mirror of
https://github.com/explosion/spaCy.git
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167 lines
5.0 KiB
Python
167 lines
5.0 KiB
Python
# coding: utf-8
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from __future__ import unicode_literals
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from ...vectors import Vectors
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import numpy
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import pytest
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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 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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def test_init_vectors_with_data(strings, data):
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v = Vectors(strings, data)
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assert v.shape == data.shape
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def test_init_vectors_with_width(strings):
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v = Vectors(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(strings, data)
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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(strings, data)
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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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#
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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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#
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#
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#@pytest.mark.xfail
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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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#
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#
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#@pytest.mark.xfail
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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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#
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#
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#@pytest.mark.xfail
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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 = get_doc(vocab, text)
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# assert list(doc.vector)
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# assert doc.vector_norm
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#
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#
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#@pytest.mark.xfail
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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 = get_doc(vocab, text)[0:2]
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# assert list(span.vector)
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# assert span.vector_norm
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#
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#
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#@pytest.mark.xfail
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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 0.0 < doc[0].similarity(doc[1]) < 1.0
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#
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#
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#@pytest.mark.xfail
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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 0.0 < token.similarity(lex) < 1.0
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#
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#
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#@pytest.mark.xfail
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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 = get_doc(vocab, text)
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# assert doc[0].similarity(doc[1:3]) == doc[1:3].similarity(doc[0])
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# assert 0.0 < doc[0].similarity(doc[1:3]) < 1.0
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#
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#
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#@pytest.mark.xfail
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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 = get_doc(vocab, text)
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# assert doc[0].similarity(doc) == doc.similarity(doc[0])
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# assert 0.0 < doc[0].similarity(doc) < 1.0
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#
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#
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#@pytest.mark.xfail
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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 = get_doc(vocab, 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 0.0 < doc.similarity(doc[1:3]) < 1.0
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#
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#
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#@pytest.mark.xfail
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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 0.0 < lex1.similarity(lex2) < 1.0
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#
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#
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#@pytest.mark.xfail
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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 = get_doc(vocab, 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 0.0 < lex.similarity(doc) < 1.0
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#
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#
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#@pytest.mark.xfail
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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 = get_doc(vocab, text)
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# assert doc[0:2].similarity(doc[1:3]) == doc[1:3].similarity(doc[0:2])
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# assert 0.0 < doc[0:2].similarity(doc[1:3]) < 1.0
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#
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#
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#@pytest.mark.xfail
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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 = get_doc(vocab, text)
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# assert doc[0:2].similarity(doc) == doc.similarity(doc[0:2])
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# assert 0.0 < doc[0:2].similarity(doc) < 1.0
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#
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#
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#@pytest.mark.xfail
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#@pytest.mark.parametrize('text1,text2', [
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# (["apple", "and", "apple", "pie"], ["orange", "juice"])])
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#def test_vectors_doc_doc_similarity(vocab, text1, text2):
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# doc1 = get_doc(vocab, text1)
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# doc2 = get_doc(vocab, text2)
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# assert doc1.similarity(doc2) == doc2.similarity(doc1)
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# assert 0.0 < doc1.similarity(doc2) < 1.0
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