spaCy/spacy/training/augment.py
Matthew Honnibal a976da168c
Support data augmentation in Corpus (#6155)
* Support data augmentation in Corpus

* Note initial docs for data augmentation

* Add augmenter to quickstart

* Fix flake8

* Format

* Fix test

* Update spacy/tests/training/test_training.py

* Improve data augmentation arguments

* Update templates

* Move randomization out into caller

* Refactor

* Update spacy/training/augment.py

* Update spacy/tests/training/test_training.py

* Fix augment

* Fix test
2020-09-28 03:03:27 +02:00

133 lines
5.2 KiB
Python

from typing import Callable
import random
import itertools
import copy
from functools import partial
from ..util import registry
@registry.augmenters("spacy.dont_augment.v1")
def create_null_augmenter():
return dont_augment
@registry.augmenters("spacy.orth_variants.v1")
def create_orth_variants_augmenter(level: float, lower: float) -> Callable:
"""Create a data augmentation callback that uses orth-variant replacement.
The callback can be added to a corpus or other data iterator during training.
"""
return partial(orth_variants_augmenter, level=level, lower=lower)
def dont_augment(nlp, example):
yield example
def orth_variants_augmenter(nlp, example, *, level: float = 0.0, lower: float=0.0):
if random.random() >= level:
yield example
else:
raw_text = example.text
orig_dict = example.to_dict()
if not orig_dict["token_annotation"]:
yield example
else:
variant_text, variant_token_annot = make_orth_variants(
nlp,
raw_text,
orig_dict["token_annotation"],
lower=raw_text is not None and random.random() < lower
)
doc = nlp.make_doc(variant_text)
orig_dict["token_annotation"] = variant_token_annot
yield example.from_dict(doc, orig_dict)
def make_orth_variants(nlp, raw, token_dict, *, lower: bool=False):
orig_token_dict = copy.deepcopy(token_dict)
orth_variants = nlp.vocab.lookups.get_table("orth_variants", {})
ndsv = orth_variants.get("single", [])
ndpv = orth_variants.get("paired", [])
words = token_dict.get("words", [])
tags = token_dict.get("tags", [])
# keep unmodified if words or tags are not defined
if words and tags:
if lower:
words = [w.lower() for w in words]
# single variants
punct_choices = [random.choice(x["variants"]) for x in ndsv]
for word_idx in range(len(words)):
for punct_idx in range(len(ndsv)):
if (
tags[word_idx] in ndsv[punct_idx]["tags"]
and words[word_idx] in ndsv[punct_idx]["variants"]
):
words[word_idx] = punct_choices[punct_idx]
# paired variants
punct_choices = [random.choice(x["variants"]) for x in ndpv]
for word_idx in range(len(words)):
for punct_idx in range(len(ndpv)):
if tags[word_idx] in ndpv[punct_idx]["tags"] and words[
word_idx
] in itertools.chain.from_iterable(ndpv[punct_idx]["variants"]):
# backup option: random left vs. right from pair
pair_idx = random.choice([0, 1])
# best option: rely on paired POS tags like `` / ''
if len(ndpv[punct_idx]["tags"]) == 2:
pair_idx = ndpv[punct_idx]["tags"].index(tags[word_idx])
# next best option: rely on position in variants
# (may not be unambiguous, so order of variants matters)
else:
for pair in ndpv[punct_idx]["variants"]:
if words[word_idx] in pair:
pair_idx = pair.index(words[word_idx])
words[word_idx] = punct_choices[punct_idx][pair_idx]
token_dict["words"] = words
token_dict["tags"] = tags
# modify raw
if raw is not None:
variants = []
for single_variants in ndsv:
variants.extend(single_variants["variants"])
for paired_variants in ndpv:
variants.extend(
list(itertools.chain.from_iterable(paired_variants["variants"]))
)
# store variants in reverse length order to be able to prioritize
# longer matches (e.g., "---" before "--")
variants = sorted(variants, key=lambda x: len(x))
variants.reverse()
variant_raw = ""
raw_idx = 0
# add initial whitespace
while raw_idx < len(raw) and raw[raw_idx].isspace():
variant_raw += raw[raw_idx]
raw_idx += 1
for word in words:
match_found = False
# skip whitespace words
if word.isspace():
match_found = True
# add identical word
elif word not in variants and raw[raw_idx:].startswith(word):
variant_raw += word
raw_idx += len(word)
match_found = True
# add variant word
else:
for variant in variants:
if not match_found and raw[raw_idx:].startswith(variant):
raw_idx += len(variant)
variant_raw += word
match_found = True
# something went wrong, abort
# (add a warning message?)
if not match_found:
return raw, orig_token_dict
# add following whitespace
while raw_idx < len(raw) and raw[raw_idx].isspace():
variant_raw += raw[raw_idx]
raw_idx += 1
raw = variant_raw
return raw, token_dict