from copy import deepcopy
from loguru import logger
from pydantic import PositiveInt
from data_juicer.utils.lazy_loader import LazyLoader
from data_juicer.utils.logger_utils import HiddenPrints
from ..base_op import OPERATORS, Mapper
nlpcda = LazyLoader('nlpcda', 'nlpcda')
OP_NAME = 'nlpcda_zh_mapper'
[docs]@OPERATORS.register_module(OP_NAME)
class NlpcdaZhMapper(Mapper):
"""Mapper to simply augment samples in Chinese based on nlpcda library."""
_batched_op = True
[docs] def __init__(self,
sequential: bool = False,
aug_num: PositiveInt = 1,
keep_original_sample: bool = True,
replace_similar_word: bool = False,
replace_homophone_char: bool = False,
delete_random_char: bool = False,
swap_random_char: bool = False,
replace_equivalent_num: 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. **Notice**: some augmentation method might not work for
some special texts, so there might be no augmented texts generated.
: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 replace_similar_word: whether to open the augmentation method of
replacing random words with their similar words in the original
texts. e.g. "这里一共有5种不同的数据增强方法" --> "这边一共有5种不同的数据增强方法"
:param replace_homophone_char: whether to open the augmentation method
of replacing random characters with their homophones in the
original texts. e.g. "这里一共有5种不同的数据增强方法" --> "这里一共有5种不同的濖据增强方法"
:param delete_random_char: whether to open the augmentation method of
deleting random characters from the original texts. e.g.
"这里一共有5种不同的数据增强方法" --> "这里一共有5种不同的数据增强"
:param swap_random_char: whether to open the augmentation method of
swapping random contiguous characters in the original texts. e.g.
"这里一共有5种不同的数据增强方法" --> "这里一共有5种不同的数据强增方法"
:param replace_equivalent_num: whether to open the augmentation method
of replacing random numbers with their equivalent representations
in the original texts. **Notice**: Only for numbers for now. e.g.
"这里一共有5种不同的数据增强方法" --> "这里一共有伍种不同的数据增强方法"
: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
# hide the redundant outputs from nlpcda library
with HiddenPrints():
import warnings
warnings.filterwarnings('ignore')
self.aug_pipeline = []
# sample level
# word level
if replace_similar_word:
# the first sample of augmented sample list is the same as the
# original sample, so we need generate one more augmented
# sample to get the expected number of augmented samples. Same
# below
create_num = (self.aug_num + 1) \
if not self.sequential or len(self.aug_pipeline) == 0 \
else 2
self.aug_pipeline.append(
nlpcda.Similarword(create_num=create_num))
# char level
if replace_homophone_char:
create_num = (self.aug_num + 1) \
if not self.sequential or len(self.aug_pipeline) == 0 \
else 2
self.aug_pipeline.append(
nlpcda.Homophone(create_num=create_num))
if delete_random_char:
create_num = (self.aug_num + 1) \
if not self.sequential or len(self.aug_pipeline) == 0 \
else 2
self.aug_pipeline.append(
nlpcda.RandomDeleteChar(create_num=create_num))
if swap_random_char:
create_num = (self.aug_num + 1) \
if not self.sequential or len(self.aug_pipeline) == 0 \
else 2
# only use char_gram=1 for relatively minor changes
self.aug_pipeline.append(
nlpcda.CharPositionExchange(create_num=create_num,
char_gram=1))
# only for numbers now
if replace_equivalent_num:
create_num = (self.aug_num + 1) \
if not self.sequential or len(self.aug_pipeline) == 0 \
else 2
self.aug_pipeline.append(
nlpcda.EquivalentChar(create_num=create_num))
[docs] def process_batched(self, samples):
# no augmentation methods are opened
if len(self.aug_pipeline) == 0:
if self.keep_original_sample:
return samples
else:
return {key: [] for key in samples}
texts_to_aug = samples[self.text_key]
res_samples = deepcopy(samples)
# get augmented texts
if self.sequential:
aug_texts = texts_to_aug
for aug_method in self.aug_pipeline:
results = []
for text in aug_texts:
# aug and skip the original text
result = aug_method.replace(text)
results += result[1:] if len(result) > 1 else result
aug_texts = results[:]
if len(aug_texts) == 1 and aug_texts[0] == texts_to_aug[0]:
aug_texts = []
else:
# apply each aug method to generate several augmented texts
aug_texts = []
for aug_method in self.aug_pipeline:
aug_texts += aug_method.replace(texts_to_aug[0])[1:]
# 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