# coding: utf-8 from __future__ import unicode_literals import pytest import numpy from numpy.testing import assert_allclose from spacy._ml import cosine from spacy.vocab import Vocab from spacy.vectors import Vectors from spacy.tokenizer import Tokenizer from spacy.strings import hash_string from spacy.tokens import Doc from ..util import add_vecs_to_vocab @pytest.fixture def strings(): return ["apple", "orange"] @pytest.fixture def vectors(): return [ ("apple", [1, 2, 3]), ("orange", [-1, -2, -3]), ('and', [-1, -1, -1]), ('juice', [5, 5, 10]), ('pie', [7, 6.3, 8.9])] @pytest.fixture def ngrams_vectors(): return [ ("apple", [1, 2, 3]), ("app", [-0.1, -0.2, -0.3]), ('ppl', [-0.2, -0.3, -0.4]), ('pl', [0.7, 0.8, 0.9]) ] @pytest.fixture() def ngrams_vocab(en_vocab, ngrams_vectors): add_vecs_to_vocab(en_vocab, ngrams_vectors) return en_vocab @pytest.fixture def data(): return numpy.asarray([[0.0, 1.0, 2.0], [3.0, -2.0, 4.0]], dtype='f') @pytest.fixture def resize_data(): return numpy.asarray([[0.0, 1.0], [2.0, 3.0]], dtype='f') @pytest.fixture() def vocab(en_vocab, vectors): add_vecs_to_vocab(en_vocab, vectors) return en_vocab @pytest.fixture() def tokenizer_v(vocab): return Tokenizer(vocab, {}, None, None, None) def test_init_vectors_with_resize_shape(strings,resize_data): v = Vectors(shape=(len(strings), 3)) v.resize(shape=resize_data.shape) assert v.shape == resize_data.shape assert v.shape != (len(strings), 3) def test_init_vectors_with_resize_data(data,resize_data): v = Vectors(data=data) v.resize(shape=resize_data.shape) assert v.shape == resize_data.shape assert v.shape != data.shape def test_get_vector_resize(strings, data,resize_data): v = Vectors(data=data) v.resize(shape=resize_data.shape) strings = [hash_string(s) for s in strings] for i, string in enumerate(strings): v.add(string, row=i) assert list(v[strings[0]]) == list(resize_data[0]) assert list(v[strings[0]]) != list(resize_data[1]) assert list(v[strings[1]]) != list(resize_data[0]) assert list(v[strings[1]]) == list(resize_data[1]) def test_init_vectors_with_data(strings, data): v = Vectors(data=data) assert v.shape == data.shape def test_init_vectors_with_shape(strings): v = Vectors(shape=(len(strings), 3)) assert v.shape == (len(strings), 3) def test_get_vector(strings, data): v = Vectors(data=data) strings = [hash_string(s) for s in strings] for i, string in enumerate(strings): v.add(string, row=i) assert list(v[strings[0]]) == list(data[0]) assert list(v[strings[0]]) != list(data[1]) assert list(v[strings[1]]) != list(data[0]) def test_set_vector(strings, data): orig = data.copy() v = Vectors(data=data) strings = [hash_string(s) for s in strings] for i, string in enumerate(strings): v.add(string, row=i) assert list(v[strings[0]]) == list(orig[0]) assert list(v[strings[0]]) != list(orig[1]) v[strings[0]] = data[1] assert list(v[strings[0]]) == list(orig[1]) assert list(v[strings[0]]) != list(orig[0]) @pytest.mark.parametrize('text', ["apple and orange"]) def test_vectors_token_vector(tokenizer_v, vectors, text): doc = tokenizer_v(text) assert vectors[0] == (doc[0].text, list(doc[0].vector)) assert vectors[1] == (doc[2].text, list(doc[2].vector)) @pytest.mark.parametrize('text', ["apple"]) def test_vectors__ngrams_word(ngrams_vocab, text): assert list(ngrams_vocab.get_vector(text)) == list(ngrams_vectors()[0][1]) @pytest.mark.parametrize('text', ["applpie"]) def test_vectors__ngrams_subword(ngrams_vocab, text): truth = list(ngrams_vocab.get_vector(text,1,6)) 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]))]) eps = [abs(truth[i] - test[i]) for i in range(len(truth))] for i in eps: assert i<1e-6 @pytest.mark.parametrize('text', ["apple", "orange"]) def test_vectors_lexeme_vector(vocab, text): lex = vocab[text] assert list(lex.vector) assert lex.vector_norm @pytest.mark.parametrize('text', [["apple", "and", "orange"]]) def test_vectors_doc_vector(vocab, text): doc = Doc(vocab, words=text) assert list(doc.vector) assert doc.vector_norm @pytest.mark.parametrize('text', [["apple", "and", "orange"]]) def test_vectors_span_vector(vocab, text): span = Doc(vocab, words=text)[0:2] assert list(span.vector) assert span.vector_norm @pytest.mark.parametrize('text', ["apple orange"]) def test_vectors_token_token_similarity(tokenizer_v, text): doc = tokenizer_v(text) assert doc[0].similarity(doc[1]) == doc[1].similarity(doc[0]) assert -1. < doc[0].similarity(doc[1]) < 1.0 @pytest.mark.parametrize('text1,text2', [("apple", "orange")]) def test_vectors_token_lexeme_similarity(tokenizer_v, vocab, text1, text2): token = tokenizer_v(text1) lex = vocab[text2] assert token.similarity(lex) == lex.similarity(token) assert -1. < token.similarity(lex) < 1.0 @pytest.mark.parametrize('text', [["apple", "orange", "juice"]]) def test_vectors_token_span_similarity(vocab, text): doc = Doc(vocab, words=text) assert doc[0].similarity(doc[1:3]) == doc[1:3].similarity(doc[0]) assert -1. < doc[0].similarity(doc[1:3]) < 1.0 @pytest.mark.parametrize('text', [["apple", "orange", "juice"]]) def test_vectors_token_doc_similarity(vocab, text): doc = Doc(vocab, words=text) assert doc[0].similarity(doc) == doc.similarity(doc[0]) assert -1. < doc[0].similarity(doc) < 1.0 @pytest.mark.parametrize('text', [["apple", "orange", "juice"]]) def test_vectors_lexeme_span_similarity(vocab, text): doc = Doc(vocab, words=text) lex = vocab[text[0]] assert lex.similarity(doc[1:3]) == doc[1:3].similarity(lex) assert -1. < doc.similarity(doc[1:3]) < 1.0 @pytest.mark.parametrize('text1,text2', [("apple", "orange")]) def test_vectors_lexeme_lexeme_similarity(vocab, text1, text2): lex1 = vocab[text1] lex2 = vocab[text2] assert lex1.similarity(lex2) == lex2.similarity(lex1) assert -1. < lex1.similarity(lex2) < 1.0 @pytest.mark.parametrize('text', [["apple", "orange", "juice"]]) def test_vectors_lexeme_doc_similarity(vocab, text): doc = Doc(vocab, words=text) lex = vocab[text[0]] assert lex.similarity(doc) == doc.similarity(lex) assert -1. < lex.similarity(doc) < 1.0 @pytest.mark.parametrize('text', [["apple", "orange", "juice"]]) def test_vectors_span_span_similarity(vocab, text): doc = Doc(vocab, words=text) with pytest.warns(None): assert doc[0:2].similarity(doc[1:3]) == doc[1:3].similarity(doc[0:2]) assert -1. < doc[0:2].similarity(doc[1:3]) < 1.0 @pytest.mark.parametrize('text', [["apple", "orange", "juice"]]) def test_vectors_span_doc_similarity(vocab, text): doc = Doc(vocab, words=text) with pytest.warns(None): assert doc[0:2].similarity(doc) == doc.similarity(doc[0:2]) assert -1. < doc[0:2].similarity(doc) < 1.0 @pytest.mark.parametrize('text1,text2', [ (["apple", "and", "apple", "pie"], ["orange", "juice"])]) def test_vectors_doc_doc_similarity(vocab, text1, text2): doc1 = Doc(vocab, words=text1) doc2 = Doc(vocab, words=text2) assert doc1.similarity(doc2) == doc2.similarity(doc1) assert -1. < doc1.similarity(doc2) < 1.0 def test_vocab_add_vector(): vocab = Vocab() data = numpy.ndarray((5,3), dtype='f') data[0] = 1. data[1] = 2. vocab.set_vector('cat', data[0]) vocab.set_vector('dog', data[1]) cat = vocab['cat'] assert list(cat.vector) == [1., 1., 1.] dog = vocab['dog'] assert list(dog.vector) == [2., 2., 2.] def test_vocab_prune_vectors(): vocab = Vocab() _ = vocab['cat'] _ = vocab['dog'] _ = vocab['kitten'] data = numpy.ndarray((5,3), dtype='f') data[0] = 1. data[1] = 2. data[2] = 1.1 vocab.set_vector('cat', data[0]) vocab.set_vector('dog', data[1]) vocab.set_vector('kitten', data[2]) remap = vocab.prune_vectors(2) assert list(remap.keys()) == ['kitten'] neighbour, similarity = list(remap.values())[0] assert neighbour == 'cat', remap assert_allclose(similarity, cosine(data[0], data[2]), atol=1e-6)