mirror of
https://github.com/explosion/spaCy.git
synced 2024-09-21 19:39:13 +03:00
433 lines
15 KiB
Python
433 lines
15 KiB
Python
import pytest
|
|
from spacy.pipeline import TrainablePipe
|
|
from spacy.matcher import PhraseMatcher, Matcher
|
|
from spacy.tokens import Doc, Span, DocBin
|
|
from spacy.training import Example, Corpus
|
|
from spacy.training.converters import json_to_docs
|
|
from spacy.vocab import Vocab
|
|
from spacy.lang.en import English
|
|
from spacy.util import minibatch, ensure_path, load_model
|
|
from spacy.util import compile_prefix_regex, compile_suffix_regex, compile_infix_regex
|
|
from spacy.tokenizer import Tokenizer
|
|
from spacy.lang.el import Greek
|
|
from spacy.language import Language
|
|
import spacy
|
|
from thinc.api import compounding
|
|
|
|
from ..util import make_tempdir
|
|
|
|
|
|
def test_issue4002(en_vocab):
|
|
"""Test that the PhraseMatcher can match on overwritten NORM attributes."""
|
|
matcher = PhraseMatcher(en_vocab, attr="NORM")
|
|
pattern1 = Doc(en_vocab, words=["c", "d"])
|
|
assert [t.norm_ for t in pattern1] == ["c", "d"]
|
|
matcher.add("TEST", [pattern1])
|
|
doc = Doc(en_vocab, words=["a", "b", "c", "d"])
|
|
assert [t.norm_ for t in doc] == ["a", "b", "c", "d"]
|
|
matches = matcher(doc)
|
|
assert len(matches) == 1
|
|
matcher = PhraseMatcher(en_vocab, attr="NORM")
|
|
pattern2 = Doc(en_vocab, words=["1", "2"])
|
|
pattern2[0].norm_ = "c"
|
|
pattern2[1].norm_ = "d"
|
|
assert [t.norm_ for t in pattern2] == ["c", "d"]
|
|
matcher.add("TEST", [pattern2])
|
|
matches = matcher(doc)
|
|
assert len(matches) == 1
|
|
|
|
|
|
def test_issue4030():
|
|
""" Test whether textcat works fine with empty doc """
|
|
unique_classes = ["offensive", "inoffensive"]
|
|
x_train = [
|
|
"This is an offensive text",
|
|
"This is the second offensive text",
|
|
"inoff",
|
|
]
|
|
y_train = ["offensive", "offensive", "inoffensive"]
|
|
nlp = spacy.blank("en")
|
|
# preparing the data
|
|
train_data = []
|
|
for text, train_instance in zip(x_train, y_train):
|
|
cat_dict = {label: label == train_instance for label in unique_classes}
|
|
train_data.append(Example.from_dict(nlp.make_doc(text), {"cats": cat_dict}))
|
|
# add a text categorizer component
|
|
model = {
|
|
"@architectures": "spacy.TextCatBOW.v1",
|
|
"exclusive_classes": True,
|
|
"ngram_size": 2,
|
|
"no_output_layer": False,
|
|
}
|
|
textcat = nlp.add_pipe("textcat", config={"model": model}, last=True)
|
|
for label in unique_classes:
|
|
textcat.add_label(label)
|
|
# training the network
|
|
with nlp.select_pipes(enable="textcat"):
|
|
optimizer = nlp.initialize()
|
|
for i in range(3):
|
|
losses = {}
|
|
batches = minibatch(train_data, size=compounding(4.0, 32.0, 1.001))
|
|
|
|
for batch in batches:
|
|
nlp.update(examples=batch, sgd=optimizer, drop=0.1, losses=losses)
|
|
# processing of an empty doc should result in 0.0 for all categories
|
|
doc = nlp("")
|
|
assert doc.cats["offensive"] == 0.0
|
|
assert doc.cats["inoffensive"] == 0.0
|
|
|
|
|
|
def test_issue4042():
|
|
"""Test that serialization of an EntityRuler before NER works fine."""
