def test_missing_groups_transform_noglobal(dataset_with_single_grouping,
scaling_range):
X, y, groups, X_with_groups, grouper = dataset_with_single_grouping
trf = MinMaxScaler(scaling_range)
transformer = GroupedTransformer(trf,
groups=grouper,
use_global_model=False)
transformer.fit(X_with_groups, y)
# Array with 2 rows, first column a new group. Remaining top are out of range so should be the range
X_test = np.concatenate([
np.array([[3], [3]]),
np.stack([X.min(axis=0) - 1, X.max(axis=0) + 1], axis=0)
],
axis=1)
with pytest.raises(ValueError):
transformer.transform(X_test)
def test_missing_groups_transform_global(dataset_with_single_grouping,
scaling_range):
X, y, groups, X_with_groups, grouper = dataset_with_single_grouping
trf = MinMaxScaler(scaling_range)
transformer = GroupedTransformer(trf, groups=grouper)
transformer.fit(X_with_groups, y)
# Array with 2 rows, first column a new group. Remaining top are out of range so should be the range
X_test = np.concatenate([
np.array([[3], [3]]),
np.stack([X.min(axis=0), X.max(axis=0)], axis=0)
],
axis=1)
transformed = transformer.transform(X_test)
# Top row should all be equal to the small value of the range, bottom the other
assert np.allclose(transformed[0, :], scaling_range[0])
assert np.allclose(transformed[1, :], scaling_range[1])