mirror of https://github.com/explosion/spaCy.git
594 lines
20 KiB
Python
594 lines
20 KiB
Python
import numpy
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import pytest
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from numpy.testing import assert_allclose, assert_almost_equal, assert_equal
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from thinc.api import NumpyOps, get_current_ops
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from spacy.lang.en import English
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from spacy.strings import hash_string # type: ignore
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from spacy.tokenizer import Tokenizer
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from spacy.tokens import Doc
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from spacy.training.initialize import convert_vectors
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from spacy.vectors import Vectors
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from spacy.vocab import Vocab
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from ..util import add_vecs_to_vocab, get_cosine, make_tempdir
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OPS = get_current_ops()
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@pytest.fixture
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def strings():
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return ["apple", "orange"]
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@pytest.fixture
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def vectors():
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return [
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("apple", OPS.asarray([1, 2, 3])),
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("orange", OPS.asarray([-1, -2, -3])),
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("and", OPS.asarray([-1, -1, -1])),
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("juice", OPS.asarray([5, 5, 10])),
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("pie", OPS.asarray([7, 6.3, 8.9])),
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]
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@pytest.fixture
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def data():
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return numpy.asarray([[0.0, 1.0, 2.0], [3.0, -2.0, 4.0]], dtype="f")
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@pytest.fixture
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def most_similar_vectors_data():
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return numpy.asarray(
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[[0.0, 1.0, 2.0], [1.0, -2.0, 4.0], [1.0, 1.0, -1.0], [2.0, 3.0, 1.0]],
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dtype="f",
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)
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@pytest.fixture
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def most_similar_vectors_keys():
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return ["a", "b", "c", "d"]
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@pytest.fixture
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def resize_data():
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return numpy.asarray([[0.0, 1.0], [2.0, 3.0]], dtype="f")
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@pytest.fixture()
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def vocab(en_vocab, vectors):
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add_vecs_to_vocab(en_vocab, vectors)
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return en_vocab
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@pytest.fixture()
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def tokenizer_v(vocab):
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return Tokenizer(vocab, {}, None, None, None)
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@pytest.mark.issue(1518)
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def test_issue1518():
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"""Test vectors.resize() works."""
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vectors = Vectors(shape=(10, 10))
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vectors.add("hello", row=2)
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vectors.resize((5, 9))
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@pytest.mark.issue(1539)
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def test_issue1539():
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"""Ensure vectors.resize() doesn't try to modify dictionary during iteration."""
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v = Vectors(shape=(10, 10), keys=[5, 3, 98, 100])
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v.resize((100, 100))
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@pytest.mark.issue(1807)
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def test_issue1807():
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"""Test vocab.set_vector also adds the word to the vocab."""
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vocab = Vocab(vectors_name="test_issue1807")
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assert "hello" not in vocab
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vocab.set_vector("hello", numpy.ones((50,), dtype="f"))
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assert "hello" in vocab
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@pytest.mark.issue(2871)
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def test_issue2871():
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"""Test that vectors recover the correct key for spaCy reserved words."""
