spaCy/spacy/tests/doc/test_add_entities.py
2020-09-28 21:35:09 +02:00

67 lines
2.0 KiB
Python

from spacy.pipeline.ner import DEFAULT_NER_MODEL
from spacy.training import Example
from spacy.pipeline import EntityRecognizer
from spacy.tokens import Span, Doc
from spacy import registry
import pytest
def _ner_example(ner):
doc = Doc(
ner.vocab,
words=["Joe", "loves", "visiting", "London", "during", "the", "weekend"],
)
gold = {"entities": [(0, 3, "PERSON"), (19, 25, "LOC")]}
return Example.from_dict(doc, gold)
def test_doc_add_entities_set_ents_iob(en_vocab):
text = ["This", "is", "a", "lion"]
doc = Doc(en_vocab, words=text)
config = {
"learn_tokens": False,
"min_action_freq": 30,
"update_with_oracle_cut_size": 100,
}
cfg = {"model": DEFAULT_NER_MODEL}
model = registry.resolve(cfg, validate=True)["model"]
ner = EntityRecognizer(en_vocab, model, **config)
ner.initialize(lambda: [_ner_example(ner)])
ner(doc)
doc.ents = [("ANIMAL", 3, 4)]
assert [w.ent_iob_ for w in doc] == ["O", "O", "O", "B"]
doc.ents = [("WORD", 0, 2)]
assert [w.ent_iob_ for w in doc] == ["B", "I", "O", "O"]
def test_ents_reset(en_vocab):
"""Ensure that resetting doc.ents does not change anything"""
text = ["This", "is", "a", "lion"]
doc = Doc(en_vocab, words=text)
config = {
"learn_tokens": False,
"min_action_freq": 30,
"update_with_oracle_cut_size": 100,
}
cfg = {"model": DEFAULT_NER_MODEL}
model = registry.resolve(cfg, validate=True)["model"]
ner = EntityRecognizer(en_vocab, model, **config)
ner.initialize(lambda: [_ner_example(ner)])
ner(doc)
orig_iobs = [t.ent_iob_ for t in doc]
doc.ents = list(doc.ents)
assert [t.ent_iob_ for t in doc] == orig_iobs
def test_add_overlapping_entities(en_vocab):
text = ["Louisiana", "Office", "of", "Conservation"]
doc = Doc(en_vocab, words=text)
entity = Span(doc, 0, 4, label=391)
doc.ents = [entity]
new_entity = Span(doc, 0, 1, label=392)
with pytest.raises(ValueError):
doc.ents = list(doc.ents) + [new_entity]