mirror of https://github.com/explosion/spaCy.git
473 lines
18 KiB
Python
473 lines
18 KiB
Python
import pytest
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from spacy.language import Language
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from spacy.vocab import Vocab
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from spacy.pipeline import EntityRuler, DependencyParser
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from spacy.pipeline.dep_parser import DEFAULT_PARSER_MODEL
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from spacy import displacy, load
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from spacy.displacy import parse_deps
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from spacy.tokens import Doc, Token
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from spacy.matcher import Matcher, PhraseMatcher
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from spacy.errors import MatchPatternError
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from spacy.util import minibatch
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from spacy.training import Example
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from spacy.lang.hi import Hindi
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from spacy.lang.es import Spanish
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from spacy.lang.en import English
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from spacy.attrs import IS_ALPHA
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from spacy import registry
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from thinc.api import compounding
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import spacy
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import srsly
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import numpy
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from ..util import make_tempdir
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@pytest.mark.parametrize("word", ["don't", "don’t", "I'd", "I’d"])
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def test_issue3521(en_tokenizer, word):
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tok = en_tokenizer(word)[1]
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# 'not' and 'would' should be stopwords, also in their abbreviated forms
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assert tok.is_stop
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def test_issue_3526_1(en_vocab):
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patterns = [
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{"label": "HELLO", "pattern": "hello world"},
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{"label": "BYE", "pattern": [{"LOWER": "bye"}, {"LOWER": "bye"}]},
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{"label": "HELLO", "pattern": [{"ORTH": "HELLO"}]},
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{"label": "COMPLEX", "pattern": [{"ORTH": "foo", "OP": "*"}]},
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{"label": "TECH_ORG", "pattern": "Apple", "id": "a1"},
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]
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nlp = Language(vocab=en_vocab)
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ruler = EntityRuler(nlp, patterns=patterns, overwrite_ents=True)
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ruler_bytes = ruler.to_bytes()
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assert len(ruler) == len(patterns)
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assert len(ruler.labels) == 4
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assert ruler.overwrite
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new_ruler = EntityRuler(nlp)
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new_ruler = new_ruler.from_bytes(ruler_bytes)
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assert len(new_ruler) == len(ruler)
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assert len(new_ruler.labels) == 4
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assert new_ruler.overwrite == ruler.overwrite
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assert new_ruler.ent_id_sep == ruler.ent_id_sep
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def test_issue_3526_2(en_vocab):
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patterns = [
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{"label": "HELLO", "pattern": "hello world"},
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{"label": "BYE", "pattern": [{"LOWER": "bye"}, {"LOWER": "bye"}]},
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{"label": "HELLO", "pattern": [{"ORTH": "HELLO"}]},
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{"label": "COMPLEX", "pattern": [{"ORTH": "foo", "OP": "*"}]},
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{"label": "TECH_ORG", "pattern": "Apple", "id": "a1"},
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]
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nlp = Language(vocab=en_vocab)
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ruler = EntityRuler(nlp, patterns=patterns, overwrite_ents=True)
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bytes_old_style = srsly.msgpack_dumps(ruler.patterns)
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new_ruler = EntityRuler(nlp)
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new_ruler = new_ruler.from_bytes(bytes_old_style)
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assert len(new_ruler) == len(ruler)
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for pattern in ruler.patterns:
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assert pattern in new_ruler.patterns
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assert new_ruler.overwrite is not ruler.overwrite
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def test_issue_3526_3(en_vocab):
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patterns = [
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{"label": "HELLO", "pattern": "hello world"},
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{"label": "BYE", "pattern": [{"LOWER": "bye"}, {"LOWER": "bye"}]},
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{"label": "HELLO", "pattern": [{"ORTH": "HELLO"}]},
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{"label": "COMPLEX", "pattern": [{"ORTH": "foo", "OP": "*"}]},
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{"label": "TECH_ORG", "pattern": "Apple", "id": "a1"},
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]
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nlp = Language(vocab=en_vocab)
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ruler = EntityRuler(nlp, patterns=patterns, overwrite_ents=True)
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with make_tempdir() as tmpdir:
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out_file = tmpdir / "entity_ruler"
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srsly.write_jsonl(out_file.with_suffix(".jsonl"), ruler.patterns)
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new_ruler = EntityRuler(nlp).from_disk(out_file)
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for pattern in ruler.patterns:
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assert pattern in new_ruler.patterns
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assert len(new_ruler) == len(ruler)
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assert new_ruler.overwrite is not ruler.overwrite
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def test_issue_3526_4(en_vocab):
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nlp = Language(vocab=en_vocab)
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patterns = [{"label": "ORG", "pattern": "Apple"}]
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config = {"overwrite_ents": True}
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ruler = nlp.add_pipe("entity_ruler", config=config)
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ruler.add_patterns(patterns)
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with make_tempdir() as tmpdir:
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nlp.to_disk(tmpdir)
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ruler = nlp.get_pipe("entity_ruler")
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assert ruler.patterns == [{"label": "ORG", "pattern": "Apple"}]
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assert ruler.overwrite is True
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nlp2 = load(tmpdir)
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new_ruler = nlp2.get_pipe("entity_ruler")
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assert new_ruler.patterns == [{"label": "ORG", "pattern": "Apple"}]
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assert new_ruler.overwrite is True
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def test_issue3531():
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"""Test that displaCy renderer doesn't require "settings" key."""
