mirror of
https://github.com/explosion/spaCy.git
synced 2024-12-30 20:06:30 +03:00
ebecaddb76
Previously the data and width options were one argument in Vectors, which meant you couldn't say vectors = Vectors(strings, width=300). It's better to have two keywords.
177 lines
5.2 KiB
Python
177 lines
5.2 KiB
Python
# coding: utf-8
|
|
from __future__ import unicode_literals
|
|
|
|
from ...vectors import Vectors
|
|
from ...tokenizer import Tokenizer
|
|
from ..util import add_vecs_to_vocab, get_doc
|
|
|
|
import numpy
|
|
import pytest
|
|
|
|
|
|
@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 data():
|
|
return numpy.asarray([[0.0, 1.0, 2.0], [3.0, -2.0, 4.0]], dtype='f')
|
|
|
|
|
|
@pytest.fixture()
|
|
def vocab(en_vocab, vectors):
|
|
add_vecs_to_vocab(en_vocab, vectors)
|
|
return en_vocab
|
|
|
|
|
|
def test_init_vectors_with_data(strings, data):
|
|
v = Vectors(strings, data=data)
|
|
assert v.shape == data.shape
|
|
|
|
def test_init_vectors_with_width(strings):
|
|
v = Vectors(strings, width=3)
|
|
for string in strings:
|
|
v.add(string)
|
|
assert v.shape == (len(strings), 3)
|
|
|
|
|
|
def test_get_vector(strings, data):
|
|
v = Vectors(strings, data=data)
|
|
for string in strings:
|
|
v.add(string)
|
|
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(strings, data=data)
|
|
for string in strings:
|
|
v.add(string)
|
|
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.fixture()
|
|
def tokenizer_v(vocab):
|
|
return Tokenizer(vocab, {}, None, None, None)
|
|
|
|
|
|
@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", "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 = get_doc(vocab, text)
|
|
assert list(doc.vector)
|
|
assert doc.vector_norm
|
|
|
|
|
|
@pytest.mark.parametrize('text', [["apple", "and", "orange"]])
|
|
def test_vectors_span_vector(vocab, text):
|
|
span = get_doc(vocab, 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 = get_doc(vocab, 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 = get_doc(vocab, 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 = get_doc(vocab, 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 = get_doc(vocab, 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 = get_doc(vocab, text)
|
|
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 = get_doc(vocab, text)
|
|
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 = get_doc(vocab, text1)
|
|
doc2 = get_doc(vocab, text2)
|
|
assert doc1.similarity(doc2) == doc2.similarity(doc1)
|
|
assert -1. < doc1.similarity(doc2) < 1.0
|