spaCy/spacy/tests/regression/test_issue3001-3500.py
Sofie Van Landeghem c9da9605f7
Test suite clean up (#5781)
* step_through tests: skip instead of xfail

* test_empty_doc should be fixed with new Thinc version

* remove outdated test (there are other misaligned tests now)

* xfail reason

* fix test according to french exceptions

* clarified some skipped tests

* skip ukranian test instead of xfail

* skip instead of xfail

* skip + reason instead of xfail

* removed obsolete tests referring to removed "set_frozen" functionality

* fix test 999

* remove unused AlignmentError

* remove xfail where possible, skip otherwise

* increment thinc release for empty_doc test
2020-07-20 14:49:54 +02:00

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import pytest
from spacy.lang.en import English
from spacy.lang.de import German
from spacy.pipeline.defaults import default_ner
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
from spacy import displacy
import numpy
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 = [
[{"ORTH": "has"}, {"LOWER": "to"}, {"LOWER": "do"}, {"TAG": "IN"}],
[
{"ORTH": "has"},
{"IS_ASCII": True, "IS_PUNCT": False, "OP": "*"},
{"LOWER": "to"},
{"LOWER": "do"},
{"TAG": "IN"},
],
[
{"ORTH": "has"},
{"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"]
pos = ["ADV", "VERB", "ADP", "VERB", "ADP"]
doc = get_doc(en_vocab, words=words, tags=tags, pos=pos)
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) == []
@pytest.mark.filterwarnings("ignore::UserWarning")
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"))
model = nlp2.get_pipe("ner").model
model.attrs["resize_output"](model, ner.moves.n_moves)
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"}])
config = {
"learn_tokens": False,
"min_action_freq": 30,
"beam_width": 1,
"beam_update_prob": 1.0,
}
ner = EntityRecognizer(doc.vocab, default_ner(), **config)
# 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")
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
@pytest.mark.skip(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