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example_dep = {
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"words": [
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{"text": "But", "tag": "CCONJ"},
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{"text": "Google", "tag": "PROPN"},
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{"text": "is", "tag": "VERB"},
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{"text": "starting", "tag": "VERB"},
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{"text": "from", "tag": "ADP"},
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{"text": "behind.", "tag": "ADV"},
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],
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"arcs": [
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{"start": 0, "end": 3, "label": "cc", "dir": "left"},
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{"start": 1, "end": 3, "label": "nsubj", "dir": "left"},
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{"start": 2, "end": 3, "label": "aux", "dir": "left"},
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{"start": 3, "end": 4, "label": "prep", "dir": "right"},
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{"start": 4, "end": 5, "label": "pcomp", "dir": "right"},
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],
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}
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example_ent = {
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"text": "But Google is starting from behind.",
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"ents": [{"start": 4, "end": 10, "label": "ORG"}],
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}
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dep_html = displacy.render(example_dep, style="dep", manual=True)
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assert dep_html
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ent_html = displacy.render(example_ent, style="ent", manual=True)
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assert ent_html
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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_issue3549(en_vocab):
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"""Test that match pattern validation doesn't raise on empty errors."""
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matcher = Matcher(en_vocab, validate=True)
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pattern = [{"LOWER": "hello"}, {"LOWER": "world"}]
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matcher.add("GOOD", [pattern])
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with pytest.raises(MatchPatternError):
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matcher.add("BAD", [[{"X": "Y"}]])
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@pytest.mark.skip("Matching currently only works on strings and integers")
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def test_issue3555(en_vocab):
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"""Test that custom extensions with default None don't break matcher."""
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Token.set_extension("issue3555", default=None)
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matcher = Matcher(en_vocab)
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pattern = [{"ORTH": "have"}, {"_": {"issue3555": True}}]
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matcher.add("TEST", [pattern])
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doc = Doc(en_vocab, words=["have", "apple"])
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matcher(doc)
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def test_issue3611():
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""" Test whether adding n-grams in the textcat works even when n > token length of some docs """
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unique_classes = ["offensive", "inoffensive"]
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x_train = [
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"This is an offensive text",
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"This is the second offensive text",
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"inoff",
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]
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y_train = ["offensive", "offensive", "inoffensive"]
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nlp = spacy.blank("en")
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# preparing the data
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train_data = []
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for text, train_instance in zip(x_train, y_train):
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cat_dict = {label: label == train_instance for label in unique_classes}
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train_data.append(Example.from_dict(nlp.make_doc(text), {"cats": cat_dict}))
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# add a text categorizer component
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model = {
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"@architectures": "spacy.TextCatBOW.v1",
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"exclusive_classes": True,
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"ngram_size": 2,
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"no_output_layer": False,
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}
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textcat = nlp.add_pipe("textcat", config={"model": model}, last=True)
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for label in unique_classes:
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textcat.add_label(label)
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# training the network
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with nlp.select_pipes(enable="textcat"):
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optimizer = nlp.initialize()
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for i in range(3):
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losses = {}
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batches = minibatch(train_data, size=compounding(4.0, 32.0, 1.001))
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for batch in batches:
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nlp.update(examples=batch, sgd=optimizer, drop=0.1, losses=losses)
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def test_issue3625():
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"""Test that default punctuation rules applies to hindi unicode characters"""
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nlp = Hindi()
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doc = nlp("hi. how हुए. होटल, होटल")
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expected = ["hi", ".", "how", "हुए", ".", "होटल", ",", "होटल"]
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assert [token.text for token in doc] == expected
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def test_issue3803():
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"""Test that spanish num-like tokens have True for like_num attribute."""
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nlp = Spanish()
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text = "2 dos 1000 mil 12 doce"
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doc = nlp(text)
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assert [t.like_num for t in doc] == [True, True, True, True, True, True]
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def _parser_example(parser):
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doc = Doc(parser.vocab, words=["a", "b", "c", "d"])
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gold = {"heads": [1, 1, 3, 3], "deps": ["right", "ROOT", "left", "ROOT"]}
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return Example.from_dict(doc, gold)
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def test_issue3830_no_subtok():
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"""Test that the parser doesn't have subtok label if not learn_tokens"""
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config = {
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"learn_tokens": False,
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}
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model = registry.resolve({"model": DEFAULT_PARSER_MODEL}, validate=True)["model"]
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parser = DependencyParser(Vocab(), model, **config)
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parser.add_label("nsubj")
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assert "subtok" not in parser.labels
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parser.initialize(lambda: [_parser_example(parser)])
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assert "subtok" not in parser.labels
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def test_issue3830_with_subtok():
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"""Test that the parser does have subtok label if learn_tokens=True."""
