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Fix CategoricalImputer when fill_value is already a category #1114

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Fix CategoricalImputer when fill_value is already a category #1114

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8 changes: 6 additions & 2 deletions feature_engine/imputation/categorical.py
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Original file line number Diff line number Diff line change
Expand Up @@ -243,9 +243,13 @@ def transform(self, X: pd.DataFrame) -> pd.DataFrame:
X = self._transform(X)

# if variable is of type category, we need to add the new
# category, before filling in the nan
# category, before filling in the nan, unless it is already
# one of the categories
for variable in self.variables_:
if X[variable].dtype.name == "category":
if (
X[variable].dtype.name == "category"
and self.imputer_dict_[variable] not in X[variable].cat.categories
):
X[variable] = X[variable].cat.add_categories(
self.imputer_dict_[variable]
)
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27 changes: 27 additions & 0 deletions tests/test_imputation/test_categorical_imputer.py
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Original file line number Diff line number Diff line change
Expand Up @@ -294,6 +294,33 @@ def test_variables_cast_as_category_frequent(df_na):
pd.testing.assert_frame_equal(X_transformed, X_reference)


@pytest.mark.parametrize("ordered", [False, True])
def test_variables_cast_as_category_with_fill_value_already_a_category(ordered):
categories = ["unknown", "west", "east"]
X = pd.DataFrame(
{
"region": pd.Categorical(
["east", None, "west"], categories=categories, ordered=ordered
)
}
)

imputer = CategoricalImputer(fill_value="unknown", variables=["region"])
X_transformed = imputer.fit_transform(X)

X_reference = pd.DataFrame(
{
"region": pd.Categorical(
["east", "unknown", "west"], categories=categories, ordered=ordered
)
}
)
pd.testing.assert_frame_equal(X_transformed, X_reference)

# transforming the imputed data again leaves it unchanged
pd.testing.assert_frame_equal(imputer.transform(X_transformed), X_reference)


@pytest.mark.parametrize(
"ignore_format",
[22.3, 1, "HOLA", {"key1": "value1", "key2": "value2", "key3": "value3"}],
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