|
|
nlp = English()
|
|
# add ner pipe
|
|
ner = nlp.add_pipe("ner")
|
|
ner.add_label("SOME_LABEL")
|
|
nlp.initialize()
|
|
# Add entity ruler
|
|
patterns = [
|
|
{"label": "MY_ORG", "pattern": "Apple"},
|
|
{"label": "MY_GPE", "pattern": [{"lower": "san"}, {"lower": "francisco"}]},
|
|
]
|
|
# works fine with "after"
|
|
ruler = nlp.add_pipe("entity_ruler", before="ner")
|
|
ruler.add_patterns(patterns)
|
|
doc1 = nlp("What do you think about Apple ?")
|
|
assert doc1.ents[0].label_ == "MY_ORG"
|
|
|
|
with make_tempdir() as d:
|
|
output_dir = ensure_path(d)
|
|
if not output_dir.exists():
|
|
output_dir.mkdir()
|
|
nlp.to_disk(output_dir)
|
|
nlp2 = load_model(output_dir)
|
|
doc2 = nlp2("What do you think about Apple ?")
|
|
assert doc2.ents[0].label_ == "MY_ORG"
|
|
|
|
|
|
def test_issue4042_bug2():
|
|
"""
|
|
Test that serialization of an NER works fine when new labels were added.
|
|
This is the second bug of two bugs underlying the issue 4042.
|
|
"""
|
|
nlp1 = English()
|
|
# add ner pipe
|
|
ner1 = nlp1.add_pipe("ner")
|
|
ner1.add_label("SOME_LABEL")
|
|
nlp1.initialize()
|
|
# add a new label to the doc
|
|
doc1 = nlp1("What do you think about Apple ?")
|
|
assert len(ner1.labels) == 1
|
|
assert "SOME_LABEL" in ner1.labels
|
|
apple_ent = Span(doc1, 5, 6, label="MY_ORG")
|
|
doc1.ents = list(doc1.ents) + [apple_ent]
|
|
# Add the label explicitly. Previously we didn't require this.
|
|
ner1.add_label("MY_ORG")
|
|
ner1(doc1)
|
|
assert len(ner1.labels) == 2
|
|
assert "SOME_LABEL" in ner1.labels
|
|
assert "MY_ORG" in ner1.labels
|
|
with make_tempdir() as d:
|
|
# assert IO goes fine
|
|
output_dir = ensure_path(d)
|
|
if not output_dir.exists():
|
|
output_dir.mkdir()
|
|
ner1.to_disk(output_dir)
|
|
config = {}
|
|
ner2 = nlp1.create_pipe("ner", config=config)
|
|
ner2.from_disk(output_dir)
|
|
assert len(ner2.labels) == 2
|
|
|
|
|
|
def test_issue4054(en_vocab):
|
|
"""Test that a new blank model can be made with a vocab from file,
|
|
and that serialization does not drop the language at any point."""
|
|
nlp1 = English()
|
|
vocab1 = nlp1.vocab
|
|
with make_tempdir() as d:
|
|
vocab_dir = ensure_path(d / "vocab")
|
|
if not vocab_dir.exists():
|
|
vocab_dir.mkdir()
|
|
vocab1.to_disk(vocab_dir)
|
|
vocab2 = Vocab().from_disk(vocab_dir)
|
|
nlp2 = spacy.blank("en", vocab=vocab2)
|
|
nlp_dir = ensure_path(d / "nlp")
|
|
if not nlp_dir.exists():
|
|
nlp_dir.mkdir()
|
|
nlp2.to_disk(nlp_dir)
|
|
nlp3 = load_model(nlp_dir)
|
|
assert nlp3.lang == "en"
|
|
|
|
|
|
def test_issue4120(en_vocab):
|
|
"""Test that matches without a final {OP: ?} token are returned."""