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config = {
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"learn_tokens": True,
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}
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model = registry.resolve({"model": DEFAULT_PARSER_MODEL}, validate=True)["model"]
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parser = DependencyParser(Vocab(), model, **config)
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parser.add_label("nsubj")
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assert "subtok" not in parser.labels
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parser.initialize(lambda: [_parser_example(parser)])
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assert "subtok" in parser.labels
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def test_issue3839(en_vocab):
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"""Test that match IDs returned by the matcher are correct, are in the string """
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doc = Doc(en_vocab, words=["terrific", "group", "of", "people"])
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matcher = Matcher(en_vocab)
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match_id = "PATTERN"
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pattern1 = [{"LOWER": "terrific"}, {"OP": "?"}, {"LOWER": "group"}]
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pattern2 = [{"LOWER": "terrific"}, {"OP": "?"}, {"OP": "?"}, {"LOWER": "group"}]
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matcher.add(match_id, [pattern1])
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matches = matcher(doc)
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assert matches[0][0] == en_vocab.strings[match_id]
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matcher = Matcher(en_vocab)
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matcher.add(match_id, [pattern2])
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matches = matcher(doc)
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assert matches[0][0] == en_vocab.strings[match_id]
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@pytest.mark.parametrize(
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"sentence",
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[
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"The story was to the effect that a young American student recently called on Professor Christlieb with a letter of introduction.",
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"The next month Barry Siddall joined Stoke City on a free transfer, after Chris Pearce had established himself as the Vale's #1.",
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"The next month Barry Siddall joined Stoke City on a free transfer, after Chris Pearce had established himself as the Vale's number one",
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"Indeed, making the one who remains do all the work has installed him into a position of such insolent tyranny, it will take a month at least to reduce him to his proper proportions.",
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"It was a missed assignment, but it shouldn't have resulted in a turnover ...",
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],
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)
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def test_issue3869(sentence):
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"""Test that the Doc's count_by function works consistently"""
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nlp = English()
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doc = nlp(sentence)
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count = 0
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for token in doc:
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count += token.is_alpha
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assert count == doc.count_by(IS_ALPHA).get(1, 0)
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def test_issue3879(en_vocab):
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doc = Doc(en_vocab, words=["This", "is", "a", "test", "."])
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assert len(doc) == 5
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pattern = [{"ORTH": "This", "OP": "?"}, {"OP": "?"}, {"ORTH": "test"}]
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matcher = Matcher(en_vocab)
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matcher.add("TEST", [pattern])
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assert len(matcher(doc)) == 2 # fails because of a FP match 'is a test'
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def test_issue3880():
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"""Test that `nlp.pipe()` works when an empty string ends the batch.
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Fixed in v7.0.5 of Thinc.
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"""
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texts = ["hello", "world", "", ""]
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nlp = English()
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nlp.add_pipe("parser").add_label("dep")
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nlp.add_pipe("ner").add_label("PERSON")
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nlp.add_pipe("tagger").add_label("NN")
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nlp.initialize()
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for doc in nlp.pipe(texts):
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pass
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def test_issue3882(en_vocab):
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"""Test that displaCy doesn't serialize the doc.user_data when making a
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copy of the Doc.
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"""
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doc = Doc(en_vocab, words=["Hello", "world"], deps=["dep", "dep"])
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doc.user_data["test"] = set()
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parse_deps(doc)
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def test_issue3951(en_vocab):
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"""Test that combinations of optional rules are matched correctly."""
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matcher = Matcher(en_vocab)
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pattern = [
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{"LOWER": "hello"},
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{"LOWER": "this", "OP": "?"},
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{"OP": "?"},
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{"LOWER": "world"},
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]
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matcher.add("TEST", [pattern])
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doc = Doc(en_vocab, words=["Hello", "my", "new", "world"])
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matches = matcher(doc)
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assert len(matches) == 0
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def test_issue3959():
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""" Ensure that a modified pos attribute is serialized correctly."""
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nlp = English()
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doc = nlp(
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"displaCy uses JavaScript, SVG and CSS to show you how computers understand language"
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)
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assert doc[0].pos_ == ""
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doc[0].pos_ = "NOUN"
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assert doc[0].pos_ == "NOUN"
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# usually this is already True when starting from proper models instead of blank English
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with make_tempdir() as tmp_dir:
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file_path = tmp_dir / "my_doc"
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doc.to_disk(file_path)
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doc2 = nlp("")
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doc2.from_disk(file_path)
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assert doc2[0].pos_ == "NOUN"
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def test_issue3962(en_vocab):
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"""Ensure that as_doc does not result in out-of-bound access of tokens.
