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Conditional GAN for Tabular Data

Project description

This repository is part of The Synthetic Data Vault Project, a project from DataCebo.

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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 clones with high fidelity.

Important Links
:computer: Website Check out the SDV Website for more information about the project.
:orange_book: SDV Blog Regular publshing of useful content about Synthetic Data Generation.
:book: Documentation Quickstarts, User and Development Guides, and API Reference.
:octocat: Repository The link to the Github Repository of this library.
:scroll: License The entire ecosystem is published under the MIT License.
:keyboard: Development Status This software is in its Pre-Alpha stage.
Community Join our Slack Workspace for announcements and discussions.
Tutorials Run the SDV Tutorials in a Binder environment.

Implemented Models

Currently, this library implements the CTGAN and TVAE models proposed in the Modeling Tabular data using Conditional GAN paper. For more information about these models, please check out the respective user guides:


CTGAN is part of the SDV project and is automatically installed alongside it. For details about this process please visit the SDV Installation Guide

Optionally, CTGAN can also be installed as a standalone library using the following commands:

Using pip:

pip install ctgan

Using conda:

conda install -c pytorch -c conda-forge ctgan

For more installation options please visit the CTGAN installation Guide

Usage Example

:warning: WARNING: If you're just getting started with synthetic data, we recommend using the SDV library which provides user-friendly APIs for interacting with CTGAN. To learn more about using CTGAN through SDV, check out the user guide here.

To get started with CTGAN, you should prepare your data as either a numpy.ndarray or a pandas.DataFrame object with two types of columns:

  • Continuous Columns: can contain any numerical value.
  • Discrete Columns: contain a finite number values, whether these are string values or not.

In this example we load the Adult Census Dataset which is a built-in demo dataset. We then model it using the CTGANSynthesizer and generate a synthetic copy of it.

from ctgan import CTGANSynthesizer
from ctgan import load_demo

data = load_demo()

# Names of the columns that are discrete
discrete_columns = [

ctgan = CTGANSynthesizer(epochs=10), discrete_columns)

# Synthetic copy
samples = ctgan.sample(1000)

Join our community

  1. Please have a look at the Contributing Guide to see how you can contribute to the project.
  2. If you have any doubts, feature requests or detect an error, please open an issue on github or join our Slack Workspace.
  3. Also, do not forget to check the project documentation site!

Citing TGAN

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.
  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},

Related Projects

Please note that these libraries are external contributions and are not maintained nor supervised by the MIT DAI-Lab team.

R interface for CTGAN

A wrapper around CTGAN has been implemented by Kevin Kuo @kevinykuo, bringing the functionalities of CTGAN to R users.

More details can be found in the corresponding repository:


A package to easily deploy CTGAN onto a remote server. This package is developed by Timothy Pillow @oregonpillow.

More details can be found in the corresponding repository:

The Synthetic Data Vault Project was first created at MIT's Data to AI Lab in 2016. After 4 years of research and traction with enterprise, we created DataCebo 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 -- a fully integrated solution and your one-stop shop for synthetic data. Or, use the standalone libraries for specific needs.


v0.5.1 - 2022-02-25

This release fixes a bug with the decoder instantiation, and also allows users to set a random state for the model fitting and sampling.

Issues closed

  • Update self.decoder with correct variable name - Issue #203 by @tejuafonja
  • Add random state - Issue #204 by @katxiao

v0.5.0 - 2021-11-18

This release adds support for Python 3.9 and updates dependencies to ensure compatibility with the rest of the SDV ecosystem, and upgrades to the latests RDT release.

Issues closed

  • Add support for Python 3.9 - Issue #177 by @pvk-developer
  • Add pip check to CI workflows - Issue #174 by @pvk-developer
  • Typo in CTGAN code - Issue #158 by @ori-katz100 and @fealho

v0.4.3 - 2021-07-12

Dependency upgrades to ensure compatibility with the rest of the SDV ecosystem.

v0.4.2 - 2021-04-27

In this release, the way in which the loss function of the TVAE model was computed has been fixed. In addition, the default value of the discriminator_decay has been changed to a more optimal value. Also some improvements to the tests were added.

