mirror of
https://github.com/explosion/spaCy.git
synced 2024-12-29 11:26:28 +03:00
877671e09a
Preserve both `-` and `O` annotation in augmenters rather than relying on `Example.to_dict`'s default support for one option outside of labeled entity spans. This is intended as a temporary workaround for augmenters for v3.4.x. The behavior of `Example` and related IOB utils could be improved in the general case for v3.5.
242 lines
10 KiB
Python
242 lines
10 KiB
Python
import pytest
|
||
from spacy.pipeline._parser_internals.nonproj import contains_cycle
|
||
from spacy.training import Corpus, Example
|
||
from spacy.training.augment import create_orth_variants_augmenter
|
||
from spacy.training.augment import create_lower_casing_augmenter
|
||
from spacy.training.augment import make_whitespace_variant
|
||
from spacy.lang.en import English
|
||
from spacy.tokens import DocBin, Doc, Span
|
||
from contextlib import contextmanager
|
||
import random
|
||
|
||
from ..util import make_tempdir
|
||
|
||
|
||
@contextmanager
|
||
def make_docbin(docs, name="roundtrip.spacy"):
|
||
with make_tempdir() as tmpdir:
|
||
output_file = tmpdir / name
|
||
DocBin(docs=docs).to_disk(output_file)
|
||
yield output_file
|
||
|
||
|
||
@pytest.fixture
|
||
def nlp():
|
||
return English()
|
||
|
||
|
||
@pytest.fixture
|
||
def doc(nlp):
|
||
# fmt: off
|
||
words = ["Sarah", "'s", "sister", "flew", "to", "Silicon", "Valley", "via", "London", "."]
|
||
tags = ["NNP", "POS", "NN", "VBD", "IN", "NNP", "NNP", "IN", "NNP", "."]
|
||
pos = ["PROPN", "PART", "NOUN", "VERB", "ADP", "PROPN", "PROPN", "ADP", "PROPN", "PUNCT"]
|
||
ents = ["B-PERSON", "I-PERSON", "O", "", "O", "B-LOC", "I-LOC", "O", "B-GPE", "O"]
|
||
cats = {"TRAVEL": 1.0, "BAKING": 0.0}
|
||
# fmt: on
|
||
doc = Doc(nlp.vocab, words=words, tags=tags, pos=pos, ents=ents)
|
||
doc.cats = cats
|
||
return doc
|
||
|
||
|
||
@pytest.mark.filterwarnings("ignore::UserWarning")
|
||
def test_make_orth_variants(nlp):
|
||
single = [
|
||
{"tags": ["NFP"], "variants": ["…", "..."]},
|
||
{"tags": [":"], "variants": ["-", "—", "–", "--", "---", "——"]},
|
||
]
|
||
# fmt: off
|
||
words = ["\n\n", "A", "\t", "B", "a", "b", "…", "...", "-", "—", "–", "--", "---", "——"]
|
||
tags = ["_SP", "NN", "\t", "NN", "NN", "NN", "NFP", "NFP", ":", ":", ":", ":", ":", ":"]
|
||
# fmt: on
|
||
spaces = [True] * len(words)
|
||
spaces[0] = False
|
||
spaces[2] = False
|
||
doc = Doc(nlp.vocab, words=words, spaces=spaces, tags=tags)
|
||
augmenter = create_orth_variants_augmenter(
|
||
level=0.2, lower=0.5, orth_variants={"single": single}
|
||
)
|
||
with make_docbin([doc] * 10) as output_file:
|
||
reader = Corpus(output_file, augmenter=augmenter)
|
||
# Due to randomness, only test that it works without errors
|
||
list(reader(nlp))
|
||
|
||
# check that the following settings lowercase everything
|
||
augmenter = create_orth_variants_augmenter(
|
||
level=1.0, lower=1.0, orth_variants={"single": single}
|
||
)
|
||
with make_docbin([doc] * 10) as output_file:
|
||
reader = Corpus(output_file, augmenter=augmenter)
|
||
for example in reader(nlp):
|
||
for token in example.reference:
|
||
assert token.text == token.text.lower()
|
||
|
||
# check that lowercasing is applied without tags
|
||
doc = Doc(nlp.vocab, words=words, spaces=[True] * len(words))
|
||
augmenter = create_orth_variants_augmenter(
|
||
level=1.0, lower=1.0, orth_variants={"single": single}
|
||
)
|
||
with make_docbin([doc] * 10) as output_file:
|
||
reader = Corpus(output_file, augmenter=augmenter)
|
||
for example in reader(nlp):
|
||
for ex_token, doc_token in zip(example.reference, doc):
