mirror of https://github.com/explosion/spaCy.git
139 lines
4.5 KiB
Python
139 lines
4.5 KiB
Python
import pytest
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import numpy
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from spacy.tokens import Doc
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from spacy.matcher import Matcher
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from spacy.displacy import render
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from spacy.gold import iob_to_biluo
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from spacy.lang.it import Italian
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from spacy.lang.en import English
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from ..util import add_vecs_to_vocab, get_doc
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@pytest.mark.xfail
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def test_issue2070():
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"""Test that checks that a dot followed by a quote is handled
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appropriately.
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"""
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# Problem: The dot is now properly split off, but the prefix/suffix rules
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# are not applied again afterwards. This means that the quote will still be
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# attached to the remaining token.
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nlp = English()
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doc = nlp('First sentence."A quoted sentence" he said ...')
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assert len(doc) == 11
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def test_issue2179():
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"""Test that spurious 'extra_labels' aren't created when initializing NER."""
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nlp = Italian()
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ner = nlp.create_pipe("ner")
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ner.add_label("CITIZENSHIP")
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nlp.add_pipe(ner)
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nlp.begin_training()
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nlp2 = Italian()
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nlp2.add_pipe(nlp2.create_pipe("ner"))
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nlp2.from_bytes(nlp.to_bytes())
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assert "extra_labels" not in nlp2.get_pipe("ner").cfg
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assert nlp2.get_pipe("ner").labels == ("CITIZENSHIP",)
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def test_issue2203(en_vocab):
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"""Test that lemmas are set correctly in doc.from_array."""
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words = ["I", "'ll", "survive"]
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tags = ["PRP", "MD", "VB"]
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lemmas = ["-PRON-", "will", "survive"]
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tag_ids = [en_vocab.strings.add(tag) for tag in tags]
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lemma_ids = [en_vocab.strings.add(lemma) for lemma in lemmas]
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doc = Doc(en_vocab, words=words)
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# Work around lemma corrpution problem and set lemmas after tags
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doc.from_array("TAG", numpy.array(tag_ids, dtype="uint64"))
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doc.from_array("LEMMA", numpy.array(lemma_ids, dtype="uint64"))
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assert [t.tag_ for t in doc] == tags
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assert [t.lemma_ for t in doc] == lemmas
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# We need to serialize both tag and lemma, since this is what causes the bug
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doc_array = doc.to_array(["TAG", "LEMMA"])
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new_doc = Doc(doc.vocab, words=words).from_array(["TAG", "LEMMA"], doc_array)
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assert [t.tag_ for t in new_doc] == tags
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assert [t.lemma_ for t in new_doc] == lemmas
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def test_issue2219(en_vocab):
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vectors = [("a", [1, 2, 3]), ("letter", [4, 5, 6])]
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add_vecs_to_vocab(en_vocab, vectors)
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[(word1, vec1), (word2, vec2)] = vectors
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doc = Doc(en_vocab, words=[word1, word2])
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assert doc[0].similarity(doc[1]) == doc[1].similarity(doc[0])
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def test_issue2361(de_tokenizer):
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chars = ("<", ">", "&", """)
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doc = de_tokenizer('< > & " ')
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doc.is_parsed = True
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doc.is_tagged = True
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html = render(doc)
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for char in chars:
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assert char in html
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def test_issue2385():
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"""Test that IOB tags are correctly converted to BILUO tags."""
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# fix bug in labels with a 'b' character
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tags1 = ("B-BRAWLER", "I-BRAWLER", "I-BRAWLER")
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assert iob_to_biluo(tags1) == ["B-BRAWLER", "I-BRAWLER", "L-BRAWLER"]
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# maintain support for iob1 format
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tags2 = ("I-ORG", "I-ORG", "B-ORG")
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assert iob_to_biluo(tags2) == ["B-ORG", "L-ORG", "U-ORG"]
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# maintain support for iob2 format
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tags3 = ("B-PERSON", "I-PERSON", "B-PERSON")
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assert iob_to_biluo(tags3) == ["B-PERSON", "L-PERSON", "U-PERSON"]
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@pytest.mark.parametrize(
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"tags",
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[
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("B-ORG", "L-ORG"),
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("B-PERSON", "I-PERSON", "L-PERSON"),
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("U-BRAWLER", "U-BRAWLER"),
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],
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)
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def test_issue2385_biluo(tags):
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"""Test that BILUO-compatible tags aren't modified."""
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assert iob_to_biluo(tags) == list(tags)
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def test_issue2396(en_vocab):
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words = ["She", "created", "a", "test", "for", "spacy"]
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heads = [1, 0, 1, -2, -1, -1]
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matrix = numpy.array(
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[
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[0, 1, 1, 1, 1, 1],
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[1, 1, 1, 1, 1, 1],
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[1, 1, 2, 3, 3, 3],
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[1, 1, 3, 3, 3, 3],
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[1, 1, 3, 3, 4, 4],
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[1, 1, 3, 3, 4, 5],
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],
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dtype=numpy.int32,
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)
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doc = get_doc(en_vocab, words=words, heads=heads)
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span = doc[:]
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assert (doc.get_lca_matrix() == matrix).all()
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assert (span.get_lca_matrix() == matrix).all()
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def test_issue2464(en_vocab):
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"""Test problem with successive ?. This is the same bug, so putting it here."""
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matcher = Matcher(en_vocab)
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doc = Doc(en_vocab, words=["a", "b"])
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matcher.add("4", [[{"OP": "?"}, {"OP": "?"}]])
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matches = matcher(doc)
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assert len(matches) == 3
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def test_issue2482():
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"""Test we can serialize and deserialize a blank NER or parser model."""
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nlp = Italian()
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nlp.add_pipe(nlp.create_pipe("ner"))
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b = nlp.to_bytes()
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Italian().from_bytes(b)
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