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7d50804644
* Migrate regressions 1-1000 * Move serialize test to correct file * Remove tests that won't work in v3 * Migrate regressions 1000-1500 Removed regression test 1250 because v3 doesn't support the old LEX scheme anymore. * Add missing imports in serializer tests * Migrate tests 1500-2000 * Migrate regressions from 2000-2500 * Migrate regressions from 2501-3000 * Migrate regressions from 3000-3501 * Migrate regressions from 3501-4000 * Migrate regressions from 4001-4500 * Migrate regressions from 4501-5000 * Migrate regressions from 5001-5501 * Migrate regressions from 5501 to 7000 * Migrate regressions from 7001 to 8000 * Migrate remaining regression tests * Fixing missing imports * Update docs with new system [ci skip] * Update CONTRIBUTING.md - Fix formatting - Update wording * Remove lemmatizer tests in el lang * Move a few tests into the general tokenizer * Separate Doc and DocBin tests
72 lines
2.3 KiB
Python
72 lines
2.3 KiB
Python
import pytest
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import numpy
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from spacy.tokens import Doc
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from ..util import get_cosine, add_vecs_to_vocab
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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 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 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_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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with pytest.warns(UserWarning):
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assert doc.similarity(doc[0]) == doc[0].similarity(doc)
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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 doc.similarity(doc[:2]) == doc[:2].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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with pytest.warns(UserWarning):
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assert doc[:2].similarity(doc[0]) == doc[0].similarity(doc[:2])
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