SyGNet

Synthetic data using Generative Adversarial Networks

Principal Investigator: Dr Thomas Robinson (thomas.robinson@durham.ac.uk)

Research team: Artem Nesterov, Maksim Zubok

sygnet is a Python package for generating synthetic data within social science contexts. The sygnet algorithm uses cutting-edge advances in deep learning methods to learn the underlying relationships between variables in a dataset. Users can then generate brand-new, synthetic observations that mimic the real data.

Installation

To install via pip, you can run the following command at the command line: pip install sygnet

sygnet requires:

numpy>=1.20
torch>=1.11.0
scikit-learn>=1.0
pandas>=1.4
datetime
tqdm

Example implementation

You can find a demonstration of sygnet under examples/basic_example.

Current version: 0.0.3 (alpha release)

Alpha release: You should expect both functionality and pipelines to change (rapidly). Comments and bug reports are very welcome!

Changes:

• Fixes column ordering issue when using mixed activation layer
• Updates example

Previous releases

0.0.2

• Fixes mixed activation bug where final layer wasn't sent to device
• Adds SygnetModel.transform() alias for SygnetModel.sample()

0.0.1 Our first release! This version has been lightly tested and the core functionality has been implemented.