OpenCompass/opencompass/datasets/SeedBench.py

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import random
import re
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from typing import List
import datasets
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import jieba
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import numpy as np
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from rouge_chinese import Rouge
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from opencompass.openicl.icl_evaluator.icl_base_evaluator import BaseEvaluator
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from opencompass.registry import ICL_EVALUATORS, TEXT_POSTPROCESSORS
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from .base import BaseDataset
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class SeedBenchDataset(BaseDataset):
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@staticmethod
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def load(data_files: str,
path: str = 'json',
split: str = None,
**kwargs) -> datasets.Dataset:
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dataset = datasets.load_dataset(path, data_files=data_files, **kwargs)
if split is None:
split = list(dataset.keys())[0]
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print(f'my datasets split : {split}')
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if split not in dataset:
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raise ValueError(f"Split '{split}' not found. \
Available splits: {list(dataset.keys())}")
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return dataset[split]
class F1Evaluator(BaseEvaluator):
"""F1 Score evaluator for multiple choice questions.
Args:
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seed (int): Seed for randomness, ensuring reproducibility.
Defaults to 0.
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"""
def __init__(self, seed: int = 0) -> None:
self.seed = seed
super().__init__()
def _preprocess(self, predictions: List, references: List) -> dict:
return {
'predictions': predictions,
'references': references,
}
def _postprocess(self, scores: dict) -> dict:
return scores
def score(self, predictions: List, references: List) -> dict:
random_state = random.getstate()
np_random_state = np.random.get_state()
details = []
random.seed(self.seed)
np.random.seed(self.seed)
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if len(predictions) != len(references):
return {
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'error':
'predictions and references have different '
f'length. len(predictions): {len(predictions)}, '
f'len(references): {len(references)}'
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}
true_positives = 0
false_positives = 0
false_negatives = 0
for hyp, ref in zip(predictions, references):
hyp = re.sub(r'[^A-Da-d,]+', '', hyp.lower())
ref = re.sub(r'[^A-Da-d,]+', '', ref.lower())
ref_set = set(ref.split(','))
hyp_set = set(hyp.split(','))
ref_set = {r.strip() for r in ref_set}
hyp_set = {h.strip() for h in hyp_set}
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sample_tp = len(hyp_set.intersection(ref_set))
sample_fp = len(hyp_set - ref_set)
sample_fn = len(ref_set - hyp_set)
true_positives += sample_tp
false_positives += sample_fp
false_negatives += sample_fn
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sample_precision = sample_tp / (sample_tp + sample_fp) if (
sample_tp + sample_fp) > 0 else 0
sample_recall = sample_tp / (sample_tp + sample_fn) if (
sample_tp + sample_fn) > 0 else 0
sample_f1 = (2 * sample_precision * sample_recall) / (
sample_precision + sample_recall) if (sample_precision +
sample_recall) > 0 else 0
details.append({
'pred': hyp,
'answer': ref,
'correct': sample_f1 * 100
})
precision = true_positives / (true_positives + false_positives) if (
true_positives + false_positives) > 0 else 0
recall = true_positives / (true_positives + false_negatives) if (
true_positives + false_negatives) > 0 else 0
f1 = (2 * precision *
recall) / (precision + recall) if (precision + recall) > 0 else 0
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result = {
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'ours_F1Score': f1 * 100, # 总体 F1 分数
'details': details
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}
random.setstate(random_state)
np.random.set_state(np_random_state)
return self._postprocess(result)
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@ICL_EVALUATORS.register_module()
class F1ScoreEvaluator(F1Evaluator):
"""F1 Score evaluator for multiple choice questions."""
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def __init__(self) -> None:
super().__init__()
# 定义自己的多选后处理逻辑输入回答为ABC ---> A,B,C)
@TEXT_POSTPROCESSORS.register_module('my_multiple_select_postprocess')
def my_multiple_select_postprocess(text: str) -> str:
selected_options = [t for t in text if t.isupper()]
selected_options = sorted(set(selected_options))
res = ', '.join(selected_options)
return res
class AverageRougeEvaluator(BaseEvaluator):
"""Average Rouge Score evaluator for fill-in-the-blank tasks.
Args:
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seed (int): Seed for randomness, ensuring reproducibility.
Defaults to 0.