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words = ["dog", "cat", "SUFFIX"]
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vocab = Vocab(vectors_name="test_issue2871")
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vocab.vectors.resize(shape=(3, 10))
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vector_data = numpy.zeros((3, 10), dtype="f")
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for word in words:
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_ = vocab[word] # noqa: F841
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vocab.set_vector(word, vector_data[0])
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vocab.vectors.name = "dummy_vectors"
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assert vocab["dog"].rank == 0
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assert vocab["cat"].rank == 1
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assert vocab["SUFFIX"].rank == 2
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assert vocab.vectors.find(key="dog") == 0
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assert vocab.vectors.find(key="cat") == 1
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assert vocab.vectors.find(key="SUFFIX") == 2
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@pytest.mark.issue(3412)
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def test_issue3412():
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data = numpy.asarray([[0, 0, 0], [1, 2, 3], [9, 8, 7]], dtype="f")
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vectors = Vectors(data=data, keys=["A", "B", "C"])
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keys, best_rows, scores = vectors.most_similar(
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numpy.asarray([[9, 8, 7], [0, 0, 0]], dtype="f")
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)
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assert best_rows[0] == 2
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@pytest.mark.issue(4725)
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def test_issue4725_2():
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if isinstance(get_current_ops, NumpyOps):
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# ensures that this runs correctly and doesn't hang or crash because of the global vectors
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# if it does crash, it's usually because of calling 'spawn' for multiprocessing (e.g. on Windows),
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# or because of issues with pickling the NER (cf test_issue4725_1)
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vocab = Vocab(vectors_name="test_vocab_add_vector")
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data = numpy.ndarray((5, 3), dtype="f")
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data[0] = 1.0
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data[1] = 2.0
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vocab.set_vector("cat", data[0])
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vocab.set_vector("dog", data[1])
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nlp = English(vocab=vocab)
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nlp.add_pipe("ner")
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nlp.initialize()
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docs = ["Kurt is in London."] * 10
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for _ in nlp.pipe(docs, batch_size=2, n_process=2):
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pass
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def test_init_vectors_with_resize_shape(strings, resize_data):
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v = Vectors(shape=(len(strings), 3))
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v.resize(shape=resize_data.shape)
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assert v.shape == resize_data.shape
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assert v.shape != (len(strings), 3)
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def test_init_vectors_with_resize_data(data, resize_data):
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v = Vectors(data=data)
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v.resize(shape=resize_data.shape)
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assert v.shape == resize_data.shape
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assert v.shape != data.shape
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def test_get_vector_resize(strings, data):
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strings = [hash_string(s) for s in strings]
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# decrease vector dimension (truncate)
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v = Vectors(data=data)
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resized_dim = v.shape[1] - 1
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v.resize(shape=(v.shape[0], resized_dim))
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for i, string in enumerate(strings):
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v.add(string, row=i)
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assert list(v[strings[0]]) == list(data[0, :resized_dim])
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assert list(v[strings[1]]) == list(data[1, :resized_dim])
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# increase vector dimension (pad with zeros)
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v = Vectors(data=data)
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resized_dim = v.shape[1] + 1
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v.resize(shape=(v.shape[0], resized_dim))
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for i, string in enumerate(strings):
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v.add(string, row=i)
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assert list(v[strings[0]]) == list(data[0]) + [0]
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assert list(v[strings[1]]) == list(data[1]) + [0]
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def test_init_vectors_with_data(strings, data):
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v = Vectors(data=data)
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assert v.shape == data.shape
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def test_init_vectors_with_shape(strings):
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v = Vectors(shape=(len(strings), 3))
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assert v.shape == (len(strings), 3)
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assert v.is_full is False
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def test_get_vector(strings, data):
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v = Vectors(data=data)
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strings = [hash_string(s) for s in strings]
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for i, string in enumerate(strings):
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v.add(string, row=i)
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assert list(v[strings[0]]) == list(data[0])
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assert list(v[strings[0]]) != list(data[1])
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assert list(v[strings[1]]) != list(data[0])
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def test_set_vector(strings, data):
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orig = data.copy()
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v = Vectors(data=data)
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strings = [hash_string(s) for s in strings]
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for i, string in enumerate(strings):
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v.add(string, row=i)
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assert list(v[strings[0]]) == list(orig[0])
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assert list(v[strings[0]]) != list(orig[1])
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v[strings[0]] = data[1]
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assert list(v[strings[0]]) == list(orig[1])
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assert list(v[strings[0]]) != list(orig[0])
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def test_vectors_most_similar(most_similar_vectors_data, most_similar_vectors_keys):
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v = Vectors(data=most_similar_vectors_data, keys=most_similar_vectors_keys)
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_, best_rows, _ = v.most_similar(v.data, batch_size=2, n=2, sort=True)
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assert all(row[0] == i for i, row in enumerate(best_rows))
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with pytest.raises(ValueError):
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v.most_similar(v.data, batch_size=2, n=10, sort=True)
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def test_vectors_most_similar_identical():
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"""Test that most similar identical vectors are assigned a score of 1.0."""