|
|
matcher = Matcher(en_vocab)
|
|
matcher.add("TEST", [[{"ORTH": "a"}, {"OP": "?"}]])
|
|
doc1 = Doc(en_vocab, words=["a"])
|
|
assert len(matcher(doc1)) == 1 # works
|
|
doc2 = Doc(en_vocab, words=["a", "b", "c"])
|
|
assert len(matcher(doc2)) == 2 # fixed
|
|
matcher = Matcher(en_vocab)
|
|
matcher.add("TEST", [[{"ORTH": "a"}, {"OP": "?"}, {"ORTH": "b"}]])
|
|
doc3 = Doc(en_vocab, words=["a", "b", "b", "c"])
|
|
assert len(matcher(doc3)) == 2 # works
|
|
matcher = Matcher(en_vocab)
|
|
matcher.add("TEST", [[{"ORTH": "a"}, {"OP": "?"}, {"ORTH": "b", "OP": "?"}]])
|
|
doc4 = Doc(en_vocab, words=["a", "b", "b", "c"])
|
|
assert len(matcher(doc4)) == 3 # fixed
|
|
|
|
|
|
def test_issue4133(en_vocab):
|
|
nlp = English()
|
|
vocab_bytes = nlp.vocab.to_bytes()
|
|
words = ["Apple", "is", "looking", "at", "buying", "a", "startup"]
|
|
pos = ["NOUN", "VERB", "ADP", "VERB", "PROPN", "NOUN", "ADP"]
|
|
doc = Doc(en_vocab, words=words)
|
|
for i, token in enumerate(doc):
|
|
token.pos_ = pos[i]
|
|
# usually this is already True when starting from proper models instead of blank English
|
|
doc_bytes = doc.to_bytes()
|
|
vocab = Vocab()
|
|
vocab = vocab.from_bytes(vocab_bytes)
|
|
doc = Doc(vocab).from_bytes(doc_bytes)
|
|
actual = []
|
|
for token in doc:
|
|
actual.append(token.pos_)
|
|
assert actual == pos
|
|
|
|
|
|
def test_issue4190():
|
|
def customize_tokenizer(nlp):
|
|
prefix_re = compile_prefix_regex(nlp.Defaults.prefixes)
|
|
suffix_re = compile_suffix_regex(nlp.Defaults.suffixes)
|
|
infix_re = compile_infix_regex(nlp.Defaults.infixes)
|
|
# Remove all exceptions where a single letter is followed by a period (e.g. 'h.')
|
|
exceptions = {
|
|
k: v
|
|
for k, v in dict(nlp.Defaults.tokenizer_exceptions).items()
|
|
if not (len(k) == 2 and k[1] == ".")
|
|
}
|
|
new_tokenizer = Tokenizer(
|
|
nlp.vocab,
|
|
exceptions,
|
|
prefix_search=prefix_re.search,
|
|
suffix_search=suffix_re.search,
|
|
infix_finditer=infix_re.finditer,
|
|
token_match=nlp.tokenizer.token_match,
|
|
)
|
|
nlp.tokenizer = new_tokenizer
|
|
|
|
test_string = "Test c."
|
|
# Load default language
|
|
nlp_1 = English()
|
|
doc_1a = nlp_1(test_string)
|
|
result_1a = [token.text for token in doc_1a] # noqa: F841
|
|
# Modify tokenizer
|
|
customize_tokenizer(nlp_1)
|
|
doc_1b = nlp_1(test_string)
|
|
result_1b = [token.text for token in doc_1b]
|
|
# Save and Reload
|
|
with make_tempdir() as model_dir:
|
|
nlp_1.to_disk(model_dir)
|
|
nlp_2 = load_model(model_dir)
|
|
# This should be the modified tokenizer
|
|
doc_2 = nlp_2(test_string)
|
|
result_2 = [token.text for token in doc_2]
|
|
assert result_1b == result_2
|
|
|
|
|
|
def test_issue4267():
|
|
""" Test that running an entity_ruler after ner gives consistent results"""
|
|
nlp = English()
|
|
ner = nlp.add_pipe("ner")
|
|
ner.add_label("PEOPLE")
|
|
nlp.initialize()
|
|
assert "ner" in nlp.pipe_names
|
|
# assert that we have correct IOB annotations
|
|
doc1 = nlp("hi")
|
|
assert doc1.has_annotation("ENT_IOB")
|
|
for token in doc1:
|
|
assert token.ent_iob == 2
|
|
# add entity ruler and run again
|
|
patterns = [{"label": "SOFTWARE", "pattern": "spacy"}]
|
|
ruler = nlp.add_pipe("entity_ruler")
|
|
ruler.add_patterns(patterns)
|
|
assert "entity_ruler" in nlp.pipe_names
|
|
assert "ner" in nlp.pipe_names
|
|
# assert that we still have correct IOB annotations
|
|
doc2 = nlp("hi")
|
|
assert doc2.has_annotation("ENT_IOB")
|
|
for token in doc2:
|
|
assert token.ent_iob == 2
|
|
|
|
|
|
@pytest.mark.skip(reason="lemmatizer lookups no longer in vocab")
|
|
def test_issue4272():
|
|
"""Test that lookup table can be accessed from Token.lemma if no POS tags
|
|
are available."""
