mirror of https://github.com/explosion/spaCy.git
435 lines
16 KiB
Python
435 lines
16 KiB
Python
# coding: utf-8
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from __future__ import unicode_literals
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import pytest
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from spacy.attrs import LEMMA
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from spacy.vocab import Vocab
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from spacy.tokens import Doc, Token
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from ..util import get_doc
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def test_doc_retokenize_merge(en_tokenizer):
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text = "WKRO played songs by the beach boys all night"
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attrs = {"tag": "NAMED", "lemma": "LEMMA", "ent_type": "TYPE"}
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doc = en_tokenizer(text)
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assert len(doc) == 9
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with doc.retokenize() as retokenizer:
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retokenizer.merge(doc[4:7], attrs=attrs)
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retokenizer.merge(doc[7:9], attrs=attrs)
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assert len(doc) == 6
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assert doc[4].text == "the beach boys"
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assert doc[4].text_with_ws == "the beach boys "
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assert doc[4].tag_ == "NAMED"
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assert doc[5].text == "all night"
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assert doc[5].text_with_ws == "all night"
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assert doc[5].tag_ == "NAMED"
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def test_doc_retokenize_merge_children(en_tokenizer):
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"""Test that attachments work correctly after merging."""
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text = "WKRO played songs by the beach boys all night"
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attrs = {"tag": "NAMED", "lemma": "LEMMA", "ent_type": "TYPE"}
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doc = en_tokenizer(text)
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assert len(doc) == 9
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with doc.retokenize() as retokenizer:
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retokenizer.merge(doc[4:7], attrs=attrs)
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for word in doc:
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if word.i < word.head.i:
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assert word in list(word.head.lefts)
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elif word.i > word.head.i:
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assert word in list(word.head.rights)
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def test_doc_retokenize_merge_hang(en_tokenizer):
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text = "through North and South Carolina"
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doc = en_tokenizer(text)
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with doc.retokenize() as retokenizer:
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retokenizer.merge(doc[3:5], attrs={"lemma": "", "ent_type": "ORG"})
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retokenizer.merge(doc[1:2], attrs={"lemma": "", "ent_type": "ORG"})
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def test_doc_retokenize_retokenizer(en_tokenizer):
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doc = en_tokenizer("WKRO played songs by the beach boys all night")
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with doc.retokenize() as retokenizer:
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retokenizer.merge(doc[4:7])
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assert len(doc) == 7
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assert doc[4].text == "the beach boys"
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def test_doc_retokenize_retokenizer_attrs(en_tokenizer):
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doc = en_tokenizer("WKRO played songs by the beach boys all night")
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# test both string and integer attributes and values
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attrs = {LEMMA: "boys", "ENT_TYPE": doc.vocab.strings["ORG"]}
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with doc.retokenize() as retokenizer:
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retokenizer.merge(doc[4:7], attrs=attrs)
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assert len(doc) == 7
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assert doc[4].text == "the beach boys"
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assert doc[4].lemma_ == "boys"
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assert doc[4].ent_type_ == "ORG"
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def test_doc_retokenize_lex_attrs(en_tokenizer):
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"""Test that lexical attributes can be changed (see #2390)."""
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doc = en_tokenizer("WKRO played beach boys songs")
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assert not any(token.is_stop for token in doc)
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with doc.retokenize() as retokenizer:
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retokenizer.merge(doc[2:4], attrs={"LEMMA": "boys", "IS_STOP": True})
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assert doc[2].text == "beach boys"
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assert doc[2].lemma_ == "boys"
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assert doc[2].is_stop
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new_doc = Doc(doc.vocab, words=["beach boys"])
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assert new_doc[0].is_stop
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def test_doc_retokenize_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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with doc.retokenize() as retokenizer:
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attrs = {"tag": "NNP", "lemma": "Los Angeles", "ent_type": "GPE"}
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retokenizer.merge(doc[0:2], attrs=attrs)
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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_doc_retokenize_spans_merge_tokens_default_attrs(en_tokenizer):
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text = "The players 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(
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tokens.vocab,
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words=[t.text for t in tokens],
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tags=["DT", "NN", "VBZ", "."],
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pos=["DET", "NOUN", "VERB", "PUNCT"],
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heads=heads,
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)
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assert len(doc) == 4
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assert doc[0].text == "The"
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assert doc[0].tag_ == "DT"
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assert doc[0].pos_ == "DET"
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with doc.retokenize() as retokenizer:
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retokenizer.merge(doc[0:2])
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assert len(doc) == 3
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assert doc[0].text == "The players"
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assert doc[0].tag_ == "NN"
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assert doc[0].pos_ == "NOUN"
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assert doc[0].lemma_ == "The players"
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doc = get_doc(
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tokens.vocab,
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words=[t.text for t in tokens],
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tags=["DT", "NN", "VBZ", "."],
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pos=["DET", "NOUN", "VERB", "PUNCT"],
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heads=heads,
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)
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assert len(doc) == 4
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assert doc[0].text == "The"
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assert doc[0].tag_ == "DT"
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assert doc[0].pos_ == "DET"
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with doc.retokenize() as retokenizer:
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retokenizer.merge(doc[0:2])
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retokenizer.merge(doc[2:4])
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assert len(doc) == 2
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assert doc[0].text == "The players"
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assert doc[0].tag_ == "NN"
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assert doc[0].pos_ == "NOUN"
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assert doc[0].lemma_ == "The players"
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assert doc[1].text == "start ."
