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