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data = numpy.asarray([[4, 2, 2, 2], [4, 2, 2, 2], [1, 1, 1, 1]], dtype="f")
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v = Vectors(data=data, keys=["A", "B", "C"])
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keys, _, scores = v.most_similar(numpy.asarray([[4, 2, 2, 2]], dtype="f"))
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assert scores[0][0] == 1.0 # not 1.0000002
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data = numpy.asarray([[1, 2, 3], [1, 2, 3], [1, 1, 1]], dtype="f")
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v = Vectors(data=data, keys=["A", "B", "C"])
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keys, _, scores = v.most_similar(numpy.asarray([[1, 2, 3]], dtype="f"))
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assert scores[0][0] == 1.0 # not 0.9999999
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@pytest.mark.parametrize("text", ["apple and orange"])
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def test_vectors_token_vector(tokenizer_v, vectors, text):
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doc = tokenizer_v(text)
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assert vectors[0][0] == doc[0].text
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assert all([a == b for a, b in zip(vectors[0][1], doc[0].vector)])
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assert vectors[1][0] == doc[2].text
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assert all([a == b for a, b in zip(vectors[1][1], doc[2].vector)])
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@pytest.mark.parametrize("text", ["apple", "orange"])
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def test_vectors_lexeme_vector(vocab, text):
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lex = vocab[text]
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assert list(lex.vector)
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assert lex.vector_norm
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@pytest.mark.parametrize("text", [["apple", "and", "orange"]])
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def test_vectors_doc_vector(vocab, text):
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doc = Doc(vocab, words=text)
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assert list(doc.vector)
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assert doc.vector_norm
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@pytest.mark.parametrize("text", [["apple", "and", "orange"]])
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def test_vectors_span_vector(vocab, text):
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span = Doc(vocab, words=text)[0:2]
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assert list(span.vector)
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assert span.vector_norm
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@pytest.mark.parametrize("text", ["apple orange"])
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def test_vectors_token_token_similarity(tokenizer_v, text):
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doc = tokenizer_v(text)
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assert doc[0].similarity(doc[1]) == doc[1].similarity(doc[0])
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assert -1.0 < doc[0].similarity(doc[1]) < 1.0
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@pytest.mark.parametrize("text1,text2", [("apple", "orange")])
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def test_vectors_token_lexeme_similarity(tokenizer_v, vocab, text1, text2):
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token = tokenizer_v(text1)
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lex = vocab[text2]
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assert token.similarity(lex) == lex.similarity(token)
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assert -1.0 < token.similarity(lex) < 1.0
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@pytest.mark.parametrize("text", [["apple", "orange", "juice"]])
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def test_vectors_token_span_similarity(vocab, text):
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doc = Doc(vocab, words=text)
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assert doc[0].similarity(doc[1:3]) == doc[1:3].similarity(doc[0])
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assert -1.0 < doc[0].similarity(doc[1:3]) < 1.0
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@pytest.mark.parametrize("text", [["apple", "orange", "juice"]])
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def test_vectors_token_doc_similarity(vocab, text):
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doc = Doc(vocab, words=text)
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assert doc[0].similarity(doc) == doc.similarity(doc[0])
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assert -1.0 < doc[0].similarity(doc) < 1.0
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@pytest.mark.parametrize("text", [["apple", "orange", "juice"]])
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def test_vectors_lexeme_span_similarity(vocab, text):
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doc = Doc(vocab, words=text)
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lex = vocab[text[0]]
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assert lex.similarity(doc[1:3]) == doc[1:3].similarity(lex)
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assert -1.0 < doc.similarity(doc[1:3]) < 1.0
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@pytest.mark.parametrize("text1,text2", [("apple", "orange")])
