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 | |
|---|---|
| 💻 Website | Check out the SDV Website for more information about the project. |
| 📙 SDV Blog | Regular publshing of useful content about Synthetic Data Generation. |
| 📖 Documentation | Quickstarts, User and Development Guides, and API Reference. |
 Repository |
The link to the Github Repository of this library. |
| 📜 License | The entire ecosystem is published under the MIT License. |
| ⌨️ 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. |
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 ctganUsing conda:
conda install -c pytorch -c conda-forge ctganFor more installation options please visit the CTGAN installation Guide
⚠️ 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 = [
'workclass',
'education',
'marital-status',
'occupation',
'relationship',
'race',
'sex',
'native-country',
'income'
]
ctgan = CTGANSynthesizer(epochs=10)
ctgan.fit(data, discrete_columns)
# Synthetic copy
samples = ctgan.sample(1000)- Please have a look at the Contributing Guide to see how you can contribute to the project.
- If you have any doubts, feature requests or detect an error, please open an issue on github or join our Slack Workspace.
- Also, do not forget to check the project documentation site!
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.
@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}
}Please note that these libraries are external contributions and are not maintained nor supervised by the MIT DAI-Lab team.
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: https://github.com/kasaai/ctgan
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: https://github.com/oregonpillow/ctgan-server-cli
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.