The Synthetic Data Vault (SDV) is a Synthetic Data Generation ecosystem of libraries that allows users to easily learn single-table, multi-table and timeseries datasets to later on generate new Synthetic Data that has the same format and statistical properties as the original dataset.
Synthetic data can then be used to supplement, augment and in some cases replace real data when training Machine Learning models. Additionally, it enables the testing of Machine Learning or other data dependent software systems without the risk of exposure that comes with data disclosure.
Underneath the hood it uses several probabilistic graphical modeling and deep learning based techniques. To enable a variety of data storage structures, we employ unique hierarchical generative modeling and recursive sampling techniques.
| 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. |
| ⌨️ 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. |
- Synthetic data generators for single tables with the following
features:
- Using Copulas and Deep Learning based models.
- Handling of multiple data types and missing data with minimum user input.
- Support for pre-defined and custom constraints and data validation.
- Synthetic data generators for complex multi-table, relational datasets with the following
features:
- Definition of entire multi-table datasets metadata with a custom and flexible JSON schema.
- Using Copulas and recursive modeling techniques.
- Synthetic data generators for multi-type, multi-variate timeseries with the following features:
- Using statistical, Autoregressive and Deep Learning models.
- Conditional sampling based on contextual attributes.
If you want to quickly discover SDV, simply click the button below and follow the tutorials!
If you want to be part of the SDV community to receive announcements of the latest releases, ask questions, suggest new features or participate in the development meetings, please join our Slack Workspace!
Using pip:
pip install sdvUsing conda:
conda install -c pytorch -c conda-forge sdvFor more installation options please visit the SDV installation Guide
In this short tutorial we will guide you through a series of steps that will help you getting started using SDV.
To model a multi table, relational dataset, we follow two steps. In the first step, we will load the data and configures the meta data. In the second step, we will use the sdv API to fit and save a hierarchical model. We will cover these two steps in this section using an example dataset.
SDV comes with a toy dataset to play with, which can be loaded using the sdv.load_demo
function:
from sdv import load_demo
metadata, tables = load_demo(metadata=True)This will return two objects:
- A
Metadataobject with all the information that SDV needs to know about the dataset.
For more details about how to build the Metadata for your own dataset, please refer to the
Working with Metadata
tutorial.
- A dictionary containing three
pandas.DataFrameswith the tables described in the metadata object.
The returned objects contain the following information:
{
'users':
user_id country gender age
0 0 USA M 34
1 1 UK F 23
2 2 ES None 44
3 3 UK M 22
4 4 USA F 54
5 5 DE M 57
6 6 BG F 45
7 7 ES None 41
8 8 FR F 23
9 9 UK None 30,
'sessions':
session_id user_id device os
0 0 0 mobile android
1 1 1 tablet ios
2 2 1 tablet android
3 3 2 mobile android
4 4 4 mobile ios
5 5 5 mobile android
6 6 6 mobile ios
7 7 6 tablet ios
8 8 6 mobile ios
9 9 8 tablet ios,
'transactions':
transaction_id session_id timestamp amount approved
0 0 0 2019-01-01 12:34:32 100.0 True
1 1 0 2019-01-01 12:42:21 55.3 True
2 2 1 2019-01-07 17:23:11 79.5 True
3 3 3 2019-01-10 11:08:57 112.1 False
4 4 5 2019-01-10 21:54:08 110.0 False
5 5 5 2019-01-11 11:21:20 76.3 True
6 6 7 2019-01-22 14:44:10 89.5 True
7 7 8 2019-01-23 10:14:09 132.1 False
8 8 9 2019-01-27 16:09:17 68.0 True
9 9 9 2019-01-29 12:10:48 99.9 True
}
First, we build a hierarchical statistical model of the data using SDV. For this we will
create an instance of the sdv.SDV class and use its fit method.
During this process, SDV will traverse across all the tables in your dataset following the primary key-foreign key relationships and learn the probability distributions of the values in the columns.
from sdv import SDV
sdv = SDV()
sdv.fit(metadata, tables)Once the modeling has finished, you can save your fitted SDV instance for later usage
using the save method of your instance.
sdv.save('sdv.pkl')The generated pkl file will not include any of the original data in it, so it can be
safely sent to where the synthetic data will be generated without any privacy concerns.
In order to sample data from the fitted model, we will first need to load it from its
pkl file. Note that you can skip this step if you are running all the steps sequentially
within the same python session.
sdv = SDV.load('sdv.pkl')After loading the instance, we can sample synthetic data by calling its sample method.
samples = sdv.sample()The output will be a dictionary with the same structure as the original tables dict,
but filled with synthetic data instead of the real one.
Finally, if you want to evaluate how similar the sampled tables are to the real data, please have a look at our evaluation framework or visit the SDMetrics library.
- If you would like to see more usage examples, please have a look at the tutorials folder of the repository. Please contact us if you have a usage example that you would want to share with the community.
- Please have a look at the Contributing Guide to see how you can contribute to the project.
- If you have any doubts, feature requests or detect an error, please open an issue on github or join our Slack Workspace
- Also, do not forget to check the project documentation site!
If you use SDV for your research, please consider citing the following paper:
Neha Patki, Roy Wedge, Kalyan Veeramachaneni. The Synthetic Data Vault. IEEE DSAA 2016.
@inproceedings{
7796926,
author={N. {Patki} and R. {Wedge} and K. {Veeramachaneni}},
booktitle={2016 IEEE International Conference on Data Science and Advanced Analytics (DSAA)},
title={The Synthetic Data Vault},
year={2016},
volume={},
number={},
pages={399-410},
keywords={data analysis;relational databases;synthetic data vault;SDV;generative model;relational database;multivariate modelling;predictive model;data analysis;data science;Data models;Databases;Computational modeling;Predictive models;Hidden Markov models;Numerical models;Synthetic data generation;crowd sourcing;data science;predictive modeling},
doi={10.1109/DSAA.2016.49},
ISSN={},
month={Oct}
}
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.