v0.4.3 - 2020-09-28

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 modeling is based on the recently introduced sdv.tabular models.

v0.4.2 - 2020-09-19

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

v0.4.1 - 2020-09-07

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 by @csala
• Anonymization mappings leak - Issue #187 by @csala

v0.4.0 - 2020-08-08

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.

v0.3.6 - 2020-07-23

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 by @csala

v0.3.5 - 2020-07-09

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.

v0.3.4 - 2020-07-04

New Features

• Support for Multiple Parents - Issue #162 by @csala
• Sample by default the same number of rows as in the original table - Issue #163 by @csala

General Improvements

• Add benchmark - Issue #165 by @csala

v0.3.3 - 2020-06-26

General Improvements

• Use SDMetrics for evaluation - Issue #159 by @csala

v0.3.2 - 2020-02-03

General Improvements

• Improve metadata visualization - Issue #151 @csala @JDTheRipperPC

v0.3.1 - 2020-01-22

New Features

• Add Metadata Validation - Issue #134 by @csala @JDTheRipperPC

• Add Metadata Visualization - Issue #135 by @JDTheRipperPC

General Improvements

• Add path to metadata JSON - Issue #143 by @JDTheRipperPC

• Use new Copulas and RDT versions - Issue #147 by @csala @JDTheRipperPC