README.Rmd

---
output: github_document
---

<!-- README.md is generated from README.Rmd. Please edit that file -->

```{r, include = FALSE}
Sys.setenv("RETICULATE_REMAP_OUTPUT_STREAMS" = 1)
knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>",
  fig.path = "man/figures/README-",
  out.width = "100%"
)
```

# ctgan

<!-- badges: start -->
[![Codecov test coverage](https://codecov.io/gh/kasaai/ctgan/branch/master/graph/badge.svg)](https://codecov.io/gh/kasaai/ctgan?branch=master)
[![R build status](https://github.com/kasaai/ctgan/workflows/R-CMD-check/badge.svg)](https://github.com/kasaai/ctgan/actions)
<!-- badges: end -->

The ctgan package provides an R interface to [CTGAN](https://github.com/DAI-Lab/CTGAN),
a GAN-based data synthesizer. The package enables one to create synthetic samples
of confidential or proprietary datasets for sharing. For more details and use cases,
see the papers in the [References](#references) section.

## Installation

You can install the development version from [GitHub](https://github.com/) with:

``` r
# install.packages("remotes")
remotes::install_github("kasaai/ctgan")
```
## Example

A quick example:

```{r example}
library(ctgan)

# Install dependencies before first usage
# install_ctgan()

synthesizer <- ctgan()
synthesizer %>% 
  fit(iris, epochs = 20)
synthesizer %>% 
  ctgan_sample() %>% 
  # Dataset-specific post-processing
  dplyr::mutate_if(is.numeric, ~ pmax(.x, 0.1))
```

This generated dataset can then be shared, but one can also serialize and share the trained synthesizer:

```{r}
model_dir <- tempdir()
synthesizer %>% 
  ctgan_save(model_dir)

ctgan_load(model_dir)
```


## References

If you use ctgan, please consider citing the original work,

- *Lei Xu, Maria Skoularidou, Alfredo Cuesta-Infante, Kalyan Veeramachaneni.* **Modeling Tabular data using Conditional GAN**. NeurIPS, 2019.

and the work implementing the R package,

- *Kevin Kuo.* **Generative Synthesis of Insurance Datasets**. [arXiv:1912.02423](https://arxiv.org/abs/1912.02423), 2019.

```LaTeX
@inproceedings{xu2019modeling,
  title={Modeling Tabular data using Conditional GAN},
  author={Xu, Lei and Skoularidou, Maria and Cuesta-Infante, Alfredo and Veeramachaneni, Kalyan},
  booktitle={Advances in Neural Information Processing Systems},
  year={2019}
}

@misc{kuo2019generative,
    title={Generative Synthesis of Insurance Datasets},
    author={Kevin Kuo},
    year={2019},
    eprint={1912.02423},
    archivePrefix={arXiv},
    primaryClass={stat.AP}
}
```