def test_blend_array_shape():
"""[Base] BlendIndex: test the array shape on generation."""
tr = np.array([0, 1])
te = np.array([2, 3])
for tri, tei in BlendIndex(0.45, 0.55, X=X).generate(as_array=True):
np.testing.assert_array_equal(tri, tr)
np.testing.assert_array_equal(tei, te)
def test_blend_index_is_fitted():
"""[Base] BlendIndex: check fit methods."""
attrs = ['n_samples', 'n_test_samples', 'n_train', 'n_test']
idx = BlendIndex(2, 3)
for attr in attrs: assert not hasattr(idx, attr)
idx.fit(X)
for attr in attrs: assert hasattr(idx, attr)
idx = BlendIndex(2, 3)
for attr in attrs: assert not hasattr(idx, attr)
for _ in idx.generate(X): pass
for attr in attrs: assert hasattr(idx, attr)
idx = BlendIndex(2, 3, X=X)
for attr in attrs: assert hasattr(idx, attr)
def test_blend_raises_empty_train():
"""[Base] BlendIndex: check raises error on empty train set."""
with np.testing.assert_raises(ValueError):
BlendIndex(10, 0, X=X)
def test_blend_raises_on_empty():
"""[Base] BlendIndex: check raises error on singular array."""
with np.testing.assert_raises(ValueError):
BlendIndex(2, 2, X=np.empty(1))
def test_blend_raises_on_oversampling():
"""[Base] BlendIndex: check raises error on float sums > 1.0."""
for tup in [(1.2, None), (0.6, 0.6)]:
with np.testing.assert_raises(ValueError):
BlendIndex(*tup, X=X)
def test_blend_tuple_shape():
"""[Base] BlendIndex: test the tuple shape on generation."""
tup = [(tri, tei) for tri, tei in BlendIndex(0.4, 0.5).generate(X)]
assert tup == [((0, 2), (2, 4))]