Sdv

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`

This release fixes a couple of minor issues and introduces an important rework of the
User Guides section of the documentation.

Issues fixed

* Error Message: "make sure the Graphviz executables are on your systems' PATH" - [Issue 182](https://github.com/sdv-dev/SDV/issues/182) by csala
* Anonymization mappings leak - [Issue 187](https://github.com/sdv-dev/SDV/issues/187) by csala

In this release SDV gets new documentation, new tutorials, improvements to the Tabular API
and broader python and dependency support.

Complete list of changes:

* New Documentation site based on the `pydata-sphinx-theme`.
* New User Guides and Notebook tutorials.
* New Developer Guides section within the docs with details about the SDV architecture,
the ecosystem libraries and how to extend and contribute to the project.
* Improved API for the Tabular models with focus on ease of use.
* Support for Python 3.8 and the newest versions of pandas, scipy and scikit-learn.
* New Slack Workspace for development discussions and community support.

This release introduces a new concept of `Constraints`, which allow the user to define
special relationships between columns that will not be handled via modeling.

This is done via a new `sdv.constraints` subpackage which defines some well-known pre-defined
constraints, as well as a generic framework that allows the user to customize the constraints
to their needs as much as necessary.

New Features

* Support for Constraints - [Issue 169](https://github.com/sdv-dev/SDV/issues/169) by csala

This release introduces a new subpackage `sdv.tabular` with models designed specifically
for single table modeling, while still providing all the usual conveniences from SDV, such
as:

* Seamless multi-type support
* Missing data handling
* PII anonymization

Currently implemented models are:

* GaussianCopula: Multivariate distributions modeled using copula functions. This is stronger
version, with more marginal distributions and options, than the one used to model multi-table
datasets.
* CTGAN: GAN-based data synthesizer that can generate synthetic tabular data with high fidelity.