spaCy/spacy/tests/regression/test_issue2001-2500.py
Sofie 9a478b6db8 Clean up of char classes, few tokenizer fixes and faster default French tokenizer (#3293)
* splitting up latin unicode interval

* removing hyphen as infix for French

* adding failing test for issue 1235

* test for issue #3002 which now works

* partial fix for issue #2070

* keep the hyphen as infix for French (as it was)

* restore french expressions with hyphen as infix (as it was)

* added succeeding unit test for Issue #2656

* Fix issue #2822 with custom Italian exception

* Fix issue #2926 by allowing numbers right before infix /

* splitting up latin unicode interval

* removing hyphen as infix for French

* adding failing test for issue 1235

* test for issue #3002 which now works

* partial fix for issue #2070

* keep the hyphen as infix for French (as it was)

* restore french expressions with hyphen as infix (as it was)

* added succeeding unit test for Issue #2656

* Fix issue #2822 with custom Italian exception

* Fix issue #2926 by allowing numbers right before infix /

* remove duplicate

* remove xfail for Issue #2179 fixed by Matt

* adjust documentation and remove reference to regex lib
2019-02-20 22:10:13 +01:00

110 lines
3.3 KiB
Python

# coding: utf8
from __future__ import unicode_literals
import pytest
from spacy.tokens import Doc
from spacy.displacy import render
from spacy.gold import iob_to_biluo
from spacy.lang.it import Italian
import numpy
from spacy.lang.en import English
from ..util import add_vecs_to_vocab, get_doc
@pytest.mark.xfail(
reason="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."
)
def test_issue2070():
"""Test that checks that a dot followed by a quote is handled appropriately."""
nlp = English()
doc = nlp('First sentence."A quoted sentence" he said ...')
assert len(doc) == 11
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"))
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_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_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)