spaCy/spacy/tests/regression/test_issue3001-3500.py

# coding: utf8
from __future__ import unicode_literals

import pytest
from spacy.lang.en import English
from spacy.lang.de import German
from spacy.pipeline import EntityRuler, EntityRecognizer
from spacy.matcher import Matcher, PhraseMatcher
from spacy.tokens import Doc
from spacy.vocab import Vocab
from spacy.attrs import ENT_IOB, ENT_TYPE
from spacy.compat import pickle, is_python2, unescape_unicode
from spacy import displacy
from spacy.util import decaying
import numpy
import re

from spacy.vectors import Vectors
from ..util import get_doc


def test_issue3002():
    """Test that the tokenizer doesn't hang on a long list of dots"""
    nlp = German()
    doc = nlp(
        "880.794.982.218.444.893.023.439.794.626.120.190.780.624.990.275.671 ist eine lange Zahl"
    )
    assert len(doc) == 5


def test_issue3009(en_vocab):
    """Test problem with matcher quantifiers"""
    patterns = [
        [{"LEMMA": "have"}, {"LOWER": "to"}, {"LOWER": "do"}, {"TAG": "IN"}],
        [
            {"LEMMA": "have"},
            {"IS_ASCII": True, "IS_PUNCT": False, "OP": "*"},
            {"LOWER": "to"},
            {"LOWER": "do"},
            {"TAG": "IN"},
        ],
        [
            {"LEMMA": "have"},
            {"IS_ASCII": True, "IS_PUNCT": False, "OP": "?"},
            {"LOWER": "to"},
            {"LOWER": "do"},
            {"TAG": "IN"},
        ],
    ]
    words = ["also", "has", "to", "do", "with"]
    tags = ["RB", "VBZ", "TO", "VB", "IN"]
    doc = get_doc(en_vocab, words=words, tags=tags)
    matcher = Matcher(en_vocab)
    for i, pattern in enumerate(patterns):
        matcher.add(str(i), [pattern])
        matches = matcher(doc)
        assert matches


def test_issue3012(en_vocab):
    """Test that the is_tagged attribute doesn't get overwritten when we from_array
    without tag information."""
    words = ["This", "is", "10", "%", "."]
    tags = ["DT", "VBZ", "CD", "NN", "."]
    pos = ["DET", "VERB", "NUM", "NOUN", "PUNCT"]
    ents = [(2, 4, "PERCENT")]
    doc = get_doc(en_vocab, words=words, tags=tags, pos=pos, ents=ents)
    assert doc.is_tagged

    expected = ("10", "NUM", "CD", "PERCENT")
    assert (doc[2].text, doc[2].pos_, doc[2].tag_, doc[2].ent_type_) == expected

    header = [ENT_IOB, ENT_TYPE]
    ent_array = doc.to_array(header)
    doc.from_array(header, ent_array)

    assert (doc[2].text, doc[2].pos_, doc[2].tag_, doc[2].ent_type_) == expected

    # Serializing then deserializing
    doc_bytes = doc.to_bytes()
    doc2 = Doc(en_vocab).from_bytes(doc_bytes)
    assert (doc2[2].text, doc2[2].pos_, doc2[2].tag_, doc2[2].ent_type_) == expected


def test_issue3199():
    """Test that Span.noun_chunks works correctly if no noun chunks iterator
    is available. To make this test future-proof, we're constructing a Doc
    with a new Vocab here and setting is_parsed to make sure the noun chunks run.
    """
    doc = Doc(Vocab(), words=["This", "is", "a", "sentence"])
    doc.is_parsed = True
    assert list(doc[0:3].noun_chunks) == []


def test_issue3209():
    """Test issue that occurred in spaCy nightly where NER labels were being
    mapped to classes incorrectly after loading the model, when the labels
    were added using ner.add_label().
    """
    nlp = English()
    ner = nlp.create_pipe("ner")
    nlp.add_pipe(ner)

