mirror of https://github.com/explosion/spaCy.git
199 lines
7.4 KiB
Python
199 lines
7.4 KiB
Python
import pytest
|
|
from spacy.vocab import Vocab
|
|
from spacy.tokens import Doc, Token
|
|
|
|
|
|
def test_doc_retokenize_split(en_vocab):
|
|
words = ["LosAngeles", "start", "."]
|
|
heads = [1, 2, 2]
|
|
doc = Doc(en_vocab, words=words, heads=heads)
|
|
assert len(doc) == 3
|
|
assert len(str(doc)) == 19
|
|
assert doc[0].head.text == "start"
|
|
assert doc[1].head.text == "."
|
|
with doc.retokenize() as retokenizer:
|
|
retokenizer.split(
|
|
doc[0],
|
|
["Los", "Angeles"],
|
|
[(doc[0], 1), doc[1]],
|
|
attrs={
|
|
"tag": ["NNP"] * 2,
|
|
"lemma": ["Los", "Angeles"],
|
|
"ent_type": ["GPE"] * 2,
|
|
"morph": ["Number=Sing"] * 2,
|
|
},
|
|
)
|
|
assert len(doc) == 4
|
|
assert doc[0].text == "Los"
|
|
assert doc[0].head.text == "Angeles"
|
|
assert doc[0].idx == 0
|
|
assert doc[0].morph_ == "Number=Sing"
|
|
assert doc[1].idx == 3
|
|
assert doc[1].text == "Angeles"
|
|
assert doc[1].head.text == "start"
|
|
assert doc[1].morph_ == "Number=Sing"
|
|
assert doc[2].text == "start"
|
|
assert doc[2].head.text == "."
|
|
assert doc[3].text == "."
|
|
assert doc[3].head.text == "."
|
|
assert len(str(doc)) == 19
|
|
|
|
|
|
def test_doc_retokenize_split_dependencies(en_vocab):
|
|
doc = Doc(en_vocab, words=["LosAngeles", "start", "."])
|
|
dep1 = doc.vocab.strings.add("amod")
|
|
dep2 = doc.vocab.strings.add("subject")
|
|
with doc.retokenize() as retokenizer:
|
|
retokenizer.split(
|
|
doc[0],
|
|
["Los", "Angeles"],
|
|
[(doc[0], 1), doc[1]],
|
|
attrs={"dep": [dep1, dep2]},
|
|
)
|
|
assert doc[0].dep == dep1
|
|
assert doc[1].dep == dep2
|
|
|
|
|
|
def test_doc_retokenize_split_heads_error(en_vocab):
|
|
doc = Doc(en_vocab, words=["LosAngeles", "start", "."])
|
|
# Not enough heads
|
|
with pytest.raises(ValueError):
|
|
with doc.retokenize() as retokenizer:
|
|
retokenizer.split(doc[0], ["Los", "Angeles"], [doc[1]])
|
|
|
|
# Too many heads
|
|
with pytest.raises(ValueError):
|
|
with doc.retokenize() as retokenizer:
|
|
retokenizer.split(doc[0], ["Los", "Angeles"], [doc[1], doc[1], doc[1]])
|
|
|
|
|
|
def test_doc_retokenize_spans_entity_split_iob():
|
|
# Test entity IOB stays consistent after merging
|
|
words = ["abc", "d", "e"]
|
|
doc = Doc(Vocab(), words=words)
|
|
doc.ents = [(doc.vocab.strings.add("ent-abcd"), 0, 2)]
|
|
assert doc[0].ent_iob_ == "B"
|
|
assert doc[1].ent_iob_ == "I"
|
|
with doc.retokenize() as retokenizer:
|
|
retokenizer.split(doc[0], ["a", "b", "c"], [(doc[0], 1), (doc[0], 2), doc[1]])
|
|
assert doc[0].ent_iob_ == "B"
|
|
assert doc[1].ent_iob_ == "I"
|
|
assert doc[2].ent_iob_ == "I"
|
|
assert doc[3].ent_iob_ == "I"
|
|
|
|
|
|
def test_doc_retokenize_spans_sentence_update_after_split(en_vocab):
|
|
# fmt: off
|
|
words = ["StewartLee", "is", "a", "stand", "up", "comedian", ".", "He",
|
|
"lives", "in", "England", "and", "loves", "JoePasquale", "."]
|
|
heads = [1, 1, 3, 5, 3, 1, 1, 8, 8, 8, 9, 8, 8, 14, 12]
|
|
deps = ["nsubj", "ROOT", "det", "amod", "prt", "attr", "punct", "nsubj",
|
|
"ROOT", "prep", "pobj", "cc", "conj", "compound", "punct"]
|
|
# fmt: on
|
|
doc = Doc(en_vocab, words=words, heads=heads, deps=deps)
|
|
sent1, sent2 = list(doc.sents)
|
|
init_len = len(sent1)
|
|
init_len2 = len(sent2)
|
|
with doc.retokenize() as retokenizer:
|
|
retokenizer.split(
|
|
doc[0],
|
|
["Stewart", "Lee"],
|
|
[(doc[0], 1), doc[1]],
|
|
attrs={"dep": ["compound", "nsubj"]},
|
|
)
|
|
retokenizer.split(
|
|
doc[13],
|
|
["Joe", "Pasquale"],
|
|
[(doc[13], 1), doc[12]],
|
|
attrs={"dep": ["compound", "dobj"]},
|
|
)
|
|
sent1, sent2 = list(doc.sents)
|
|
assert len(sent1) == init_len + 1
|
|
assert len(sent2) == init_len2 + 1
|
|
|
|
|
|
def test_doc_retokenize_split_orths_mismatch(en_vocab):
|
|
"""Test that the regular retokenizer.split raises an error if the orths
|
|
don't match the original token text. There might still be a method that
|
|
allows this, but for the default use cases, merging and splitting should
|
|
always conform with spaCy's non-destructive tokenization policy. Otherwise,
|
|
it can lead to very confusing and unexpected results.