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def test_vectors_lexeme_lexeme_similarity(vocab, text1, text2):
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lex1 = vocab[text1]
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lex2 = vocab[text2]
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assert lex1.similarity(lex2) == lex2.similarity(lex1)
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assert -1.0 < lex1.similarity(lex2) < 1.0
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@pytest.mark.parametrize("text", [["apple", "orange", "juice"]])
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def test_vectors_lexeme_doc_similarity(vocab, text):
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doc = Doc(vocab, words=text)
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lex = vocab[text[0]]
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assert lex.similarity(doc) == doc.similarity(lex)
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assert -1.0 < lex.similarity(doc) < 1.0
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@pytest.mark.parametrize("text", [["apple", "orange", "juice"]])
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def test_vectors_span_span_similarity(vocab, text):
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doc = Doc(vocab, words=text)
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with pytest.warns(UserWarning):
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assert doc[0:2].similarity(doc[1:3]) == doc[1:3].similarity(doc[0:2])
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assert -1.0 < doc[0:2].similarity(doc[1:3]) < 1.0
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@pytest.mark.parametrize("text", [["apple", "orange", "juice"]])
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def test_vectors_span_doc_similarity(vocab, text):
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doc = Doc(vocab, words=text)
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with pytest.warns(UserWarning):
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assert doc[0:2].similarity(doc) == doc.similarity(doc[0:2])
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assert -1.0 < doc[0:2].similarity(doc) < 1.0
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@pytest.mark.parametrize(
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"text1,text2", [(["apple", "and", "apple", "pie"], ["orange", "juice"])]
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)
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def test_vectors_doc_doc_similarity(vocab, text1, text2):
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doc1 = Doc(vocab, words=text1)
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doc2 = Doc(vocab, words=text2)
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assert doc1.similarity(doc2) == doc2.similarity(doc1)
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assert -1.0 < doc1.similarity(doc2) < 1.0
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def test_vocab_add_vector():
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vocab = Vocab(vectors_name="test_vocab_add_vector")
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data = OPS.xp.ndarray((5, 3), dtype="f")
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data[0] = 1.0
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data[1] = 2.0
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vocab.set_vector("cat", data[0])
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vocab.set_vector("dog", data[1])
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cat = vocab["cat"]
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assert list(cat.vector) == [1.0, 1.0, 1.0]
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dog = vocab["dog"]
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assert list(dog.vector) == [2.0, 2.0, 2.0]
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with pytest.raises(ValueError):
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vocab.vectors.add(vocab["hamster"].orth, row=1000000)
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def test_vocab_prune_vectors():
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vocab = Vocab(vectors_name="test_vocab_prune_vectors")
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_ = vocab["cat"] # noqa: F841
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_ = vocab["dog"] # noqa: F841
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_ = vocab["kitten"] # noqa: F841
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data = OPS.xp.ndarray((5, 3), dtype="f")
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data[0] = OPS.asarray([1.0, 1.2, 1.1])
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data[1] = OPS.asarray([0.3, 1.3, 1.0])
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data[2] = OPS.asarray([0.9, 1.22, 1.05])
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vocab.set_vector("cat", data[0])
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vocab.set_vector("dog", data[1])
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vocab.set_vector("kitten", data[2])
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remap = vocab.prune_vectors(2, batch_size=2)
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assert list(remap.keys()) == ["kitten"]
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neighbour, similarity = list(remap.values())[0]
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assert neighbour == "cat", remap
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cosine = get_cosine(data[0], data[2])
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assert_allclose(float(similarity), cosine, atol=1e-4, rtol=1e-3)
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def test_vectors_serialize():
