mirror of
https://github.com/explosion/spaCy.git
synced 2024-11-11 04:08:09 +03:00
84e06f9fb7
Improve GoldParse NER alignment by including all cases where the start and end of the NER span can be aligned, regardless of internal tokenization differences. To do this, convert BILUO tags to character offsets, check start/end alignment with `doc.char_span()`, and assign the BILUO tags for the aligned spans. Alignment for `O/-` tags is handled through the one-to-one and multi alignments.
288 lines
11 KiB
Python
288 lines
11 KiB
Python
# coding: utf-8
|
|
from __future__ import unicode_literals
|
|
|
|
from spacy.gold import biluo_tags_from_offsets, offsets_from_biluo_tags
|
|
from spacy.gold import spans_from_biluo_tags, GoldParse, iob_to_biluo
|
|
from spacy.gold import GoldCorpus, docs_to_json, align
|
|
from spacy.lang.en import English
|
|
from spacy.tokens import Doc
|
|
from spacy.util import get_words_and_spaces
|
|
from .util import make_tempdir
|
|
import pytest
|
|
import srsly
|
|
|
|
|
|
def test_gold_biluo_U(en_vocab):
|
|
words = ["I", "flew", "to", "London", "."]
|
|
spaces = [True, True, True, False, True]
|
|
doc = Doc(en_vocab, words=words, spaces=spaces)
|
|
entities = [(len("I flew to "), len("I flew to London"), "LOC")]
|
|
tags = biluo_tags_from_offsets(doc, entities)
|
|
assert tags == ["O", "O", "O", "U-LOC", "O"]
|
|
|
|
|
|
def test_gold_biluo_BL(en_vocab):
|
|
words = ["I", "flew", "to", "San", "Francisco", "."]
|
|
spaces = [True, True, True, True, False, True]
|
|
doc = Doc(en_vocab, words=words, spaces=spaces)
|
|
entities = [(len("I flew to "), len("I flew to San Francisco"), "LOC")]
|
|
tags = biluo_tags_from_offsets(doc, entities)
|
|
assert tags == ["O", "O", "O", "B-LOC", "L-LOC", "O"]
|
|
|
|
|
|
def test_gold_biluo_BIL(en_vocab):
|
|
words = ["I", "flew", "to", "San", "Francisco", "Valley", "."]
|
|
spaces = [True, True, True, True, True, False, True]
|
|
doc = Doc(en_vocab, words=words, spaces=spaces)
|
|
entities = [(len("I flew to "), len("I flew to San Francisco Valley"), "LOC")]
|
|
tags = biluo_tags_from_offsets(doc, entities)
|
|
assert tags == ["O", "O", "O", "B-LOC", "I-LOC", "L-LOC", "O"]
|
|
|
|
|
|
def test_gold_biluo_overlap(en_vocab):
|
|
words = ["I", "flew", "to", "San", "Francisco", "Valley", "."]
|
|
spaces = [True, True, True, True, True, False, True]
|
|
doc = Doc(en_vocab, words=words, spaces=spaces)
|
|
entities = [
|
|
(len("I flew to "), len("I flew to San Francisco Valley"), "LOC"),
|
|
(len("I flew to "), len("I flew to San Francisco"), "LOC"),
|
|
]
|
|
with pytest.raises(ValueError):
|
|
biluo_tags_from_offsets(doc, entities)
|
|
|
|
|
|
def test_gold_biluo_misalign(en_vocab):
|
|
words = ["I", "flew", "to", "San", "Francisco", "Valley."]
|
|
spaces = [True, True, True, True, True, False]
|
|
doc = Doc(en_vocab, words=words, spaces=spaces)
|
|
entities = [(len("I flew to "), len("I flew to San Francisco Valley"), "LOC")]
|
|
tags = biluo_tags_from_offsets(doc, entities)
|
|
assert tags == ["O", "O", "O", "-", "-", "-"]
|
|
|
|
|
|
def test_gold_biluo_different_tokenization(en_vocab, en_tokenizer):
|
|
# one-to-many
|
|
words = ["I", "flew to", "San Francisco Valley", "."]
