from copy import deepcopy
from loguru import logger
from pydantic import PositiveInt
from data_juicer.utils.lazy_loader import LazyLoader
from ..base_op import OPERATORS, Mapper
nlpaug = LazyLoader('nlpaug', 'nlpaug')
nac = LazyLoader('nac', 'nlpaug.augmenter.char')
naw = LazyLoader('naw', 'nlpaug.augmenter.word')
naf = LazyLoader('naf', 'nlpaug.flow')
OP_NAME = 'nlpaug_en_mapper'
[docs]
@OPERATORS.register_module(OP_NAME)
class NlpaugEnMapper(Mapper):
"""Mapper to simply augment samples in English based on nlpaug library."""
_batched_op = True
[docs]
def __init__(self,
sequential: bool = False,
aug_num: PositiveInt = 1,
keep_original_sample: bool = True,
delete_random_word: bool = False,
swap_random_word: bool = False,
spelling_error_word: bool = False,
split_random_word: bool = False,
keyboard_error_char: bool = False,
ocr_error_char: bool = False,
delete_random_char: bool = False,
swap_random_char: bool = False,
insert_random_char: bool = False,
*args,
**kwargs):
"""
Initialization method. All augmentation methods use default parameters
in default. We recommend you to only use 1-3 augmentation methods at a
time. Otherwise, the semantics of samples might be changed
significantly.
:param sequential: whether combine all augmentation methods to a
sequence. If it's True, a sample will be augmented by all opened
augmentation methods sequentially. If it's False, each opened
augmentation method would generate its augmented samples
independently.
:param aug_num: number of augmented samples to be generated. If
`sequential` is True, there will be total aug_num augmented samples
generated. If it's False, there will be (aug_num *
#opened_aug_method) augmented samples generated.
:param keep_original_sample: whether to keep the original sample. If
it's set to False, there will be only generated texts in the final
datasets and the original texts will be removed. It's True in
default.
:param delete_random_word: whether to open the augmentation method of
deleting random words from the original texts. e.g. "I love LLM"
--> "I LLM"
:param swap_random_word: whether to open the augmentation method of
swapping random contiguous words in the original texts. e.g. "I
love LLM" --> "Love I LLM"
:param spelling_error_word: whether to open the augmentation method of
simulating the spelling error for words in the original texts. e.g.
"I love LLM" --> "Ai love LLM"
:param split_random_word: whether to open the augmentation method of
splitting words randomly with whitespaces in the original texts.
e.g. "I love LLM" --> "I love LL M"
:param keyboard_error_char: whether to open the augmentation method of
simulating the keyboard error for characters in the original texts.
e.g. "I love LLM" --> "I ;ov4 LLM"
:param ocr_error_char: whether to open the augmentation method of
simulating the OCR error for characters in the original texts.
e.g. "I love LLM" --> "I 10ve LLM"
:param delete_random_char: whether to open the augmentation method of
deleting random characters from the original texts. e.g. "I love
LLM" --> "I oe LLM"
:param swap_random_char: whether to open the augmentation method of
swapping random contiguous characters in the original texts.
e.g. "I love LLM" --> "I ovle LLM"
:param insert_random_char: whether to open the augmentation method of
inserting random characters into the original texts. e.g. "I love
LLM" --> "I ^lKove LLM"
:param args: extra args
:param kwargs: extra args
"""
super().__init__(*args, **kwargs)
self.aug_num = aug_num
if aug_num >= 10:
logger.warning(f'Relatively large augmentation number [{aug_num}]'
f' might generate large number of new samples and '
f'requires more memory and disk space.')
self.sequential = sequential
self.keep_original_sample = keep_original_sample
aug_pipeline = []
# word level
Action = nlpaug.util.Action
if delete_random_word:
aug_pipeline.append(naw.RandomWordAug(action=Action.DELETE))
if swap_random_word:
aug_pipeline.append(naw.RandomWordAug(action=Action.SWAP))
if spelling_error_word:
aug_pipeline.append(naw.SpellingAug())
if split_random_word:
aug_pipeline.append(naw.SplitAug())
# char level
if keyboard_error_char:
aug_pipeline.append(nac.KeyboardAug())
if ocr_error_char:
aug_pipeline.append(nac.OcrAug())
if delete_random_char:
aug_pipeline.append(nac.RandomCharAug(action=Action.DELETE))
if swap_random_char:
aug_pipeline.append(nac.RandomCharAug(action=Action.SWAP))
if insert_random_char:
aug_pipeline.append(nac.RandomCharAug(action=Action.INSERT))
if self.sequential:
self.aug = naf.Sequential(aug_pipeline)
else:
self.aug = aug_pipeline
[docs]
def process_batched(self, samples):
# no augmentation methods are opened
if len(self.aug) == 0:
if self.keep_original_sample:
return samples
else:
return {key: [] for key in samples}
texts_to_aug = samples[self.text_key][0] # batch_size = 1
res_samples = deepcopy(samples)
# get augmented texts
if self.sequential:
aug_texts = self.aug.augment(texts_to_aug, n=self.aug_num)
else:
# apply each aug method to generate several augmented texts
aug_texts = []
for aug_method in self.aug:
aug_texts += aug_method.augment(texts_to_aug, n=self.aug_num)
# add augmented samples to the batch with other replicate fields
if self.keep_original_sample:
res_samples[self.text_key] += aug_texts
else:
res_samples[self.text_key] = aug_texts
# add other replicate fields
for key in res_samples:
if key != self.text_key:
res_samples[key] = res_samples[key] * \
len(res_samples[self.text_key])
return res_samples