mirror of https://github.com/explosion/spaCy.git
353 lines
14 KiB
Python
353 lines
14 KiB
Python
# coding: utf8
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from __future__ import unicode_literals
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import pytest
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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 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, is_python2, unescape_unicode
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from spacy import displacy
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from spacy.util import decaying
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import numpy
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import re
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from spacy.vectors import Vectors
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from ..util import get_doc
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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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[{"LEMMA": "have"}, {"LOWER": "to"}, {"LOWER": "do"}, {"TAG": "IN"}],
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[
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{"LEMMA": "have"},
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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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{"LEMMA": "have"},
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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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doc = get_doc(en_vocab, words=words, tags=tags)
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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 = [(2, 4, "PERCENT")]
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doc = get_doc(en_vocab, words=words, tags=tags, pos=pos, ents=ents)
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assert doc.is_tagged
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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 setting is_parsed to make sure the noun chunks run.
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"""
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doc = Doc(Vocab(), words=["This", "is", "a", "sentence"])
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doc.is_parsed = True
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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.create_pipe("ner")
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nlp.add_pipe(ner)
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ner.add_label("ANIMAL")
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nlp.begin_training()
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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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nlp2.add_pipe(nlp2.create_pipe("ner"))
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nlp2.from_bytes(nlp.to_bytes())
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assert nlp2.get_pipe("ner").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, 0, -1, 1, 0, 1, -2, -3]
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deps = ["intj", "ROOT", "punct", "advmod", "ROOT", "det", "nsubj", "punct"]
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doc = get_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(nlp.create_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(nlp.create_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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ner = EntityRecognizer(doc.vocab)
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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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if is_python2:
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# If we have this test in Python 3, pytest chokes, as it can't print the
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# string above in the xpass message.
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prefix_search = (
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b"^\xc2\xa7|^%|^=|^\xe2\x80\x94|^\xe2\x80\x93|^\\+(?![0-9])"
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b"|^\xe2\x80\xa6|^\xe2\x80\xa6\xe2\x80\xa6|^,|^:|^;|^\\!|^\\?"
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b"|^\xc2\xbf|^\xd8\x9f|^\xc2\xa1|^\\(|^\\)|^\\[|^\\]|^\\{|^\\}"
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b"|^<|^>|^_|^#|^\\*|^&|^\xe3\x80\x82|^\xef\xbc\x9f|^\xef\xbc\x81|"
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b"^\xef\xbc\x8c|^\xe3\x80\x81|^\xef\xbc\x9b|^\xef\xbc\x9a|"
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b"^\xef\xbd\x9e|^\xc2\xb7|^\xe0\xa5\xa4|^\xd8\x8c|^\xd8\x9b|"
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b"^\xd9\xaa|^\\.\\.+|^\xe2\x80\xa6|^\\'|^\"|^\xe2\x80\x9d|"
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b"^\xe2\x80\x9c|^`|^\xe2\x80\x98|^\xc2\xb4|^\xe2\x80\x99|"
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b"^\xe2\x80\x9a|^,|^\xe2\x80\x9e|^\xc2\xbb|^\xc2\xab|^\xe3\x80\x8c|"
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b"^\xe3\x80\x8d|^\xe3\x80\x8e|^\xe3\x80\x8f|^\xef\xbc\x88|"
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b"^\xef\xbc\x89|^\xe3\x80\x94|^\xe3\x80\x95|^\xe3\x80\x90|"
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b"^\xe3\x80\x91|^\xe3\x80\x8a|^\xe3\x80\x8b|^\xe3\x80\x88|"
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b"^\xe3\x80\x89|^\\$|^\xc2\xa3|^\xe2\x82\xac|^\xc2\xa5|^\xe0\xb8\xbf|"
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b"^US\\$|^C\\$|^A\\$|^\xe2\x82\xbd|^\xef\xb7\xbc|^\xe2\x82\xb4|"
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b"^[\\u00A6\\u00A9\\u00AE\\u00B0\\u0482\\u058D\\u058E\\u060E\\u060F"
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b"\\u06DE\\u06E9\\u06FD\\u06FE\\u07F6\\u09FA\\u0B70\\u0BF3-\\u0BF8"
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b"\\u0BFA\\u0C7F\\u0D4F\\u0D79\\u0F01-\\u0F03\\u0F13\\u0F15-\\u0F17"
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b"\\u0F1A-\\u0F1F\\u0F34\\u0F36\\u0F38\\u0FBE-\\u0FC5\\u0FC7-\\u0FCC"
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b"\\u0FCE\\u0FCF\\u0FD5-\\u0FD8\\u109E\\u109F\\u1390-\\u1399\\u1940"
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b"\\u19DE-\\u19FF\\u1B61-\\u1B6A\\u1B74-\\u1B7C\\u2100\\u2101\\u2103"
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b"-\\u2106\\u2108\\u2109\\u2114\\u2116\\u2117\\u211E-\\u2123\\u2125"
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b"\\u2127\\u2129\\u212E\\u213A\\u213B\\u214A\\u214C\\u214D\\u214F"
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b"\\u218A\\u218B\\u2195-\\u2199\\u219C-\\u219F\\u21A1\\u21A2\\u21A4"
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b"\\u21A5\\u21A7-\\u21AD\\u21AF-\\u21CD\\u21D0\\u21D1\\u21D3\\u21D5"
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b"-\\u21F3\\u2300-\\u2307\\u230C-\\u231F\\u2322-\\u2328\\u232B"
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b"-\\u237B\\u237D-\\u239A\\u23B4-\\u23DB\\u23E2-\\u2426\\u2440"
