import pytest from spacy import displacy from spacy.gold import Example from spacy.lang.en import English from spacy.lang.ja import Japanese from spacy.lang.xx import MultiLanguage from spacy.language import Language from spacy.matcher import Matcher from spacy.tokens import Doc, Span from spacy.vocab import Vocab from spacy.compat import pickle from spacy.util import link_vectors_to_models import numpy import random from ..util import get_doc def test_issue2564(): """Test the tagger sets is_tagged correctly when used via Language.pipe.""" nlp = Language() tagger = nlp.create_pipe("tagger") with pytest.warns(UserWarning): tagger.begin_training() # initialise weights nlp.add_pipe(tagger) doc = nlp("hello world") assert doc.is_tagged docs = nlp.pipe(["hello", "world"]) piped_doc = next(docs) assert piped_doc.is_tagged def test_issue2569(en_tokenizer): """Test that operator + is greedy.""" doc = en_tokenizer("It is May 15, 1993.") doc.ents = [Span(doc, 2, 6, label=doc.vocab.strings["DATE"])] matcher = Matcher(doc.vocab) matcher.add("RULE", [[{"ENT_TYPE": "DATE", "OP": "+"}]]) matched = [doc[start:end] for _, start, end in matcher(doc)] matched = sorted(matched, key=len, reverse=True) assert len(matched) == 10 assert len(matched[0]) == 4 assert matched[0].text == "May 15, 1993" @pytest.mark.parametrize( "text", [ "ABLEItemColumn IAcceptance Limits of ErrorIn-Service Limits of ErrorColumn IIColumn IIIColumn IVColumn VComputed VolumeUnder Registration of\xa0VolumeOver Registration of\xa0VolumeUnder Registration of\xa0VolumeOver Registration of\xa0VolumeCubic FeetCubic FeetCubic FeetCubic FeetCubic Feet1Up to 10.0100.0050.0100.005220.0200.0100.0200.010350.0360.0180.0360.0184100.0500.0250.0500.0255Over 100.5% of computed volume0.25% of computed volume0.5% of computed volume0.25% of computed volume TABLE ItemColumn IAcceptance Limits of ErrorIn-Service Limits of ErrorColumn IIColumn IIIColumn IVColumn VComputed VolumeUnder Registration of\xa0VolumeOver Registration of\xa0VolumeUnder Registration of\xa0VolumeOver Registration of\xa0VolumeCubic FeetCubic FeetCubic FeetCubic FeetCubic Feet1Up to 10.0100.0050.0100.005220.0200.0100.0200.010350.0360.0180.0360.0184100.0500.0250.0500.0255Over 100.5% of computed volume0.25% of computed volume0.5% of computed volume0.25% of computed volume ItemColumn IAcceptance Limits of ErrorIn-Service Limits of ErrorColumn IIColumn IIIColumn IVColumn VComputed VolumeUnder Registration of\xa0VolumeOver Registration of\xa0VolumeUnder Registration of\xa0VolumeOver Registration of\xa0VolumeCubic FeetCubic FeetCubic FeetCubic FeetCubic Feet1Up to 10.0100.0050.0100.005220.0200.0100.0200.010350.0360.0180.0360.0184100.0500.0250.0500.0255Over 100.5% of computed volume0.25% of computed volume0.5% of computed volume0.25% of computed volume", "oow.jspsearch.eventoracleopenworldsearch.technologyoraclesolarissearch.technologystoragesearch.technologylinuxsearch.technologyserverssearch.technologyvirtualizationsearch.technologyengineeredsystemspcodewwmkmppscem:", ], ) def test_issue2626_2835(en_tokenizer, text): """Check that sentence doesn't cause an infinite loop in the tokenizer.""" doc = en_tokenizer(text) assert doc def test_issue2656(en_tokenizer): """Test that tokenizer correctly splits of punctuation after numbers with decimal points. """ doc = en_tokenizer("I went for 40.3, and got home by 10.0.") assert len(doc) == 11 assert doc[0].text == "I" assert doc[1].text == "went" assert doc[2].text == "for" assert doc[3].text == "40.3" assert doc[4].text == "," assert doc[5].text == "and" assert doc[6].text == "got" assert doc[7].text == "home" assert doc[8].text == "by" assert doc[9].text == "10.0" assert doc[10].text == "." def test_issue2671(): """Ensure the correct entity ID is returned for matches with quantifiers. See also #2675 """ nlp = English() matcher = Matcher(nlp.vocab) pattern_id = "test_pattern" pattern = [ {"LOWER": "high"}, {"IS_PUNCT": True, "OP": "?"