spaCy/spacy/tests/regression/test_issue2001-2500.py
2020-07-06 14:05:59 +02:00

145 lines
4.8 KiB
Python

import pytest
import numpy
from spacy.tokens import Doc
from spacy.matcher import Matcher
from spacy.displacy import render
from spacy.gold import iob_to_biluo
from spacy.lang.it import Italian
from spacy.lang.en import English
from ..util import add_vecs_to_vocab, get_doc
@pytest.mark.xfail
def test_issue2070():
"""Test that checks that a dot followed by a quote is handled
appropriately.
"""
# Problem: The dot is now properly split off, but the prefix/suffix rules
# are not applied again afterwards. This means that the quote will still be
# attached to the remaining token.
nlp = English()
doc = nlp('First sentence."A quoted sentence" he said ...')
assert len(doc) == 11
@pytest.mark.filterwarnings("ignore::UserWarning")
def test_issue2179():
"""Test that spurious 'extra_labels' aren't created when initializing NER."""
nlp = Italian()
ner = nlp.create_pipe("ner")
ner.add_label("CITIZENSHIP")
nlp.add_pipe(ner)
nlp.begin_training()
nlp2 = Italian()
nlp2.add_pipe(nlp2.create_pipe("ner"))
assert len(nlp2.get_pipe("ner").labels) == 0
model = nlp2.get_pipe("ner").model
model.attrs["resize_output"](model, nlp.get_pipe("ner").moves.n_moves)
nlp2.from_bytes(nlp.to_bytes())
assert "extra_labels" not in nlp2.get_pipe("ner").cfg
assert nlp2.get_pipe("ner").labels == ("CITIZENSHIP",)
def test_issue2203(en_vocab):
"""Test that lemmas are set correctly in doc.from_array."""
words = ["I", "'ll", "survive"]
tags = ["PRP", "MD", "VB"]
lemmas = ["-PRON-", "will", "survive"]
tag_ids = [en_vocab.strings.add(tag) for tag in tags]
lemma_ids = [en_vocab.strings.add(lemma) for lemma in lemmas]
doc = Doc(en_vocab, words=words)
# Work around lemma corruption problem and set lemmas after tags
doc.from_array("TAG", numpy.array(tag_ids, dtype="uint64"))
doc.from_array("LEMMA", numpy.array(lemma_ids, dtype="uint64"))
assert [t.tag_ for t in doc] == tags
assert [t.lemma_ for t in doc] == lemmas
# We need to serialize both tag and lemma, since this is what causes the bug
doc_array = doc.to_array(["TAG", "LEMMA"])
new_doc = Doc(doc.vocab, words=words).from_array(["TAG", "LEMMA"], doc_array)
assert [t.tag_ for t in new_doc] == tags
assert [t.lemma_ for t in new_doc] == lemmas
def test_issue2219(en_vocab):
vectors = [("a", [1, 2, 3]), ("letter", [4, 5, 6])]
add_vecs_to_vocab(en_vocab, vectors)
[(word1, vec1), (word2, vec2)] = vectors
doc = Doc(en_vocab, words=[word1, word2])
assert doc[0].similarity(doc[1]) == doc[1].similarity(doc[0])
def test_issue2361(de_tokenizer):
chars = ("<", ">", "&", """)
doc = de_tokenizer('< > & " ')
doc.is_parsed = True
doc.is_tagged = True
html = render(doc)
for char in chars:
assert char in html
def test_issue2385():
"""Test that IOB tags are correctly converted to BILUO tags."""
# fix bug in labels with a 'b' character
tags1 = ("B-BRAWLER", "I-BRAWLER", "I-BRAWLER")
assert iob_to_biluo(tags1) == ["B-BRAWLER", "I-BRAWLER", "L-BRAWLER"]
# maintain support for iob1 format
tags2 = ("I-ORG", "I-ORG", "B-ORG")
assert iob_to_biluo(tags2) == ["B-ORG", "L-ORG", "U-ORG"]
# maintain support for iob2 format
tags3 = ("B-PERSON", "I-PERSON", "B-PERSON")
assert iob_to_biluo(tags3) == ["B-PERSON", "L-PERSON", "U-PERSON"]
@pytest.mark.parametrize(
"tags",
[
("B-ORG", "L-ORG"),
("B-PERSON", "I-PERSON", "L-PERSON"),
("U-BRAWLER", "U-BRAWLER"),
],
)
def test_issue2385_biluo(tags):
"""Test that BILUO-compatible tags aren't modified."""
assert iob_to_biluo(tags) == list(tags)
def test_issue2396(en_vocab):
words = ["She", "created", "a", "test", "for", "spacy"]
heads = [1, 0, 1, -2, -1, -1]
matrix = numpy.array(
[
[0, 1, 1, 1, 1, 1],
[1, 1, 1, 1, 1, 1],
[1, 1, 2, 3, 3, 3],
[1, 1, 3, 3, 3, 3],
[1, 1, 3, 3, 4, 4],
[1, 1, 3, 3, 4, 5],
],
dtype=numpy.int32,
)
doc = get_doc(en_vocab, words=words, heads=heads)
span = doc[:]
assert (doc.get_lca_matrix() == matrix).all()
assert (span.get_lca_matrix() == matrix).all()
def test_issue2464(en_vocab):
"""Test problem with successive ?. This is the same bug, so putting it here."""
matcher = Matcher(en_vocab)
doc = Doc(en_vocab, words=["a", "b"])
matcher.add("4", [[{"OP": "?"}, {"OP": "?"}]])
matches = matcher(doc)
assert len(matches) == 3
@pytest.mark.filterwarnings("ignore::UserWarning")
def test_issue2482():
"""Test we can serialize and deserialize a blank NER or parser model."""
nlp = Italian()
nlp.add_pipe(nlp.create_pipe("ner"))
b = nlp.to_bytes()
Italian().from_bytes(b)