mirror of
https://github.com/explosion/spaCy.git
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cae4457c38
* Add logic to filter out warning IDs via environment variable Usage: SPACY_WARNING_EXCLUDE=W001,W007 * Add warnings for empty vectors * Add warning if no word vectors are used in .similarity methods For example, if only tensors are available in small models – should hopefully clear up some confusion around this * Capture warnings in tests * Rename SPACY_WARNING_EXCLUDE to SPACY_WARNING_IGNORE
229 lines
7.2 KiB
Python
229 lines
7.2 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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from ...tokenizer import Tokenizer
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from ...strings import hash_string
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from ..util import add_vecs_to_vocab, get_doc
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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 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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@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 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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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,resize_data):
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v = Vectors(data=data)
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v.resize(shape=resize_data.shape)
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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(resize_data[0])
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assert list(v[strings[0]]) != list(resize_data[1])
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assert list(v[strings[1]]) != list(resize_data[0])
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assert list(v[strings[1]]) == list(resize_data[1])
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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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@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.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, 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, text):
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truth = list(ngrams_vocab.get_vector(text,1,6))
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test = list([(ngrams_vectors()[1][1][i] + ngrams_vectors()[2][1][i] + ngrams_vectors()[3][1][i])/3 for i in range(len(ngrams_vectors()[1][1]))])
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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 = 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 -1. < 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. < 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 -1. < 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 -1. < 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 -1. < 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. < 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 -1. < 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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with pytest.warns(None):
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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. < 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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with pytest.warns(None):
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assert doc[0:2].similarity(doc) == doc.similarity(doc[0:2])
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assert -1. < 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 -1. < doc1.similarity(doc2) < 1.0
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