|
|
"""
|
|
doc = Doc(en_vocab, words=["LosAngeles", "start", "."])
|
|
with pytest.raises(ValueError):
|
|
with doc.retokenize() as retokenizer:
|
|
retokenizer.split(doc[0], ["L", "A"], [(doc[0], 0), (doc[0], 0)])
|
|
|
|
|
|
def test_doc_retokenize_split_extension_attrs(en_vocab):
|
|
Token.set_extension("a", default=False, force=True)
|
|
Token.set_extension("b", default="nothing", force=True)
|
|
doc = Doc(en_vocab, words=["LosAngeles", "start"])
|
|
with doc.retokenize() as retokenizer:
|
|
heads = [(doc[0], 1), doc[1]]
|
|
underscore = [{"a": True, "b": "1"}, {"b": "2"}]
|
|
attrs = {"lemma": ["los", "angeles"], "_": underscore}
|
|
retokenizer.split(doc[0], ["Los", "Angeles"], heads, attrs=attrs)
|
|
assert doc[0].lemma_ == "los"
|
|
assert doc[0]._.a is True
|
|
assert doc[0]._.b == "1"
|
|
assert doc[1].lemma_ == "angeles"
|
|
assert doc[1]._.a is False
|
|
assert doc[1]._.b == "2"
|
|
|
|
|
|
@pytest.mark.parametrize(
|
|
"underscore_attrs",
|
|
[
|
|
[{"a": "x"}, {}], # Overwriting getter without setter
|
|
[{"b": "x"}, {}], # Overwriting method
|
|
[{"c": "x"}, {}], # Overwriting nonexistent attribute
|
|
[{"a": "x"}, {"x": "x"}], # Combination
|
|
[{"a": "x", "x": "x"}, {"x": "x"}], # Combination
|
|
{"x": "x"}, # Not a list of dicts
|
|
],
|
|
)
|
|
def test_doc_retokenize_split_extension_attrs_invalid(en_vocab, underscore_attrs):
|
|
Token.set_extension("x", default=False, force=True)
|
|
Token.set_extension("a", getter=lambda x: x, force=True)
|
|
Token.set_extension("b", method=lambda x: x, force=True)
|
|
doc = Doc(en_vocab, words=["LosAngeles", "start"])
|
|
attrs = {"_": underscore_attrs}
|
|
with pytest.raises(ValueError):
|
|
with doc.retokenize() as retokenizer:
|
|
heads = [(doc[0], 1), doc[1]]
|
|
retokenizer.split(doc[0], ["Los", "Angeles"], heads, attrs=attrs)
|
|
|
|
|
|
def test_doc_retokenizer_split_lex_attrs(en_vocab):
|
|
"""Test that retokenization also sets attributes on the lexeme if they're
|
|
lexical attributes. For example, if a user sets IS_STOP, it should mean that
|
|
"all tokens with that lexeme" are marked as a stop word, so the ambiguity
|
|
here is acceptable. Also see #2390.
|
|
"""
|
|
assert not Doc(en_vocab, words=["Los"])[0].is_stop
|
|
assert not Doc(en_vocab, words=["Angeles"])[0].is_stop
|
|
doc = Doc(en_vocab, words=["LosAngeles", "start"])
|
|
assert not doc[0].is_stop
|
|
with doc.retokenize() as retokenizer:
|
|
attrs = {"is_stop": [True, False]}
|
|
heads = [(doc[0], 1), doc[1]]
|
|
retokenizer.split(doc[0], ["Los", "Angeles"], heads, attrs=attrs)
|
|
assert doc[0].is_stop
|
|
assert not doc[1].is_stop
|
|
|
|
|
|
def test_doc_retokenizer_realloc(en_vocab):
|
|
"""#4604: realloc correctly when new tokens outnumber original tokens"""
|
|
text = "Hyperglycemic adverse events following antipsychotic drug administration in the"
|
|
doc = Doc(en_vocab, words=text.split()[:-1])
|
|
with doc.retokenize() as retokenizer:
|
|
token = doc[0]
|
|
heads = [(token, 0)] * len(token)
|
|
retokenizer.split(doc[token.i], list(token.text), heads=heads)
|
|
doc = Doc(en_vocab, words=text.split())
|
|
with doc.retokenize() as retokenizer:
|
|
token = doc[0]
|
|
heads = [(token, 0)] * len(token)
|
|
retokenizer.split(doc[token.i], list(token.text), heads=heads)
|