Back out some incorrect changes, ignore lint rule E721

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

@@ -93,7 +93,7 @@ def fit(self, X, y=None):
             return self
 
         X = X[self._cols_to_impute]
-        if (X.dtypes is bool).all():
+        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. This is needed because sklearn doesn't allow
             # data with only bool dtype to be passed in.
@@ -119,7 +119,7 @@ def transform(self, X, y=None):
 
         # separate out just the columns we are imputing
         X_t = X[self._cols_to_impute]
-        if not self._cols_to_impute or (X_t.dtypes is bool).all():
+        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 will never have null values, return the original data without any fully null columns
             not_all_null_cols = [

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

@@ -95,7 +95,7 @@ def fit(self, X, y):
         y = y.to_frame()
 
         # Return early if all the columns are bool dtype, which will never have null values
-        if (y.dtypes is bool).all():
+        if (y.dtypes == bool).all():
             return y
 
         self._component_obj.fit(y)
@@ -119,7 +119,7 @@ def transform(self, X, y):
         y_df = y_ww.ww.to_frame()
 
         # Return early if all the columns are bool dtype, which will never have null values
-        if (y_df.dtypes is bool).all():
+        if (y_df.dtypes == bool).all():
             return X, y_ww
 
         transformed = self._component_obj.transform(y_df)

pyproject.toml

@@ -163,7 +163,7 @@ lint.select = [
     # isort
     "I001"
 ]
-lint.ignore = ["E501", "D107", "D401"]
+lint.ignore = ["E501", "D107", "D401", "E721"]
 src = ["evalml"]
 
 [tool.ruff.lint.per-file-ignores]