def test_vocab_prune_vectors():
vocab = Vocab()
_ = vocab["cat"] # noqa: F841
_ = vocab["dog"] # noqa: F841
_ = vocab["kitten"] # noqa: F841
data = numpy.ndarray((5, 3), dtype="f")
data[0] = 1.0
data[1] = 2.0
data[2] = 1.1
vocab.set_vector("cat", data[0])
vocab.set_vector("dog", data[1])
vocab.set_vector("kitten", data[2])
remap = vocab.prune_vectors(2)
assert list(remap.keys()) == ["kitten"]
neighbour, similarity = list(remap.values())[0]
assert neighbour == "cat", remap
assert_allclose(similarity, cosine(data[0], data[2]), atol=1e-6)
def test_vocab_prune_vectors():
vocab = Vocab(vectors_name="test_vocab_prune_vectors")
_ = vocab["cat"] # noqa: F841
_ = vocab["dog"] # noqa: F841
_ = vocab["kitten"] # noqa: F841
data = numpy.ndarray((5, 3), dtype="f")
data[0] = [1.0, 1.2, 1.1]
data[1] = [0.3, 1.3, 1.0]
data[2] = [0.9, 1.22, 1.05]
vocab.set_vector("cat", data[0])
vocab.set_vector("dog", data[1])
vocab.set_vector("kitten", data[2])
remap = vocab.prune_vectors(2, batch_size=2)
assert list(remap.keys()) == ["kitten"]
neighbour, similarity = list(remap.values())[0]
assert neighbour == "cat", remap
assert_allclose(similarity, cosine(data[0], data[2]), atol=1e-4, rtol=1e-3)