Synthetic Data Gym (SDGym) is a framework to benchmark the performance of synthetic data generators based on SDV and SDMetrics.

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.

A Synthetic Data Generator is a Python function (or method) that takes as input some data, which we call the real data, learns a model from it, and outputs new synthetic data that has the same structure and similar mathematical properties as the real one.

Please refer to the synthesizers documentation for instructions about how to implement your own Synthetic Data Generator and integrate with SDGym. You can also read about how to use the ones already included in SDGym and see how to run them.

SDGym evaluates the performance of Synthetic Data Generators using single table, multi table and timeseries datasets stored as CSV files alongside an SDV Metadata JSON file.

Further details about the list of available datasets and how to add your own datasets to the collection can be found in the datasets documentation.

SDGym can be installed using the following commands:

Using pip:

pip install sdgym

Using conda:

conda install -c pytorch -c conda-forge sdgym

For more installation options please visit the SDGym installation Guide

SDGym evaluates Synthetic Data Generators, which are Python functions (or classes) that take as input some data, which we call the real data, learn a model from it, and output new synthetic data that has the same structure and similar mathematical properties as the real one.

As an example, let use define a synthesizer function that applies the GaussianCopula model from SDV with gaussian distribution.

import numpy as np
from sdv.tabular import GaussianCopula


def gaussian_copula(real_data, metadata):
    gc = GaussianCopula(default_distribution='gaussian')
    table_name = metadata.get_tables()[0]
    gc.fit(real_data[table_name])
    return {table_name: gc.sample()}

We can now try to evaluate this function on the asia and alarm datasets:

import sdgym

scores = sdgym.run(synthesizers=gaussian_copula, datasets=['asia', 'alarm'])

The output of the sdgym.run function will be a pd.DataFrame containing the results obtained by your synthesizer on each dataset.

If you want to run the SDGym benchmark on the SDGym Synthesizers you can directly pass the corresponding class, or a list of classes, to the sdgym.run function.

For example, if you want to run the complete benchmark suite to evaluate all the existing synthesizers you can run (:warning: this will take a lot of time to run!):

from sdgym.synthesizers import (
    CLBN, CopulaGAN, CTGAN, HMA1, Identity, Independent,
    MedGAN, PAR, PrivBN, SDV, TableGAN, TVAE,
    Uniform, VEEGAN)

all_synthesizers = [
    CLBN,
    CTGAN,
    CopulaGAN,
    HMA1,
    Identity,
    Independent,
    MedGAN,
    PAR,
    PrivBN,
    SDV,
    TVAE,
    TableGAN,
    Uniform,
    VEEGAN,
]
scores = sdgym.run(synthesizers=all_synthesizers)

For further details about all the arguments and possibilities that the benchmark function offers please refer to the benchmark documentation

• Datasets used in SDGym are detailed here.
• How to write a synthesizer is detailed here.
• How to use benchmark function is detailed here.
• Detailed leaderboard results for all the releases are available here.

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.