|
|
spaces = [True, True, False, False]
|
|
doc = Doc(en_vocab, words=words, spaces=spaces)
|
|
entities = [(len("I flew to "), len("I flew to San Francisco Valley"), "LOC")]
|
|
gp = GoldParse(
|
|
doc,
|
|
words=["I", "flew", "to", "San", "Francisco", "Valley", "."],
|
|
entities=entities,
|
|
)
|
|
assert gp.ner == ["O", "O", "U-LOC", "O"]
|
|
|
|
# many-to-one
|
|
words = ["I", "flew", "to", "San", "Francisco", "Valley", "."]
|
|
spaces = [True, True, True, True, True, False, False]
|
|
doc = Doc(en_vocab, words=words, spaces=spaces)
|
|
entities = [(len("I flew to "), len("I flew to San Francisco Valley"), "LOC")]
|
|
gp = GoldParse(
|
|
doc, words=["I", "flew to", "San Francisco Valley", "."], entities=entities
|
|
)
|
|
assert gp.ner == ["O", "O", "O", "B-LOC", "I-LOC", "L-LOC", "O"]
|
|
|
|
# misaligned
|
|
words = ["I flew", "to", "San Francisco", "Valley", "."]
|
|
spaces = [True, True, True, False, False]
|
|
doc = Doc(en_vocab, words=words, spaces=spaces)
|
|
entities = [(len("I flew to "), len("I flew to San Francisco Valley"), "LOC")]
|
|
gp = GoldParse(
|
|
doc, words=["I", "flew to", "San", "Francisco Valley", "."], entities=entities,
|
|
)
|
|
assert gp.ner == ["O", "O", "B-LOC", "L-LOC", "O"]
|
|
|
|
# additional whitespace tokens in GoldParse words
|
|
words, spaces = get_words_and_spaces(
|
|
["I", "flew", "to", "San Francisco", "Valley", "."],
|
|
"I flew to San Francisco Valley.",
|
|
)
|
|
doc = Doc(en_vocab, words=words, spaces=spaces)
|
|
entities = [(len("I flew to "), len("I flew to San Francisco Valley"), "LOC")]
|
|
gp = GoldParse(
|
|
doc,
|
|
words=["I", "flew", " ", "to", "San Francisco Valley", "."],
|
|
entities=entities,
|
|
)
|
|
assert gp.ner == ["O", "O", "O", "O", "B-LOC", "L-LOC", "O"]
|
|
|
|
# from issue #4791
|
|
data = (
|
|
"I'll return the ₹54 amount",
|
|
{
|
|
"words": ["I", "'ll", "return", "the", "₹", "54", "amount",],
|
|
"entities": [(16, 19, "MONEY")],
|
|
},
|
|
)
|
|
gp = GoldParse(en_tokenizer(data[0]), **data[1])
|
|
assert gp.ner == ["O", "O", "O", "O", "U-MONEY", "O"]
|
|
|
|
data = (
|
|
"I'll return the $54 amount",
|
|
{
|
|
"words": ["I", "'ll", "return", "the", "$", "54", "amount",],
|
|
"entities": [(16, 19, "MONEY")],
|
|
},
|
|
)
|
|
gp = GoldParse(en_tokenizer(data[0]), **data[1])
|
|
assert gp.ner == ["O", "O", "O", "O", "B-MONEY", "L-MONEY", "O"]
|
|
|
|
|
|
def test_roundtrip_offsets_biluo_conversion(en_tokenizer):
|
|
text = "I flew to Silicon Valley via London."
|
|
biluo_tags = ["O", "O", "O", "B-LOC", "L-LOC", "O", "U-GPE", "O"]
|
|
offsets = [(10, 24, "LOC"), (29, 35, "GPE")]
|
|
doc = en_tokenizer(text)
|
|
biluo_tags_converted = biluo_tags_from_offsets(doc, offsets)
|
|
assert biluo_tags_converted == biluo_tags
|
|
offsets_converted = offsets_from_biluo_tags(doc, biluo_tags)
|
|
assert offsets_converted == offsets
|
|
|
|
|
|
def test_biluo_spans(en_tokenizer):
|
|
doc = en_tokenizer("I flew to Silicon Valley via London.")
