This release adds support for SDV 1.0 and PyTorch 2.0!
- Add functions to top level import - Issue #229 by @fealho
- Cleanup SDGym to the new SDV 1.0 metadata and synthesizers - Issue #212 by @fealho
- limit_dataset_size causes sdgym to crash - Issue #231 by @fealho
- benchmark_single_table crashes with metadata dict - Issue #232 by @fealho
- Passing None as synthesizers runs all of them - Issue #233 by @fealho
- timeout parameter causes sdgym to crash - Issue #234 by @pvk-developer
- SDGym is not working with latest torch - Issue #210 by @amontanez24
- Fix sdgym --help - Issue #206 by @katxiao
- Increase code style lint - Issue #123 by @fealho
- Remove code support for synthesizers that are not strings/classes - PR #236 by @fealho
- Code Refactoring - Issue #215 by @fealho
- Remove pomegranate - Issue #230 by @amontanez24
This release introduces methods for benchmarking single table data and creating custom synthesizers, which can be based on existing SDGym-defined synthesizers or on user-defined functions. This release also adds support for Python 3.10 and drops support for Python 3.6.
- Benchmarking progress bar should update on one line - Issue #204 by @katxiao
- Support local additional datasets folder with zip files - Issue #186 by @katxiao
- Enforce that each synthesizer is unique in benchmark_single_table - Issue #190 by @katxiao
- Simplify the file names inside the detailed_results_folder - Issue #191 by @katxiao
- Use SDMetrics silent report generation - Issue #179 by @katxiao
- Remove arguments in get_available_datasets - Issue #197 by @katxiao
- Accept metadata.json as valid metadata file - Issue #194 by @katxiao
- Check if file or folder exists before writing benchmarking results - Issue #196 by @katxiao
- Rename benchmarking argument "evaluate_quality" to "compute_quality_score" - Issue #195 by @katxiao
- Add option to disable sdmetrics in benchmarking - Issue #182 by @katxiao
- Prefix remote bucket with 's3' - Issue #183 by @katxiao
- Benchmarking error handling - Issue #177 by @katxiao
- Allow users to specify custom synthesizers' display names - Issue #174 by @katxiao
- Update benchmarking results columns - Issue #172 by @katxiao
- Allow custom datasets - Issue #166 by @katxiao
- Use new datasets s3 bucket - Issue #161 by @katxiao
- Create benchmark_single_table method - Issue #151 by @katxiao
- Update summary metrics - Issue #134 by @katxiao
- Benchmark individual methods - Issue #159 by @katxiao
- Add method to create a sdv variant synthesizer - Issue #152 by @katxiao
- Add method to generate a multi table synthesizer - Issue #149 by @katxiao
- Add method to create single table synthesizers - Issue #148 by @katxiao
- Updating existing synthesizers to new API - Issue #154 by @katxiao
- Pip encounters dependency issues with ipython - Issue #187 by @katxiao
- IndependentSynthesizer is printing out ConvergeWarning too many times - Issue #192 by @katxiao
- Size values in benchmarking results seems inaccurate - Issue #184 by @katxiao
- Import error in the example for benchmarking the synthesizers - Issue #139 by @katxiao
- Updates and bugfixes - Issue #132 by @csala
- Update README - Issue #203 by @katxiao
- Support Python Versions >=3.7 and <3.11 - Issue #170 by @katxiao
- SDGym Package Maintenance Updates documentation - Issue #163 by @katxiao
- Remove YData - Issue #168 by @katxiao
- Update to newest SDV - Issue #157 by @katxiao
- Update slack invite link. - Issue #144 by @pvk-developer
- updating workflows to work with windows - Issue #136 by @amontanez24
- Update conda dependencies - Issue #130 by @katxiao
This release adds support for Python 3.9, and updates dependencies to accept the latest versions when possible.
- Add support for Python 3.9 - Issue #127 by @katxiao
- Add pip check worflow - Issue #124 by @pvk-developer
- Fix meta.yaml dependencies - PR #119 by @fealho
- Upgrade dependency ranges - Issue #118 by @katxiao
This release fixed a bug where passing a json file as configuration for a multi-table synthesizer crashed the model.
It also adds a number of fixes and enhancements, including: (1) a function and CLI command to list the available synthesizer names,
(2) a curate set of dependencies and making Gretel into an optional dependency, (3) updating Gretel to use temp directories,
(4) using nvidia-smi to get the number of gpus and (5) multiple dockerfile updates to improve functionality.
- Bug when using JSON configuration for multiple multi-table evaluation - Issue #115 by @pvk-developer
- Use nvidia-smi to get number of gpus - PR #113 by @katxiao
- List synthesizer names - Issue #82 by @fealho
- Use nvidia base for dockerfile - PR #108 by @katxiao
- Add Makefile target to install gretel and ydata - PR #107 by @katxiao
- Curate dependencies and make Gretel optional - PR #106 by @csala
- Update gretel checkpoints to use temp directory - PR #105 by @katxiao
- Initialize variable before reference - PR #104 by @katxiao
This release adds new synthesizers for Gretel and ydata, and creates a Docker image for SDGym. It also includes enhancements to the accepted SDGym arguments, adds a summary command to aggregate metrics, and adds the normalized score to the benchmark results.
- Add normalized score to benchmark results - Issue #102 by @katxiao
- Add max rows and max columns args - Issue #96 by @katxiao
- Automatically detect number of workers - Issue #97 by @katxiao
- Add summary function and command - Issue #92 by @amontanez24
- Allow jobs list/JSON to be passed - Issue #93 by @fealho
- Add ydata to sdgym - Issue #90 by @fealho
- Add dockerfile for sdgym - Issue #88 by @katxiao
- Add Gretel to SDGym synthesizer - Issue #87 by @amontanez24
This release adds new features to store results and cache contents into an S3 bucket as well as a script to collect results from a cache dir and compile a single results CSV file.
- Collect cached results from s3 bucket - Issue #85 by @katxiao
- Store cache contents into an S3 bucket - Issue #81 by @katxiao
- Store SDGym results into an S3 bucket - Issue #80 by @katxiao
- Add a way to collect cached results - Issue #79 by @katxiao
- Allow reading datasets from private s3 bucket - Issue #74 by @katxiao
- Typos in the sdgym.run function docstring documentation - Issue #69 by @sbrugman
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