mirror of
https://github.com/explosion/spaCy.git
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e2b70df012
* Use isort with Black profile * isort all the things * Fix import cycles as a result of import sorting * Add DOCBIN_ALL_ATTRS type definition * Add isort to requirements * Remove isort from build dependencies check * Typo
297 lines
11 KiB
Python
297 lines
11 KiB
Python
import numpy
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import pytest
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from spacy.tokens import Doc, Token
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from spacy.vocab import Vocab
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@pytest.mark.issue(3540)
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def test_issue3540(en_vocab):
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words = ["I", "live", "in", "NewYork", "right", "now"]
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tensor = numpy.asarray(
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[[1.0, 1.1], [2.0, 2.1], [3.0, 3.1], [4.0, 4.1], [5.0, 5.1], [6.0, 6.1]],
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dtype="f",
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)
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doc = Doc(en_vocab, words=words)
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doc.tensor = tensor
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gold_text = ["I", "live", "in", "NewYork", "right", "now"]
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assert [token.text for token in doc] == gold_text
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gold_lemma = ["I", "live", "in", "NewYork", "right", "now"]
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for i, lemma in enumerate(gold_lemma):
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doc[i].lemma_ = lemma
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assert [token.lemma_ for token in doc] == gold_lemma
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vectors_1 = [token.vector for token in doc]
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assert len(vectors_1) == len(doc)
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with doc.retokenize() as retokenizer:
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heads = [(doc[3], 1), doc[2]]
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attrs = {
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"POS": ["PROPN", "PROPN"],
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"LEMMA": ["New", "York"],
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"DEP": ["pobj", "compound"],
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}
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retokenizer.split(doc[3], ["New", "York"], heads=heads, attrs=attrs)
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gold_text = ["I", "live", "in", "New", "York", "right", "now"]
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assert [token.text for token in doc] == gold_text
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gold_lemma = ["I", "live", "in", "New", "York", "right", "now"]
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assert [token.lemma_ for token in doc] == gold_lemma
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vectors_2 = [token.vector for token in doc]
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assert len(vectors_2) == len(doc)
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assert vectors_1[0].tolist() == vectors_2[0].tolist()
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assert vectors_1[1].tolist() == vectors_2[1].tolist()
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assert vectors_1[2].tolist() == vectors_2[2].tolist()
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assert vectors_1[4].tolist() == vectors_2[5].tolist()
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assert vectors_1[5].tolist() == vectors_2[6].tolist()
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def test_doc_retokenize_split(en_vocab):
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words = ["LosAngeles", "start", "."]
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heads = [1, 2, 2]
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deps = ["dep"] * len(heads)
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doc = Doc(en_vocab, words=words, heads=heads, deps=deps)
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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 str(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 str(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_lemmas(en_vocab):
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# If lemmas are not set, leave unset
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words = ["LosAngeles", "start", "."]
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heads = [1, 2, 2]
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deps = ["dep"] * len(heads)
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doc = Doc(en_vocab, words=words, heads=heads, deps=deps)
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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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)
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assert doc[0].lemma_ == ""
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assert doc[1].lemma_ == ""
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# If lemmas are set, use split orth as default lemma
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words = ["LosAngeles", "start", "."]
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heads = [1, 2, 2]
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deps = ["dep"] * len(heads)
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doc = Doc(en_vocab, words=words, heads=heads, deps=deps)
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for t in doc:
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t.lemma_ = "a"
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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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)
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assert doc[0].lemma_ == "Los"
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assert doc[1].lemma_ == "Angeles"
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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, 1, 3, 5, 3, 1, 1, 8, 8, 8, 9, 8, 8, 14, 12]
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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 = 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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def test_doc_retokenizer_split_norm(en_vocab):
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"""#6060: reset norm in split"""
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text = "The quick brownfoxjumpsoverthe lazy dog w/ white spots"
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doc = Doc(en_vocab, words=text.split())
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# Set custom norm on the w/ token.
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doc[5].norm_ = "with"
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# Retokenize to split out the words in the token at doc[2].
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token = doc[2]
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with doc.retokenize() as retokenizer:
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retokenizer.split(
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token,
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["brown", "fox", "jumps", "over", "the"],
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heads=[(token, idx) for idx in range(5)],
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)
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assert doc[9].text == "w/"
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assert doc[9].norm_ == "with"
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assert doc[5].text == "over"
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assert doc[5].norm_ == "over"
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