mirror of
https://github.com/explosion/spaCy.git
synced 2025-10-31 07:57:35 +03:00
196 lines
7.1 KiB
Python
196 lines
7.1 KiB
Python
# coding: utf-8
|
|
from __future__ import unicode_literals
|
|
|
|
from spacy.vocab import Vocab
|
|
from spacy.tokens import Doc
|
|
import pytest
|
|
|
|
from ..util import get_doc
|
|
|
|
|
|
def test_spans_merge_tokens(en_tokenizer):
|
|
text = "Los Angeles start."
|
|
heads = [1, 1, 0, -1]
|
|
tokens = en_tokenizer(text)
|
|
doc = get_doc(tokens.vocab, words=[t.text for t in tokens], heads=heads)
|
|
assert len(doc) == 4
|
|
assert doc[0].head.text == "Angeles"
|
|
assert doc[1].head.text == "start"
|
|
doc.merge(0, len("Los Angeles"), tag="NNP", lemma="Los Angeles", ent_type="GPE")
|
|
assert len(doc) == 3
|
|
assert doc[0].text == "Los Angeles"
|
|
assert doc[0].head.text == "start"
|
|
|
|
doc = get_doc(tokens.vocab, words=[t.text for t in tokens], heads=heads)
|
|
assert len(doc) == 4
|
|
assert doc[0].head.text == "Angeles"
|
|
assert doc[1].head.text == "start"
|
|
doc.merge(0, len("Los Angeles"), tag="NNP", lemma="Los Angeles", label="GPE")
|
|
|
|
assert len(doc) == 3
|
|
assert doc[0].text == "Los Angeles"
|
|
assert doc[0].head.text == "start"
|
|
assert doc[0].ent_type_ == "GPE"
|
|
|
|
|
|
def test_spans_merge_heads(en_tokenizer):
|
|
text = "I found a pilates class near work."
|
|
heads = [1, 0, 2, 1, -3, -1, -1, -6]
|
|
tokens = en_tokenizer(text)
|
|
doc = get_doc(tokens.vocab, words=[t.text for t in tokens], heads=heads)
|
|
assert len(doc) == 8
|
|
with doc.retokenize() as retokenizer:
|
|
attrs = {"tag": doc[4].tag_, "lemma": "pilates class", "ent_type": "O"}
|
|
retokenizer.merge(doc[3:5], attrs=attrs)
|
|
assert len(doc) == 7
|
|
assert doc[0].head.i == 1
|
|
assert doc[1].head.i == 1
|
|
assert doc[2].head.i == 3
|
|
assert doc[3].head.i == 1
|
|
assert doc[4].head.i in [1, 3]
|
|
assert doc[5].head.i == 4
|
|
|
|
|
|
def test_spans_merge_non_disjoint(en_tokenizer):
|
|
text = "Los Angeles start."
|
|
doc = en_tokenizer(text)
|
|
with pytest.raises(ValueError):
|
|
with doc.retokenize() as retokenizer:
|
|
retokenizer.merge(
|
|
doc[0:2],
|
|
attrs={"tag": "NNP", "lemma": "Los Angeles", "ent_type": "GPE"},
|
|
)
|
|
retokenizer.merge(
|
|
doc[0:1],
|
|
attrs={"tag": "NNP", "lemma": "Los Angeles", "ent_type": "GPE"},
|
|
)
|
|
|
|
|
|
def test_span_np_merges(en_tokenizer):
|
|
text = "displaCy is a parse tool built with Javascript"
|
|
heads = [1, 0, 2, 1, -3, -1, -1, -1]
|
|
tokens = en_tokenizer(text)
|
|
doc = get_doc(tokens.vocab, words=[t.text for t in tokens], heads=heads)
|
|
|
|
assert doc[4].head.i == 1
|
|
doc.merge(
|
|
doc[2].idx, doc[4].idx + len(doc[4]), tag="NP", lemma="tool", ent_type="O"
|
|
)
|
|
assert doc[2].head.i == 1
|
|
|
|
text = "displaCy is a lightweight and modern dependency parse tree visualization tool built with CSS3 and JavaScript."
