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https://github.com/explosion/spaCy.git
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73538782a0
* Switch Doc.__init__(ents=) to IOB tags * Fix check for "-" * Allow "" or None as missing IOB tag
260 lines
9.0 KiB
Python
260 lines
9.0 KiB
Python
import pytest
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from spacy import registry
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from spacy.lang.en import English
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from spacy.lang.de import German
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from spacy.pipeline.ner import DEFAULT_NER_MODEL
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from spacy.pipeline import EntityRuler, EntityRecognizer
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from spacy.matcher import Matcher, PhraseMatcher
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from spacy.tokens import Doc
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from spacy.vocab import Vocab
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from spacy.attrs import ENT_IOB, ENT_TYPE
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from spacy.compat import pickle
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from spacy import displacy
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from spacy.vectors import Vectors
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import numpy
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def test_issue3002():
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"""Test that the tokenizer doesn't hang on a long list of dots"""
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nlp = German()
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doc = nlp(
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"880.794.982.218.444.893.023.439.794.626.120.190.780.624.990.275.671 ist eine lange Zahl"
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)
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assert len(doc) == 5
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def test_issue3009(en_vocab):
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"""Test problem with matcher quantifiers"""
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patterns = [
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[{"ORTH": "has"}, {"LOWER": "to"}, {"LOWER": "do"}, {"TAG": "IN"}],
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[
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{"ORTH": "has"},
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{"IS_ASCII": True, "IS_PUNCT": False, "OP": "*"},
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{"LOWER": "to"},
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{"LOWER": "do"},
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{"TAG": "IN"},
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],
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[
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{"ORTH": "has"},
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{"IS_ASCII": True, "IS_PUNCT": False, "OP": "?"},
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{"LOWER": "to"},
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{"LOWER": "do"},
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{"TAG": "IN"},
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],
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]
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words = ["also", "has", "to", "do", "with"]
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tags = ["RB", "VBZ", "TO", "VB", "IN"]
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pos = ["ADV", "VERB", "ADP", "VERB", "ADP"]
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doc = Doc(en_vocab, words=words, tags=tags, pos=pos)
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matcher = Matcher(en_vocab)
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for i, pattern in enumerate(patterns):
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matcher.add(str(i), [pattern])
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matches = matcher(doc)
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assert matches
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def test_issue3012(en_vocab):
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"""Test that the is_tagged attribute doesn't get overwritten when we from_array
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without tag information."""
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words = ["This", "is", "10", "%", "."]
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tags = ["DT", "VBZ", "CD", "NN", "."]
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pos = ["DET", "VERB", "NUM", "NOUN", "PUNCT"]
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ents = ["O", "O", "B-PERCENT", "I-PERCENT", "O"]
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doc = Doc(en_vocab, words=words, tags=tags, pos=pos, ents=ents)
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assert doc.has_annotation("TAG")
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expected = ("10", "NUM", "CD", "PERCENT")
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assert (doc[2].text, doc[2].pos_, doc[2].tag_, doc[2].ent_type_) == expected
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header = [ENT_IOB, ENT_TYPE]
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ent_array = doc.to_array(header)
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doc.from_array(header, ent_array)
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assert (doc[2].text, doc[2].pos_, doc[2].tag_, doc[2].ent_type_) == expected
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# Serializing then deserializing
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doc_bytes = doc.to_bytes()
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doc2 = Doc(en_vocab).from_bytes(doc_bytes)
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assert (doc2[2].text, doc2[2].pos_, doc2[2].tag_, doc2[2].ent_type_) == expected
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def test_issue3199():
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"""Test that Span.noun_chunks works correctly if no noun chunks iterator
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is available. To make this test future-proof, we're constructing a Doc
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with a new Vocab here and a parse tree to make sure the noun chunks run.
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"""
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words = ["This", "is", "a", "sentence"]
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doc = Doc(Vocab(), words=words, heads=[0] * len(words), deps=["dep"] * len(words))
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assert list(doc[0:3].noun_chunks) == []
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def test_issue3209():
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"""Test issue that occurred in spaCy nightly where NER labels were being
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mapped to classes incorrectly after loading the model, when the labels
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were added using ner.add_label().
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"""
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nlp = English()
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ner = nlp.add_pipe("ner")
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ner.add_label("ANIMAL")
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nlp.initialize()
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move_names = ["O", "B-ANIMAL", "I-ANIMAL", "L-ANIMAL", "U-ANIMAL"]
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assert ner.move_names == move_names
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nlp2 = English()
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ner2 = nlp2.add_pipe("ner")
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model = ner2.model
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model.attrs["resize_output"](model, ner.moves.n_moves)
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nlp2.from_bytes(nlp.to_bytes())
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assert ner2.move_names == move_names
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def test_issue3248_1():
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"""Test that the PhraseMatcher correctly reports its number of rules, not
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total number of patterns."""
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nlp = English()
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matcher = PhraseMatcher(nlp.vocab)
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matcher.add("TEST1", [nlp("a"), nlp("b"), nlp("c")])
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matcher.add("TEST2", [nlp("d")])
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assert len(matcher) == 2
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def test_issue3248_2():
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"""Test that the PhraseMatcher can be pickled correctly."""
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nlp = English()
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matcher = PhraseMatcher(nlp.vocab)
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matcher.add("TEST1", [nlp("a"), nlp("b"), nlp("c")])
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matcher.add("TEST2", [nlp("d")])
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data = pickle.dumps(matcher)
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new_matcher = pickle.loads(data)
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assert len(new_matcher) == len(matcher)
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def test_issue3277(es_tokenizer):
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"""Test that hyphens are split correctly as prefixes."""
