Major rework of the SDGym functionality to support a collection of new features:
- Add relational and timeseries model benchmarking.
- Use SDMetrics for model scoring.
- Update datasets format to match SDV metadata based storage format.
- Centralize default datasets collection in the
sdv-datasetsS3 bucket. - Add options to download and use datasets from different S3 buckets.
- Rename synthesizers to baselines and adapt to the new metadata format.
- Add model execution and metric computation time logging.
- Add optional synthetic data and error traceback caching.
This version adds a rework of the benchmark function and a few new synthesizers.
- New CLI with
run,make-leaderboardandmake-summarycommands - Parallel execution via Dask or Multiprocessing
- Download datasets without executing the benchmark
- Support for python from 3.6 to 3.8
sdv.tabular.CTGANsdv.tabular.CopulaGANsdv.tabular.GaussianCopulaOneHotsdv.tabular.GaussianCopulaCategoricalsdv.tabular.GaussianCopulaCategoricalFuzzy
New updated leaderboard and minor improvements.
- Add parameters for PrivBNSynthesizer - Issue #37 by @csala
New Becnhmark API and lots of improved documentation.
- The benchmark function now returns a complete leaderboard instead of only one score
- Class Synthesizers can be directly passed to the benchmark function
- One hot encoding errors in the Independent, VEEGAN and Medgan Synthesizers.
- Proper usage of the
evalmode during sampling. - Fix improperly configured datasets.
First release to PyPi