Conver all bool dfs to boolean nullable instead of refitting

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

@@ -90,13 +90,14 @@ def fit(self, X, y=None):
         # If there are no columns to impute, return early
         if not self._cols_to_impute:
             return self
-        elif (X.dtypes == bool).all():
-            self._fit_with_all_bool_dtypes = True
-            return self
-        else:
-            self._fit_with_all_bool_dtypes = False
 
-        self._component_obj.fit(X[self._cols_to_impute], y)
+        X = X[self._cols_to_impute]
+        if (X.dtypes == bool).all():
+            # Ensure that _component_obj still gets fit so that if any of the dtypes are different
+            # at transform, we've fit the component
+            X = X.astype("boolean")
+
+        self._component_obj.fit(X, y)
         return self
 
     def transform(self, X, y=None):
@@ -113,17 +114,14 @@ def transform(self, X, y=None):
         original_schema = X.ww.schema
         original_index = X.index
 
+        X_t = X[self._cols_to_impute]
         not_all_null_cols = [col for col in X.columns if col not in self._all_null_cols]
-        if not self._cols_to_impute:
-            # If there are no columns to impute, return the original data without any fully null columns
+        if not self._cols_to_impute or (X_t.dtypes == bool).all():
+            # If there are no columns to impute or all columns to impute are bool dtype, which sklearn errors on,
+            # return the original data without any fully null columns
             return X.ww[not_all_null_cols]
-        elif (X.dtypes == bool).all():
-            return X
-        # If the dtypes are not all bool but it was with with all bool dtypes we need to fit the _component_obj
-        elif self._fit_with_all_bool_dtypes:
-            self._component_obj.fit(X[self._cols_to_impute])
 
-        X_t = self._component_obj.transform(X[self._cols_to_impute])
+        X_t = self._component_obj.transform(X_t)
         X_t = pd.DataFrame(X_t, columns=self._cols_to_impute)
 
         # Get Woodwork types for the imputed data

evalml/tests/component_tests/test_simple_imputer.py

@@ -3,7 +3,6 @@
 import pandas as pd
 import pytest
 import woodwork as ww
-import woodwork.exceptions
 from pandas.testing import assert_frame_equal
 from woodwork.logical_types import (
     Boolean,
@@ -628,7 +627,47 @@ def test_simple_imputer_boolean_nullable_valid_train_empty_test():
     assert isinstance(X_t.ww.logical_types["a"], BooleanNullable)
 
 
+def test_simple_imputer_all_bools_at_fit_and_transform():
+    """Confirms that the simple imputer can handle data with only the bool dtype
+    which sklearn would error on."""
+    X = pd.DataFrame(
+        {
+            "bools1": pd.Series([True, False, True, True] * 20),
+            "bools2": pd.Series([True, False, True, False] * 20),
+        },
+    )
+    X.ww.init(
+        logical_types={
+            "bools1": "Boolean",
+            "bools2": "Boolean",
+        },
+    )
+
+    imp = SimpleImputer()
+    imp.fit(X)
+
+    X_imputed = imp.transform(X)
+    pd.testing.assert_frame_equal(X, X_imputed)
+
+
+def test_simple_imputer_all_bools_at_fit_and_transform_with_all_null_and_nl_cols(
+    imputer_test_data,
+):
+    """Confirm that the simple imputer, which doesn't pass all null or natural language columns
+    to sklearn works when the remaining columns are all teh bool dtype, which sklearn would error on."""
+    X = imputer_test_data.ww[["all nan", "bool col", "natural language col"]]
+    X_copy = X.ww.copy()
+
+    imp = SimpleImputer()
+    imp.fit(X)
+
+    X_imputed = imp.transform(X)
+    pd.testing.assert_frame_equal(X_copy.ww.drop("all nan"), X_imputed)
+
+
 def test_simple_imputer_all_bools_at_fit_with_nans_at_transform():
+    """Confirm that the simple imputer can handle data whose dtype is different at transform
+    when originally the data only had bool dtype columns."""
     # X_train will be only bool dtypes so the _component_obj won't be fit
     X_train = pd.DataFrame(
         {