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fix test checking for variants

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This commit is contained in:
svlandeg 2020-06-19 14:05:35 +02:00 committed by Matthew Honnibal
parent a427ca9355
commit 6d5bfd6f6a
9 changed files with 27 additions and 59 deletions
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1
examples/experiments/onto-joint/defaults.cfg
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@ -9,7 +9,6 @@ max_length = 0
limit = 0
# Data augmentation
orth_variant_level = 0.0
noise_level = 0.0
dropout = 0.1
# Controls early-stopping. 0 or -1 mean unlimited.
patience = 1600

1
examples/experiments/onto-joint/pretrain.cfg
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@ -9,7 +9,6 @@ max_length = 0
limit = 0
# Data augmentation
orth_variant_level = 0.0
noise_level = 0.0
dropout = 0.1
# Controls early-stopping. 0 or -1 mean unlimited.
patience = 1600

1
examples/experiments/ptb-joint-pos-dep/bilstm_tok2vec.cfg
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@ -6,7 +6,6 @@ init_tok2vec = null
vectors = null
max_epochs = 100
orth_variant_level = 0.0
noise_level = 0.0
gold_preproc = true
max_length = 0
use_gpu = 0

1
examples/experiments/ptb-joint-pos-dep/defaults.cfg
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@ -6,7 +6,6 @@ init_tok2vec = null
vectors = null
max_epochs = 100
orth_variant_level = 0.0
noise_level = 0.0
gold_preproc = true
max_length = 0
use_gpu = -1

2
spacy/cli/__init__.py
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@ -4,7 +4,7 @@ from .download import download # noqa: F401
from .info import info # noqa: F401
from .package import package # noqa: F401
from .profile import profile # noqa: F401
from .train_from_config import train_cli # noqa: F401
from .train import train_cli # noqa: F401
from .pretrain import pretrain # noqa: F401
from .debug_data import debug_data # noqa: F401
from .evaluate import evaluate # noqa: F401

1
spacy/cli/train_from_config.py → spacy/cli/train.py
View File

@ -371,7 +371,6 @@ def create_train_batches(nlp, corpus, cfg):
train_examples = list(
corpus.train_dataset(
nlp,
noise_level=cfg["noise_level"], # I think this is deprecated?
orth_variant_level=cfg["orth_variant_level"],
gold_preproc=cfg["gold_preproc"],
max_length=cfg["max_length"],

29
spacy/gold/augment.py
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@ -2,6 +2,15 @@ import random
import itertools
def make_orth_variants_example(nlp, example, orth_variant_level=0.0): # TODO: naming
raw_text = example.text
orig_dict = example.to_dict()
variant_text, variant_token_annot = make_orth_variants(nlp, raw_text, orig_dict["token_annotation"], orth_variant_level)
doc = nlp.make_doc(variant_text)
orig_dict["token_annotation"] = variant_token_annot
return example.from_dict(doc, orig_dict)
def make_orth_variants(nlp, raw_text, orig_token_dict, orth_variant_level=0.0):
if random.random() >= orth_variant_level:
return raw_text, orig_token_dict
@ -98,23 +107,3 @@ def make_orth_variants(nlp, raw_text, orig_token_dict, orth_variant_level=0.0):
raw_idx += 1
raw = variant_raw
return raw, token_dict
def add_noise(orig, noise_level):
if random.random() >= noise_level:
return orig
elif type(orig) == list:
corrupted = [_corrupt(word, noise_level) for word in orig]
corrupted = [w for w in corrupted if w]
return corrupted
else:
return "".join(_corrupt(c, noise_level) for c in orig)
def _corrupt(c, noise_level):
if random.random() >= noise_level:
return c
elif c in [".", "'", "!", "?", ","]:
return "\n"
else:
return c.lower()

32
spacy/gold/corpus.py
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@ -8,7 +8,7 @@ from ..tokens import Doc
from .. import util
from ..errors import Errors, AlignmentError
from .gold_io import read_json_file, json_to_annotations
from .augment import make_orth_variants, add_noise
from .augment import make_orth_variants
from .example import Example
@ -148,7 +148,6 @@ class GoldCorpus(object):
nlp,
gold_preproc=False,
max_length=None,
noise_level=0.0,
orth_variant_level=0.0,
ignore_misaligned=False,
):
@ -160,7 +159,6 @@ class GoldCorpus(object):
train_annotations,
gold_preproc,
max_length=max_length,
noise_level=noise_level,
orth_variant_level=orth_variant_level,
make_projective=True,
ignore_misaligned=ignore_misaligned,
@ -194,33 +192,31 @@ class GoldCorpus(object):
annotations,
gold_preproc,
max_length=None,
noise_level=0.0,
orth_variant_level=0.0,
make_projective=False,
ignore_misaligned=False,
):
""" Setting gold_preproc will result in creating a doc per sentence """
for eg_dict in annotations:
token_annot = eg_dict.get("token_annotation", {})
if eg_dict["text"]:
example = Example.from_dict(
nlp.make_doc(eg_dict["text"]),
eg_dict
)
doc = nlp.make_doc(eg_dict["text"])
elif "words" in token_annot:
doc = Doc(nlp.vocab, words=token_annot["words"])
else:
example = Example.from_dict(
Doc(nlp.vocab, words=eg_dict["words"]),
eg_dict
)
raise ValueError("Expecting either 'text' or token_annotation.words annotation")
if gold_preproc:
# TODO: Data augmentation
variant_text, variant_token_annot = make_orth_variants(nlp, doc.text, token_annot, orth_variant_level)
doc = nlp.make_doc(variant_text)
eg_dict["token_annotation"] = variant_token_annot
example = Example.from_dict(doc, eg_dict)
examples = example.split_sents()
else:
example = Example.from_dict(doc, eg_dict)
examples = [example]
for eg in examples:
if (not max_length) or len(eg.predicted) < max_length:
if ignore_misaligned:
try:
_ = eg._deprecated_get_gold()
except AlignmentError:
continue
yield eg

18
spacy/tests/test_gold.py
View File

@ -11,6 +11,7 @@ import pytest
import srsly
from .util import make_tempdir
from ..gold.augment import make_orth_variants_example
@pytest.fixture
@ -387,8 +388,8 @@ def test_make_orth_variants(doc):
goldcorpus = GoldCorpus(str(json_file), str(json_file))
# due to randomness, test only that this runs with no errors for now
train_reloaded_example = next(goldcorpus.train_dataset(nlp, orth_variant_level=0.2))
train_goldparse = get_parses_from_example(train_reloaded_example)[0][1]
train_example = next(goldcorpus.train_dataset(nlp))
variant_example = make_orth_variants_example(nlp, train_example, orth_variant_level=0.2)
@pytest.mark.parametrize(
@ -499,19 +500,6 @@ def test_split_sents(merged_dict):
)
assert example.text == "Hi there everyone It is just me"
assert len(get_parses_from_example(
example,
merge=False,
vocab=nlp.vocab,
make_projective=False)
) == 2
assert len(get_parses_from_example(
example,
merge=True,
vocab=nlp.vocab,
make_projective=False
)) == 1
split_examples = example.split_sents()
assert len(split_examples) == 2
assert split_examples[0].text == "Hi there everyone "