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data = OPS.asarray([[4, 2, 2, 2], [4, 2, 2, 2], [1, 1, 1, 1]], dtype="f")
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v = Vectors(data=data, keys=["A", "B", "C"])
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b = v.to_bytes()
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v_r = Vectors()
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v_r.from_bytes(b)
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assert_equal(OPS.to_numpy(v.data), OPS.to_numpy(v_r.data))
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assert v.key2row == v_r.key2row
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v.resize((5, 4))
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v_r.resize((5, 4))
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row = v.add("D", vector=OPS.asarray([1, 2, 3, 4], dtype="f"))
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row_r = v_r.add("D", vector=OPS.asarray([1, 2, 3, 4], dtype="f"))
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assert row == row_r
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assert_equal(OPS.to_numpy(v.data), OPS.to_numpy(v_r.data))
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assert v.is_full == v_r.is_full
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with make_tempdir() as d:
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v.to_disk(d)
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v_r.from_disk(d)
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assert_equal(OPS.to_numpy(v.data), OPS.to_numpy(v_r.data))
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assert v.key2row == v_r.key2row
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v.resize((5, 4))
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v_r.resize((5, 4))
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row = v.add("D", vector=OPS.asarray([10, 20, 30, 40], dtype="f"))
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row_r = v_r.add("D", vector=OPS.asarray([10, 20, 30, 40], dtype="f"))
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assert row == row_r
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assert_equal(OPS.to_numpy(v.data), OPS.to_numpy(v_r.data))
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def test_vector_is_oov():
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vocab = Vocab(vectors_name="test_vocab_is_oov")
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data = OPS.xp.ndarray((5, 3), dtype="f")
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data[0] = 1.0
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data[1] = 2.0
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vocab.set_vector("cat", data[0])
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vocab.set_vector("dog", data[1])
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assert vocab["cat"].is_oov is False
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assert vocab["dog"].is_oov is False
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assert vocab["hamster"].is_oov is True
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def test_init_vectors_unset():
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v = Vectors(shape=(10, 10))
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assert v.is_full is False
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assert v.shape == (10, 10)
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with pytest.raises(ValueError):
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v = Vectors(shape=(10, 10), mode="floret")
|
|
|
|
v = Vectors(data=OPS.xp.zeros((10, 10)), mode="floret", hash_count=1)
|
|
assert v.is_full is True
|
|
|
|
|
|
def test_vectors_clear():
|
|
data = OPS.asarray([[4, 2, 2, 2], [4, 2, 2, 2], [1, 1, 1, 1]], dtype="f")
|
|
v = Vectors(data=data, keys=["A", "B", "C"])
|
|
assert v.is_full is True
|
|
assert hash_string("A") in v
|
|
v.clear()
|
|
# no keys
|
|
assert v.key2row == {}
|
|
assert list(v) == []
|
|
assert v.is_full is False
|
|
assert "A" not in v
|
|
with pytest.raises(KeyError):
|
|
v["A"]
|
|
|
|
|
|
def test_vectors_get_batch():
|
|
data = OPS.asarray([[4, 2, 2, 2], [4, 2, 2, 2], [1, 1, 1, 1]], dtype="f")
|
|
v = Vectors(data=data, keys=["A", "B", "C"])
|
|
# check with mixed int/str keys
|
|
words = ["C", "B", "A", v.strings["B"]]
|
|
rows = v.find(keys=words)
|
|
vecs = OPS.as_contig(v.data[rows])
|
|
assert_equal(OPS.to_numpy(vecs), OPS.to_numpy(v.get_batch(words)))
|
|
|
|
|
|
@pytest.fixture()
|
|
def floret_vectors_hashvec_str():
|
|
"""The full hashvec table from floret with the settings:
|
|
bucket 10, dim 10, minn 2, maxn 3, hash count 2, hash seed 2166136261,
|
|
bow <, eow >"""
|
|
return """10 10 2 3 2 2166136261 < >
|
|
0 -2.2611 3.9302 2.6676 -11.233 0.093715 -10.52 -9.6463 -0.11853 2.101 -0.10145
|
|
1 -3.12 -1.7981 10.7 -6.171 4.4527 10.967 9.073 6.2056 -6.1199 -2.0402
|
|
2 9.5689 5.6721 -8.4832 -1.2249 2.1871 -3.0264 -2.391 -5.3308 -3.2847 -4.0382
|
|
3 3.6268 4.2759 -1.7007 1.5002 5.5266 1.8716 -12.063 0.26314 2.7645 2.4929
|
|
4 -11.683 -7.7068 2.1102 2.214 7.2202 0.69799 3.2173 -5.382 -2.0838 5.0314
|
|
5 -4.3024 8.0241 2.0714 -1.0174 -0.28369 1.7622 7.8797 -1.7795 6.7541 5.6703
|
|
6 8.3574 -5.225 8.6529 8.5605 -8.9465 3.767 -5.4636 -1.4635 -0.98947 -0.58025
|
|
7 -10.01 3.3894 -4.4487 1.1669 -11.904 6.5158 4.3681 0.79913 -6.9131 -8.687
|
|
8 -5.4576 7.1019 -8.8259 1.7189 4.955 -8.9157 -3.8905 -0.60086 -2.1233 5.892
|
|
9 8.0678 -4.4142 3.6236 4.5889 -2.7611 2.4455 0.67096 -4.2822 2.0875 4.6274
|
|
"""
|
|
|
|
|
|
@pytest.fixture()
|
|
def floret_vectors_vec_str():
|
|
"""The top 10 rows from floret with the settings above, to verify
|
|
that the spacy floret vectors are equivalent to the fasttext static
|
|
vectors."""