    ner.add_label("ANIMAL")
    nlp.begin_training()
    move_names = ["O", "B-ANIMAL", "I-ANIMAL", "L-ANIMAL", "U-ANIMAL"]
    assert ner.move_names == move_names
    nlp2 = English()
    nlp2.add_pipe(nlp2.create_pipe("ner"))
    nlp2.from_bytes(nlp.to_bytes())
    assert nlp2.get_pipe("ner").move_names == move_names


def test_issue3248_1():
    """Test that the PhraseMatcher correctly reports its number of rules, not
    total number of patterns."""
    nlp = English()
    matcher = PhraseMatcher(nlp.vocab)
    matcher.add("TEST1", [nlp("a"), nlp("b"), nlp("c")])
    matcher.add("TEST2", [nlp("d")])
    assert len(matcher) == 2


def test_issue3248_2():
    """Test that the PhraseMatcher can be pickled correctly."""
    nlp = English()
    matcher = PhraseMatcher(nlp.vocab)
    matcher.add("TEST1", [nlp("a"), nlp("b"), nlp("c")])
    matcher.add("TEST2", [nlp("d")])
    data = pickle.dumps(matcher)
    new_matcher = pickle.loads(data)
    assert len(new_matcher) == len(matcher)


def test_issue3277(es_tokenizer):
    """Test that hyphens are split correctly as prefixes."""
    doc = es_tokenizer("—Yo me llamo... –murmuró el niño– Emilio Sánchez Pérez.")
    assert len(doc) == 14
    assert doc[0].text == "\u2014"
    assert doc[5].text == "\u2013"
    assert doc[9].text == "\u2013"


def test_issue3288(en_vocab):
    """Test that retokenization works correctly via displaCy when punctuation
    is merged onto the preceeding token and tensor is resized."""
    words = ["Hello", "World", "!", "When", "is", "this", "breaking", "?"]
    heads = [1, 0, -1, 1, 0, 1, -2, -3]
    deps = ["intj", "ROOT", "punct", "advmod", "ROOT", "det", "nsubj", "punct"]
    doc = get_doc(en_vocab, words=words, heads=heads, deps=deps)
    doc.tensor = numpy.zeros((len(words), 96), dtype="float32")
    displacy.render(doc)


def test_issue3289():
    """Test that Language.to_bytes handles serializing a pipeline component
    with an uninitialized model."""
    nlp = English()
    nlp.add_pipe(nlp.create_pipe("textcat"))
    bytes_data = nlp.to_bytes()
    new_nlp = English()
    new_nlp.add_pipe(nlp.create_pipe("textcat"))
    new_nlp.from_bytes(bytes_data)


def test_issue3328(en_vocab):
    doc = Doc(en_vocab, words=["Hello", ",", "how", "are", "you", "doing", "?"])
    matcher = Matcher(en_vocab)
    patterns = [
        [{"LOWER": {"IN": ["hello", "how"]}}],
        [{"LOWER": {"IN": ["you", "doing"]}}],
    ]
    matcher.add("TEST", patterns)
    matches = matcher(doc)
    assert len(matches) == 4
    matched_texts = [doc[start:end].text for _, start, end in matches]
    assert matched_texts == ["Hello", "how", "you", "doing"]


def test_issue3331(en_vocab):
    """Test that duplicate patterns for different rules result in multiple
    matches, one per rule.
    """
    matcher = PhraseMatcher(en_vocab)
    matcher.add("A", [Doc(en_vocab, words=["Barack", "Obama"])])
    matcher.add("B", [Doc(en_vocab, words=["Barack", "Obama"])])
    doc = Doc(en_vocab, words=["Barack", "Obama", "lifts", "America"])
    matches = matcher(doc)
    assert len(matches) == 2
    match_ids = [en_vocab.strings[matches[0][0]], en_vocab.strings[matches[1][0]]]
    assert sorted(match_ids) == ["A", "B"]


