spaCy/spacy/tests/doc/test_span_merge.py

166 lines
6.6 KiB
Python

# coding: utf-8
from __future__ import unicode_literals
from ..util import get_doc
from ...vocab import Vocab
from ...tokens import Doc
from ...tokens import Span
import pytest
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, [t.text for t in tokens], heads=heads)
assert len(doc) == 4
assert doc[0].head.text == 'Angeles'
assert doc[1].head.text == 'start'
with doc.retokenize() as retokenizer:
retokenizer.merge(doc[0 : 2], attrs={'tag':'NNP', 'lemma':'Los Angeles', 'ent_type':'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, [t.text for t in tokens], heads=heads)
assert len(doc) == 8
with doc.retokenize() as retokenizer:
retokenizer.merge(doc[3 : 5], attrs={'tag':doc[4].tag_, 'lemma':'pilates class', 'ent_type':'O'})
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."
tokens = en_tokenizer(text)
doc = get_doc(tokens.vocab, [t.text for t in tokens])
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, [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, [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 != 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, [t.text for t in tokens], heads=heads)
for span in doc.ents:
merged = doc.merge()
assert merged != None, (span.start, span.end, span.label_, span.lemma_)
def test_spans_entity_merge(en_tokenizer):
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 = [('Stewart Lee', 'PERSON', 0, 2), ('England', 'GPE', 10, 11), ('Joe Pasquale', 'PERSON', 13, 15)]
tokens = en_tokenizer(text)
doc = get_doc(tokens.vocab, [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):
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']
tokens = en_tokenizer(text)
doc = get_doc(tokens.vocab, [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):
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']
tokens = en_tokenizer(text)
doc = get_doc(tokens.vocab, [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