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]))