From 59b66644896325e44eac9b324a4d872dff620afa Mon Sep 17 00:00:00 2001 From: chukarsten <64713315+chukarsten@users.noreply.github.com> Date: Wed, 27 Oct 2021 18:13:17 -0400 Subject: [PATCH] Initial. (#2974) Co-authored-by: Angela Lin --- docs/source/release_notes.rst | 12 +++++++++++- evalml/__init__.py | 2 +- setup.py | 2 +- 3 files changed, 13 insertions(+), 3 deletions(-) diff --git a/docs/source/release_notes.rst b/docs/source/release_notes.rst index c2a88ad67a..afeb87f702 100644 --- a/docs/source/release_notes.rst +++ b/docs/source/release_notes.rst @@ -1,6 +1,17 @@ Release Notes ------------- **Future Releases** + * Enhancements + * Fixes + * Changes + * Documentation Changes + * Testing Changes + +.. warning:: + + **Breaking Changes** + +**v0.36.0 Oct. 27, 2021** * Enhancements * Added LIME as an algorithm option for ``explain_predictions`` and ``explain_predictions_best_worst`` :pr:`2905` * Standardized data check messages and added default "rows" and "columns" to data check message details dictionary :pr:`2869` @@ -34,7 +45,6 @@ Release Notes * Standardized data check messages and added default "rows" and "columns" to data check message details dictionary. This may change the number of messages returned from a data check. :pr:`2869` - **v0.35.0 Oct. 14, 2021** * Enhancements * Added human-readable pipeline explanations to model understanding :pr:`2861` diff --git a/evalml/__init__.py b/evalml/__init__.py index 9d862aa06e..2eb0cf665a 100644 --- a/evalml/__init__.py +++ b/evalml/__init__.py @@ -23,4 +23,4 @@ warnings.filterwarnings("ignore", category=FutureWarning) warnings.filterwarnings("ignore", category=DeprecationWarning) -__version__ = "0.35.0" +__version__ = "0.36.0" diff --git a/setup.py b/setup.py index 9afd7574bc..4adc150b4a 100644 --- a/setup.py +++ b/setup.py @@ -11,7 +11,7 @@ setup( name='evalml', - version='0.35.0', + version='0.36.0', author='Alteryx, Inc.', author_email='support@featurelabs.com', description='EvalML is an AutoML library that builds, optimizes, and evaluates machine learning pipelines using domain-specific objective functions.',