|
|
return """10 10
|
|
, -5.7814 2.6918 0.57029 -3.6985 -2.7079 1.4406 1.0084 1.7463 -3.8625 -3.0565
|
|
. 3.8016 -1.759 0.59118 3.3044 -0.72975 0.45221 -2.1412 -3.8933 -2.1238 -0.47409
|
|
der 0.08224 2.6601 -1.173 1.1549 -0.42821 -0.097268 -2.5589 -1.609 -0.16968 0.84687
|
|
die -2.8781 0.082576 1.9286 -0.33279 0.79488 3.36 3.5609 -0.64328 -2.4152 0.17266
|
|
und 2.1558 1.8606 -1.382 0.45424 -0.65889 1.2706 0.5929 -2.0592 -2.6949 -1.6015
|
|
" -1.1242 1.4588 -1.6263 1.0382 -2.7609 -0.99794 -0.83478 -1.5711 -1.2137 1.0239
|
|
in -0.87635 2.0958 4.0018 -2.2473 -1.2429 2.3474 1.8846 0.46521 -0.506 -0.26653
|
|
von -0.10589 1.196 1.1143 -0.40907 -1.0848 -0.054756 -2.5016 -1.0381 -0.41598 0.36982
|
|
( 0.59263 2.1856 0.67346 1.0769 1.0701 1.2151 1.718 -3.0441 2.7291 3.719
|
|
) 0.13812 3.3267 1.657 0.34729 -3.5459 0.72372 0.63034 -1.6145 1.2733 0.37798
|
|
"""
|
|
|
|
|
|
def test_floret_vectors(floret_vectors_vec_str, floret_vectors_hashvec_str):
|
|
nlp = English()
|
|
nlp_plain = English()
|
|
# load both vec and hashvec tables
|
|
with make_tempdir() as tmpdir:
|
|
p = tmpdir / "test.hashvec"
|
|
with open(p, "w") as fileh:
|
|
fileh.write(floret_vectors_hashvec_str)
|
|
convert_vectors(nlp, p, truncate=0, prune=-1, mode="floret")
|
|
p = tmpdir / "test.vec"
|
|
with open(p, "w") as fileh:
|
|
fileh.write(floret_vectors_vec_str)
|
|
convert_vectors(nlp_plain, p, truncate=0, prune=-1)
|
|
|
|
word = "der"
|
|
# ngrams: full padded word + padded 2-grams + padded 3-grams
|
|
ngrams = nlp.vocab.vectors._get_ngrams(word)
|
|
assert ngrams == ["<der>", "<d", "de", "er", "r>", "<de", "der", "er>"]
|
|
# rows: 2 rows per ngram
|
|
rows = OPS.xp.asarray(
|
|
[
|
|
h % nlp.vocab.vectors.shape[0]
|
|
for ngram in ngrams
|
|
for h in nlp.vocab.vectors._get_ngram_hashes(ngram)
|
|
],
|
|
dtype="uint32",
|
|
)
|
|
assert_equal(
|
|
OPS.to_numpy(rows),
|
|
numpy.asarray([5, 6, 7, 5, 8, 2, 8, 9, 3, 3, 4, 6, 7, 3, 0, 2]),
|
|
)
|
|
assert len(rows) == len(ngrams) * nlp.vocab.vectors.hash_count
|
|
# all vectors are equivalent for plain static table vs. hash ngrams
|
|
for word in nlp_plain.vocab.vectors:
|
|
word = nlp_plain.vocab.strings.as_string(word)
|
|
assert_almost_equal(
|
|
nlp.vocab[word].vector, nlp_plain.vocab[word].vector, decimal=3
|
|
)
|
|
|
|