def test_issue3345():
    """Test case where preset entity crosses sentence boundary."""
    nlp = English()
    doc = Doc(nlp.vocab, words=["I", "live", "in", "New", "York"])
    doc[4].is_sent_start = True
    ruler = EntityRuler(nlp, patterns=[{"label": "GPE", "pattern": "New York"}])
    ner = EntityRecognizer(doc.vocab)
    # Add the OUT action. I wouldn't have thought this would be necessary...
    ner.moves.add_action(5, "")
    ner.add_label("GPE")
    doc = ruler(doc)
    # Get into the state just before "New"
    state = ner.moves.init_batch([doc])[0]
    ner.moves.apply_transition(state, "O")
    ner.moves.apply_transition(state, "O")
    ner.moves.apply_transition(state, "O")
    # Check that B-GPE is valid.
    assert ner.moves.is_valid(state, "B-GPE")


if is_python2:
    # If we have this test in Python 3, pytest chokes, as it can't print the
    # string above in the xpass message.
    prefix_search = (
        b"^\xc2\xa7|^%|^=|^\xe2\x80\x94|^\xe2\x80\x93|^\\+(?![0-9])"
        b"|^\xe2\x80\xa6|^\xe2\x80\xa6\xe2\x80\xa6|^,|^:|^;|^\\!|^\\?"
        b"|^\xc2\xbf|^\xd8\x9f|^\xc2\xa1|^\\(|^\\)|^\\[|^\\]|^\\{|^\\}"
        b"|^<|^>|^_|^#|^\\*|^&|^\xe3\x80\x82|^\xef\xbc\x9f|^\xef\xbc\x81|"
        b"^\xef\xbc\x8c|^\xe3\x80\x81|^\xef\xbc\x9b|^\xef\xbc\x9a|"
        b"^\xef\xbd\x9e|^\xc2\xb7|^\xe0\xa5\xa4|^\xd8\x8c|^\xd8\x9b|"
        b"^\xd9\xaa|^\\.\\.+|^\xe2\x80\xa6|^\\'|^\"|^\xe2\x80\x9d|"
        b"^\xe2\x80\x9c|^`|^\xe2\x80\x98|^\xc2\xb4|^\xe2\x80\x99|"
        b"^\xe2\x80\x9a|^,|^\xe2\x80\x9e|^\xc2\xbb|^\xc2\xab|^\xe3\x80\x8c|"
        b"^\xe3\x80\x8d|^\xe3\x80\x8e|^\xe3\x80\x8f|^\xef\xbc\x88|"
        b"^\xef\xbc\x89|^\xe3\x80\x94|^\xe3\x80\x95|^\xe3\x80\x90|"
        b"^\xe3\x80\x91|^\xe3\x80\x8a|^\xe3\x80\x8b|^\xe3\x80\x88|"
        b"^\xe3\x80\x89|^\\$|^\xc2\xa3|^\xe2\x82\xac|^\xc2\xa5|^\xe0\xb8\xbf|"
        b"^US\\$|^C\\$|^A\\$|^\xe2\x82\xbd|^\xef\xb7\xbc|^\xe2\x82\xb4|"
        b"^[\\u00A6\\u00A9\\u00AE\\u00B0\\u0482\\u058D\\u058E\\u060E\\u060F"
        b"\\u06DE\\u06E9\\u06FD\\u06FE\\u07F6\\u09FA\\u0B70\\u0BF3-\\u0BF8"
        b"\\u0BFA\\u0C7F\\u0D4F\\u0D79\\u0F01-\\u0F03\\u0F13\\u0F15-\\u0F17"
        b"\\u0F1A-\\u0F1F\\u0F34\\u0F36\\u0F38\\u0FBE-\\u0FC5\\u0FC7-\\u0FCC"
        b"\\u0FCE\\u0FCF\\u0FD5-\\u0FD8\\u109E\\u109F\\u1390-\\u1399\\u1940"
        b"\\u19DE-\\u19FF\\u1B61-\\u1B6A\\u1B74-\\u1B7C\\u2100\\u2101\\u2103"
        b"-\\u2106\\u2108\\u2109\\u2114\\u2116\\u2117\\u211E-\\u2123\\u2125"
        b"\\u2127\\u2129\\u212E\\u213A\\u213B\\u214A\\u214C\\u214D\\u214F"
