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https://github.com/sdv-dev/ctgan
Conditional GAN for generating synthetic tabular data.
https://github.com/sdv-dev/ctgan
data-generation generative-adversarial-network synthetic-data synthetic-data-generation tabular-data
Last synced: 29 days ago
JSON representation
Conditional GAN for generating synthetic tabular data.
- Host: GitHub
- URL: https://github.com/sdv-dev/ctgan
- Owner: sdv-dev
- License: other
- Created: 2019-09-08T19:24:12.000Z (about 5 years ago)
- Default Branch: main
- Last Pushed: 2024-10-14T00:44:39.000Z (about 1 month ago)
- Last Synced: 2024-10-15T09:42:06.082Z (29 days ago)
- Topics: data-generation, generative-adversarial-network, synthetic-data, synthetic-data-generation, tabular-data
- Language: Python
- Homepage:
- Size: 1.79 MB
- Stars: 1,255
- Watchers: 23
- Forks: 282
- Open Issues: 35
-
Metadata Files:
- Readme: README.md
- Changelog: HISTORY.md
- Contributing: CONTRIBUTING.rst
- License: LICENSE
- Codeowners: .github/CODEOWNERS
- Authors: AUTHORS.rst
Awesome Lists containing this project
README
This repository is part of The Synthetic Data Vault Project, a project from DataCebo.
[](https://pypi.org/search/?c=Development+Status+%3A%3A+2+-+Pre-Alpha)
[](https://pypi.python.org/pypi/ctgan)
[](https://github.com/sdv-dev/CTGAN/actions/workflows/unit.yml)
[](https://pepy.tech/project/ctgan)
[](https://codecov.io/gh/sdv-dev/CTGAN)
# Overview
CTGAN is a collection of Deep Learning based synthetic data generators for single table data, which are able to learn from real data and generate synthetic data with high fidelity.
| Important Links | |
| --------------------------------------------- | -------------------------------------------------------------------- |
| :computer: **[Website]** | Check out the SDV Website for more information about our overall synthetic data ecosystem.|
| :orange_book: **[Blog]** | A deeper look at open source, synthetic data creation and evaluation.|
| :book: **[Documentation]** | Quickstarts, User and Development Guides, and API Reference. |
| :octocat: **[Repository]** | The link to the Github Repository of this library. |
| :keyboard: **[Development Status]** | This software is in its Pre-Alpha stage. |
| [![][Slack Logo] **Community**][Community] | Join our Slack Workspace for announcements and discussions. |
[Website]: https://sdv.dev
[Blog]: https://datacebo.com/blog
[Documentation]: https://bit.ly/sdv-docs
[Repository]: https://github.com/sdv-dev/CTGAN
[License]: https://github.com/sdv-dev/CTGAN/blob/main/LICENSE
[Development Status]: https://pypi.org/search/?c=Development+Status+%3A%3A+2+-+Pre-Alpha
[Slack Logo]: https://github.com/sdv-dev/SDV/blob/stable/docs/images/slack.png
[Community]: https://bit.ly/sdv-slack-invite
Currently, this library implements the **CTGAN** and **TVAE** models described in the [Modeling Tabular data using Conditional GAN](https://arxiv.org/abs/1907.00503) paper, presented at the 2019 NeurIPS conference.
# Install
## Use CTGAN through the SDV library
:warning: If you're just getting started with synthetic data, we recommend installing the SDV library which provides user-friendly APIs for accessing CTGAN. :warning:
The SDV library provides wrappers for preprocessing your data as well as additional usability features like constraints. See the [SDV documentation](https://bit.ly/sdv-docs) to get started.
## Use the CTGAN standalone library
Alternatively, you can also install and use **CTGAN** directly, as a standalone library:
**Using `pip`:**
```bash
pip install ctgan
```
**Using `conda`:**
```bash
conda install -c pytorch -c conda-forge ctgan
```
When using the CTGAN library directly, you may need to manually preprocess your data into the correct format, for example:
* Continuous data must be represented as floats
* Discrete data must be represented as ints or strings
* The data should not contain any missing values
# Usage Example
In this example we load the [Adult Census Dataset](https://archive.ics.uci.edu/ml/datasets/adult)* which is a built-in demo dataset. We use CTGAN to learn from the real data and then generate some synthetic data.
```python3
from ctgan import CTGAN
from ctgan import load_demo
real_data = load_demo()
# Names of the columns that are discrete
discrete_columns = [
'workclass',
'education',
'marital-status',
'occupation',
'relationship',
'race',
'sex',
'native-country',
'income'
]
ctgan = CTGAN(epochs=10)
ctgan.fit(real_data, discrete_columns)
# Create synthetic data
synthetic_data = ctgan.sample(1000)
```
*For more information about the dataset see:
Dua, D. and Graff, C. (2019). UCI Machine Learning Repository [http://archive.ics.uci.edu/ml].
Irvine, CA: University of California, School of Information and Computer Science.
# Join our community
Join our [Slack channel](https://bit.ly/sdv-slack-invite) to discuss more about CTGAN and synthetic data. If you find a bug or have a feature request, you can also [open an issue](https://github.com/sdv-dev/CTGAN/issues) on our GitHub.
**Interested in contributing to CTGAN?** Read our [Contribution Guide](CONTRIBUTING.rst) to get started.
# Citing CTGAN
If you use CTGAN, please cite the following work:
*Lei Xu, Maria Skoularidou, Alfredo Cuesta-Infante, Kalyan Veeramachaneni.* **Modeling Tabular data using Conditional GAN**. NeurIPS, 2019.
```LaTeX
@inproceedings{ctgan,
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}
}
```
# Related Projects
Please note that these projects are external to the SDV Ecosystem. They are not affiliated with or maintained by DataCebo.
* **R Interface for CTGAN**: A wrapper around **CTGAN** that brings the functionalities to **R** users.
More details can be found in the corresponding repository: https://github.com/kasaai/ctgan
* **CTGAN Server CLI**: A package to easily deploy CTGAN onto a remote server. Created by Timothy Pillow @oregonpillow at: https://github.com/oregonpillow/ctgan-server-cli
---
[The Synthetic Data Vault Project](https://sdv.dev) was first created at MIT's [Data to AI Lab](
https://dai.lids.mit.edu/) in 2016. After 4 years of research and traction with enterprise, we
created [DataCebo](https://datacebo.com) in 2020 with the goal of growing the project.
Today, DataCebo is the proud developer of SDV, the largest ecosystem for
synthetic data generation & evaluation. It is home to multiple libraries that support synthetic
data, including:
* 🔄 Data discovery & transformation. Reverse the transforms to reproduce realistic data.
* 🧠 Multiple machine learning models -- ranging from Copulas to Deep Learning -- to create tabular,
multi table and time series data.
* 📊 Measuring quality and privacy of synthetic data, and comparing different synthetic data
generation models.
[Get started using the SDV package](https://sdv.dev/SDV/getting_started/install.html) -- a fully
integrated solution and your one-stop shop for synthetic data. Or, use the standalone libraries
for specific needs.