v0.14.1 - 2022-05-03

This release adds a TabularPreset, available in the sdv.lite module, which allows users to easily optimize a tabular model for speed.
In this release, we also include bug fixes for sampling with conditions, an unresolved warning, and setting field distributions. Finally,
we include documentation updates for sampling and the new TabularPreset.

Bugs Fixed

• Sampling with conditions={column: 0.0} for float columns doesn't work - Issue #525 by @shlomihod and @tssbas
• resolved FutureWarning with Pandas replaced append by concat - Issue #759 by @Deathn0t
• Field distributions bug in CopulaGAN - Issue #747 by @katxiao
• Field distributions bug in GaussianCopula - Issue #746 by @katxiao

New Features

• Set default transformer to categorical_fuzzy - Issue #768 by @amontanez24
• Model nulls normally when tabular preset has constraints - Issue #764 by @katxiao
• Don't modify my metadata object - Issue #754 by @amontanez24
• Presets should be able to handle constraints - Issue #753 by @katxiao
• Change preset optimize_for --> name - Issue #749 by @katxiao
• Create a speed optimized Preset - Issue #716 by @katxiao

Documentation Changes

• Add tabular preset docs - Issue #777 by @katxiao
• sdv.sampling module is missing from the API - Issue #740 by @katxiao