Sdv

This release updates to the latest CTGAN, RDT and SDMetrics libraries to introduce a
new TVAE model, multiple new metrics for single table and multi table, and fixes
issues in the re-creation of tabular models from a metadata dict.

Issues resolved

* Upgrade to SDMetrics v0.1.0 and add `sdv.metrics` module - [Issue 281](https://github.com/sdv-dev/SDV/issues/281) by csala
* Upgrade to CTGAN 0.3.0 and add TVAE model - [Issue 278](https://github.com/sdv-dev/SDV/issues/278) by fealho
* Add `dtype_transformers` to `Table.from_dict` - [Issue 276](https://github.com/sdv-dev/SDV/issues/276) by csala
* Fix Metadata `from_dict` behavior - [Issue 275](https://github.com/sdv-dev/SDV/issues/275) by csala

This version updates the dependencies and makes a few internal changes in order
to ensure that SDV works properly on Windows Systems, making this the first
release to be officially supported on Windows.

Apart from this, some more internal changes have been made to solve a few minor
issues from the older versions while also improving the processing speed when
processing relational datasets with the default parameters.

API breaking changes

* The `distribution` argument of the `GaussianCopula` has been renamed to `field_distributions`.
* The `HMA1` and `SDV` classes now use the `categorical_fuzzy` transformer by default instead of
the `one_hot_encoding` one.

Issues resolved

* GaussianCopula: rename `distribution` argument to `field_distributions` - [Issue 237](https://github.com/sdv-dev/SDV/issues/237) by csala
* GaussianCopula: Improve error message if an invalid distribution name is passed - [Issue 220](https://github.com/sdv-dev/SDV/issues/220) by csala
* Import urllib.request explicitly - [Issue 227](https://github.com/sdv-dev/SDV/issues/227) by csala
* TypeError: cannot astype a datetimelike from [datetime64[ns]] to [int32] - [Issue 218](https://github.com/sdv-dev/SDV/issues/218) by csala
* Change default categorical transformer to `categorical_fuzzy` in HMA1 - [Issue 214](https://github.com/sdv-dev/SDV/issues/214) by csala
* Integer categoricals being sampled as strings instead of integer values - [Issue 194](https://github.com/sdv-dev/SDV/issues/194) by csala

In this version a new family of models for Synthetic Time Series Generation is introduced
under the `sdv.timeseries` sub-package. The new family of models now includes a new class
called `PAR`, which implements a *Probabilistic AutoRegressive* model.

This version also adds support for composite primary keys and regex based generation of id
fields in tabular models and drops Python 3.5 support.

Issues resolved

* Drop python 3.5 support - [Issue 204](https://github.com/sdv-dev/SDV/issues/204) by csala
* Support composite primary keys in tabular models - [Issue 207](https://github.com/sdv-dev/SDV/issues/207) by csala
* Add the option to generate string `id` fields based on regex on tabular models - [Issue 208](https://github.com/sdv-dev/SDV/issues/208) by csala
* Synthetic Time Series - [Issue 142](https://github.com/sdv-dev/SDV/issues/142) by csala

This version adds a new tabular model based on combining the CTGAN model with the reversible
transformation applied in the GaussianCopula model that converts random variables with
arbitrary distributions to new random variables with standard normal distribution.

The reversible transformation is handled by the GaussianCopulaTransformer recently added to RDT.

Issues resolved

* Add CopulaGAN Model - [Issue 202](https://github.com/sdv-dev/SDV/issues/202) by csala

This release moves the models and algorithms related to generation of synthetic
relational data to a new `sdv.relational` subpackage (Issue 198)

As part of the change, also the old `sdv.models` have been removed and now
relational model is based on the recently introduced `sdv.tabular` models.

In this release the `sdv.evaluation` module has been reworked to include 4 different
metrics and in all cases return a normalized score between 0 and 1.

Included metrics are:
- `cstest`
- `kstest`
- `logistic_detection`
- `svc_detection`