def test_fit_not_changing_data(self):
"""Test fit does not change X."""
df = d.create_df_1()
x = NominalToIntegerTransformer(columns=["a", "b"])
x.fit(df)
h.assert_equal_dispatch(
expected=d.create_df_1(),
actual=df,
msg="Check X not changing during fit",
)
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)
expected_call_args = {0: {"args": (["mappings"], ), "kwargs": {}}}
with h.assert_function_call(mocker, tubular.base.BaseTransformer,
"check_is_fitted", expected_call_args):
x.transform(df)
def test_non_mappable_rows_raises_error(self):
"""Test that rows that cannot be mapped result in an exception."""
df = d.create_df_1()
x = NominalToIntegerTransformer(columns=["a", "b"])
x.fit(df)
df["a"] = df["a"] + 1
with pytest.raises(
ValueError,
match=
"nulls would be introduced into column a from levels not present in mapping",
):
x.transform(df)
def test_learnt_values_not_modified(self):
"""Test that the mappings from fit are not changed in transform."""
df = d.create_df_1()
x = NominalToIntegerTransformer(columns=["a", "b"])
x.fit(df)
x2 = NominalToIntegerTransformer(columns=["a", "b"])
x2.fit_transform(df)
h.assert_equal_dispatch(
expected=x.mappings,
actual=x2.mappings,
msg="Impute values not changed in transform",
)
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_super_transform_called(self, mocker):
"""Test that BaseTransformer.transform called."""
df = d.create_df_1()
x = NominalToIntegerTransformer(columns="a")
x.fit(df)
expected_call_args = {0: {"args": (d.create_df_1(), ), "kwargs": {}}}
with h.assert_function_call(
mocker,
tubular.base.BaseTransformer,
"transform",
expected_call_args,
return_value=d.create_df_1(),
):
x.transform(df)
def test_fit_returns_self(self):
"""Test fit returns self?"""
df = d.create_df_1()
x = NominalToIntegerTransformer(columns=["a", "b"])
x_fitted = x.fit(df)
assert (
x_fitted is x
), "Returned value from NominalToIntegerTransformer.fit not as expected."
def test_learnt_values(self):
"""Test that the impute values learnt during fit are expected."""
df = d.create_df_1()
x = NominalToIntegerTransformer(columns=["a", "b"], start_encoding=1)
x.fit(df)
h.test_object_attributes(
obj=x,
expected_attributes={
"mappings": {
"a": {k: i
for i, k in enumerate(df["a"].unique(), 1)},
"b": {k: i
for i, k in enumerate(df["b"].unique(), 1)},
}
},
msg="mappings attribute",
)
def test_super_fit_called(self, mocker):
"""Test that fit calls BaseTransformer.fit."""
df = d.create_df_1()
x = NominalToIntegerTransformer(columns=["a", "b"])
spy = mocker.spy(tubular.base.BaseTransformer, "fit")
x.fit(df)
assert spy.call_count == 1, "unexpected number of calls to BaseTransformer.fit"
call_args = spy.call_args_list[0]
call_pos_args = call_args[0]
call_kwargs = call_args[1]
expected_kwargs = {}
assert (call_kwargs == expected_kwargs
), "unexpected kwargs in BaseTransformer.fit call"
expected_pos_args = (x, d.create_df_1(), None)
assert len(expected_pos_args) == len(
call_pos_args
), "unexpected # positional args in BaseTransformer.fit call"
assert (expected_pos_args[0] == call_pos_args[0]
), "unexpected 1st positional arg in BaseTransformer.fit call"
h.assert_equal_dispatch(
expected_pos_args[1:3],
call_pos_args[1:3],
"unexpected 2nd, 3rd positional arg in BaseTransformer.fit call",
)