Revert "Add pipeline.should_skip_featurization flag (#3849)" (#3862)

* Revert "Add pipeline.should_skip_featurization flag (#3849)"

This reverts commit 61a0a0e.

* release notes

docs/source/release_notes.rst

@@ -11,7 +11,6 @@ Release Notes
         * Updated demo dataset links to point to new endpoint :pr:`3826`
         * Updated ``STLDecomposer`` to infer the time index frequency if it's not present :pr:`3829`
         * Updated ``_drop_time_index`` to move the time index from X to both ``X.index`` and ``y.index`` :pr:`3829`
-        * Added ``TimeSeriesPipeline.should_skip_featurization`` to fix bug where data would get featurized unnecessarily :pr:`3849`
         * Fixed bug where engineered features lost their origin attribute in partial dependence, causing it to fail :pr:`3830`
         * Fixed bug where partial dependence's fast mode handling for the DFS Transformer wouldn't work with multi output features :pr:`3830`
         * Allowed target to be present and ignored in partial dependence's DFS Transformer fast mode handling  :pr:`3830`
@@ -20,6 +19,7 @@ Release Notes
         * Removed Featuretools version upper bound and prevent Woodwork 0.20.0 from being installed :pr:`3813`
         * Updated min Featuretools version to 0.16.0, min nlp-primitives version to 2.9.0 and min Dask version to 2022.2.0 :pr:`3823`
         * Rename issue templates config.yaml to config.yml :pr:`3844`
+        * Reverted change adding a ``should_skip_featurization`` flag to time series pipelines :pr:`3862`
     * Documentation Changes
         * Added information about STL Decomposition to the time series docs :pr:`3835`
     * Testing Changes

evalml/pipelines/time_series_pipeline_base.py

@@ -65,13 +65,10 @@ def __init__(
             "ARIMA Regressor",
             "Prophet Regressor",
         ]
-        self.should_skip_featurization = (
+        self.should_drop_time_index = (
             not datetime_featurizer_included
             and not time_series_featurizer_included
             and self.estimator is not None
-        )
-        self.should_drop_time_index = (
-            self.should_skip_featurization
             and self.estimator.name not in time_series_native_estimators
         )
 
@@ -188,7 +185,7 @@ def transform_all_but_final(self, X, y=None, X_train=None, y_train=None):
         X, y = self._convert_to_woodwork(X, y)
 
         empty_training_data = X_train.empty or y_train.empty
-        if empty_training_data or self.should_skip_featurization:
+        if empty_training_data or self.should_drop_time_index:
             features_holdout = super().transform_all_but_final(X, y)
         else:
             padded_features, padded_target = self._add_training_data_to_X_Y(

evalml/tests/integration_tests/test_time_series_integration.py

@@ -1,5 +1,3 @@
-from unittest.mock import patch
-
 import pandas as pd
 import pytest
 from featuretools import EntitySet, Feature, calculate_feature_matrix, dfs
@@ -157,9 +155,6 @@ def test_can_run_automl_for_time_series_known_in_advance(
     automl.best_pipeline.predict(X_valid, X_train=X, y_train=y)
 
 
-@patch(
-    "evalml.pipelines.time_series_pipeline_base.TimeSeriesPipelineBase._add_training_data_to_X_Y",
-)
 @pytest.mark.parametrize(
     "problem_type",
     [
@@ -169,7 +164,6 @@ def test_can_run_automl_for_time_series_known_in_advance(
     ],
 )
 def test_can_run_automl_for_time_series_with_exclude_featurizers(
-    mock_add_X_y,
     problem_type,
 ):
 
@@ -218,7 +212,6 @@ def test_can_run_automl_for_time_series_with_exclude_featurizers(
         exclude_featurizers=["DatetimeFeaturizer", "TimeSeriesFeaturizer"],
     )
     automl.search()
-    assert mock_add_X_y.call_count == 0
 
     rankings = automl.rankings
     for score in rankings["validation_score"].values:
@@ -231,4 +224,3 @@ def test_can_run_automl_for_time_series_with_exclude_featurizers(
             assert not pipeline.should_drop_time_index
         else:
             assert pipeline.should_drop_time_index
-        assert pipeline.should_skip_featurization