spaCy/spacy/tests/regression/test_issue4001-4500.py
Adriane Boyd 7e4cd7575c
Refactor Docs.is_ flags (#6044)
* Refactor Docs.is_ flags

* Add derived `Doc.has_annotation` method

  * `Doc.has_annotation(attr)` returns `True` for partial annotation

  * `Doc.has_annotation(attr, require_complete=True)` returns `True` for
    complete annotation

* Add deprecation warnings to `is_tagged`, `is_parsed`, `is_sentenced`
and `is_nered`

* Add `Doc._get_array_attrs()`, which returns a full list of `Doc` attrs
for use with `Doc.to_array`, `Doc.to_bytes` and `Doc.from_docs`. The
list is the `DocBin` attributes list plus `SPACY` and `LENGTH`.

Notes on `Doc.has_annotation`:

* `HEAD` is converted to `DEP` because heads don't have an unset state

* Accept `IS_SENT_START` as a synonym of `SENT_START`

Additional changes:

* Add `NORM`, `ENT_ID` and `SENT_START` to default attributes for
`DocBin`

* In `Doc.from_array()` the presence of `DEP` causes `HEAD` to override
`SENT_START`

* In `Doc.from_array()` using `attrs` other than
`Doc._get_array_attrs()` (i.e., a user's custom list rather than our
default internal list) with both `HEAD` and `SENT_START` shows a warning
that `HEAD` will override `SENT_START`

* `set_children_from_heads` does not require dependency labels to set
sentence boundaries and sets `sent_start` for all non-sentence starts to
`-1`

* Fix call to set_children_form_heads

Co-authored-by: Matthew Honnibal <honnibal+gh@gmail.com>
2020-09-17 00:14:01 +02:00

440 lines
16 KiB
Python

import pytest
from spacy.pipeline import Pipe
from spacy.matcher import PhraseMatcher, Matcher
from spacy.tokens import Doc, Span, DocBin
from spacy.training import Example, Corpus
from spacy.training.converters import json2docs
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 collections import defaultdict
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.begin_training()
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.begin_training()
# 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.begin_training()
# 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]
# reapply the NER - at this point it should resize itself
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.begin_training()
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(Pipe):
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)
@pytest.mark.skip(reason="removed Beam stuff during the Example/GoldParse refactor")
def test_issue4313():
""" This should not crash or exit with some strange error code """
beam_width = 16
beam_density = 0.0001
nlp = English()
config = {}
ner = nlp.create_pipe("ner", config=config)
ner.add_label("SOME_LABEL")
ner.begin_training(lambda: [])
# 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]
beams = nlp.entity.beam_parse(
docs, beam_width=beam_width, beam_density=beam_density
)
for doc, beam in zip(docs, beams):
entity_scores = defaultdict(float)
for score, ents in nlp.entity.moves.get_beam_parses(beam):
for start, end, label in ents:
entity_scores[(start, end, label)] += score
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.begin_training()
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 = json2docs([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