        b"\\u218A\\u218B\\u2195-\\u2199\\u219C-\\u219F\\u21A1\\u21A2\\u21A4"
        b"\\u21A5\\u21A7-\\u21AD\\u21AF-\\u21CD\\u21D0\\u21D1\\u21D3\\u21D5"
        b"-\\u21F3\\u2300-\\u2307\\u230C-\\u231F\\u2322-\\u2328\\u232B"
        b"-\\u237B\\u237D-\\u239A\\u23B4-\\u23DB\\u23E2-\\u2426\\u2440"
        b"-\\u244A\\u249C-\\u24E9\\u2500-\\u25B6\\u25B8-\\u25C0\\u25C2"
        b"-\\u25F7\\u2600-\\u266E\\u2670-\\u2767\\u2794-\\u27BF\\u2800"
        b"-\\u28FF\\u2B00-\\u2B2F\\u2B45\\u2B46\\u2B4D-\\u2B73\\u2B76"
        b"-\\u2B95\\u2B98-\\u2BC8\\u2BCA-\\u2BFE\\u2CE5-\\u2CEA\\u2E80"
        b"-\\u2E99\\u2E9B-\\u2EF3\\u2F00-\\u2FD5\\u2FF0-\\u2FFB\\u3004"
        b"\\u3012\\u3013\\u3020\\u3036\\u3037\\u303E\\u303F\\u3190\\u3191"
        b"\\u3196-\\u319F\\u31C0-\\u31E3\\u3200-\\u321E\\u322A-\\u3247\\u3250"
        b"\\u3260-\\u327F\\u328A-\\u32B0\\u32C0-\\u32FE\\u3300-\\u33FF\\u4DC0"
        b"-\\u4DFF\\uA490-\\uA4C6\\uA828-\\uA82B\\uA836\\uA837\\uA839\\uAA77"
        b"-\\uAA79\\uFDFD\\uFFE4\\uFFE8\\uFFED\\uFFEE\\uFFFC\\uFFFD\\U00010137"
        b"-\\U0001013F\\U00010179-\\U00010189\\U0001018C-\\U0001018E"
        b"\\U00010190-\\U0001019B\\U000101A0\\U000101D0-\\U000101FC\\U00010877"
        b"\\U00010878\\U00010AC8\\U0001173F\\U00016B3C-\\U00016B3F\\U00016B45"
        b"\\U0001BC9C\\U0001D000-\\U0001D0F5\\U0001D100-\\U0001D126\\U0001D129"
        b"-\\U0001D164\\U0001D16A-\\U0001D16C\\U0001D183\\U0001D184\\U0001D18C"
        b"-\\U0001D1A9\\U0001D1AE-\\U0001D1E8\\U0001D200-\\U0001D241\\U0001D245"
        b"\\U0001D300-\\U0001D356\\U0001D800-\\U0001D9FF\\U0001DA37-\\U0001DA3A"
        b"\\U0001DA6D-\\U0001DA74\\U0001DA76-\\U0001DA83\\U0001DA85\\U0001DA86"
        b"\\U0001ECAC\\U0001F000-\\U0001F02B\\U0001F030-\\U0001F093\\U0001F0A0"
        b"-\\U0001F0AE\\U0001F0B1-\\U0001F0BF\\U0001F0C1-\\U0001F0CF\\U0001F0D1"
        b"-\\U0001F0F5\\U0001F110-\\U0001F16B\\U0001F170-\\U0001F1AC\\U0001F1E6"
        b"-\\U0001F202\\U0001F210-\\U0001F23B\\U0001F240-\\U0001F248\\U0001F250"
        b"\\U0001F251\\U0001F260-\\U0001F265\\U0001F300-\\U0001F3FA\\U0001F400"
        b"-\\U0001F6D4\\U0001F6E0-\\U0001F6EC\\U0001F6F0-\\U0001F6F9\\U0001F700"
        b"-\\U0001F773\\U0001F780-\\U0001F7D8\\U0001F800-\\U0001F80B\\U0001F810"
        b"-\\U0001F847\\U0001F850-\\U0001F859\\U0001F860-\\U0001F887\\U0001F890"
        b"-\\U0001F8AD\\U0001F900-\\U0001F90B\\U0001F910-\\U0001F93E\\U0001F940"
        b"-\\U0001F970\\U0001F973-\\U0001F976\\U0001F97A\\U0001F97C-\\U0001F9A2"
        b"\\U0001F9B0-\\U0001F9B9\\U0001F9C0-\\U0001F9C2\\U0001F9D0-\\U0001F9FF"
        b"\\U0001FA60-\\U0001FA6D]"
    )

