def test_expected_output(self, df, expected):
"""Test that transform then inverse_transform gets back to the original df."""
x = NominalToIntegerTransformer(columns=["a", "b"])
# set the mapping dict directly rather than fitting x on df so test works with helpers
x.mappings = {
"a": {
1: 0,
2: 1,
3: 2,
4: 3,
5: 4,
6: 5
},
"b": {
"a": 0,
"b": 1,
"c": 2,
"d": 3,
"e": 4,
"f": 5
},
}
df_transformed = x.transform(df)
df_transformed_back = x.inverse_transform(df_transformed)
h.assert_frame_equal_msg(
actual=df_transformed_back,
expected=expected,
msg_tag="transform reverse does not get back to original",
)
def test_check_is_fitted_called(self, mocker):
"""Test that BaseTransformer check_is_fitted called."""
df = d.create_df_1()
x = NominalToIntegerTransformer(columns=["a", "b"])
x.fit(df)
df_transformed = x.transform(df)
expected_call_args = {0: {"args": (["mappings"], ), "kwargs": {}}}
with h.assert_function_call(mocker, tubular.base.BaseTransformer,
"check_is_fitted", expected_call_args):
x.inverse_transform(df_transformed)
def test_non_mappable_rows_raises_error(self):
"""Test that rows that cannot be mapped result in an exception."""
x = NominalToIntegerTransformer(columns=["a", "b"])
df = d.create_df_1()
x.fit(df)
df_transformed = x.transform(df)
df_transformed["b"] = df_transformed["b"] + 1
with pytest.raises(
ValueError,
match=
"nulls introduced from levels not present in mapping for column: b",
):
x.inverse_transform(df_transformed)
def test_learnt_values_not_modified(self):
"""Test that the mappings from fit are not changed in inverse_transform."""
df = d.create_df_1()
x = NominalToIntegerTransformer(columns=["a", "b"])
x.fit(df)
x2 = NominalToIntegerTransformer(columns=["a", "b"])
x2.fit(df)
df_transformed = x2.transform(df)
x2.inverse_transform(df_transformed)
h.assert_equal_dispatch(
expected=x.mappings,
actual=x2.mappings,
msg="Impute values not changed in inverse_transform",
)