|
|
nlp = Greek()
|
|
doc = nlp("Χθες")
|
|
assert doc[0].lemma_
|
|
|
|
|
|
def test_multiple_predictions():
|
|
class DummyPipe(TrainablePipe):
|
|
def __init__(self):
|
|
self.model = "dummy_model"
|
|
|
|
def predict(self, docs):
|
|
return ([1, 2, 3], [4, 5, 6])
|
|
|
|
def set_annotations(self, docs, scores):
|
|
return docs
|
|
|
|
nlp = Language()
|
|
doc = nlp.make_doc("foo")
|
|
dummy_pipe = DummyPipe()
|
|
dummy_pipe(doc)
|
|
|
|
|
|
def test_issue4313():
|
|
""" This should not crash or exit with some strange error code """
|
|
beam_width = 16
|
|
beam_density = 0.0001
|
|
nlp = English()
|
|
config = {
|
|
"beam_width": beam_width,
|
|
"beam_density": beam_density,
|
|
}
|
|
ner = nlp.add_pipe("beam_ner", config=config)
|
|
ner.add_label("SOME_LABEL")
|
|
nlp.initialize()
|
|
# add a new label to the doc
|
|
doc = nlp("What do you think about Apple ?")
|
|
assert len(ner.labels) == 1
|
|
assert "SOME_LABEL" in ner.labels
|
|
apple_ent = Span(doc, 5, 6, label="MY_ORG")
|
|
doc.ents = list(doc.ents) + [apple_ent]
|
|
|
|
# ensure the beam_parse still works with the new label
|
|
docs = [doc]
|
|
ner.beam_parse(docs, drop=0.0, beam_width=beam_width, beam_density=beam_density)
|
|
assert len(ner.labels) == 2
|
|
assert "MY_ORG" in ner.labels
|
|
|
|
|
|
def test_issue4348():
|
|
"""Test that training the tagger with empty data, doesn't throw errors"""
|
|
nlp = English()
|
|
example = Example.from_dict(nlp.make_doc(""), {"tags": []})
|
|
TRAIN_DATA = [example, example]
|
|
tagger = nlp.add_pipe("tagger")
|
|
tagger.add_label("A")
|
|
optimizer = nlp.initialize()
|
|
for i in range(5):
|
|
losses = {}
|
|
batches = minibatch(TRAIN_DATA, size=compounding(4.0, 32.0, 1.001))
|
|
for batch in batches:
|
|
nlp.update(batch, sgd=optimizer, losses=losses)
|
|
|
|
|
|
def test_issue4367():
|
|
"""Test that docbin init goes well"""
|
|
DocBin()
|
|
DocBin(attrs=["LEMMA"])
|
|
DocBin(attrs=["LEMMA", "ENT_IOB", "ENT_TYPE"])
|
|
|
|
|
|
def test_issue4373():
|
|
"""Test that PhraseMatcher.vocab can be accessed (like Matcher.vocab)."""