}, {"LOWER": "adrenaline"}, ] matcher.add(pattern_id, [pattern]) doc1 = nlp("This is a high-adrenaline situation.") doc2 = nlp("This is a high adrenaline situation.") matches1 = matcher(doc1) for match_id, start, end in matches1: assert nlp.vocab.strings[match_id] == pattern_id matches2 = matcher(doc2) for match_id, start, end in matches2: assert nlp.vocab.strings[match_id] == pattern_id def test_issue2728(en_vocab): """Test that displaCy ENT visualizer escapes HTML correctly.""" doc = Doc(en_vocab, words=["test", "", "test"]) doc.ents = [Span(doc, 0, 1, label="TEST")] html = displacy.render(doc, style="ent") assert "<RELEASE>" in html doc.ents = [Span(doc, 1, 2, label="TEST")] html = displacy.render(doc, style="ent") assert "<RELEASE>" in html def test_issue2754(en_tokenizer): """Test that words like 'a' and 'a.m.' don't get exceptional norm values.""" a = en_tokenizer("a") assert a[0].norm_ == "a" am = en_tokenizer("am") assert am[0].norm_ == "am" def test_issue2772(en_vocab): """Test that deprojectivization doesn't mess up sentence boundaries.""" words = "When we write or communicate virtually , we can hide our true feelings .".split() # A tree with a non-projective (i.e. crossing) arc # The arcs (0, 4) and (2, 9) cross. heads = [4, 1, 7, -1, -2, -1, 3, 2, 1, 0, 2, 1, -3, -4] deps = ["dep"] * len(heads) doc = get_doc(en_vocab, words=words, heads=heads, deps=deps) assert doc[1].is_sent_start is None @pytest.mark.parametrize("text", ["-0.23", "+123,456", "±1"]) @pytest.mark.parametrize("lang_cls", [English, MultiLanguage]) def test_issue2782(text, lang_cls): """Check that like_num handles + and - before number.""" nlp = lang_cls() doc = nlp(text) assert len(doc) == 1 assert doc[0].like_num @pytest.mark.filterwarnings("ignore::UserWarning") def test_issue2800(): """Test issue that arises when too many labels are added to NER model. Used to cause segfault. """ nlp = English() train_data = [] train_data.extend( [Example.from_dict(nlp.make_doc("One sentence"), {"entities": []})] ) entity_types = [str(i) for i in range(1000)] ner = nlp.create_pipe("ner") nlp.add_pipe(ner) for entity_type in list(entity_types): ner.add_label(entity_type) optimizer = nlp.begin_training() for i in range(20): losses = {} random.shuffle(train_data) for example in train_data: nlp.update([example], sgd=optimizer, losses=losses, drop=0.5) def test_issue2822(it_tokenizer): """Test that the abbreviation of poco is kept as one word.""" doc = it_tokenizer("Vuoi un po' di zucchero?") assert len(doc) == 6 assert doc[0].text == "Vuoi" assert doc[1].text == "un" assert doc[2].text == "po'" assert doc[2].lemma_ == "poco" assert doc[3].text == "di" assert doc[4].text == "zucchero" assert doc[5].text == "?" def test_issue2833(en_vocab): """Test that a custom error is raised if a token or span is pickled.""" doc = Doc(en_vocab, words=["Hello", "world"]) with pytest.raises(NotImplementedError): pickle.dumps(doc[0]) with pytest.raises(NotImplementedError): pickle.dumps(doc[0:2]) def test_issue2871(): """Test that vectors recover the correct key for spaCy reserved words.""" words = ["dog", "cat", "SUFFIX"] vocab = Vocab(vectors_name="test_issue2871") vocab.vectors.resize(shape=(3, 10)) vector_data = numpy.zeros((3, 10), dtype="f") for word in words: _ = vocab[word] # noqa: F841 vocab.set_vector(word, vector_data[0]) vocab.vectors.name = "dummy_vectors" link_vectors_to_models(vocab) assert vocab["dog"].rank == 0 assert vocab["cat"].rank == 1 assert vocab["SUFFIX"].rank == 2 assert vocab.vectors.find(key="dog") == 0 assert vocab.vectors.find(key="cat") == 1 assert vocab.vectors.find(key="SUFFIX") == 2 def test_issue2901(): """Test that `nlp` doesn't fail.""" try: nlp = Japanese() except ImportError: pytest.skip() doc = nlp("pythonが大好きです") assert doc def test_issue2926(fr_tokenizer): """Test that the tokenizer correctly splits tokens separated by a slash (/) ending in a digit. """ doc = fr_tokenizer("Learn html5/css3/javascript/jquery") assert len(doc) == 8 assert doc[0].text == "Learn" assert doc[1].text == "html5" assert doc[2].text == "/" assert doc[3].text == "css3" assert doc[4].text == "/" assert doc[5].text == "javascript" assert doc[6].text == "/" assert doc[7].text == "jquery"