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doc = es_tokenizer("—Yo me llamo... –murmuró el niño– Emilio Sánchez Pérez.")
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assert len(doc) == 14
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assert doc[0].text == "\u2014"
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assert doc[5].text == "\u2013"
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assert doc[9].text == "\u2013"
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def test_issue3288(en_vocab):
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"""Test that retokenization works correctly via displaCy when punctuation
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is merged onto the preceeding token and tensor is resized."""
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words = ["Hello", "World", "!", "When", "is", "this", "breaking", "?"]
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heads = [1, 1, 1, 4, 4, 6, 4, 4]
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deps = ["intj", "ROOT", "punct", "advmod", "ROOT", "det", "nsubj", "punct"]
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doc = Doc(en_vocab, words=words, heads=heads, deps=deps)
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doc.tensor = numpy.zeros((len(words), 96), dtype="float32")
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displacy.render(doc)
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def test_issue3289():
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"""Test that Language.to_bytes handles serializing a pipeline component
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with an uninitialized model."""
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nlp = English()
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nlp.add_pipe("textcat")
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bytes_data = nlp.to_bytes()
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new_nlp = English()
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new_nlp.add_pipe("textcat")
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new_nlp.from_bytes(bytes_data)
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def test_issue3328(en_vocab):
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doc = Doc(en_vocab, words=["Hello", ",", "how", "are", "you", "doing", "?"])
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matcher = Matcher(en_vocab)
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patterns = [
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[{"LOWER": {"IN": ["hello", "how"]}}],
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[{"LOWER": {"IN": ["you", "doing"]}}],
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]
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matcher.add("TEST", patterns)
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matches = matcher(doc)
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assert len(matches) == 4
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matched_texts = [doc[start:end].text for _, start, end in matches]
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assert matched_texts == ["Hello", "how", "you", "doing"]
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def test_issue3331(en_vocab):
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"""Test that duplicate patterns for different rules result in multiple
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matches, one per rule.
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"""
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matcher = PhraseMatcher(en_vocab)
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matcher.add("A", [Doc(en_vocab, words=["Barack", "Obama"])])
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matcher.add("B", [Doc(en_vocab, words=["Barack", "Obama"])])
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doc = Doc(en_vocab, words=["Barack", "Obama", "lifts", "America"])
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matches = matcher(doc)
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assert len(matches) == 2
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match_ids = [en_vocab.strings[matches[0][0]], en_vocab.strings[matches[1][0]]]
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assert sorted(match_ids) == ["A", "B"]
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def test_issue3345():
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"""Test case where preset entity crosses sentence boundary."""
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nlp = English()
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doc = Doc(nlp.vocab, words=["I", "live", "in", "New", "York"])
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doc[4].is_sent_start = True
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ruler = EntityRuler(nlp, patterns=[{"label": "GPE", "pattern": "New York"}])
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config = {
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"learn_tokens": False,
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"min_action_freq": 30,
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"update_with_oracle_cut_size": 100,
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}
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cfg = {"model": DEFAULT_NER_MODEL}
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model = registry.resolve(cfg, validate=True)["model"]
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ner = EntityRecognizer(doc.vocab, model, **config)
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# Add the OUT action. I wouldn't have thought this would be necessary...
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ner.moves.add_action(5, "")
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ner.add_label("GPE")
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doc = ruler(doc)
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# Get into the state just before "New"
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state = ner.moves.init_batch([doc])[0]
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ner.moves.apply_transition(state, "O")
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ner.moves.apply_transition(state, "O")
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ner.moves.apply_transition(state, "O")
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# Check that B-GPE is valid.
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assert ner.moves.is_valid(state, "B-GPE")
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def test_issue3412():
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data = numpy.asarray([[0, 0, 0], [1, 2, 3], [9, 8, 7]], dtype="f")
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vectors = Vectors(data=data, keys=["A", "B", "C"])
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keys, best_rows, scores = vectors.most_similar(
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numpy.asarray([[9, 8, 7], [0, 0, 0]], dtype="f")
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)
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assert best_rows[0] == 2
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@pytest.mark.skip(reason="default suffix rules avoid one upper-case letter before dot")
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def test_issue3449():
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nlp = English()
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nlp.add_pipe("sentencizer")
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text1 = "He gave the ball to I. Do you want to go to the movies with I?"
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text2 = "He gave the ball to I. Do you want to go to the movies with I?"
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text3 = "He gave the ball to I.\nDo you want to go to the movies with I?"
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t1 = nlp(text1)
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t2 = nlp(text2)
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t3 = nlp(text3)
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assert t1[5].text == "I"
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assert t2[5].text == "I"
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assert t3[5].text == "I"
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def test_issue3456():
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# this crashed because of a padding error in layer.ops.unflatten in thinc
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nlp = English()
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tagger = nlp.add_pipe("tagger")
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tagger.add_label("A")
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nlp.initialize()
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list(nlp.pipe(["hi", ""]))
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def test_issue3468():
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"""Test that sentence boundaries are set correctly so Doc.has_annotation("SENT_START") can
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be restored after serialization."""
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nlp = English()
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nlp.add_pipe("sentencizer")
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doc = nlp("Hello world")
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assert doc[0].is_sent_start
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assert doc.has_annotation("SENT_START")
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assert len(list(doc.sents)) == 1
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doc_bytes = doc.to_bytes()
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new_doc = Doc(nlp.vocab).from_bytes(doc_bytes)
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assert new_doc[0].is_sent_start
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assert new_doc.has_annotation("SENT_START")
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assert len(list(new_doc.sents)) == 1
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