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assert doc[1].tag_ == "VBZ"
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assert doc[1].pos_ == "VERB"
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assert doc[1].lemma_ == "start ."
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def test_doc_retokenize_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_doc_retokenize_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_doc_retokenize_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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with doc.retokenize() as retokenizer:
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attrs = {"tag": "NP", "lemma": "tool", "ent_type": "O"}
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retokenizer.merge(doc[2:5], attrs=attrs)
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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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with doc.retokenize() as retokenizer:
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for ent in doc.ents:
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attrs = {"tag": ent.label_, "lemma": ent.lemma_, "ent_type": ent.label_}
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retokenizer.merge(ent, attrs=attrs)
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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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with doc.retokenize() as retokenizer:
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for ent in doc.ents:
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retokenizer.merge(ent)
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def test_doc_retokenize_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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with doc.retokenize() as retokenizer:
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for ent in doc.ents:
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ent_type = max(w.ent_type_ for w in ent)
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attrs = {"lemma": ent.root.lemma_, "ent_type": ent_type}
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retokenizer.merge(ent, attrs=attrs)
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# check looping is ok
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assert len(doc) == 15
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def test_doc_retokenize_spans_entity_merge_iob(en_vocab):
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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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with doc.retokenize() as retokenizer:
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retokenizer.merge(doc[0:2])
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assert len(doc) == len(words) - 1
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assert doc[0].ent_iob_ == "B"
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assert doc[1].ent_iob_ == "I"
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# Test that IOB stays consistent with provided IOB
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words = ["a", "b", "c", "d", "e"]
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doc = Doc(Vocab(), words=words)
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with doc.retokenize() as retokenizer:
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attrs = {"ent_type": "ent-abc", "ent_iob": 1}
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retokenizer.merge(doc[0:3], attrs=attrs)
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retokenizer.merge(doc[3:5], attrs=attrs)
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assert doc[0].ent_iob_ == "B"
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assert doc[1].ent_iob_ == "I"
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# if no parse/heads, the first word in the span is the root and provides
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# default values
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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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assert len(doc) == 6
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assert doc[3].ent_iob_ == "B"
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assert doc[3].ent_type_ == "ent-de"
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assert doc[4].ent_iob_ == "B"
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assert doc[4].ent_type_ == "ent-fg"
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# if there is a parse, span.root provides default values
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words = ["a", "b", "c", "d", "e", "f", "g", "h", "i"]
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heads = [0, -1, 1, -3, -4, -5, -1, -7, -8]
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ents = [(3, 5, "ent-de"), (5, 7, "ent-fg")]
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deps = ["dep"] * len(words)
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en_vocab.strings.add("ent-de")
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en_vocab.strings.add("ent-fg")
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en_vocab.strings.add("dep")
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doc = get_doc(en_vocab, words=words, heads=heads, deps=deps, ents=ents)
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assert doc[2:4].root == doc[3] # root of 'c d' is d
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assert doc[4:6].root == doc[4] # root is 'e f' is e
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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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assert len(doc) == 6
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assert doc[2].ent_iob_ == "B"
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assert doc[2].ent_type_ == "ent-de"
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assert doc[3].ent_iob_ == "I"
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assert doc[3].ent_type_ == "ent-de"
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assert doc[4].ent_iob_ == "B"
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assert doc[4].ent_type_ == "ent-fg"
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# check that B is preserved if span[start] is B
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words = ["a", "b", "c", "d", "e", "f", "g", "h", "i"]
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heads = [0, -1, 1, 1, -4, -5, -1, -7, -8]
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ents = [(3, 5, "ent-de"), (5, 7, "ent-de")]
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deps = ["dep"] * len(words)
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doc = get_doc(en_vocab, words=words, heads=heads, deps=deps, ents=ents)
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with doc.retokenize() as retokenizer:
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retokenizer.merge(doc[3:5])
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retokenizer.merge(doc[5:7])
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assert len(doc) == 7
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assert doc[3].ent_iob_ == "B"
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assert doc[3].ent_type_ == "ent-de"
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assert doc[4].ent_iob_ == "B"
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assert doc[4].ent_type_ == "ent-de"
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def test_doc_retokenize_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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with doc.retokenize() as retokenizer:
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attrs = {"lemma": "none", "ent_type": "none"}
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retokenizer.merge(doc[0:2], attrs=attrs)
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retokenizer.merge(doc[-2:], attrs=attrs)
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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_doc_retokenize_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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with doc.retokenize() as retokenizer:
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attrs = {"lemma": "none", "ent_type": "none"}
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retokenizer.merge(doc[0:2], attrs=attrs)
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assert len(list(sent1.root.subtree)) == init_len - 1
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def test_doc_retokenize_merge_extension_attrs(en_vocab):
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Token.set_extension("a", default=False, force=True)
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Token.set_extension("b", default="nothing", force=True)
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doc = Doc(en_vocab, words=["hello", "world", "!"])
