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import json
from typing import List, Union
from datasets import Dataset, concatenate_datasets
from opencompass.openicl.icl_evaluator import AccEvaluator
from .base import BaseDataset
class AdvDataset(BaseDataset):
"""Base adv GLUE dataset. Adv GLUE is built on GLUE dataset. The main
purpose is to eval the accuracy drop on original set and adv set.
Args:
subset (str): The subset task of adv GLUE dataset.
filter_keys (str): The keys to be filtered to create the original
set for comparison.
"""
def __init__(
self,
subset: str,
filter_keys: Union[str, List[str]],
**kwargs,
):
self.subset = subset
if isinstance(filter_keys, str):
filter_keys = [filter_keys]
self.filter_keys = filter_keys
super().__init__(**kwargs)
def aug_with_original_data(self, dataset):
"""Create original dataset and concat to the end."""
# Remove data without original reference
dataset = dataset.filter(
lambda x: any([x[k] for k in self.filter_keys]))
def ori_preprocess(example):
for k in self.filter_keys:
if example[k]:
new_k = k.split('original_')[-1]
example[new_k] = example[k]
example['type'] = 'original'
return example
original_dataset = dataset.map(ori_preprocess)
return concatenate_datasets([dataset, original_dataset])
def load(self, path):
"""Load dataset and aug with original dataset."""
with open(path, 'r') as f:
raw_data = json.load(f)
subset = raw_data[self.subset]
# In case the missing keys in first example causes Dataset
# to ignore them in the following examples when building.
for k in self.filter_keys:
if k not in subset[0]:
subset[0][k] = None
dataset = Dataset.from_list(raw_data[self.subset])
dataset = self.aug_with_original_data(dataset)
def choices_process(example):
example['label_option'] = chr(ord('A') + example['label'])
return example
dataset = dataset.map(choices_process)
return dataset
# label 0 for A. negative
# label 1 for B. positive
class AdvSst2Dataset(AdvDataset):
"""Adv GLUE sst2 dataset."""
def __init__(self, **kwargs):
super().__init__(subset='sst2',
filter_keys='original_sentence',
**kwargs)
# label 0 for not_duplicate, A. no
# label 1 for duplicate, B. yes
class AdvQqpDataset(AdvDataset):
"""Adv GLUE qqp dataset."""
def __init__(self, **kwargs):
super().__init__(
subset='qqp',
filter_keys=['original_question1', 'original_question2'],
**kwargs)
# # label 0 for entailment, A. yes
# # label 1 for neutral, B. maybe
# # label 2 for contradiction, C. no
class AdvMnliDataset(AdvDataset):
"""Adv GLUE mnli dataset."""
def __init__(self, **kwargs):
super().__init__(
subset='mnli',
filter_keys=['original_premise', 'original_hypothesis'],
**kwargs)
# # label 0 for entailment, A. yes
# # label 1 for neutral, B. maybe
# # label 2 for contradiction, C. no
class AdvMnliMMDataset(AdvDataset):
"""Adv GLUE mnli mm dataset."""
def __init__(self, **kwargs):
super().__init__(
subset='mnli-mm',
filter_keys=['original_premise', 'original_hypothesis'],
**kwargs)
# # label 0 for entailment, A. yes
# # label 1 for not entailment, B. no
class AdvQnliDataset(AdvDataset):
"""Adv GLUE qnli dataset."""
def __init__(self, **kwargs):
super().__init__(
subset='qnli',
filter_keys=['original_question', 'original_sentence'],
**kwargs)
# # label 0 for entailment, A. yes
# # label 1 for not entailment, B. no
class AdvRteDataset(AdvDataset):
"""Adv GLUE rte dataset."""
def __init__(self, **kwargs):
super().__init__(
subset='rte',
filter_keys=['original_sentence1', 'original_sentence2'],
**kwargs)
class AccDropEvaluator(AccEvaluator):
"""Eval accuracy drop."""
def __init__(self) -> None:
super().__init__()
def score(self, predictions: List, references: List) -> dict:
"""Calculate scores and accuracy.
Args:
predictions (List): List of probabilities for each class of each
sample.
references (List): List of target labels for each sample.
Returns:
dict: calculated scores.
"""
n = len(predictions)
assert n % 2 == 0, 'Number of examples should be even.'
acc_after = super().score(predictions[:n // 2], references[:n // 2])
acc_before = super().score(predictions[n // 2:], references[n // 2:])
acc_drop = 1 - acc_after['accuracy'] / acc_before['accuracy']
return dict(acc_drop=acc_drop,
acc_after=acc_after['accuracy'],
acc_before=acc_before['accuracy'])
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