|
||
assert ex_token.text == doc_token.text.lower()
|
||
|
||
# check that no lowercasing is applied with lower=0.0
|
||
doc = Doc(nlp.vocab, words=words, spaces=[True] * len(words))
|
||
augmenter = create_orth_variants_augmenter(
|
||
level=1.0, lower=0.0, orth_variants={"single": single}
|
||
)
|
||
with make_docbin([doc] * 10) as output_file:
|
||
reader = Corpus(output_file, augmenter=augmenter)
|
||
for example in reader(nlp):
|
||
for ex_token, doc_token in zip(example.reference, doc):
|
||
assert ex_token.text == doc_token.text
|
||
|
||
|
||
def test_lowercase_augmenter(nlp, doc):
|
||
augmenter = create_lower_casing_augmenter(level=1.0)
|
||
with make_docbin([doc]) as output_file:
|
||
reader = Corpus(output_file, augmenter=augmenter)
|
||
corpus = list(reader(nlp))
|
||
eg = corpus[0]
|
||
assert eg.reference.text == doc.text.lower()
|
||
assert eg.predicted.text == doc.text.lower()
|
||
ents = [(e.start, e.end, e.label) for e in doc.ents]
|
||
assert [(e.start, e.end, e.label) for e in eg.reference.ents] == ents
|
||
for ref_ent, orig_ent in zip(eg.reference.ents, doc.ents):
|
||
assert ref_ent.text == orig_ent.text.lower()
|
||
assert [t.ent_iob for t in doc] == [t.ent_iob for t in eg.reference]
|
||
assert [t.pos_ for t in eg.reference] == [t.pos_ for t in doc]
|
||
|
||
# check that augmentation works when lowercasing leads to different
|
||
# predicted tokenization
|
||
words = ["A", "B", "CCC."]
|
||
doc = Doc(nlp.vocab, words=words)
|
||
with make_docbin([doc]) as output_file:
|
||
reader = Corpus(output_file, augmenter=augmenter)
|
||
corpus = list(reader(nlp))
|
||
eg = corpus[0]
|
||
assert eg.reference.text == doc.text.lower()
|
||
assert eg.predicted.text == doc.text.lower()
|
||
assert [t.text for t in eg.reference] == [t.lower() for t in words]
|
||
assert [t.text for t in eg.predicted] == [
|
||
t.text for t in nlp.make_doc(doc.text.lower())
|
||
]
|
||
|
||
|
||
@pytest.mark.filterwarnings("ignore::UserWarning")
|
||
def test_custom_data_augmentation(nlp, doc):
|
||
def create_spongebob_augmenter(randomize: bool = False):
|
||
def augment(nlp, example):
|
||
text = example.text
|
||
if randomize:
|
||
ch = [c.lower() if random.random() < 0.5 else c.upper() for c in text]
|
||
else:
|
||
ch = [c.lower() if i % 2 else c.upper() for i, c in enumerate(text)]
|
||
example_dict = example.to_dict()
|
||
doc = nlp.make_doc("".join(ch))
|
||
example_dict["token_annotation"]["ORTH"] = [t.text for t in doc]
|
||
yield example
|
||
yield example.from_dict(doc, example_dict)
|
||
|
||
return augment
|
||
|
||
with make_docbin([doc]) as output_file:
|
||
reader = Corpus(output_file, augmenter=create_spongebob_augmenter())
|
||
corpus = list(reader(nlp))
|
||
orig_text = "Sarah 's sister flew to Silicon Valley via London . "
|
||
augmented = "SaRaH 's sIsTeR FlEw tO SiLiCoN VaLlEy vIa lOnDoN . "
|
||
assert corpus[0].text == orig_text
|
||
assert corpus[0].reference.text == orig_text
|
||
assert corpus[0].predicted.text == orig_text
|
||
assert corpus[1].text == augmented
|
||
assert corpus[1].reference.text == augmented
|
||
assert corpus[1].predicted.text == augmented
|
||
ents = [(e.start, e.end, e.label) for e in doc.ents]
|
||
assert [(e.start, e.end, e.label) for e in corpus[0].reference.ents] == ents
|
||
assert [(e.start, e.end, e.label) for e in corpus[1].reference.ents] == ents
|
||
|
||
|
||
def test_make_whitespace_variant(nlp):
|
||
# fmt: off
|
||
text = "They flew to New York City.\nThen they drove to Washington, D.C."
|
||
words = ["They", "flew", "to", "New", "York", "City", ".", "\n", "Then", "they", "drove", "to", "Washington", ",", "D.C."]
