Welcome to synthyverse’s documentation!¶

synthyverse is an extensive ecosystem for synthetic data generation and evaluation in Python.

The synthyverse provides:

🔧 Highly modular installation - Install only those modules which you require to keep your installation lightweight.
📚 Extensive library - Any generator or metric can be quickly added without dependency conflicts due to synthyverse’s modular installation. This allows the synthyverse to host a great amount of generators and evaluation metrics. It also allows the synthyverse to wrap around any existing synthetic data library.
⚙️ Benchmarking module - The benchmarking module executes a modular pipeline of synthetic data generation and evaluation. Choose a generator, set of evaluation metrics, and pipeline parameters, and obtain results on synthetic data quality.
👷 Minimal preprocessing - All preprocessing is handled by the synthyverse, so no need for scaling, one-hot encoding, or handling missing values. Different preprocessing schemes can be used by setting simple parameters.
👍 Constraint support - You can specify inter-column constraints which you want your synthetic data to follow. Constraints are modelled explicitly by the synthyverse, not through oversampling. This ensures efficient and reliable constraint setting.

Quick Start¶

Getting Started

API Reference