Synthcity: facilitating innovative use cases of synthetic data in different data modalities

TL;DR

Synthcity is an open-source toolkit that enables innovative applications of synthetic data across various data types, supporting research, experimentation, and benchmarking in ML fairness, privacy, and augmentation.

Contribution

It provides a unified platform for synthetic data generation and benchmarking across diverse data modalities, facilitating research and practical applications.

Findings

Supports multiple data modalities including time series and censored data

Offers state-of-the-art benchmarks for synthetic data quality

Enables rapid experimentation and community collaboration

Abstract

Synthcity is an open-source software package for innovative use cases of synthetic data in ML fairness, privacy and augmentation across diverse tabular data modalities, including static data, regular and irregular time series, data with censoring, multi-source data, composite data, and more. Synthcity provides the practitioners with a single access point to cutting edge research and tools in synthetic data. It also offers the community a playground for rapid experimentation and prototyping, a one-stop-shop for SOTA benchmarks, and an opportunity for extending research impact. The library can be accessed on GitHub (https://github.com/vanderschaarlab/synthcity) and pip (https://pypi.org/project/synthcity/). We warmly invite the community to join the development effort by providing feedback, reporting bugs, and contributing code.