spaCy/spacy/tests/vocab_vectors/test_vectors.py
2020-05-21 18:39:06 +02:00

376 lines
12 KiB
Python

import pytest
import numpy
from numpy.testing import assert_allclose, assert_equal
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, get_cosine, make_tempdir
@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 most_similar_vectors_data():
return numpy.asarray(
[[0.0, 1.0, 2.0], [1.0, -2.0, 4.0], [1.0, 1.0, -1.0], [2.0, 3.0, 1.0]],
dtype="f",
)
@pytest.fixture
def most_similar_vectors_keys():
return ["a", "b", "c", "d"]
@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):
strings = [hash_string(s) for s in strings]
# decrease vector dimension (truncate)
v = Vectors(data=data)
resized_dim = v.shape[1] - 1
v.resize(shape=(v.shape[0], resized_dim))
for i, string in enumerate(strings):
v.add(string, row=i)
assert list(v[strings[0]]) == list(data[0, :resized_dim])
assert list(v[strings[1]]) == list(data[1, :resized_dim])
# increase vector dimension (pad with zeros)
v = Vectors(data=data)
resized_dim = v.shape[1] + 1
v.resize(shape=(v.shape[0], resized_dim))
for i, string in enumerate(strings):
v.add(string, row=i)
assert list(v[strings[0]]) == list(data[0]) + [0]
assert list(v[strings[1]]) == list(data[1]) + [0]
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])
def test_vectors_most_similar(most_similar_vectors_data, most_similar_vectors_keys):
v = Vectors(data=most_similar_vectors_data, keys=most_similar_vectors_keys)
_, best_rows, _ = v.most_similar(v.data, batch_size=2, n=2, sort=True)
assert all(row[0] == i for i, row in enumerate(best_rows))
with pytest.raises(ValueError):
v.most_similar(v.data, batch_size=2, n=10, sort=True)
def test_vectors_most_similar_identical():
"""Test that most similar identical vectors are assigned a score of 1.0."""
data = numpy.asarray([[4, 2, 2, 2], [4, 2, 2, 2], [1, 1, 1, 1]], dtype="f")
v = Vectors(data=data, keys=["A", "B", "C"])
keys, _, scores = v.most_similar(numpy.asarray([[4, 2, 2, 2]], dtype="f"))
assert scores[0][0] == 1.0 # not 1.0000002
data = numpy.asarray([[1, 2, 3], [1, 2, 3], [1, 1, 1]], dtype="f")
v = Vectors(data=data, keys=["A", "B", "C"])
keys, _, scores = v.most_similar(numpy.asarray([[1, 2, 3]], dtype="f"))
assert scores[0][0] == 1.0 # not 0.9999999
@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, ngrams_vectors, 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, ngrams_vectors, 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 -1.0 < 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 -1.0 < 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 -1.0 < 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 -1.0 < 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 -1.0 < 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 -1.0 < 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 -1.0 < 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(UserWarning):
assert doc[0:2].similarity(doc[1:3]) == doc[1:3].similarity(doc[0:2])
assert -1.0 < 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(UserWarning):
assert doc[0:2].similarity(doc) == doc.similarity(doc[0:2])
assert -1.0 < 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 -1.0 < doc1.similarity(doc2) < 1.0
def test_vocab_add_vector():
vocab = Vocab(vectors_name="test_vocab_add_vector")
data = numpy.ndarray((5, 3), dtype="f")
data[0] = 1.0
data[1] = 2.0
vocab.set_vector("cat", data[0])
vocab.set_vector("dog", data[1])
cat = vocab["cat"]
assert list(cat.vector) == [1.0, 1.0, 1.0]
dog = vocab["dog"]
assert list(dog.vector) == [2.0, 2.0, 2.0]
with pytest.raises(ValueError):
vocab.vectors.add(vocab["hamster"].orth, row=1000000)
def test_vocab_prune_vectors():
vocab = Vocab(vectors_name="test_vocab_prune_vectors")
_ = vocab["cat"] # noqa: F841
_ = vocab["dog"] # noqa: F841
_ = vocab["kitten"] # noqa: F841
data = numpy.ndarray((5, 3), dtype="f")
data[0] = [1.0, 1.2, 1.1]
data[1] = [0.3, 1.3, 1.0]
data[2] = [0.9, 1.22, 1.05]
vocab.set_vector("cat", data[0])
vocab.set_vector("dog", data[1])
vocab.set_vector("kitten", data[2])
remap = vocab.prune_vectors(2, batch_size=2)
assert list(remap.keys()) == ["kitten"]
neighbour, similarity = list(remap.values())[0]
assert neighbour == "cat", remap
assert_allclose(similarity, get_cosine(data[0], data[2]), atol=1e-4, rtol=1e-3)
def test_vectors_serialize():
data = numpy.asarray([[4, 2, 2, 2], [4, 2, 2, 2], [1, 1, 1, 1]], dtype="f")
v = Vectors(data=data, keys=["A", "B", "C"])
b = v.to_bytes()
v_r = Vectors()
v_r.from_bytes(b)
assert_equal(v.data, v_r.data)
assert v.key2row == v_r.key2row
v.resize((5, 4))
v_r.resize((5, 4))
row = v.add("D", vector=numpy.asarray([1, 2, 3, 4], dtype="f"))
row_r = v_r.add("D", vector=numpy.asarray([1, 2, 3, 4], dtype="f"))
assert row == row_r
assert_equal(v.data, v_r.data)
assert v.is_full == v_r.is_full
with make_tempdir() as d:
v.to_disk(d)
v_r.from_disk(d)
assert_equal(v.data, v_r.data)
assert v.key2row == v_r.key2row
v.resize((5, 4))
v_r.resize((5, 4))
row = v.add("D", vector=numpy.asarray([10, 20, 30, 40], dtype="f"))
row_r = v_r.add("D", vector=numpy.asarray([10, 20, 30, 40], dtype="f"))
assert row == row_r
assert_equal(v.data, v_r.data)
def test_vector_is_oov():
vocab = Vocab(vectors_name="test_vocab_is_oov")
data = numpy.ndarray((5, 3), dtype="f")
data[0] = 1.0
data[1] = 2.0
vocab.set_vector("cat", data[0])
vocab.set_vector("dog", data[1])
assert vocab["cat"].is_oov is True
assert vocab["dog"].is_oov is True
assert vocab["hamster"].is_oov is False