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b"-\\u244A\\u249C-\\u24E9\\u2500-\\u25B6\\u25B8-\\u25C0\\u25C2"
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b"-\\u25F7\\u2600-\\u266E\\u2670-\\u2767\\u2794-\\u27BF\\u2800"
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b"-\\u28FF\\u2B00-\\u2B2F\\u2B45\\u2B46\\u2B4D-\\u2B73\\u2B76"
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b"-\\u2B95\\u2B98-\\u2BC8\\u2BCA-\\u2BFE\\u2CE5-\\u2CEA\\u2E80"
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b"-\\u2E99\\u2E9B-\\u2EF3\\u2F00-\\u2FD5\\u2FF0-\\u2FFB\\u3004"
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b"\\u3012\\u3013\\u3020\\u3036\\u3037\\u303E\\u303F\\u3190\\u3191"
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b"\\u3196-\\u319F\\u31C0-\\u31E3\\u3200-\\u321E\\u322A-\\u3247\\u3250"
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b"\\u3260-\\u327F\\u328A-\\u32B0\\u32C0-\\u32FE\\u3300-\\u33FF\\u4DC0"
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b"-\\u4DFF\\uA490-\\uA4C6\\uA828-\\uA82B\\uA836\\uA837\\uA839\\uAA77"
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b"-\\uAA79\\uFDFD\\uFFE4\\uFFE8\\uFFED\\uFFEE\\uFFFC\\uFFFD\\U00010137"
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b"-\\U0001013F\\U00010179-\\U00010189\\U0001018C-\\U0001018E"
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b"\\U00010190-\\U0001019B\\U000101A0\\U000101D0-\\U000101FC\\U00010877"
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b"\\U00010878\\U00010AC8\\U0001173F\\U00016B3C-\\U00016B3F\\U00016B45"
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b"\\U0001BC9C\\U0001D000-\\U0001D0F5\\U0001D100-\\U0001D126\\U0001D129"
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b"-\\U0001D164\\U0001D16A-\\U0001D16C\\U0001D183\\U0001D184\\U0001D18C"
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b"-\\U0001D1A9\\U0001D1AE-\\U0001D1E8\\U0001D200-\\U0001D241\\U0001D245"
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b"\\U0001D300-\\U0001D356\\U0001D800-\\U0001D9FF\\U0001DA37-\\U0001DA3A"
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b"\\U0001DA6D-\\U0001DA74\\U0001DA76-\\U0001DA83\\U0001DA85\\U0001DA86"
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b"\\U0001ECAC\\U0001F000-\\U0001F02B\\U0001F030-\\U0001F093\\U0001F0A0"
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b"-\\U0001F0AE\\U0001F0B1-\\U0001F0BF\\U0001F0C1-\\U0001F0CF\\U0001F0D1"
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b"-\\U0001F0F5\\U0001F110-\\U0001F16B\\U0001F170-\\U0001F1AC\\U0001F1E6"
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b"-\\U0001F202\\U0001F210-\\U0001F23B\\U0001F240-\\U0001F248\\U0001F250"
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b"\\U0001F251\\U0001F260-\\U0001F265\\U0001F300-\\U0001F3FA\\U0001F400"
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b"-\\U0001F6D4\\U0001F6E0-\\U0001F6EC\\U0001F6F0-\\U0001F6F9\\U0001F700"
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b"-\\U0001F773\\U0001F780-\\U0001F7D8\\U0001F800-\\U0001F80B\\U0001F810"
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b"-\\U0001F847\\U0001F850-\\U0001F859\\U0001F860-\\U0001F887\\U0001F890"
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b"-\\U0001F8AD\\U0001F900-\\U0001F90B\\U0001F910-\\U0001F93E\\U0001F940"
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b"-\\U0001F970\\U0001F973-\\U0001F976\\U0001F97A\\U0001F97C-\\U0001F9A2"
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b"\\U0001F9B0-\\U0001F9B9\\U0001F9C0-\\U0001F9C2\\U0001F9D0-\\U0001F9FF"
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b"\\U0001FA60-\\U0001FA6D]"
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)
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def test_issue3356():
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pattern = re.compile(unescape_unicode(prefix_search.decode("utf8")))
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assert not pattern.search("hello")
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def test_issue3410():
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texts = ["Hello world", "This is a test"]
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nlp = English()
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matcher = Matcher(nlp.vocab)
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phrasematcher = PhraseMatcher(nlp.vocab)
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with pytest.deprecated_call():
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docs = list(nlp.pipe(texts, n_threads=4))
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with pytest.deprecated_call():
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docs = list(nlp.tokenizer.pipe(texts, n_threads=4))
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with pytest.deprecated_call():
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list(matcher.pipe(docs, n_threads=4))
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with pytest.deprecated_call():
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list(phrasematcher.pipe(docs, n_threads=4))
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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)
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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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def test_issue3447():
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sizes = decaying(10.0, 1.0, 0.5)
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size = next(sizes)
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assert size == 10.0
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size = next(sizes)
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assert size == 10.0 - 0.5
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size = next(sizes)
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assert size == 10.0 - 0.5 - 0.5
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@pytest.mark.xfail(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(nlp.create_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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@pytest.mark.filterwarnings("ignore::UserWarning")
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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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nlp.add_pipe(nlp.create_pipe("tagger"))
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nlp.begin_training()
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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.is_sentenced can
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be restored after serialization."""
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nlp = English()
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nlp.add_pipe(nlp.create_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.is_sentenced
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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
|
||
assert new_doc.is_sentenced
|
||
assert len(list(new_doc.sents)) == 1
|