Metadata-Version: 2.1
Name: distvae_tabular
Version: 0.0.10
Summary: DistVAE Implementation Package for Synthetic Data Generation
Home-page: https://github.com/an-seunghwan/DistVAE-Tabular
Author: Seunghwan An
Author-email: dpeltms79@gmail.com
Classifier: Programming Language :: Python :: 3
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Requires-Python: >=3.11
Description-Content-Type: text/markdown
Requires-Dist: numpy ==1.26.4
Requires-Dist: pandas ==2.2.2
Requires-Dist: scikit-learn ==1.5.1
Requires-Dist: scipy ==1.14.0
Requires-Dist: torch ==2.2.2
Requires-Dist: tqdm ==4.66.4

# DistVAE-Tabular

### 1. Install
```
pip install distvae-tabular
```

### 2. Usage
```
from distvae_tabular import distvae

distvae.DistVAE # DistVAE model
distvae.generate_data # generate synthetic data
```
- See `example.ipynb` for detailed usage with `loan` dataset.
- Link for download `loan` dataset: [https://www.kaggle.com/datasets/teertha/personal-loan-modeling](https://www.kaggle.com/datasets/teertha/personal-loan-modeling)

### Citation
If you use this code or package, please cite our associated paper:
```
@article{an2024distributional,
  title={Distributional learning of variational AutoEncoder: application to synthetic data generation},
  author={An, Seunghwan and Jeon, Jong-June},
  journal={Advances in Neural Information Processing Systems},
  volume={36},
  year={2024}
}
```
