This is the official git paper CTAB-GAN: Effective Table Data Synthesizing. The paper is published on Asian Conference on Machine Learning (ACML 2021), please check our pdf on PMLR website for our newest version of paper, it adds more content on time consumption analysis of training CTAB-GAN. If you have any question, please contact [email protected] for more information.

The required package version

numpy==1.21.0
torch==1.9.1
pandas==1.2.4
sklearn==0.24.1
dython==0.6.4.post1
scipy==1.4.1

Experiment_Script_Adult.ipynb is an example notebook for training CTAB-GAN with Adult dataset. The dataset is alread under Real_Datasets folder. The evaluation code is also provided.

If your dataset has large number of column, you may encounter the problem that our currnet code cannot encode all of your data since CTAB-GAN will wrap the encoded data into an image-like format. What you can do is changing the line 341 and 348 in model/synthesizer/ctabgan_synthesizer.py. The number in the slide list

sides = [4, 8, 16, 24, 32]

is the side size of image. You can enlarge the list to [4, 8, 16, 24, 32, 64] or [4, 8, 16, 24, 32, 64, 128] for accepting larger dataset.

To cite this paper, you could use this bibtex

@InProceedings{zhao21,
  title = 	 {CTAB-GAN: Effective Table Data Synthesizing},
  author =       {Zhao, Zilong and Kunar, Aditya and Birke, Robert and Chen, Lydia Y.},
  booktitle = 	 {Proceedings of The 13th Asian Conference on Machine Learning},
  pages = 	 {97--112},
  year = 	 {2021},
  editor = 	 {Balasubramanian, Vineeth N. and Tsang, Ivor},
  volume = 	 {157},
  series = 	 {Proceedings of Machine Learning Research},
  month = 	 {17--19 Nov},
  publisher =    {PMLR},
  pdf = 	 {https://proceedings.mlr.press/v157/zhao21a/zhao21a.pdf},
  url = 	 {https://proceedings.mlr.press/v157/zhao21a.html}
}