Synthetic data using Generative Adversarial Networks
Project description
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>=1.10.0
scikit-learn>=1.0
pandas>=1.4
datetime
tqdm
Example implementation
You can find a demonstration of sygnet under examples/basic_example.
Current version: 0.0.7 (alpha release)
Alpha release: You should expect both functionality and pipelines to change (rapidly). Comments and bug reports are very welcome!
Changes:
- Update internal
train_*
functions to return losses and improve logging - Update
tune()
function
Previous releases
0.0.6 and 0.0.5
- Internal changes to improve code efficiency
- Removes
sygnet_
from all submodule names - Lowers PyTorch requirement to 1.10 for compatability with OpenCE environments
0.0.4
- Adds
tune()
function to run hyperparameter tuning - Adds model saving functionality to
SygnetModel.fit()
- Fixes various bugs
- Improves documentation
0.0.3
- Fixes column ordering issue when using mixed activation layer
- Updates example
0.0.2
- Fixes mixed activation bug where final layer wasn't sent to
device
- Adds
SygnetModel.transform()
alias forSygnetModel.sample()
0.0.1 Our first release! This version has been lightly tested and the core functionality has been implemented.
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