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This is achieved by setting the head to itself if it would lie out of the span otherwise."""
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# fmt: off
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words = ["He", "jests", "at", "scars", ",", "that", "never", "felt", "a", "wound", "."]
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heads = [1, 7, 1, 2, 7, 7, 7, 7, 9, 7, 7]
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deps = ["nsubj", "ccomp", "prep", "pobj", "punct", "nsubj", "neg", "ROOT", "det", "dobj", "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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span2 = doc[1:5] # "jests at scars ,"
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doc2 = span2.as_doc()
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doc2_json = doc2.to_json()
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assert doc2_json
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# head set to itself, being the new artificial root
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assert doc2[0].head.text == "jests"
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assert doc2[0].dep_ == "dep"
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assert doc2[1].head.text == "jests"
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assert doc2[1].dep_ == "prep"
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assert doc2[2].head.text == "at"
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assert doc2[2].dep_ == "pobj"
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assert doc2[3].head.text == "jests" # head set to the new artificial root
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assert doc2[3].dep_ == "dep"
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# We should still have 1 sentence
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assert len(list(doc2.sents)) == 1
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span3 = doc[6:9] # "never felt a"
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doc3 = span3.as_doc()
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doc3_json = doc3.to_json()
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assert doc3_json
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assert doc3[0].head.text == "felt"
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assert doc3[0].dep_ == "neg"
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assert doc3[1].head.text == "felt"
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assert doc3[1].dep_ == "ROOT"
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assert doc3[2].head.text == "felt" # head set to ancestor
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assert doc3[2].dep_ == "dep"
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# We should still have 1 sentence as "a" can be attached to "felt" instead of "wound"
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assert len(list(doc3.sents)) == 1
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def test_issue3962_long(en_vocab):
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"""Ensure that as_doc does not result in out-of-bound access of tokens.
|
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This is achieved by setting the head to itself if it would lie out of the span otherwise."""
|
||
# fmt: off
|
||
words = ["He", "jests", "at", "scars", ".", "They", "never", "felt", "a", "wound", "."]
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heads = [1, 1, 1, 2, 1, 7, 7, 7, 9, 7, 7]
|
||
deps = ["nsubj", "ROOT", "prep", "pobj", "punct", "nsubj", "neg", "ROOT", "det", "dobj", "punct"]
|
||
# fmt: on
|
||
two_sent_doc = Doc(en_vocab, words=words, heads=heads, deps=deps)
|
||
span2 = two_sent_doc[1:7] # "jests at scars. They never"
|
||
doc2 = span2.as_doc()
|
||
doc2_json = doc2.to_json()
|
||
assert doc2_json
|
||
# head set to itself, being the new artificial root (in sentence 1)
|
||
assert doc2[0].head.text == "jests"
|
||
assert doc2[0].dep_ == "ROOT"
|
||
assert doc2[1].head.text == "jests"
|
||
assert doc2[1].dep_ == "prep"
|
||
assert doc2[2].head.text == "at"
|
||
assert doc2[2].dep_ == "pobj"
|
||
assert doc2[3].head.text == "jests"
|
||
assert doc2[3].dep_ == "punct"
|
||
# head set to itself, being the new artificial root (in sentence 2)
|
||
assert doc2[4].head.text == "They"
|
||
assert doc2[4].dep_ == "dep"
|
||
# head set to the new artificial head (in sentence 2)
|
||
assert doc2[4].head.text == "They"
|
||
assert doc2[4].dep_ == "dep"
|
||
# We should still have 2 sentences
|
||
sents = list(doc2.sents)
|
||
assert len(sents) == 2
|
||
assert sents[0].text == "jests at scars ."
|
||
assert sents[1].text == "They never"
|
||
|
||
|
||
def test_issue3972(en_vocab):
|
||
"""Test that the PhraseMatcher returns duplicates for duplicate match IDs."""
|
||
matcher = PhraseMatcher(en_vocab)
|
||
matcher.add("A", [Doc(en_vocab, words=["New", "York"])])
|
||
matcher.add("B", [Doc(en_vocab, words=["New", "York"])])
|
||
doc = Doc(en_vocab, words=["I", "live", "in", "New", "York"])
|
||
matches = matcher(doc)
|
||
|
||
assert len(matches) == 2
|
||
|
||
# We should have a match for each of the two rules
|
||
found_ids = [en_vocab.strings[ent_id] for (ent_id, _, _) in matches]
|
||
assert "A" in found_ids
|
||
assert "B" in found_ids
|