|
|
heads = [1, 0, 8, 3, -1, -2, 4, 3, 1, 1, -9, -1, -1, -1, -1, -2, -15]
|
|
tokens = en_tokenizer(text)
|
|
doc = get_doc(tokens.vocab, words=[t.text for t in tokens], heads=heads)
|
|
|
|
ents = [(e[0].idx, e[-1].idx + len(e[-1]), e.label_, e.lemma_) for e in doc.ents]
|
|
for start, end, label, lemma in ents:
|
|
merged = doc.merge(start, end, tag=label, lemma=lemma, ent_type=label)
|
|
assert merged is not None, (start, end, label, lemma)
|
|
|
|
text = "One test with entities like New York City so the ents list is not void"
|
|
heads = [1, 11, -1, -1, -1, 1, 1, -3, 4, 2, 1, 1, 0, -1, -2]
|
|
tokens = en_tokenizer(text)
|
|
doc = get_doc(tokens.vocab, words=[t.text for t in tokens], heads=heads)
|
|
for span in doc.ents:
|
|
merged = doc.merge()
|
|
assert merged is not None, (span.start, span.end, span.label_, span.lemma_)
|
|
|
|
|
|
def test_spans_entity_merge(en_tokenizer):
|
|
# fmt: off
|
|
text = "Stewart Lee is a stand up comedian who lives in England and loves Joe Pasquale.\n"
|
|
heads = [1, 1, 0, 1, 2, -1, -4, 1, -2, -1, -1, -3, -10, 1, -2, -13, -1]
|
|
tags = ["NNP", "NNP", "VBZ", "DT", "VB", "RP", "NN", "WP", "VBZ", "IN", "NNP", "CC", "VBZ", "NNP", "NNP", ".", "SP"]
|
|
ents = [(0, 2, "PERSON"), (10, 11, "GPE"), (13, 15, "PERSON")]
|
|
# fmt: on
|
|
tokens = en_tokenizer(text)
|
|
doc = get_doc(
|
|
tokens.vocab, words=[t.text for t in tokens], heads=heads, tags=tags, ents=ents
|
|
)
|
|
assert len(doc) == 17
|
|
for ent in doc.ents:
|
|
label, lemma, type_ = (
|
|
ent.root.tag_,
|
|
ent.root.lemma_,
|
|
max(w.ent_type_ for w in ent),
|
|
)
|
|
ent.merge(label=label, lemma=lemma, ent_type=type_)
|
|
# check looping is ok
|
|
assert len(doc) == 15
|
|
|
|
|
|
def test_spans_entity_merge_iob():
|
|
# Test entity IOB stays consistent after merging
|
|
words = ["a", "b", "c", "d", "e"]
|
|
doc = Doc(Vocab(), words=words)
|
|
doc.ents = [
|
|
(doc.vocab.strings.add("ent-abc"), 0, 3),
|
|
(doc.vocab.strings.add("ent-d"), 3, 4),
|
|
]
|
|
assert doc[0].ent_iob_ == "B"
|
|
assert doc[1].ent_iob_ == "I"
|
|
assert doc[2].ent_iob_ == "I"
|
|
assert doc[3].ent_iob_ == "B"
|
|
doc[0:1].merge()
|
|
assert doc[0].ent_iob_ == "B"
|
|
assert doc[1].ent_iob_ == "I"
|
|
|
|
words = ["a", "b", "c", "d", "e", "f", "g", "h", "i"]
|
|
doc = Doc(Vocab(), words=words)
|
|
doc.ents = [
|
|
(doc.vocab.strings.add("ent-de"), 3, 5),
|
|
(doc.vocab.strings.add("ent-fg"), 5, 7),
|
|
]
|
|
assert doc[3].ent_iob_ == "B"
|
|
assert doc[4].ent_iob_ == "I"
|
|
assert doc[5].ent_iob_ == "B"
|
|
assert doc[6].ent_iob_ == "I"
|
|
with doc.retokenize() as retokenizer:
|
|
retokenizer.merge(doc[2:4])
|
|
retokenizer.merge(doc[4:6])
|
|
retokenizer.merge(doc[7:9])
|
|
for token in doc:
|
|
print(token)
|
|
print(token.ent_iob)
|
|
assert len(doc) == 6
|
|
assert doc[3].ent_iob_ == "B"
|
|
assert doc[4].ent_iob_ == "I"
|
|
|
|
|
|
def test_spans_sentence_update_after_merge(en_tokenizer):
|
|
# fmt: off
|
|
text = "Stewart Lee is a stand up comedian. He lives in England and loves Joe Pasquale."
|
|
heads = [1, 1, 0, 1, 2, -1, -4, -5, 1, 0, -1, -1, -3, -4, 1, -2, -7]
|
|
deps = ['compound', 'nsubj', 'ROOT', 'det', 'amod', 'prt', 'attr',
|
|
'punct', 'nsubj', 'ROOT', 'prep', 'pobj', 'cc', 'conj',
|
|
'compound', 'dobj', 'punct']
|
|
# fmt: on
|
|
|
|
tokens = en_tokenizer(text)
|
|
doc = get_doc(tokens.vocab, words=[t.text for t in tokens], heads=heads, deps=deps)
|
|
sent1, sent2 = list(doc.sents)
|
|
init_len = len(sent1)
|
|
init_len2 = len(sent2)
|
|
doc[0:2].merge(label="none", lemma="none", ent_type="none")
|
|
doc[-2:].merge(label="none", lemma="none", ent_type="none")
|
|
assert len(sent1) == init_len - 1
|
|
assert len(sent2) == init_len2 - 1
|
|
|
|
|
|
def test_spans_subtree_size_check(en_tokenizer):
|
|
# fmt: off
|
|
text = "Stewart Lee is a stand up comedian who lives in England and loves Joe Pasquale"
|
|
heads = [1, 1, 0, 1, 2, -1, -4, 1, -2, -1, -1, -3, -10, 1, -2]
|
|
deps = ["compound", "nsubj", "ROOT", "det", "amod", "prt", "attr",
|
|
"nsubj", "relcl", "prep", "pobj", "cc", "conj", "compound",
|
|
"dobj"]
|
|
# fmt: on
|
|
|
|
tokens = en_tokenizer(text)
|
|
doc = get_doc(tokens.vocab, words=[t.text for t in tokens], heads=heads, deps=deps)
|
|
sent1 = list(doc.sents)[0]
|
|
init_len = len(list(sent1.root.subtree))
|
|
doc[0:2].merge(label="none", lemma="none", ent_type="none")
|
|
assert len(list(sent1.root.subtree)) == init_len - 1
|