Metadata-Version: 2.1
Name: sygnet
Version: 0.0.1
Summary: Synthetic data using Generative Adversarial Networks
Keywords: synthetic data,neural networks,GAN,torch
Author-email: Thomas Robinson <thomas.robinson@durham.ac.uk>
Maintainer: Artem Nesterov, Maksim Zubok
Maintainer-email: Thomas Robinson <thomas.robinson@durham.ac.uk>
Requires-Python: >=3.9
Description-Content-Type: text/markdown
Classifier: Development Status :: 3 - Alpha
Classifier: Intended Audience :: Science/Research
Classifier: Topic :: Scientific/Engineering
Classifier: License :: OSI Approved :: GNU General Public License v3 (GPLv3)
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.9
Requires-Dist: numpy>=1.20
Requires-Dist: torch>=1.11.0
Requires-Dist: scikit-learn>=1.0
Requires-Dist: pandas>=1.4
Requires-Dist: datetime
Requires-Dist: tqdm
Requires-Dist: tensorboard ; extra == "vis"
Project-URL: Bugs, https://github.com/tsrobinson/SyGNet/issues
Project-URL: Source, https://github.com/tsrobinson/SyGNet
Provides-Extra: vis

# SyGNet<img src="sygnet.png" alt="SyGNet Mascot" align="right" height="300" /></a>

## **Sy**nthetic data using **G**enerative Adversarial **Net**works

*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

### Version 0.0.1 (Alpha release)

Our first release! This version has been lightly tested and the core functionality has been implemented. You should expect both functionality and architecture to change considerably. Comments and bug reports are very welcome!
