SDGym
Mimesis
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| SDGym | Mimesis | |
|---|---|---|
| 1 | 3 | |
| 242 | 4,304 | |
| 4.5% | - | |
| 7.5 | 9.1 | |
| 4 days ago | 4 days ago | |
| Python | Python | |
| GNU General Public License v3.0 or later | MIT License |
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
SDGym
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[D] Synthetic data generation techniques for data privacy
I would suggest starting with "differentially private synthetic data generation". These methods utilize differential privacy and mostly protect against membership inference attacks, are very popular in the ML/DL community. I would also suggest reading up on privacy preserving ML methods in general and adversarial attacks against them (membership inference, inversion, reconstruction, property inference), but if you're keen on reading some code, check out sd-gym: https://github.com/sdv-dev/SDGym. The authors have collected implementations for a lot of PPSDG methods. Also I strongly suggest reading McMahan's 2016 paper: https://arxiv.org/abs/1607.00133.
Mimesis
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Mimesis allows you toeasily generate detailed dummy datasets.
Mimesis has well-structured and comprehensive documentation: https://mimesis.name
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Open source
P.S Here is a project I was talking about.
- Mimesis is a fake data generator that can be used in Data Science for generating dummy datasets.
What are some alternatives?
SDV - Synthetic data generation for tabular data
faker - Faker is a Python package that generates fake data for you.
FAST-RIR - This is the official implementation of our neural-network-based fast diffuse room impulse response generator (FAST-RIR) for generating room impulse responses (RIRs) for a given acoustic environment.
fake2db - create custom test databases that are populated with fake data
Copulas - A library to model multivariate data using copulas.
radar
DPL - [NeurIPS 2023] Multi-fidelity hyperparameter optimization with deep power laws that achieves state-of-the-art results across diverse benchmarks.
FauxFactory - Generates random data for your tests.
callee - Argument matchers for unittest.mock
aiounittest - Test python asyncio-based code with ease.
pytest-fastapi-deps - This library allows you to easily replace FastAPI dependencies in your tests. Regular mocking techniques do not work due to the inner working of FastAPI.
Schemathesis - Automate your API Testing: catch crashes, validate specs, and save time