Remove SimpleImputer DeprecationWarning (#1018)

* init remove simpleimputer

* revert and remove warning

* fix

* release and lint

* delete for codecov

* revert and add tests

docs/source/release_notes.rst

@@ -5,6 +5,7 @@ Release Notes
     * Enhancements
     * Fixes
     * Changes
+        * Removed DeprecationWarning for SimpleImputer :pr:`1018`
     * Documentation Changes
     * Testing Changes
 

evalml/pipelines/components/transformers/imputers/simple_imputer.py

@@ -1,5 +1,3 @@
-import warnings
-
 import pandas as pd
 from sklearn.impute import SimpleImputer as SkImputer
 
@@ -20,8 +18,6 @@ def __init__(self, impute_strategy="most_frequent", fill_value=None, random_stat
             fill_value (string): When impute_strategy == "constant", fill_value is used to replace missing data.
                Defaults to 0 when imputing numerical data and "missing_value" for strings or object data types.
         """
-        warnings.warn("SimpleImputer is deprecated in v0.12.0 and will be removed in 0.13.0 in favor of Imputer", DeprecationWarning)
-
         parameters = {"impute_strategy": impute_strategy,
                       "fill_value": fill_value}
         parameters.update(kwargs)

evalml/tests/component_tests/test_feature_selectors.py

@@ -1,5 +1,9 @@
+import pandas as pd
+import pytest
+
 from evalml.pipelines.components import (
     ComponentBase,
+    FeatureSelector,
     RFClassifierSelectFromModel,
     RFRegressorSelectFromModel
 )
@@ -38,3 +42,18 @@ def test_component_fit(X_y_binary, X_y_multi, X_y_regression):
     assert isinstance(rf_classifier.fit(X_binary, y_binary), ComponentBase)
     assert isinstance(rf_classifier.fit(X_multi, y_multi), ComponentBase)
     assert isinstance(rf_regressor.fit(X_reg, y_reg), ComponentBase)
+
+
+def test_feature_selector_missing_component_obj():
+    class MockFeatureSelector(FeatureSelector):
+        name = "Mock Feature Selector"
+
+        def fit(self, X, y):
+            pass
+
+    mock_feature_selector = MockFeatureSelector()
+    mock_feature_selector.fit(pd.DataFrame(), pd.Series())
+    with pytest.raises(RuntimeError, match="Transformer requires a transform method or a component_obj that implements transform"):
+        mock_feature_selector.transform(pd.DataFrame())
+    with pytest.raises(RuntimeError, match="Transformer requires a fit_transform method or a component_obj that implements fit_transform"):
+        mock_feature_selector.fit_transform(pd.DataFrame())

evalml/tests/component_tests/test_simple_imputer.py

@@ -1,5 +1,3 @@
-import warnings
-
 import numpy as np
 import pandas as pd
 import pytest
@@ -151,12 +149,3 @@ def test_numpy_input():
     np.testing.assert_almost_equal(X, np.array([[np.nan, 0, 1, np.nan],
                                                 [np.nan, 2, 3, 2],
                                                 [np.nan, 2, 3, 0]]))
-
-
-def test_simple_imputer_deprecation_warning():
-    with warnings.catch_warnings(record=True) as w:
-        warnings.simplefilter("always")
-        SimpleImputer(impute_strategy='mean')
-        assert len(w) == 1
-        assert issubclass(w[-1].category, DeprecationWarning)
-        assert "deprecated" in str(w[-1].message)