def test_all_groups_missing_raises(input_df, errors):
transform_df = mock.mock_raw_data(ids=[2, 3])
gp = GroupedPipeline(groupby=['id'], pipeline=Pipeline([col_selector]),
errors=errors)
gp.fit(input_df)
with pytest.raises(KeyError,
message='All keys missing in fitted pipelines'):
gp.transform(transform_df)
def test_one_group_missing_return_none(input_df):
transform_df = mock.mock_raw_data(ids=[0, 1, 2])
gp = GroupedPipeline(groupby=['id'], pipeline=Pipeline([val_selector]),
errors='return_empty')
gp.fit(input_df)
out = gp.transform(transform_df)
assert out.shape[1] == 1
transformed_part = transform_df[ini.Columns.target].values[:96]
np.testing.assert_array_equal(out[:96, 0], transformed_part)
assert np.isnan(out[96:]).all()
def test_one_groups_missing_return_df(input_df):
transform_df = mock.mock_raw_data(ids=[0, 1, 2])
dt_feat = PandasDateTimeFeaturizer(attributes='month')
gp = GroupedPipeline(groupby=['id'], pipeline=dt_feat, errors='return_df')
gp.fit(input_df)
out = gp.transform(transform_df)
set(out.columns) == {
'id', ini.Columns.datetime, ini.Columns.target, 'month'
}
assert (~out[out.id == 0].month.isnull()).all()
assert (~out[out.id == 1].month.isnull()).all()
assert (out[out.id == 2].month.isnull()).all()
orig_cols = ['id', ini.Columns.datetime, ini.Columns.target]
pd.testing.assert_frame_equal(out[orig_cols], transform_df)
def test_raises_when_missing_key(input_df):
transform_df = mock.mock_raw_data(ids=[0, 1, 2])
gp = GroupedPipeline(groupby=['id'], pipeline=Pipeline([col_selector]))
gp.fit(input_df)
with pytest.raises(KeyError, message="Missing key 2 in fitted pipelines"):
gp.transform(transform_df)