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"""
def __init__(self, seed: int = 0) -> None:
self.seed = seed
super().__init__()
def _preprocess(self, predictions: List, references: List) -> dict:
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pattern = r'(正确答案[:]|correct answer[:])'
cleaned_predictions = [
re.sub(pattern, '', pred, flags=re.IGNORECASE).strip()
for pred in predictions
]
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return {
'predictions': cleaned_predictions,
'references': references,
}
def _postprocess(self, scores: dict) -> dict:
return scores
def score(self, predictions: List, references: List) -> dict:
def rouge_score(hyps, refs):
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assert (len(hyps) == len(refs))
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hyps = [' '.join(jieba.cut(h)) for h in hyps]
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hyps = [h if h.strip() != '' else '无内容' for h in hyps]
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refs = [' '.join(jieba.cut(r)) for r in refs]
rouge_scores = Rouge().get_scores(hyps, refs)
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rouge_ls = [score['rouge-l']['f'] for score in rouge_scores]
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average_rouge_l = sum(rouge_ls) / len(rouge_ls)
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return {'score': average_rouge_l * 100}
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random_state = random.getstate()
np_random_state = np.random.get_state()
details = []
random.seed(self.seed)
np.random.seed(self.seed)
if len(predictions) != len(references):
return {
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'error':
'predictions and references have different '
f'length. len(predictions): {len(predictions)}, '
f'len(references): {len(references)}'
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}
preprocessed_data = self._preprocess(predictions, references)
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hyps, refs = preprocessed_data['predictions'], preprocessed_data[
'references']
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scores = []
for i in range(len(hyps)):
refs[i] = refs[i].replace('', ',')
word_level_refs = refs[i].split(',')
word_level_refs = [r.strip() for r in word_level_refs]
if len(word_level_refs) == 1:
word_level_hyps = [hyps[i]]
else:
word_level_hyps = hyps[i].split(',')
word_level_hyps = [h.strip() for h in word_level_hyps]
if len(word_level_hyps) < len(word_level_refs):
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word_level_hyps += ['无内容'] * (len(word_level_refs) -
len(word_level_hyps))
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else:
word_level_hyps = word_level_hyps[:len(word_level_refs)]
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sample_score = rouge_score(word_level_hyps,
word_level_refs)['score']
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scores.append(sample_score)
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details.append({
'pred': word_level_hyps,
'answer': word_level_refs,
'correct': sample_score
})
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average_score = sum(scores) / len(scores)
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result = {'AvgRougeScore': average_score, 'details': details}
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random.setstate(random_state)
np.random.set_state(np_random_state)
return self._postprocess(result)
@ICL_EVALUATORS.register_module()
class AverageRougeScoreEvaluator(AverageRougeEvaluator):
"""Average Rouge Score evaluator."""
def __init__(self) -> None:
super().__init__()
class AccScoreStrEvaluator(BaseEvaluator):
"""Accuracy evaluator based on string matching.
Args:
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seed (int): Seed for randomness, ensuring reproducibility.
Defaults to 0.
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"""
def __init__(self, seed: int = 0) -> None:
self.seed = seed
super().__init__()
def _preprocess(self, predictions: List, references: List) -> dict:
return {
'predictions': predictions,
'references': references,
}
def _postprocess(self, scores: dict) -> dict:
return scores
def score(self, predictions: List, references: List) -> dict:
random_state = random.getstate()
np_random_state = np.random.get_state()
details = []
random.seed(self.seed)
np.random.seed(self.seed)
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if len(predictions) != len(references):
return {
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'error':
'predictions and references have different '
f'length. len(predictions): {len(predictions)}, '
f'len(references): {len(references)}'
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}
preprocessed_data = self._preprocess(predictions, references)
correct = 0
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for hyp, ref in zip(preprocessed_data['predictions'],
preprocessed_data['references']):
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is_correct = 1 if ref.strip().lower() in hyp.strip().lower() else 0
correct += is_correct
details.append({'pred': hyp, 'answer': ref, 'correct': is_correct})
accuracy = correct / len(predictions)
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result = {'ACCStrScore': accuracy * 100, 'details': details}
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random.setstate(random_state)
np.random.set_state(np_random_state)
return self._postprocess(result)
@ICL_EVALUATORS.register_module()
class AccScoreStr_Evaluator(AccScoreStrEvaluator):
"""Accuracy evaluator wrapper for the AccScoreEvaluator."""
def __init__(self) -> None:
super().__init__()