spaCy/spacy/tests/regression/test_issue2001-2500.py
2018-09-28 15:18:30 +02:00

71 lines
2.2 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
from ..util import add_vecs_to_vocab
@pytest.mark.xfail
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_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()
nlp2 = Italian().from_bytes(b)