Issues closed

  • TVAE: loss function - Issue #143 by @fealho and @DingfanChen
  • Set discriminator_decay to 1e-6 - Pull request #145 by @fealho
  • Adds unit tests - Pull requests #140 by @fealho

v0.4.1 - 2021-03-30

This release exposes all the hyperparameters which the user may find useful for both CTGAN and TVAE. Also TVAE can now be fitted on datasets that are shorter than the batch size and drops the last batch only if the data size is not divisible by the batch size.

Issues closed

  • TVAE: Adapt batch_size to data size - Issue #135 by @fealho and @csala
  • ValueError from validate_discre_columns with uniqueCombinationConstraint - Issue 133 by @fealho and @MLjungg

v0.4.0 - 2021-02-24

Maintenance relese to upgrade dependencies to ensure compatibility with the rest of the SDV libraries.

Also add a validation on the CTGAN condition_column and condition_value inputs.


  • Validate condition_column and condition_value - Issue #124 by @fealho

v0.3.1 - 2021-01-27


  • Check discrete_columns valid before fitting - Issue #35 by @fealho

Bugs fixed

  • ValueError: max() arg is an empty sequence - Issue #115 by @fealho

v0.3.0 - 2020-12-18

In this release we add a new TVAE model which was presented in the original CTGAN paper. It also exposes more hyperparameters and moves epochs and log_frequency from fit to the constructor.

A new verbose argument has been added to optionally disable unnecessary printing, and a new hyperparameter called discriminator_steps has been added to CTGAN to control the number of optimization steps performed in the discriminator for each generator epoch.

The code has also been reorganized and cleaned up for better readability and interpretability.

Special thanks to @Baukebrenninkmeijer @fealho @leix28 @csala for the contributions!


  • Add TVAE - Issue #111 by @fealho
  • Move log_frequency to __init__ - Issue #102 by @fealho
  • Add discriminator steps hyperparameter - Issue #101 by @Baukebrenninkmeijer
  • Code cleanup / Expose hyperparameters - Issue #59 by @fealho and @leix28
  • Publish to conda repo - Issue #54 by @fealho

Bugs fixed

  • Fixed NaN != NaN counting bug. - Issue #100 by @fealho
  • Update dependencies and testing - Issue #90 by @csala

v0.2.2 - 2020-11-13

In this release we introduce several minor improvements to make CTGAN more versatile and propertly support new types of data, such as categorical NaN values, as well as conditional sampling and features to save and load models.

Additionally, the dependency ranges and python versions have been updated to support up to date runtimes.

Many thanks @fealho @leix28 @csala @oregonpillow and @lurosenb for working on making this release possible!


  • Drop Python 3.5 support - Issue #79 by @fealho
  • Support NaN values in categorical variables - Issue #78 by @fealho
  • Sample synthetic data conditioning on a discrete column - Issue #69 by @leix28
  • Support recent versions of pandas - Issue #57 by @csala
  • Easy solution for restoring original dtypes - Issue #26 by @oregonpillow

Bugs fixed

  • Loss to nan - Issue #73 by @fealho
  • Swapped the sklearn utils testing import statement - Issue #53 by @lurosenb

v0.2.1 - 2020-01-27

Minor version including changes to ensure the logs are properly printed and the option to disable the log transformation to the discrete column frequencies.

Special thanks to @kevinykuo for the contributions!

Issues Resolved:

  • Option to sample from true data frequency instead of logged frequency - Issue #16 by @kevinykuo
  • Flush stdout buffer for epoch updates - Issue #14 by @kevinykuo

v0.2.0 - 2019-12-18

Reorganization of the project structure with a new Python API, new Command Line Interface and increased data format support.

Issues Resolved:

  • Reorganize the project structure - Issue #10 by @csala
  • Move epochs to the fit method - Issue #5 by @csala

v0.1.0 - 2019-11-07

First Release - NeurIPS 2019 Version.

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