def test_multilabel_field_returns_correct_empty_sequence(self):
vocab = Vocabulary()
vocab.add_token_to_namespace("label1", namespace="test_empty_labels")
vocab.add_token_to_namespace("label2", namespace="test_empty_labels")
f = MultiLabelField([], label_namespace="test_empty_labels")
f.empty_field()
vocab = Vocabulary()
vocab.add_token_to_namespace("rel0", namespace="rel_labels")
vocab.add_token_to_namespace("rel1", namespace="rel_labels")
vocab.add_token_to_namespace("rel2", namespace="rel_labels")
f = MultiLabelField(["rel1", "rel0"], label_namespace="rel_labels")
f.index(vocab)
tensor = f.as_tensor(f.get_padding_lengths()).detach().cpu().numpy()
f.empty_field()
numpy.testing.assert_array_almost_equal(tensor, numpy.array([1, 1, 0]))
def test_multilabel_field_empty_field_works(self):
vocab = Vocabulary()
vocab.add_token_to_namespace("label1", namespace="test_empty_labels")
vocab.add_token_to_namespace("label2", namespace="test_empty_labels")
f = MultiLabelField([], label_namespace="test_empty_labels")
f.index(vocab)
tensor = f.as_tensor(f.get_padding_lengths()).detach().cpu().numpy()
numpy.testing.assert_array_almost_equal(tensor, numpy.array([0, 0]))
g = f.empty_field()
g.index(vocab)
tensor = g.as_tensor(g.get_padding_lengths()).detach().cpu().numpy()
numpy.testing.assert_array_almost_equal(tensor, numpy.array([0, 0]))
h = MultiLabelField(
[0, 0, 1], label_namespace="test_empty_labels", num_labels=3, skip_indexing=True
)
tensor = h.empty_field().as_tensor(None).detach().cpu().numpy()
numpy.testing.assert_array_almost_equal(tensor, numpy.array([0, 0, 0]))