|
||
spaces = [True, True, True, True, True, False, False, False, True, True, True, True, False, True, False]
|
||
tags = ["PRP", "VBD", "IN", "NNP", "NNP", "NNP", ".", "_SP", "RB", "PRP", "VBD", "IN", "NNP", ",", "NNP"]
|
||
lemmas = ["they", "fly", "to", "New", "York", "City", ".", "\n", "then", "they", "drive", "to", "Washington", ",", "D.C."]
|
||
heads = [1, 1, 1, 4, 5, 2, 1, 10, 10, 10, 10, 10, 11, 12, 12]
|
||
deps = ["nsubj", "ROOT", "prep", "compound", "compound", "pobj", "punct", "dep", "advmod", "nsubj", "ROOT", "prep", "pobj", "punct", "appos"]
|
||
ents = ["O", "", "O", "B-GPE", "I-GPE", "I-GPE", "O", "O", "O", "O", "O", "O", "B-GPE", "O", "B-GPE"]
|
||
# fmt: on
|
||
doc = Doc(
|
||
nlp.vocab,
|
||
words=words,
|
||
spaces=spaces,
|
||
tags=tags,
|
||
lemmas=lemmas,
|
||
heads=heads,
|
||
deps=deps,
|
||
ents=ents,
|
||
)
|
||
assert doc.text == text
|
||
example = Example(nlp.make_doc(text), doc)
|
||
# whitespace is only added internally in entity spans
|
||
mod_ex = make_whitespace_variant(nlp, example, " ", 3)
|
||
assert mod_ex.reference.ents[0].text == "New York City"
|
||
mod_ex = make_whitespace_variant(nlp, example, " ", 4)
|
||
assert mod_ex.reference.ents[0].text == "New York City"
|
||
mod_ex = make_whitespace_variant(nlp, example, " ", 5)
|
||
assert mod_ex.reference.ents[0].text == "New York City"
|
||
mod_ex = make_whitespace_variant(nlp, example, " ", 6)
|
||
assert mod_ex.reference.ents[0].text == "New York City"
|
||
# add a space at every possible position
|
||
for i in range(len(doc) + 1):
|
||
mod_ex = make_whitespace_variant(nlp, example, " ", i)
|
||
assert mod_ex.reference[i].is_space
|
||
# adds annotation when the doc contains at least partial annotation
|
||
assert [t.tag_ for t in mod_ex.reference] == tags[:i] + ["_SP"] + tags[i:]
|
||
assert [t.lemma_ for t in mod_ex.reference] == lemmas[:i] + [" "] + lemmas[i:]
|
||
assert [t.dep_ for t in mod_ex.reference] == deps[:i] + ["dep"] + deps[i:]
|
||
# does not add partial annotation if doc does not contain this feature
|
||
assert not mod_ex.reference.has_annotation("POS")
|
||
assert not mod_ex.reference.has_annotation("MORPH")
|
||
# produces well-formed trees
|
||
assert not contains_cycle([t.head.i for t in mod_ex.reference])
|
||
assert len(list(doc.sents)) == 2
|
||
if i == 0:
|
||
assert mod_ex.reference[i].head.i == 1
|
||
else:
|
||
assert mod_ex.reference[i].head.i == i - 1
|
||
# adding another space also produces well-formed trees
|
||
for j in (3, 8, 10):
|
||
mod_ex2 = make_whitespace_variant(nlp, mod_ex, "\t\t\n", j)
|
||
assert not contains_cycle([t.head.i for t in mod_ex2.reference])
|
||
assert len(list(doc.sents)) == 2
|
||
assert mod_ex2.reference[j].head.i == j - 1
|
||
# entities are well-formed
|
||
assert len(doc.ents) == len(mod_ex.reference.ents)
|
||
# there is one token with missing entity information
|
||
assert any(t.ent_iob == 0 for t in mod_ex.reference)
|
||
for ent in mod_ex.reference.ents:
|
||
assert not ent[0].is_space
|
||
assert not ent[-1].is_space
|
||
|
||
# no modifications if:
|
||
# partial dependencies
|
||
example.reference[0].dep_ = ""
|
||
mod_ex = make_whitespace_variant(nlp, example, " ", 5)
|
||
assert mod_ex.text == example.reference.text
|
||
example.reference[0].dep_ = "nsubj" # reset
|
||
|
||
# spans
|
||
example.reference.spans["spans"] = [example.reference[0:5]]
|
||
mod_ex = make_whitespace_variant(nlp, example, " ", 5)
|
||
assert mod_ex.text == example.reference.text
|
||
del example.reference.spans["spans"] # reset
|
||
|
||
# links
|
||
example.reference.ents = [Span(doc, 0, 2, label="ENT", kb_id="Q123")]
|
||
mod_ex = make_whitespace_variant(nlp, example, " ", 5)
|
||
assert mod_ex.text == example.reference.text
|