DateTime columns
• Missing values

# Explicit column type specification (optional)
forge = TabularForge(
    real_data,
    categorical_columns=['gender', 'country', 'product_type'],
    numerical_columns=['age', 'income', 'score'],
    datetime_columns=['signup_date', 'last_purchase']
)

Evaluating Synthetic Data Quality

from tabularforge import TabularForge

forge = TabularForge(real_data)
synthetic = forge.generate(n_samples=1000)

# Get comprehensive quality report
quality_report = forge.evaluate_quality(synthetic)

print(quality_report)
# Output:
# {
#     'statistical_similarity': 0.92,
#     'column_correlations': 0.89,
#     'distribution_match': 0.94,
#     'ml_utility': 0.87
# }

Conditional Generation

Generate data satisfying specific conditions:

# Generate only high-income customers
synthetic = forge.generate(
    n_samples=500,
    conditions={'income': '>100000', 'country': 'UK'}
)

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🔒 Privacy Features

Differential Privacy

TabularForge implements differential privacy to provide formal privacy guarantees:

# Lower epsilon = stronger privacy (but lower utility)
# Higher epsilon = weaker privacy (but higher utility)
forge = TabularForge(real_data, privacy_epsilon=0.1)  # Strong privacy
forge = TabularForge(real_data, privacy_epsilon=1.0)  # Balanced
forge = TabularForge(real_data, privacy_epsilon=10.0) # Weak privacy

Privacy Attack Simulation

Test your synthetic data against common privacy attacks:

# Simulate membership inference attack
attack_results = forge.simulate_attack(
    attack_type='membership_inference',
    synthetic_data=synthetic
)

print(f"Attack success rate: {attack_results['success_rate']:.2%}")
# A good synthetic dataset should have ~50% (random guess)

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📊 Use Cases

Healthcare

# Generate synthetic patient cohorts for research
patient_data = pd.read_csv("patient_records.csv")
forge = TabularForge(patient_data, privacy_epsilon=1.0)
synthetic_patients = forge.generate(n_samples=10000)
# Share with researchers without exposing real patients

Finance

# Create synthetic transactions for fraud detection R&D
transactions = pd.read_csv("transactions.csv")
forge = TabularForge(transactions)
synthetic_transactions = forge.generate(n_samples=50000)
# Develop ML models without sensitive financial data

ML Development

# Augment small datasets
small_dataset = pd.read_csv("rare_events.csv")  # Only 100 samples
forge = TabularForge(small_dataset)
augmented = forge.generate(n_samples=10000)
# Now you have enough data to train robust models

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🏗️ Architecture

tabularforge/
├── __init__.py              # Main API exports
├── forge.py                 # TabularForge main class
├── generators/              # Synthetic data generators
│   ├── base.py              # Abstract base generator
│   ├── copula.py            # Gaussian Copula generator
│   ├── ctgan.py             # CTGAN generator
│   └── tvae.py              # TVAE generator
├── preprocessing/           # Data preprocessing
│   ├── encoder.py           # Column encoding/decoding
│   └── transformer.py       # Data transformations
├── privacy/                 # Privacy mechanisms
│   ├── differential.py      # Differential privacy
│   └── attacks.py           # Privacy attack simulations
├── metrics/                 # Quality & privacy metrics
│   ├── statistical.py       # Statistical similarity
│   ├── utility.py           # ML utility metrics
│   └── privacy.py           # Privacy metrics
└── utils/                   # Utilities
    ├── config.py            # Configuration management
    └── logging.py           # Logging utilities

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🧪 Development

Setting Up Development Environment

# Clone the repository
git clone https://github.com/ganeshreddy28/tabularforge.git
cd tabularforge

# Create virtual environment
python -m venv venv
source venv/bin/activate  # On Windows: venv\Scripts\activate

# Install development dependencies
pip install -e ".[dev]"

# Run tests
pytest tests/ -v

# Run linting
flake8 tabularforge/
black tabularforge/

Running Tests

# Run all tests
pytest

# Run with coverage
pytest --cov=tabularforge --cov-report=html

# Run specific test file
pytest tests/test_generators.py -v

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📚 Documentation

• Full Documentation
• API Reference
• Tutorials
• Examples

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🤝 Contributing

Contributions are welcome! Please see our Contributing Guide for details.

1. Fork the repository
2. Create your feature branch (git checkout -b feature/amazing-feature)
3. Commit your changes (git commit -m 'Add amazing feature')
4. Push to the branch (git push origin feature/amazing-feature)
5. Open a Pull Request

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📄 License

This project is licensed under the MIT License - see the LICENSE file for details.

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🙏 Acknowledgments

• SDV for inspiration on synthetic data APIs
• CTGAN Paper for the CTGAN architecture
• The differential privacy research community

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📬 Contact

• Author: Sai Ganesh Kolan
• Email: [e-mail]
• LinkedIn: (https://linkedin.com/in/saiganeshkolan/)

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Made with ❤️ for the data science community

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