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# Test regular merging
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with doc.retokenize() as retokenizer:
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attrs = {"lemma": "hello world", "_": {"a": True, "b": "1"}}
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retokenizer.merge(doc[0:2], attrs=attrs)
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assert doc[0].lemma_ == "hello world"
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assert doc[0]._.a is True
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assert doc[0]._.b == "1"
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# Test bulk merging
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doc = Doc(en_vocab, words=["hello", "world", "!", "!"])
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with doc.retokenize() as retokenizer:
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retokenizer.merge(doc[0:2], attrs={"_": {"a": True, "b": "1"}})
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retokenizer.merge(doc[2:4], attrs={"_": {"a": None, "b": "2"}})
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assert doc[0]._.a is True
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assert doc[0]._.b == "1"
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assert doc[1]._.a is None
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assert doc[1]._.b == "2"
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@pytest.mark.parametrize("underscore_attrs", [{"a": "x"}, {"b": "x"}, {"c": "x"}, [1]])
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def test_doc_retokenize_merge_extension_attrs_invalid(en_vocab, underscore_attrs):
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Token.set_extension("a", getter=lambda x: x, force=True)
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Token.set_extension("b", method=lambda x: x, force=True)
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doc = Doc(en_vocab, words=["hello", "world", "!"])
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attrs = {"_": underscore_attrs}
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with pytest.raises(ValueError):
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with doc.retokenize() as retokenizer:
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retokenizer.merge(doc[0:2], attrs=attrs)
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def test_doc_retokenizer_merge_lex_attrs(en_vocab):
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"""Test that retokenization also sets attributes on the lexeme if they're
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lexical attributes. For example, if a user sets IS_STOP, it should mean that
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"all tokens with that lexeme" are marked as a stop word, so the ambiguity
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here is acceptable. Also see #2390.
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"""
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# Test regular merging
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doc = Doc(en_vocab, words=["hello", "world", "!"])
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assert not any(t.is_stop for t in doc)
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with doc.retokenize() as retokenizer:
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retokenizer.merge(doc[0:2], attrs={"lemma": "hello world", "is_stop": True})
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assert doc[0].lemma_ == "hello world"
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|
assert doc[0].is_stop
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# Test bulk merging
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|
doc = Doc(en_vocab, words=["eins", "zwei", "!", "!"])
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assert not any(t.like_num for t in doc)
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|
assert not any(t.is_stop for t in doc)
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|
with doc.retokenize() as retokenizer:
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|
retokenizer.merge(doc[0:2], attrs={"like_num": True})
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retokenizer.merge(doc[2:4], attrs={"is_stop": True})
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|
assert doc[0].like_num
|
|
assert doc[1].is_stop
|
|
assert not doc[0].is_stop
|
|
assert not doc[1].like_num
|
|
|
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|
|
def test_retokenize_skip_duplicates(en_vocab):
|
|
"""Test that the retokenizer automatically skips duplicate spans instead
|
|
of complaining about overlaps. See #3687."""
|
|
doc = Doc(en_vocab, words=["hello", "world", "!"])
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|
with doc.retokenize() as retokenizer:
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|
retokenizer.merge(doc[0:2])
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|
retokenizer.merge(doc[0:2])
|
|
assert len(doc) == 2
|
|
assert doc[0].text == "hello world"
|
|
|
|
|
|
def test_retokenize_disallow_zero_length(en_vocab):
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|
doc = Doc(en_vocab, words=["hello", "world", "!"])
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|
with pytest.raises(ValueError):
|
|
with doc.retokenize() as retokenizer:
|
|
retokenizer.merge(doc[1:1])
|