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The Synthetic Data Generator (SDG) is a specialized framework designed to generate high-quality structured tabular data.

Synthetic data does not contain any sensitive information, yet it retains the essential characteristics of the original data, making it exempt from privacy regulations such as GDPR and ADPPA.

High-quality synthetic data can be safely utilized across various domains including data sharing, model training and debugging, system development and testing, etc.

We are excited to have you here and look forward to your contributions, get started with the project through thisContributing Overview Guide!

Our current key achievements and timelines are as follows:

🔥 Nov 21, 2024: 1) Model Integration - We've integrated theGaussianCopula model into our Data Processor System. Check out the code example in thisPR; 2) Synthetic Quality - We implemented automatic detection of data column relationships and allowed for relationship specification, improved the quality of synthetic data(Code Example); 3) Performance Enhancement - We significantly reduced the memory usage of GaussianCopula when handling discrete data, enabling training on thousands of categorical data entries with a2C4G setup!

🔥 May 30, 2024: The Data Processor module was officially merged. This module will: 1) help SDG convert the format of some data columns (such as Datetime columns) before feeded into the model (so as to avoid being treated as discrete types), and reversely convert the model-generated data into the original format; 2) perform more customized pre-processing and post-processing on various data types; 3) easily deal with problems such as null values ​​in the original data; 4) support the plug-in system.

🔥 Feb 20, 2024: a single-table data synthesis model based on LLM is included, view colab example: LLM: Data Synthesis and LLM: Off-table Feature Inference.

🔧 Feb 7, 2024: We improvedsdgx.data_models.metadata to support metadata information describing for single tables and multiple tables, support multiple data types, support automatic data type inference. view colab example:SDG Single-Table Metadata。

🔶 Dec 20, 2023: v0.1.0 released, a CTGAN model that supports billions of data processing capabilities is included, view our benchmark against SDV, where SDG achieved less memory consumption and avoided crashing during training. For specific use, view colab example: Billion-Level-Data supported CTGAN.

🔆 Aug 10, 2023: First line of SDG code committed.

For a long time, LLM has been used to understand and generate various types of data. In fact, LLM also has certain capabilities in tabular data generation. Also, it has some abilities that cannot be achieved by traditional (based on GAN methods or statistical methods) .

Oursdgx.models.LLM.single_table.gpt.SingleTableGPTModel implements two new features:

No training data is required, synthetic data can be generated based on metadata data, view in our colab example.

Infer new column data based on the existing data in the table and the knowledge mastered by LLM, view in our colab example.

• Technological advancements:
  • Supports a wide range of statistical data synthesis algorithms, LLM-based synthetic data generation model is also integrated;
  • Optimized for big data, effectively reducing memory consumption;
  • Continuously tracking the latest advances in academia and industry, and introducing support for excellent algorithms and models in a timely manner.
• Privacy enhancements:
  • SDG supports differential privacy, anonymization and other methods to enhance the security of synthetic data.
• Easy to extend:
  • Supports expansion of models, data processing, data connectors, etc. in the form of plug-in packages.

You can use pre-built images to quickly experience the latest features.

docker pull idsteam/sdgx:latest

pip install sdgx

Use SDG by installing it through the source code.

git clone git@github.com:hitsz-ids/synthetic-data-generator.gitpip install.# Or install from gitpip install git+https://github.com/hitsz-ids/synthetic-data-generator.git

fromsdgx.data_connectors.csv_connectorimportCsvConnectorfromsdgx.models.ml.single_table.ctganimportCTGANSynthesizerModelfromsdgx.synthesizerimportSynthesizerfromsdgx.utilsimportdownload_demo_data# This will download demo data to ./datasetdataset_csv=download_demo_data()# Create data connector for csv filedata_connector=CsvConnector(path=dataset_csv)# Initialize synthesizer, use CTGAN modelsynthesizer=Synthesizer(model=CTGANSynthesizerModel(epochs=1),# For quick demodata_connector=data_connector,)# Fit the modelsynthesizer.fit()# Samplesampled_data=synthesizer.sample(1000)print(sampled_data)

Real data are as follows：

>>>data_connector.read()ageworkclassfnlwgteducation  ...capitallosshoursperweeknative-countryclass02State-gov77516Bachelors  ...02United-States<=50K13Self-emp-not-inc83311Bachelors  ...00United-States<=50K22Private215646HS-grad  ...02United-States<=50K33Private23472111th  ...02United-States<=50K41Private338409Bachelors  ...02Cuba<=50K...    ...               ...     ...        ...  ...          ...          ...            ...    ...488372Private215419Bachelors  ...02United-States<=50K488384NaN321403HS-grad  ...02United-States<=50K488392Private374983Bachelors  ...03United-States<=50K488402Private83891Bachelors  ...02United-States<=50K488411Self-emp-inc182148Bachelors  ...03United-States>50K[48842rowsx15columns]

Synthetic data are as follows：

>>>sampled_dataageworkclassfnlwgteducation  ...capitallosshoursperweeknative-countryclass01NaN28219Some-college  ...02Puerto-Rico<=50K12Private250166HS-grad  ...02United-States>50K22Private50304HS-grad  ...02United-States<=50K34Private89318Bachelors  ...02Puerto-Rico>50K41Private172149Bachelors  ...03United-States<=50K..   ...       ...     ...           ...  ...          ...          ...            ...    ...9952NaN208938Bachelors  ...01United-States<=50K9962Private166416Bachelors  ...22United-States<=50K9972NaN336022HS-grad  ...01United-States<=50K9983Private198051Masters  ...02United-States>50K9991NaN41973HS-grad  ...02United-States<=50K[1000rowsx15columns]

• CTGAN：Modeling Tabular Data using Conditional GAN
• C3-TGAN:C3-TGAN- Controllable Tabular Data Synthesis with Explicit Correlations and Property Constraints
• TVAE：Modeling Tabular Data using Conditional GAN
• table-GAN：Data Synthesis based on Generative Adversarial Networks
• CTAB-GAN:CTAB-GAN: Effective Table Data Synthesizing
• OCT-GAN:OCT-GAN: Neural ODE-based Conditional Tabular GANs

The SDG project was initiated byInstitute of Data Security, Harbin Institute of Technology. If you are interested in out project, welcome to join our community. We welcome organizations, teams, and individuals who share our commitment to data protection and security through open source:

• ReadCONTRIBUTING before draft a pull request.
• Submit an issue by viewingView Good First Issue or submit a Pull Request.
• Join our Wechat Group through QR code.

The SDG open source project uses Apache-2.0 license, please refer to theLICENSE.