Renames feature_importances and related methods to singular #883
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Codecov Report
@@ Coverage Diff @@
## master #883 +/- ##
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Coverage 99.75% 99.75%
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Files 195 195
Lines 8505 8505
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Hits 8484 8484
Misses 21 21
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|
dsherry
reviewed
Jun 25, 2020
| @@ -39,6 +41,7 @@ Changelog | |||
| **Breaking Changes** | |||
| * Pipelines' static ``component_graph`` field must contain either ``ComponentBase`` subclasses or ``str``, instead of ``ComponentBase`` subclass instances :pr:`850` | |||
| * Rename ``handle_component`` to ``handle_component_class``. Now standardizes to ``ComponentBase`` subclasses instead of ``ComponentBase`` subclass instances :pr:`850` | |||
| * Pipelines' and classifiers' `feature_importances` is renamed `feature_importance`, `graph_feature_importances` is renamed `graph_feature_importance` :pr:`883` | |||
dsherry
reviewed
Jun 25, 2020
| def feature_importances(self): | ||
| """Returns feature importances. Since baseline regressors do not use input features to calculate predictions, returns an array of zeroes. | ||
| def feature_importance(self): | ||
| """Returns importance associated with each feature. Since baseline regressors do not use input features to calculate predictions, returns an array of zeroes. |
dsherry
reviewed
Jun 25, 2020
| @@ -29,5 +29,5 @@ def __init__(self, n_estimators=100, max_depth=6, n_jobs=-1, random_state=0, **k | |||
| random_state=random_state) | |||
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| @property | |||
| def feature_importances(self): | |||
| def feature_importance(self): | |||
| return self._component_obj.feature_importances_ | |||
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I see xgboost many and sklearn models use "feature_importances_" and catboost uses "get_feature_importance". Funny to see the differences. I like our rename.
dsherry
approved these changes
Jun 25, 2020
Merged
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Closes #868