Synthetic Data Generation Toolkit

This repository provides a comprehensive toolkit for generating synthetic data using seven different models. The toolkit evaluates the generated data for utility, similarity/fidelity, and privacy, specifically tailored for tabular datasets with binary classification problems (e.g., True/False, Yes/No).

Quick Start

Step 1: Install the Package

Install the package using pip:

pip install synthius

Step 2: Usage Example

To understand how to use this package, explore the three example Jupyter notebooks included in the repository:

Go to examples/1-getting-started.ipynb to begin.

Additional Setup for Mac Users

Mac users may encounter errors during installation. To resolve these issues, install the required dependencies and set up the environment:

1. Install dependencies using Homebrew:

   brew install libomp llvm

2. Set up the environment:

   export PATH="/opt/homebrew/opt/llvm/bin:$PATH"
   export CC=$(brew --prefix llvm)/bin/clang
   export CXX=$(brew --prefix llvm)/bin/clang++
   export CXXFLAGS="-I$(brew --prefix llvm)/include -I$(brew --prefix libomp)/include"
   export LDFLAGS="-L$(brew --prefix llvm)/lib -L$(brew --prefix libomp)/lib -lomp"

Acknowledgments

Special thanks to all contributors and the libraries used in this project.