|
|
matcher = Matcher(Vocab())
|
|
assert isinstance(matcher.vocab, Vocab)
|
|
matcher = PhraseMatcher(Vocab())
|
|
assert isinstance(matcher.vocab, Vocab)
|
|
|
|
|
|
def test_issue4402():
|
|
json_data = {
|
|
"id": 0,
|
|
"paragraphs": [
|
|
{
|
|
"raw": "How should I cook bacon in an oven?\nI've heard of people cooking bacon in an oven.",
|
|
"sentences": [
|
|
{
|
|
"tokens": [
|
|
{"id": 0, "orth": "How", "ner": "O"},
|
|
{"id": 1, "orth": "should", "ner": "O"},
|
|
{"id": 2, "orth": "I", "ner": "O"},
|
|
{"id": 3, "orth": "cook", "ner": "O"},
|
|
{"id": 4, "orth": "bacon", "ner": "O"},
|
|
{"id": 5, "orth": "in", "ner": "O"},
|
|
{"id": 6, "orth": "an", "ner": "O"},
|
|
{"id": 7, "orth": "oven", "ner": "O"},
|
|
{"id": 8, "orth": "?", "ner": "O"},
|
|
],
|
|
"brackets": [],
|
|
},
|
|
{
|
|
"tokens": [
|
|
{"id": 9, "orth": "\n", "ner": "O"},
|
|
{"id": 10, "orth": "I", "ner": "O"},
|
|
{"id": 11, "orth": "'ve", "ner": "O"},
|
|
{"id": 12, "orth": "heard", "ner": "O"},
|
|
{"id": 13, "orth": "of", "ner": "O"},
|
|
{"id": 14, "orth": "people", "ner": "O"},
|
|
{"id": 15, "orth": "cooking", "ner": "O"},
|
|
{"id": 16, "orth": "bacon", "ner": "O"},
|
|
{"id": 17, "orth": "in", "ner": "O"},
|
|
{"id": 18, "orth": "an", "ner": "O"},
|
|
{"id": 19, "orth": "oven", "ner": "O"},
|
|
{"id": 20, "orth": ".", "ner": "O"},
|
|
],
|
|
"brackets": [],
|
|
},
|
|
],
|
|
"cats": [
|
|
{"label": "baking", "value": 1.0},
|
|
{"label": "not_baking", "value": 0.0},
|
|
],
|
|
},
|
|
{
|
|
"raw": "What is the difference between white and brown eggs?\n",
|
|
"sentences": [
|
|
{
|
|
"tokens": [
|
|
{"id": 0, "orth": "What", "ner": "O"},
|
|
{"id": 1, "orth": "is", "ner": "O"},
|
|
{"id": 2, "orth": "the", "ner": "O"},
|
|
{"id": 3, "orth": "difference", "ner": "O"},
|
|
{"id": 4, "orth": "between", "ner": "O"},
|
|
{"id": 5, "orth": "white", "ner": "O"},
|
|
{"id": 6, "orth": "and", "ner": "O"},
|
|
{"id": 7, "orth": "brown", "ner": "O"},
|
|
{"id": 8, "orth": "eggs", "ner": "O"},
|
|
{"id": 9, "orth": "?", "ner": "O"},
|
|
],
|
|
"brackets": [],
|
|
},
|
|
{"tokens": [{"id": 10, "orth": "\n", "ner": "O"}], "brackets": []},
|
|
],
|
|
"cats": [
|
|
{"label": "baking", "value": 0.0},
|
|
{"label": "not_baking", "value": 1.0},
|
|
],
|
|
},
|
|
],
|
|
}
|
|
nlp = English()
|
|
attrs = ["ORTH", "SENT_START", "ENT_IOB", "ENT_TYPE"]
|
|
with make_tempdir() as tmpdir:
|
|
output_file = tmpdir / "test4402.spacy"
|
|
docs = json_to_docs([json_data])
|
|
data = DocBin(docs=docs, attrs=attrs).to_bytes()
|
|
with output_file.open("wb") as file_:
|
|
file_.write(data)
|
|
reader = Corpus(output_file)
|
|
train_data = list(reader(nlp))
|
|
assert len(train_data) == 2
|
|
|
|
split_train_data = []
|
|
for eg in train_data:
|
|
split_train_data.extend(eg.split_sents())
|
|
assert len(split_train_data) == 4
|