Release v0.67.0 (#3968)

* Release v0.67.0

* Lint fix.

* Lint fix.

Author: chukarsten

docs/source/release_notes.rst

@@ -3,19 +3,24 @@ Release Notes
 **Future Releases**
     * Enhancements
     * Fixes
-        * Re-added ``TimeSeriesPipeline.should_skip_featurization`` to fix bug where data would get featurized unnecessarily :pr:`3964`
-        * Allow float categories to be passed into CatBoost estimators :pr:`3966`
     * Changes
-        * Update pyproject.toml to correctly specify the data filepaths :pr:`3967`
     * Documentation Changes
-        * Added demo for prediction intervals :pr:`3954`
     * Testing Changes
 
 .. warning::
 
     **Breaking Changes**
 
 
+**v0.67.0 Jan. 31, 2023**
+    * Fixes
+        * Re-added ``TimeSeriesPipeline.should_skip_featurization`` to fix bug where data would get featurized unnecessarily :pr:`3964`
+        * Allow float categories to be passed into CatBoost estimators :pr:`3966`
+    * Changes
+        * Update pyproject.toml to correctly specify the data filepaths :pr:`3967`
+    * Documentation Changes
+        * Added demo for prediction intervals :pr:`3954`
+
 **v0.66.1 Jan. 26, 2023**
     * Fixes
         * Updated ``LabelEncoder`` to store the original typing information :pr:`3960`

docs/source/user_guide/data_checks.ipynb

@@ -805,7 +805,6 @@
     "\n",
     "\n",
     "class MyCustomDataChecks(DataChecks):\n",
-    "\n",
     "    data_checks = [\n",
     "        NullDataCheck,\n",
     "        InvalidTargetDataCheck,\n",

docs/source/user_guide/model_understanding.ipynb

@@ -783,7 +783,6 @@
     "\n",
     "\n",
     "def hinge_loss(y_true, y_pred_proba):\n",
-    "\n",
     "    probabilities = np.clip(y_pred_proba.iloc[:, 1], 0.001, 0.999)\n",
     "    y_true[y_true == 0] = -1\n",
     "\n",

evalml/__init__.py

@@ -23,4 +23,4 @@
 warnings.filterwarnings("ignore", category=FutureWarning)
 warnings.filterwarnings("ignore", category=DeprecationWarning)
 
-__version__ = "0.66.1"
+__version__ = "0.67.0"

evalml/data_checks/null_data_check.py

@@ -280,7 +280,6 @@ def validate(self, X, y=None):
             )
 
         if moderately_null_cols:
-
             impute_strategies_dict = {}
             for col in moderately_null_cols:
                 col_in_df = X.ww[col]

evalml/model_understanding/feature_explanations.py

@@ -43,7 +43,6 @@ def readable_explanation(
         )
 
     if importance_method == "permutation":
-
         if objective == "auto":
             objective = evalml.automl.get_default_primary_search_objective(
                 pipeline.problem_type,

evalml/pipelines/component_graph.py

@@ -585,7 +585,6 @@ def _get_feature_provenance(self, input_feature_names):
         for component_instance in transformers:
             component_provenance = component_instance._get_feature_provenance()
             for component_input, component_output in component_provenance.items():
-
                 # Case 1: The transformer created features from one of the original features
                 if component_input in provenance:
                     provenance[component_input] = provenance[component_input].union(

evalml/pipelines/components/transformers/encoders/onehot_encoder.py

@@ -277,7 +277,6 @@ def _get_feature_names(self):
             unique_encoded_columns = []
             encoded_features_to_drop = []
             for cat_index, category in enumerate(column_categories):
-
                 # Drop categories specified by the user
                 if (
                     self._encoder.drop_idx_ is not None

evalml/pipelines/components/transformers/feature_selection/rf_regressor_feature_selector.py

@@ -47,7 +47,6 @@ def __init__(
         random_seed=0,
         **kwargs,
     ):
-
         parameters = {
             "number_features": number_features,
             "n_estimators": n_estimators,

evalml/pipelines/components/transformers/preprocessing/time_series_featurizer.py

@@ -241,7 +241,6 @@ def _compute_delays(self, X_ww, y):
                 X_ww[categorical_columns],
             )
             for col_name in cols_to_delay:
-
                 col = X_ww[col_name]
                 if col_name in categorical_columns:
                     col = X_categorical[col_name]