Skip to main content

Synthetic data using Generative Adversarial Networks

Project description

SyGNetSyGNet Mascot

Synthetic data using Generative Adversarial Networks

Principal Investigator: Dr Thomas Robinson (

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.


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

sygnet requires:


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!


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

Previous releases


  • 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.

Project details

Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

sygnet-0.0.3.tar.gz (5.2 MB view hashes)

Uploaded source

Built Distribution

sygnet-0.0.3-py3-none-any.whl (26.4 kB view hashes)

Uploaded py3

Supported by

AWS AWS Cloud computing Datadog Datadog Monitoring Facebook / Instagram Facebook / Instagram PSF Sponsor Fastly Fastly CDN Google Google Object Storage and Download Analytics Huawei Huawei PSF Sponsor Microsoft Microsoft PSF Sponsor NVIDIA NVIDIA PSF Sponsor Pingdom Pingdom Monitoring Salesforce Salesforce PSF Sponsor Sentry Sentry Error logging StatusPage StatusPage Status page