mirror of
https://github.com/explosion/spaCy.git
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d94e961f14
Fix `Token.is_oov` and `Lexeme.is_oov` so they return `True` when the lexeme does **not** have a vector.
382 lines
12 KiB
Python
382 lines
12 KiB
Python
# coding: utf-8
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from __future__ import unicode_literals
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import pytest
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import numpy
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from numpy.testing import assert_allclose, assert_equal
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from spacy._ml import cosine
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from spacy.vocab import Vocab
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from spacy.vectors import Vectors
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from spacy.tokenizer import Tokenizer
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from spacy.strings import hash_string
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from spacy.tokens import Doc
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from spacy.compat import is_python2
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from ..util import add_vecs_to_vocab, make_tempdir
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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", [1, 2, 3]),
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("orange", [-1, -2, -3]),
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("and", [-1, -1, -1]),
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("juice", [5, 5, 10]),
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("pie", [7, 6.3, 8.9]),
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]
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@pytest.fixture
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def ngrams_vectors():
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return [
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("apple", [1, 2, 3]),
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("app", [-0.1, -0.2, -0.3]),
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("ppl", [-0.2, -0.3, -0.4]),
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("pl", [0.7, 0.8, 0.9]),
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]
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@pytest.fixture()
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def ngrams_vocab(en_vocab, ngrams_vectors):
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add_vecs_to_vocab(en_vocab, ngrams_vectors)
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return en_vocab
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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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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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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] == (doc[0].text, list(doc[0].vector))
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assert vectors[1] == (doc[2].text, list(doc[2].vector))
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@pytest.mark.parametrize("text", ["apple"])
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def test_vectors__ngrams_word(ngrams_vocab, ngrams_vectors, text):
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assert list(ngrams_vocab.get_vector(text)) == list(ngrams_vectors[0][1])
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@pytest.mark.parametrize("text", ["applpie"])
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def test_vectors__ngrams_subword(ngrams_vocab, ngrams_vectors, text):
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truth = list(ngrams_vocab.get_vector(text, 1, 6))
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test = list(
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[
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(
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ngrams_vectors[1][1][i]
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+ ngrams_vectors[2][1][i]
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+ ngrams_vectors[3][1][i]
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)
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/ 3
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for i in range(len(ngrams_vectors[1][1]))
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]
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)
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eps = [abs(truth[i] - test[i]) for i in range(len(truth))]
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for i in eps:
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assert i < 1e-6
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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 = 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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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 = numpy.ndarray((5, 3), dtype="f")
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data[0] = [1.0, 1.2, 1.1]
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data[1] = [0.3, 1.3, 1.0]
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data[2] = [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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assert_allclose(similarity, cosine(data[0], data[2]), atol=1e-4, rtol=1e-3)
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@pytest.mark.skipif(is_python2, reason="Dict order? Not sure if worth investigating")
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def test_vectors_serialize():
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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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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(v.data, 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=numpy.asarray([1, 2, 3, 4], dtype="f"))
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row_r = v_r.add("D", vector=numpy.asarray([1, 2, 3, 4], dtype="f"))
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assert row == row_r
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assert_equal(v.data, 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(v.data, 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=numpy.asarray([10, 20, 30, 40], dtype="f"))
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row_r = v_r.add("D", vector=numpy.asarray([10, 20, 30, 40], dtype="f"))
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assert row == row_r
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assert_equal(v.data, 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 = 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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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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