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