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Modernise vector tests, use add_vecs_to_vocab and don't depend on models
This commit is contained in:
Ines Montani
parent
96f0caa28a
commit
138deb80a1
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@ -1,7 +1,7 @@
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# coding: utf-8
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from __future__ import unicode_literals
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from ..util import get_doc, get_cosine
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from ..util import get_doc, get_cosine, add_vecs_to_vocab
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import numpy
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import pytest
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@ -9,22 +9,16 @@ import pytest
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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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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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word1, vec1, word2, vec2 = vectors
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en_vocab.resize_vectors(3)
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lex1 = en_vocab[word1]
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lex2 = en_vocab[word2]
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lex1.vector = vec1
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lex2.vector = vec2
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return en_vocab
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return add_vecs_to_vocab(en_vocab, vectors)
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def test_vectors_similarity_LL(vocab, vectors):
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word1, vec1, word2, vec2 = 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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@ -37,7 +31,7 @@ def test_vectors_similarity_LL(vocab, vectors):
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def test_vectors_similarity_TT(vocab, vectors):
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word1, vec1, word2, vec2 = vectors
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[(word1, vec1), (word2, vec2)] = vectors
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doc = get_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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@ -49,18 +43,18 @@ def test_vectors_similarity_TT(vocab, vectors):
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def test_vectors_similarity_TD(vocab, vectors):
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word1, vec1, word2, vec2 = vectors
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[(word1, vec1), (word2, vec2)] = vectors
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doc = get_doc(vocab, words=[word1, word2])
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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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[(word1, vec1), (word2, vec2)] = vectors
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doc = get_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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[(word1, vec1), (word2, vec2)] = vectors
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doc = get_doc(vocab, words=[word1, word2])
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assert doc[:2].similarity(doc[0]) == doc[0].similarity(doc[:2])
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@ -1,109 +1,126 @@
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# coding: utf-8
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from __future__ import unicode_literals
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from ...tokenizer import Tokenizer
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from ..util import get_doc, add_vecs_to_vocab
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import pytest
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@pytest.mark.models
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def test_token_vector(EN):
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token = EN(u'Apples and oranges')[0]
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token.vector
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token.vector_norm
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@pytest.mark.models
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def test_lexeme_vector(EN):
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lexeme = EN.vocab[u'apples']
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lexeme.vector
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lexeme.vector_norm
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@pytest.fixture
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def vectors():
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return [("apple", [0.0, 1.0, 2.0]), ("orange", [3.0, -2.0, 4.0])]
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@pytest.mark.models
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def test_doc_vector(EN):
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doc = EN(u'Apples and oranges')
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doc.vector
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doc.vector_norm
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@pytest.mark.models
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def test_span_vector(EN):
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span = EN(u'Apples and oranges')[0:2]
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span.vector
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span.vector_norm
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@pytest.mark.models
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def test_token_token_similarity(EN):
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apples, oranges = EN(u'apples oranges')
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assert apples.similarity(oranges) == oranges.similarity(apples)
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assert 0.0 < apples.similarity(oranges) < 1.0
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@pytest.mark.models
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def test_token_lexeme_similarity(EN):
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apples = EN(u'apples')
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oranges = EN.vocab[u'oranges']
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assert apples.similarity(oranges) == oranges.similarity(apples)
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assert 0.0 < apples.similarity(oranges) < 1.0
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@pytest.mark.models
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def test_token_span_similarity(EN):
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doc = EN(u'apples orange juice')
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apples = doc[0]
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oranges = doc[1:3]
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assert apples.similarity(oranges) == oranges.similarity(apples)
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assert 0.0 < apples.similarity(oranges) < 1.0
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@pytest.mark.models
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def test_token_doc_similarity(EN):
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doc = EN(u'apples orange juice')
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apples = doc[0]
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assert apples.similarity(doc) == doc.similarity(apples)
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assert 0.0 < apples.similarity(doc) < 1.0
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@pytest.mark.models
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def test_lexeme_span_similarity(EN):
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doc = EN(u'apples orange juice')
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apples = EN.vocab[u'apples']
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span = doc[1:3]
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assert apples.similarity(span) == span.similarity(apples)
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assert 0.0 < apples.similarity(span) < 1.0
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@pytest.fixture()
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def vocab(en_vocab, vectors):
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return add_vecs_to_vocab(en_vocab, vectors)
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@pytest.mark.models
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def test_lexeme_lexeme_similarity(EN):
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apples = EN.vocab[u'apples']
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oranges = EN.vocab[u'oranges']
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assert apples.similarity(oranges) == oranges.similarity(apples)
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assert 0.0 < apples.similarity(oranges) < 1.0
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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.models
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def test_lexeme_doc_similarity(EN):
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doc = EN(u'apples orange juice')
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apples = EN.vocab[u'apples']
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assert apples.similarity(doc) == doc.similarity(apples)
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assert 0.0 < apples.similarity(doc) < 1.0
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@pytest.mark.models
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def test_span_span_similarity(EN):
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doc = EN(u'apples orange juice')
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apples = doc[0:2]
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oj = doc[1:3]
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assert apples.similarity(oj) == oj.similarity(apples)
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assert 0.0 < apples.similarity(oj) < 1.0
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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.models
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def test_span_doc_similarity(EN):
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doc = EN(u'apples orange juice')
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apples = doc[0:2]
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oj = doc[1:3]
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assert apples.similarity(doc) == doc.similarity(apples)
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assert 0.0 < apples.similarity(doc) < 1.0
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@pytest.mark.models
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def test_doc_doc_similarity(EN):
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apples = EN(u'apples and apple pie')
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oranges = EN(u'orange juice')
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assert apples.similarity(oranges) == apples.similarity(oranges)
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assert 0.0 < apples.similarity(oranges) < 1.0
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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 = get_doc(vocab, 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 = 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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@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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@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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@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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@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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@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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@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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@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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@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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@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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@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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