# every word has a vector
|
|
assert nlp.vocab[word * 5].has_vector
|
|
|
|
# check that single and batched vector lookups are identical
|
|
words = [s for s in nlp_plain.vocab.vectors]
|
|
single_vecs = OPS.to_numpy(OPS.asarray([nlp.vocab[word].vector for word in words]))
|
|
batch_vecs = OPS.to_numpy(nlp.vocab.vectors.get_batch(words))
|
|
assert_equal(single_vecs, batch_vecs)
|
|
|
|
# an empty key returns 0s
|
|
assert_equal(
|
|
OPS.to_numpy(nlp.vocab[""].vector),
|
|
numpy.zeros((nlp.vocab.vectors.shape[0],)),
|
|
)
|
|
# an empty batch returns 0s
|
|
assert_equal(
|
|
OPS.to_numpy(nlp.vocab.vectors.get_batch([""])),
|
|
numpy.zeros((1, nlp.vocab.vectors.shape[0])),
|
|
)
|
|
# an empty key within a batch returns 0s
|
|
assert_equal(
|
|
OPS.to_numpy(nlp.vocab.vectors.get_batch(["a", "", "b"])[1]),
|
|
numpy.zeros((nlp.vocab.vectors.shape[0],)),
|
|
)
|
|
|
|
# the loaded ngram vector table cannot be modified
|
|
# except for clear: warning, then return without modifications
|
|
vector = list(range(nlp.vocab.vectors.shape[1]))
|
|
orig_bytes = nlp.vocab.vectors.to_bytes(exclude=["strings"])
|
|
with pytest.warns(UserWarning):
|
|
nlp.vocab.set_vector("the", vector)
|
|
assert orig_bytes == nlp.vocab.vectors.to_bytes(exclude=["strings"])
|
|
with pytest.warns(UserWarning):
|
|
nlp.vocab[word].vector = vector
|
|
assert orig_bytes == nlp.vocab.vectors.to_bytes(exclude=["strings"])
|
|
with pytest.warns(UserWarning):
|
|
nlp.vocab.vectors.add("the", row=6)
|
|
assert orig_bytes == nlp.vocab.vectors.to_bytes(exclude=["strings"])
|
|
with pytest.warns(UserWarning):
|
|
nlp.vocab.vectors.resize(shape=(100, 10))
|
|
assert orig_bytes == nlp.vocab.vectors.to_bytes(exclude=["strings"])
|
|
with pytest.raises(ValueError):
|
|
nlp.vocab.vectors.clear()
|
|
|
|
# data and settings are serialized correctly
|
|
with make_tempdir() as d:
|
|
nlp.vocab.to_disk(d)
|
|
vocab_r = Vocab()
|
|
vocab_r.from_disk(d)
|
|
assert nlp.vocab.vectors.to_bytes() == vocab_r.vectors.to_bytes()
|
|
assert_equal(
|
|
OPS.to_numpy(nlp.vocab.vectors.data), OPS.to_numpy(vocab_r.vectors.data)
|
|
)
|
|
assert_equal(nlp.vocab.vectors._get_cfg(), vocab_r.vectors._get_cfg())
|
|
assert_almost_equal(
|
|
OPS.to_numpy(nlp.vocab[word].vector),
|
|
OPS.to_numpy(vocab_r[word].vector),
|
|
decimal=6,
|
|
)
|