def test_is_multilabel(setup):
raws = [
[[1, 2]],
[0, 1, 0, 1],
[[1], [0, 2], []],
np.array([[1, 0], [0, 0]]),
np.array([[1], [0], [0]]),
np.array([[1, 0, 0]]),
np.array([[1., 0.], [0., 0.]]),
sps.csr_matrix([[1, 0], [0, 1]]),
]
for raw in raws:
assert is_multilabel(raw).to_numpy() == sklearn_is_multilabel(raw)
t = mt.tensor(raws[3], chunk_size=1)
assert is_multilabel(t).to_numpy() == sklearn_is_multilabel(raws[3])
def testIsMultilabel(self):
raws = [
[[1, 2]],
[0, 1, 0, 1],
[[1], [0, 2], []],
np.array([[1, 0], [0, 0]]),
np.array([[1], [0], [0]]),
np.array([[1, 0, 0]]),
np.array([[1., 0.], [0., 0.]]),
sps.csr_matrix([[1, 0], [0, 1]]),
]
for raw in raws:
self.assertEqual(is_multilabel(raw).execute(),
sklearn_is_multilabel(raw),
'raw: {}'.format(raw))
t = mt.tensor(raws[3], chunk_size=1)
self.assertEqual(is_multilabel(t).execute(),
sklearn_is_multilabel(raws[3]))