v0.7.0 - 2021-01-27

This release introduces a few changes in the HMA1 relational algorithm to decrease modeling
and sampling times, while also ensuring that correlations are properly kept across tables
and also adding support for some relational schemas that were not supported before.

A few changes in constraints and tabular models also ensure that situations that produced
errors before now work without errors.

Issues resolved

• Fix unique key generation - Issue #306 by @fealho
• Ensure tables that contain nothing but ids can be modeled - Issue #302 by @csala
• Metadata visualization improvements - Issue #301 by @csala
• Multi-parent re-model and re-sample issue - Issue #298 by @csala
• Support datetimes in GreaterThan constraint - Issue #266 by @rollervan
• Support for multiple foreign keys in one table - Issue #185 by @csala

v0.6.1 - 2020-12-31

SDMetrics version is updated to include the new Time Series metrics, which have also
been added to the API Reference and User Guides documentation. Additionally,
a few code has been refactored to reduce external dependencies and a few minor bugs
related to single table constraints have been fixed

Issues resolved:

• Add timeseries metrics and user guides - Issue #289 by @csala
• Add functions to generate regex ids - Issue #288 by @csala
• Saving a fitted tabular model with UniqueCombinations constraint raises PicklingError -
  Issue #286 by @csala
• Constraints: handling_strategy='reject_sampling' causes 'ZeroDivisionError: division by zero' -
  Issue #285 by @csala

v0.6.0 - 2020-12-22

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 by @csala
• Upgrade to CTGAN 0.3.0 and add TVAE model - Issue #278 by @fealho
• Add dtype_transformers to Table.from_dict - Issue #276 by @csala
• Fix Metadata from_dict behavior - Issue #275 by @csala

v0.5.0 - 2020-11-25

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 by @csala
• GaussianCopula: Improve error message if an invalid distribution name is passed - Issue #220 by @csala
• Import urllib.request explicitly - Issue #227 by @csala
• TypeError: cannot astype a datetimelike from [datetime64[ns]] to [int32] - Issue #218 by @csala
• Change default categorical transformer to categorical_fuzzy in HMA1 - Issue #214 by @csala
• Integer categoricals being sampled as strings instead of integer values - Issue #194 by @csala

v0.4.5 - 2020-10-17

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 by @csala
• Support composite primary keys in tabular models - Issue #207 by @csala
• Add the option to generate string id fields based on regex on tabular models - Issue #208 by @csala
• Synthetic Time Series - Issue #142 by @csala

v0.4.4 - 2020-10-06

This release 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.

New Features

• Add CopulaGAN Model - Issue #202 by @csala

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.