spaCy/spacy/tests/regression/test_issue7716.py

56 lines
1.7 KiB
Python

import pytest
from thinc.api import Adam
from spacy.attrs import NORM
from spacy.vocab import Vocab
from spacy import registry
from spacy.training import Example
from spacy.pipeline.dep_parser import DEFAULT_PARSER_MODEL
from spacy.tokens import Doc
from spacy.pipeline import DependencyParser
@pytest.fixture
def vocab():
return Vocab(lex_attr_getters={NORM: lambda s: s})
def _parser_example(parser):
doc = Doc(parser.vocab, words=["a", "b", "c", "d"])
gold = {"heads": [1, 1, 3, 3], "deps": ["right", "ROOT", "left", "ROOT"]}
return Example.from_dict(doc, gold)
@pytest.fixture
def parser(vocab):
vocab.strings.add("ROOT")
cfg = {"model": DEFAULT_PARSER_MODEL}
model = registry.resolve(cfg, validate=True)["model"]
parser = DependencyParser(vocab, model)
parser.cfg["token_vector_width"] = 4
parser.cfg["hidden_width"] = 32
# parser.add_label('right')
parser.add_label("left")
parser.initialize(lambda: [_parser_example(parser)])
sgd = Adam(0.001)
for i in range(10):
losses = {}
doc = Doc(vocab, words=["a", "b", "c", "d"])
example = Example.from_dict(
doc, {"heads": [1, 1, 3, 3], "deps": ["left", "ROOT", "left", "ROOT"]}
)
parser.update([example], sgd=sgd, losses=losses)
return parser
@pytest.mark.issue(7716)
@pytest.mark.xfail(reason="Not fixed yet")
def test_partial_annotation(parser):
doc = Doc(parser.vocab, words=["a", "b", "c", "d"])
doc[2].is_sent_start = False
# Note that if the following line is used, then doc[2].is_sent_start == False
# doc[3].is_sent_start = False
doc = parser(doc)
assert doc[2].is_sent_start == False