    def test_issue3356():
        pattern = re.compile(unescape_unicode(prefix_search.decode("utf8")))
        assert not pattern.search("hello")


def test_issue3410():
    texts = ["Hello world", "This is a test"]
    nlp = English()
    matcher = Matcher(nlp.vocab)
    phrasematcher = PhraseMatcher(nlp.vocab)
    with pytest.deprecated_call():
        docs = list(nlp.pipe(texts, n_threads=4))
    with pytest.deprecated_call():
        docs = list(nlp.tokenizer.pipe(texts, n_threads=4))
    with pytest.deprecated_call():
        list(matcher.pipe(docs, n_threads=4))
    with pytest.deprecated_call():
        list(phrasematcher.pipe(docs, n_threads=4))


def test_issue3412():
    data = numpy.asarray([[0, 0, 0], [1, 2, 3], [9, 8, 7]], dtype="f")
    vectors = Vectors(data=data, keys=["A", "B", "C"])
    keys, best_rows, scores = vectors.most_similar(
        numpy.asarray([[9, 8, 7], [0, 0, 0]], dtype="f")
    )
    assert best_rows[0] == 2


def test_issue3447():
    sizes = decaying(10.0, 1.0, 0.5)
    size = next(sizes)
    assert size == 10.0
    size = next(sizes)
    assert size == 10.0 - 0.5
    size = next(sizes)
    assert size == 10.0 - 0.5 - 0.5


@pytest.mark.xfail(reason="default suffix rules avoid one upper-case letter before dot")
def test_issue3449():
    nlp = English()
    nlp.add_pipe(nlp.create_pipe("sentencizer"))
    text1 = "He gave the ball to I. Do you want to go to the movies with I?"
    text2 = "He gave the ball to I.  Do you want to go to the movies with I?"
    text3 = "He gave the ball to I.\nDo you want to go to the movies with I?"
    t1 = nlp(text1)
    t2 = nlp(text2)
    t3 = nlp(text3)
    assert t1[5].text == "I"
    assert t2[5].text == "I"
    assert t3[5].text == "I"


@pytest.mark.filterwarnings("ignore::UserWarning")
def test_issue3456():
    # this crashed because of a padding error in layer.ops.unflatten in thinc
    nlp = English()
    nlp.add_pipe(nlp.create_pipe("tagger"))
    nlp.begin_training()
    list(nlp.pipe(["hi", ""]))


def test_issue3468():
    """Test that sentence boundaries are set correctly so Doc.is_sentenced can
    be restored after serialization."""
    nlp = English()
    nlp.add_pipe(nlp.create_pipe("sentencizer"))
    doc = nlp("Hello world")
    assert doc[0].is_sent_start
    assert doc.is_sentenced
    assert len(list(doc.sents)) == 1
    doc_bytes = doc.to_bytes()
    new_doc = Doc(nlp.vocab).from_bytes(doc_bytes)
    assert new_doc[0].is_sent_start
    assert new_doc.is_sentenced
    assert len(list(new_doc.sents)) == 1