Sdgym

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-datasets` S3 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 Features

* New CLI with `run`, `make-leaderboard` and `make-summary` commands
* Parallel execution via Dask or Multiprocessing
* Download datasets without executing the benchmark
* Support for python from 3.6 to 3.8

New Synthesizers

* `sdv.tabular.CTGAN`
* `sdv.tabular.CopulaGAN`
* `sdv.tabular.GaussianCopulaOneHot`
* `sdv.tabular.GaussianCopulaCategorical`
* `sdv.tabular.GaussianCopulaCategoricalFuzzy`

New updated leaderboard and minor improvements.

New Features

* Add parameters for PrivBNSynthesizer - [Issue 37](https://github.com/sdv-dev/SDGym/issues/37) by csala

New Becnhmark API and lots of improved documentation.

New Features

* The benchmark function now returns a complete leaderboard instead of only one score
* Class Synthesizers can be directly passed to the benchmark function

Bug Fixes

* One hot encoding errors in the Independent, VEEGAN and Medgan Synthesizers.
* Proper usage of the `eval` mode during sampling.
* Fix improperly configured datasets.

First release to PyPi