mirror of https://github.com/explosion/spaCy.git
201 lines
7.4 KiB
Python
201 lines
7.4 KiB
Python
import pytest
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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_split(en_vocab):
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words = ["LosAngeles", "start", "."]
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heads = [1, 1, 0]
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doc = get_doc(en_vocab, words=words, heads=heads)
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assert len(doc) == 3
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assert len(str(doc)) == 19
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assert doc[0].head.text == "start"
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assert doc[1].head.text == "."
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with doc.retokenize() as retokenizer:
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retokenizer.split(
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doc[0],
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["Los", "Angeles"],
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[(doc[0], 1), doc[1]],
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attrs={
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"tag": ["NNP"] * 2,
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"lemma": ["Los", "Angeles"],
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"ent_type": ["GPE"] * 2,
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"morph": ["Number=Sing"] * 2,
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},
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)
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assert len(doc) == 4
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assert doc[0].text == "Los"
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assert doc[0].head.text == "Angeles"
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assert doc[0].idx == 0
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assert doc[0].morph_ == "Number=Sing"
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assert doc[1].idx == 3
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assert doc[1].text == "Angeles"
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assert doc[1].head.text == "start"
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assert doc[1].morph_ == "Number=Sing"
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assert doc[2].text == "start"
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assert doc[2].head.text == "."
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assert doc[3].text == "."
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assert doc[3].head.text == "."
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assert len(str(doc)) == 19
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def test_doc_retokenize_split_dependencies(en_vocab):
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doc = Doc(en_vocab, words=["LosAngeles", "start", "."])
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dep1 = doc.vocab.strings.add("amod")
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dep2 = doc.vocab.strings.add("subject")
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with doc.retokenize() as retokenizer:
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retokenizer.split(
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doc[0],
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["Los", "Angeles"],
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[(doc[0], 1), doc[1]],
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attrs={"dep": [dep1, dep2]},
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)
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assert doc[0].dep == dep1
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assert doc[1].dep == dep2
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def test_doc_retokenize_split_heads_error(en_vocab):
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doc = Doc(en_vocab, words=["LosAngeles", "start", "."])
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# Not enough heads
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with pytest.raises(ValueError):
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with doc.retokenize() as retokenizer:
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retokenizer.split(doc[0], ["Los", "Angeles"], [doc[1]])
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# Too many heads
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with pytest.raises(ValueError):
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with doc.retokenize() as retokenizer:
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retokenizer.split(doc[0], ["Los", "Angeles"], [doc[1], doc[1], doc[1]])
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def test_doc_retokenize_spans_entity_split_iob():
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# Test entity IOB stays consistent after merging
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words = ["abc", "d", "e"]
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doc = Doc(Vocab(), words=words)
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doc.ents = [(doc.vocab.strings.add("ent-abcd"), 0, 2)]
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assert doc[0].ent_iob_ == "B"
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assert doc[1].ent_iob_ == "I"
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with doc.retokenize() as retokenizer:
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retokenizer.split(doc[0], ["a", "b", "c"], [(doc[0], 1), (doc[0], 2), doc[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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assert doc[2].ent_iob_ == "I"
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assert doc[3].ent_iob_ == "I"
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def test_doc_retokenize_spans_sentence_update_after_split(en_vocab):
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# fmt: off
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words = ["StewartLee", "is", "a", "stand", "up", "comedian", ".", "He",
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"lives", "in", "England", "and", "loves", "JoePasquale", "."]
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heads = [1, 0, 1, 2, -1, -4, -5, 1, 0, -1, -1, -3, -4, 1, -2]
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deps = ["nsubj", "ROOT", "det", "amod", "prt", "attr", "punct", "nsubj",
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"ROOT", "prep", "pobj", "cc", "conj", "compound", "punct"]
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# fmt: on
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doc = get_doc(en_vocab, words=words, 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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retokenizer.split(
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doc[0],
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["Stewart", "Lee"],
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[(doc[0], 1), doc[1]],
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attrs={"dep": ["compound", "nsubj"]},
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)
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retokenizer.split(
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doc[13],
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["Joe", "Pasquale"],
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[(doc[13], 1), doc[12]],
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attrs={"dep": ["compound", "dobj"]},
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)
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sent1, sent2 = list(doc.sents)
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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_split_orths_mismatch(en_vocab):
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"""Test that the regular retokenizer.split raises an error if the orths
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don't match the original token text. There might still be a method that
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allows this, but for the default use cases, merging and splitting should
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always conform with spaCy's non-destructive tokenization policy. Otherwise,
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it can lead to very confusing and unexpected results.
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"""
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doc = Doc(en_vocab, words=["LosAngeles", "start", "."])
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with pytest.raises(ValueError):
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with doc.retokenize() as retokenizer:
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retokenizer.split(doc[0], ["L", "A"], [(doc[0], 0), (doc[0], 0)])
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def test_doc_retokenize_split_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=["LosAngeles", "start"])
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with doc.retokenize() as retokenizer:
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heads = [(doc[0], 1), doc[1]]
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underscore = [{"a": True, "b": "1"}, {"b": "2"}]
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attrs = {"lemma": ["los", "angeles"], "_": underscore}
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retokenizer.split(doc[0], ["Los", "Angeles"], heads, attrs=attrs)
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assert doc[0].lemma_ == "los"
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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].lemma_ == "angeles"
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assert doc[1]._.a is False
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assert doc[1]._.b == "2"
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@pytest.mark.parametrize(
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"underscore_attrs",
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[
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[{"a": "x"}, {}], # Overwriting getter without setter
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[{"b": "x"}, {}], # Overwriting method
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[{"c": "x"}, {}], # Overwriting nonexistent attribute
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[{"a": "x"}, {"x": "x"}], # Combination
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[{"a": "x", "x": "x"}, {"x": "x"}], # Combination
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{"x": "x"}, # Not a list of dicts
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],
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)
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def test_doc_retokenize_split_extension_attrs_invalid(en_vocab, underscore_attrs):
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Token.set_extension("x", default=False, force=True)
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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=["LosAngeles", "start"])
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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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heads = [(doc[0], 1), doc[1]]
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retokenizer.split(doc[0], ["Los", "Angeles"], heads, attrs=attrs)
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def test_doc_retokenizer_split_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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assert not Doc(en_vocab, words=["Los"])[0].is_stop
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assert not Doc(en_vocab, words=["Angeles"])[0].is_stop
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doc = Doc(en_vocab, words=["LosAngeles", "start"])
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assert not doc[0].is_stop
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with doc.retokenize() as retokenizer:
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attrs = {"is_stop": [True, False]}
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heads = [(doc[0], 1), doc[1]]
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retokenizer.split(doc[0], ["Los", "Angeles"], heads, attrs=attrs)
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assert doc[0].is_stop
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assert not doc[1].is_stop
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def test_doc_retokenizer_realloc(en_vocab):
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"""#4604: realloc correctly when new tokens outnumber original tokens"""
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text = "Hyperglycemic adverse events following antipsychotic drug administration in the"
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doc = Doc(en_vocab, words=text.split()[:-1])
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with doc.retokenize() as retokenizer:
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token = doc[0]
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heads = [(token, 0)] * len(token)
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retokenizer.split(doc[token.i], list(token.text), heads=heads)
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doc = Doc(en_vocab, words=text.split())
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with doc.retokenize() as retokenizer:
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token = doc[0]
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heads = [(token, 0)] * len(token)
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retokenizer.split(doc[token.i], list(token.text), heads=heads)
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