|
|
biluo_tags = ["O", "O", "O", "B-LOC", "L-LOC", "O", "U-GPE", "O"]
|
|
spans = spans_from_biluo_tags(doc, biluo_tags)
|
|
assert len(spans) == 2
|
|
assert spans[0].text == "Silicon Valley"
|
|
assert spans[0].label_ == "LOC"
|
|
assert spans[1].text == "London"
|
|
assert spans[1].label_ == "GPE"
|
|
|
|
|
|
def test_gold_ner_missing_tags(en_tokenizer):
|
|
doc = en_tokenizer("I flew to Silicon Valley via London.")
|
|
biluo_tags = [None, "O", "O", "B-LOC", "L-LOC", "O", "U-GPE", "O"]
|
|
gold = GoldParse(doc, entities=biluo_tags) # noqa: F841
|
|
|
|
|
|
def test_iob_to_biluo():
|
|
good_iob = ["O", "O", "B-LOC", "I-LOC", "O", "B-PERSON"]
|
|
good_biluo = ["O", "O", "B-LOC", "L-LOC", "O", "U-PERSON"]
|
|
bad_iob = ["O", "O", '"', "B-LOC", "I-LOC"]
|
|
converted_biluo = iob_to_biluo(good_iob)
|
|
assert good_biluo == converted_biluo
|
|
with pytest.raises(ValueError):
|
|
iob_to_biluo(bad_iob)
|
|
|
|
|
|
def test_roundtrip_docs_to_json():
|
|
text = "I flew to Silicon Valley via London."
|
|
tags = ["PRP", "VBD", "IN", "NNP", "NNP", "IN", "NNP", "."]
|
|
heads = [1, 1, 1, 4, 2, 1, 5, 1]
|
|
deps = ["nsubj", "ROOT", "prep", "compound", "pobj", "prep", "pobj", "punct"]
|
|
biluo_tags = ["O", "O", "O", "B-LOC", "L-LOC", "O", "U-GPE", "O"]
|
|
cats = {"TRAVEL": 1.0, "BAKING": 0.0}
|
|
nlp = English()
|
|
doc = nlp(text)
|
|
for i in range(len(tags)):
|
|
doc[i].tag_ = tags[i]
|
|
doc[i].dep_ = deps[i]
|
|
doc[i].head = doc[heads[i]]
|
|
doc.ents = spans_from_biluo_tags(doc, biluo_tags)
|
|
doc.cats = cats
|
|
doc.is_tagged = True
|
|
doc.is_parsed = True
|
|
|
|
# roundtrip to JSON
|
|
with make_tempdir() as tmpdir:
|
|
json_file = tmpdir / "roundtrip.json"
|
|
srsly.write_json(json_file, [docs_to_json(doc)])
|
|
goldcorpus = GoldCorpus(str(json_file), str(json_file))
|
|
|
|
reloaded_doc, goldparse = next(goldcorpus.train_docs(nlp))
|
|
|
|
assert len(doc) == goldcorpus.count_train()
|
|
assert text == reloaded_doc.text
|
|
assert tags == goldparse.tags
|
|
assert deps == goldparse.labels
|
|
assert heads == goldparse.heads
|
|
assert biluo_tags == goldparse.ner
|
|
assert "TRAVEL" in goldparse.cats
|
|
assert "BAKING" in goldparse.cats
|
|
assert cats["TRAVEL"] == goldparse.cats["TRAVEL"]
|
|
assert cats["BAKING"] == goldparse.cats["BAKING"]
|
|
|
|
# roundtrip to JSONL train dicts
|
|
with make_tempdir() as tmpdir:
|
|
jsonl_file = tmpdir / "roundtrip.jsonl"
|
|
srsly.write_jsonl(jsonl_file, [docs_to_json(doc)])
|
|
goldcorpus = GoldCorpus(str(jsonl_file), str(jsonl_file))
|
|
|
|
reloaded_doc, goldparse = next(goldcorpus.train_docs(nlp))
|
|
|
|
