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341 lines
12 KiB
Python
341 lines
12 KiB
Python
import os
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import random
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import datasets
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from typing import List
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from .base import BaseDataset
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from opencompass.openicl.icl_evaluator.icl_base_evaluator import BaseEvaluator
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import numpy as np
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import re
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import jieba
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from rouge_chinese import Rouge
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from opencompass.registry import ICL_EVALUATORS, TEXT_POSTPROCESSORS
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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)
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if split is None:
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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]
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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. 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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"""Preprocess the final predictions and references to needed format.
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Args:
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predictions (List): List of predictions for each sample.
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references (List): List of reference answers for each sample.
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Returns:
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dict: Preprocessed predictions and references in the required format.
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"""
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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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"""Postprocess the final score for F1.
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Args:
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scores (dict): Dictionary of calculated F1 score.
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Returns:
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dict: Postprocessed F1 score.
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"""
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return scores
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def score(self, predictions: List, references: List) -> dict:
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"""Calculate F1 score.
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Args:
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predictions (List): List of predicted answers for each sample.
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references (List): List of reference answers for each sample.
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Returns:
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dict: Calculated F1 score.
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"""
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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': '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 (sample_tp + sample_fp) > 0 else 0
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sample_recall = sample_tp / (sample_tp + sample_fn) if (sample_tp + sample_fn) > 0 else 0
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sample_f1 = (2 * sample_precision * sample_recall) / (sample_precision + sample_recall) if (sample_precision + sample_recall) > 0 else 0
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details.append({'pred': hyp, 'answer': ref, 'correct': sample_f1 * 100})
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precision = true_positives / (true_positives + false_positives) if (true_positives + false_positives) > 0 else 0
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recall = true_positives / (true_positives + false_negatives) if (true_positives + false_negatives) > 0 else 0
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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
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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. 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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"""Preprocess the final predictions and references to needed format.
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Args:
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predictions (List): List of predictions for each sample.
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references (List): List of reference answers for each sample.
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Returns:
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dict: Preprocessed predictions and references in the required format.
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"""
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pattern = r"(正确答案[:：]|correct answer[:：])"
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cleaned_predictions = [re.sub(pattern, "", pred, flags=re.IGNORECASE).strip() for pred in predictions]
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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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"""Postprocess the final Rouge scores.
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Args:
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scores (dict): Dictionary of calculated average Rouge scores.
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Returns:
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dict: Postprocessed Rouge scores.
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"""
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return scores
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def score(self, predictions: List, references: List) -> dict:
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"""Calculate average Rouge-L score.
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Args:
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predictions (List): List of predicted strings for each sample.
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references (List): List of reference strings for each sample.
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Returns:
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dict: Calculated average Rouge-L score.
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"""
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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': '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['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) - 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, 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 = {
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"AvgRougeScore": average_score,
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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 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. 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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"""Preprocess the final predictions and references to needed format.
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Args:
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predictions (List): List of predictions for each sample.
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references (List): List of reference answers for each sample.
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Returns:
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dict: Preprocessed predictions and references in the required format.
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"""
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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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"""Postprocess the final accuracy score.
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Args:
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scores (dict): Dictionary of calculated accuracy score.
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Returns:
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dict: Postprocessed accuracy score.
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"""
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return scores
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def score(self, predictions: List, references: List) -> dict:
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"""Calculate accuracy score.
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Args:
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predictions (List): List of predicted strings for each sample.
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references (List): List of reference strings for each sample.
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Returns:
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dict: Calculated accuracy score.
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"""
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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': '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'], 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 = {
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"ACCStrScore": accuracy * 100,
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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 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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