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. If you're just getting started with synthetic data, we recommend installing the SDV library which provides user-friendly APIs for accessing CTGAN. The SDV library provides wrappers for preprocessing your data as well as additional usability features like constraints. 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.
Features
- Use the CTGAN standalone library
- Use CTGAN through the SDV library
- Continuous data must be represented as floats
- Discrete data must be represented as ints or strings
- The data should not contain any missing values
- Currently, this library implements the CTGAN and TVAE models described in the Modeling Tabular data using Conditional GAN paper
Project Samples
