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https://github.com/rcamino/multi-categorical-gans

Code for the paper "Generating Multi-Categorical Samples with Generative Adversarial Networks"
https://github.com/rcamino/multi-categorical-gans

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Code for the paper "Generating Multi-Categorical Samples with Generative Adversarial Networks"

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README

        

# IMPORTANT UPDATE!

Please consider checking the [code](https://github.com/rcamino/imputation-dgm) for my new work [Improving Missing Value Imputation with Deep Generative Models](https://arxiv.org/abs/1902.10666).

# Multi-Categorical GANs

Code for the paper [Generating Multi-Categorical Samples with Generative Adversarial Networks](https://arxiv.org/abs/1807.01202)

## Pre-requisites

The project was developed using python 3.6.7 with the following packages:

- future==0.17.1
- numpy==1.16.0
- scikit-learn==0.20.2
- scipy==1.2.0
- torch==1.0.0

Installation with pip:

```bash
pip install -r requirements.txt
```

## Contents
- [Datasets](multi_categorical_gans/datasets)
- [Synthetic data generation](multi_categorical_gans/datasets/synthetic/)
- [US Census 1990](multi_categorical_gans/datasets/uscensus/)
- [Methods](multi_categorical_gans/methods)
- [ARAE and MC-ARAE](multi_categorical_gans/methods/arae/)
- [MedGAN and MC-MedGAN](multi_categorical_gans/methods/medgan/)
- [MC-Gumbel](multi_categorical_gans/methods/mc_gumbel/)
- [MC-WGAN-GP](multi_categorical_gans/methods/mc_wgan_gp/)
- [Metrics](multi_categorical_gans/metrics)

## Changelog

- 2019-01-28: changed to Python 3 as suggested (and still compatible with 2.7 ... I hope).
- 2018-07-25: now we use WGAN-GP for ARAE following the author updates.