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We are happy to announce that our new model for synthetic data called CTGAN is open-sourced. The new model is simpler and gives better performance on many datasets. TGAN is a tabular data synthesizer. It can generate fully synthetic data from real data. Currently, TGAN can generate numerical columns and categorical columns. TGAN has been developed and runs on Python 3.5, 3.6 and 3.7. Also, although it is not strictly required, the usage of a virtualenv is highly recommended in order to avoid interfering with other software installed in the system where TGAN is run. For development, you can use make install-develop instead in order to install all the required dependencies for testing and code listing. In order to be able to sample new synthetic data, TGAN first needs to be fitted to existing data.
Features
- Generative adversarial training for synthesizing tabular data
- Currently, TGAN can generate numerical columns and categorical columns
- Requires Python
- TGAN has been developed and runs on Python 3.5, 3.6 and 3.7
- The output of TGAN is a table of sampled data with the same columns as the input table and as many rows as requested
- Import TGAN and create an instance of the model
Project Samples
