mirror of
https://github.com/explosion/spaCy.git
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e2b70df012
* Use isort with Black profile * isort all the things * Fix import cycles as a result of import sorting * Add DOCBIN_ALL_ATTRS type definition * Add isort to requirements * Remove isort from build dependencies check * Typo
112 lines
3.7 KiB
Python
112 lines
3.7 KiB
Python
import numpy
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import pytest
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from spacy.tokens import Doc
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from spacy.vocab import Vocab
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from ..util import add_vecs_to_vocab, get_cosine
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@pytest.fixture
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def vectors():
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return [("apple", [1, 2, 3]), ("orange", [-1, -2, -3])]
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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.mark.issue(2219)
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def test_issue2219(en_vocab):
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"""Test if indexing issue still occurs during Token-Token similarity"""
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vectors = [("a", [1, 2, 3]), ("letter", [4, 5, 6])]
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add_vecs_to_vocab(en_vocab, vectors)
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[(word1, vec1), (word2, vec2)] = vectors
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doc = Doc(en_vocab, words=[word1, word2])
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assert doc[0].similarity(doc[1]) == doc[1].similarity(doc[0])
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def test_vectors_similarity_LL(vocab, vectors):
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[(word1, vec1), (word2, vec2)] = vectors
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lex1 = vocab[word1]
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lex2 = vocab[word2]
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assert lex1.has_vector
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assert lex2.has_vector
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assert lex1.vector_norm != 0
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assert lex2.vector_norm != 0
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assert lex1.vector[0] != lex2.vector[0] and lex1.vector[1] != lex2.vector[1]
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assert isinstance(lex1.similarity(lex2), float)
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assert numpy.isclose(lex1.similarity(lex2), get_cosine(vec1, vec2))
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assert numpy.isclose(lex2.similarity(lex2), lex1.similarity(lex1))
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def test_vectors_similarity_TT(vocab, vectors):
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[(word1, vec1), (word2, vec2)] = vectors
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doc = Doc(vocab, words=[word1, word2])
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assert doc[0].has_vector
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assert doc[1].has_vector
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assert doc[0].vector_norm != 0
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assert doc[1].vector_norm != 0
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assert doc[0].vector[0] != doc[1].vector[0] and doc[0].vector[1] != doc[1].vector[1]
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assert isinstance(doc[0].similarity(doc[1]), float)
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assert numpy.isclose(doc[0].similarity(doc[1]), get_cosine(vec1, vec2))
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assert numpy.isclose(doc[1].similarity(doc[0]), doc[0].similarity(doc[1]))
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def test_vectors_similarity_SS(vocab, vectors):
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[(word1, vec1), (word2, vec2)] = vectors
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doc = Doc(vocab, words=[word1, word2])
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assert isinstance(doc[0:1].similarity(doc[0:2]), float)
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assert doc[0:1].similarity(doc[0:2]) == doc[0:2].similarity(doc[0:1])
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def test_vectors_similarity_DD(vocab, vectors):
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[(word1, vec1), (word2, vec2)] = vectors
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doc1 = Doc(vocab, words=[word1, word2])
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doc2 = Doc(vocab, words=[word2, word1])
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assert isinstance(doc1.similarity(doc2), float)
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assert doc1.similarity(doc2) == doc2.similarity(doc1)
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def test_vectors_similarity_TD(vocab, vectors):
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[(word1, vec1), (word2, vec2)] = vectors
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doc = Doc(vocab, words=[word1, word2])
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assert isinstance(doc.similarity(doc[0]), float)
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assert isinstance(doc[0].similarity(doc), float)
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assert doc.similarity(doc[0]) == doc[0].similarity(doc)
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def test_vectors_similarity_TS(vocab, vectors):
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[(word1, vec1), (word2, vec2)] = vectors
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doc = Doc(vocab, words=[word1, word2])
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assert isinstance(doc[:2].similarity(doc[0]), float)
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assert isinstance(doc[0].similarity(doc[:2]), float)
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assert doc[:2].similarity(doc[0]) == doc[0].similarity(doc[:2])
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def test_vectors_similarity_DS(vocab, vectors):
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[(word1, vec1), (word2, vec2)] = vectors
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doc = Doc(vocab, words=[word1, word2])
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assert isinstance(doc.similarity(doc[:2]), float)
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assert doc.similarity(doc[:2]) == doc[:2].similarity(doc)
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def test_vectors_similarity_no_vectors():
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vocab = Vocab()
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doc1 = Doc(vocab, words=["a", "b"])
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doc2 = Doc(vocab, words=["c", "d", "e"])
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with pytest.warns(UserWarning):
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doc1.similarity(doc2)
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with pytest.warns(UserWarning):
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doc1.similarity(doc2[1])
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with pytest.warns(UserWarning):
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doc1.similarity(doc2[:2])
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with pytest.warns(UserWarning):
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doc2.similarity(doc1)
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with pytest.warns(UserWarning):
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doc2[1].similarity(doc1)
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with pytest.warns(UserWarning):
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doc2[:2].similarity(doc1)
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