assert len(doc) == goldcorpus.count_train()
|
|
assert text == reloaded_doc.text
|
|
assert tags == goldparse.tags
|
|
assert deps == goldparse.labels
|
|
assert heads == goldparse.heads
|
|
assert biluo_tags == goldparse.ner
|
|
assert "TRAVEL" in goldparse.cats
|
|
assert "BAKING" in goldparse.cats
|
|
assert cats["TRAVEL"] == goldparse.cats["TRAVEL"]
|
|
assert cats["BAKING"] == goldparse.cats["BAKING"]
|
|
|
|
# roundtrip to JSONL tuples
|
|
with make_tempdir() as tmpdir:
|
|
jsonl_file = tmpdir / "roundtrip.jsonl"
|
|
# write to JSONL train dicts
|
|
srsly.write_jsonl(jsonl_file, [docs_to_json(doc)])
|
|
goldcorpus = GoldCorpus(str(jsonl_file), str(jsonl_file))
|
|
# load and rewrite as JSONL tuples
|
|
srsly.write_jsonl(jsonl_file, goldcorpus.train_tuples)
|
|
goldcorpus = GoldCorpus(str(jsonl_file), str(jsonl_file))
|
|
|
|
reloaded_doc, goldparse = next(goldcorpus.train_docs(nlp))
|
|
|
|
assert len(doc) == goldcorpus.count_train()
|
|
assert text == reloaded_doc.text
|
|
assert tags == goldparse.tags
|
|
assert deps == goldparse.labels
|
|
assert heads == goldparse.heads
|
|
assert biluo_tags == goldparse.ner
|
|
assert "TRAVEL" in goldparse.cats
|
|
assert "BAKING" in goldparse.cats
|
|
assert cats["TRAVEL"] == goldparse.cats["TRAVEL"]
|
|
assert cats["BAKING"] == goldparse.cats["BAKING"]
|
|
|
|
|
|
@pytest.mark.parametrize(
|
|
"tokens_a,tokens_b,expected",
|
|
[
|
|
(["a", "b", "c"], ["ab", "c"], (3, [-1, -1, 1], [-1, 2], {0: 0, 1: 0}, {})),
|
|
(
|
|
["a", "b", '"', "c"],
|
|
['ab"', "c"],
|
|
(4, [-1, -1, -1, 1], [-1, 3], {0: 0, 1: 0, 2: 0}, {}),
|
|
),
|
|
(["a", "bc"], ["ab", "c"], (4, [-1, -1], [-1, -1], {0: 0}, {1: 1})),
|
|
(
|
|
["ab", "c", "d"],
|
|
["a", "b", "cd"],
|
|
(6, [-1, -1, -1], [-1, -1, -1], {1: 2, 2: 2}, {0: 0, 1: 0}),
|
|
),
|
|
(
|
|
["a", "b", "cd"],
|
|
["a", "b", "c", "d"],
|
|
(3, [0, 1, -1], [0, 1, -1, -1], {}, {2: 2, 3: 2}),
|
|
),
|
|
([" ", "a"], ["a"], (1, [-1, 0], [1], {}, {})),
|
|
],
|
|
)
|
|
def test_align(tokens_a, tokens_b, expected):
|
|
cost, a2b, b2a, a2b_multi, b2a_multi = align(tokens_a, tokens_b)
|
|
assert (cost, list(a2b), list(b2a), a2b_multi, b2a_multi) == expected
|
|
# check symmetry
|
|
cost, a2b, b2a, a2b_multi, b2a_multi = align(tokens_b, tokens_a)
|
|
assert (cost, list(b2a), list(a2b), b2a_multi, a2b_multi) == expected
|
|
|
|
|
|
def test_goldparse_startswith_space(en_tokenizer):
|
|
text = " a"
|
|
doc = en_tokenizer(text)
|
|
g = GoldParse(doc, words=["a"], entities=["U-DATE"], deps=["ROOT"], heads=[0])
|
|
assert g.words == [" ", "a"]
|
|
assert g.ner == [None, "U-DATE"]
|
|
assert g.labels == [None, "ROOT"]
|