OpenCompass/opencompass/datasets/SeedBench.py

import os
import random
import datasets
from typing import List
from .base import BaseDataset
from opencompass.openicl.icl_evaluator.icl_base_evaluator import BaseEvaluator
import numpy as np
import re
import jieba
from rouge_chinese import Rouge
from opencompass.registry import ICL_EVALUATORS, TEXT_POSTPROCESSORS


class SeedBenchDataset(BaseDataset):
    @staticmethod
    def load(data_files: str, path: str = 'json', split: str = None, **kwargs) -> datasets.Dataset:
        dataset = datasets.load_dataset(path, data_files=data_files, **kwargs)

        if split is None:
            split = list(dataset.keys())[0]
            print(f"my datasets split :  {split}")

        if split not in dataset:
            raise ValueError(f"Split '{split}' not found. Available splits: {list(dataset.keys())}")

        return dataset[split]


class F1Evaluator(BaseEvaluator):
    """F1 Score evaluator for multiple choice questions.

    Args:
        seed (int): Seed for randomness, ensuring reproducibility. Defaults to 0.
    """

    def __init__(self, seed: int = 0) -> None:
        self.seed = seed
        super().__init__()

    def _preprocess(self, predictions: List, references: List) -> dict:
        """Preprocess the final predictions and references to needed format.

        Args:
            predictions (List): List of predictions for each sample.
            references (List): List of reference answers for each sample.

        Returns:
            dict: Preprocessed predictions and references in the required format.
        """
        return {
            'predictions': predictions,
            'references': references,
        }

    def _postprocess(self, scores: dict) -> dict:
        """Postprocess the final score for F1.

        Args:
            scores (dict): Dictionary of calculated F1 score.

        Returns:
            dict: Postprocessed F1 score.
        """
        return scores

    def score(self, predictions: List, references: List) -> dict:
        """Calculate F1 score.

        Args:
            predictions (List): List of predicted answers for each sample.
            references (List): List of reference answers for each sample.

        Returns:
            dict: Calculated F1 score.
        """
        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 {
                'error': 'predictions and references have different '
                         f'length. len(predictions): {len(predictions)}, '
                         f'len(references): {len(references)}'
            }

        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}
            
            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
            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

        result = {
            "ours_F1Score": f1 * 100,  # 总体 F1 分数
            "details": details
        }
        random.setstate(random_state)
        np.random.set_state(np_random_state)
        return self._postprocess(result)
    
@ICL_EVALUATORS.register_module()
class F1ScoreEvaluator(F1Evaluator):
    """F1 Score evaluator for multiple choice questions."""
    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:
        seed (int): Seed for randomness, ensuring reproducibility. Defaults to 0.
    """

    def __init__(self, seed: int = 0) -> None:
        self.seed = seed
        super().__init__()

    def _preprocess(self, predictions: List, references: List) -> dict:
        """Preprocess the final predictions and references to needed format.

        Args:
            predictions (List): List of predictions for each sample.
            references (List): List of reference answers for each sample.

        Returns:
            dict: Preprocessed predictions and references in the required format.
        """
        pattern = r"(正确答案[:：]|correct answer[:：])"
        cleaned_predictions = [re.sub(pattern, "", pred, flags=re.IGNORECASE).strip() for pred in predictions]

        return {
            'predictions': cleaned_predictions,
            'references': references,
        }

    def _postprocess(self, scores: dict) -> dict:
        """Postprocess the final Rouge scores.

        Args:
            scores (dict): Dictionary of calculated average Rouge scores.

        Returns:
            dict: Postprocessed Rouge scores.
        """
        return scores

    def score(self, predictions: List, references: List) -> dict:
        """Calculate average Rouge-L score.

        Args:
            predictions (List): List of predicted strings for each sample.
            references (List): List of reference strings for each sample.

        Returns:
            dict: Calculated average Rouge-L score.
        """
        def rouge_score(hyps, refs):
            assert(len(hyps) == len(refs))
            hyps = [' '.join(jieba.cut(h)) for h in hyps]
            hyps = [h if h.strip() != "" else "无内容" for h in hyps]
            refs = [' '.join(jieba.cut(r)) for r in refs]
            rouge_scores = Rouge().get_scores(hyps, refs)
            rouge_ls = [score["rouge-l"]["f"] for score in rouge_scores]
            average_rouge_l = sum(rouge_ls) / len(rouge_ls)
            return {"score": average_rouge_l * 100}
        
        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 {
                'error': 'predictions and references have different '
                         f'length. len(predictions): {len(predictions)}, '
                         f'len(references): {len(references)}'
            }

        preprocessed_data = self._preprocess(predictions, references)
        hyps, refs = preprocessed_data['predictions'], preprocessed_data['references']

        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):
                    word_level_hyps += ['无内容'] * (len(word_level_refs) - len(word_level_hyps))
                else:
                    word_level_hyps = word_level_hyps[:len(word_level_refs)]

            sample_score = rouge_score(word_level_hyps, word_level_refs)["score"]
            scores.append(sample_score)
            details.append({'pred': word_level_hyps, 'answer': word_level_refs, 'correct': sample_score})

        average_score = sum(scores) / len(scores)
        result = {
            "AvgRougeScore": average_score,
            "details": details
        }
        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:
        seed (int): Seed for randomness, ensuring reproducibility. Defaults to 0.
    """

    def __init__(self, seed: int = 0) -> None:
        self.seed = seed
        super().__init__()

    def _preprocess(self, predictions: List, references: List) -> dict:
        """Preprocess the final predictions and references to needed format.

        Args:
            predictions (List): List of predictions for each sample.
            references (List): List of reference answers for each sample.

        Returns:
            dict: Preprocessed predictions and references in the required format.
        """
        return {
            'predictions': predictions,
            'references': references,
        }

    def _postprocess(self, scores: dict) -> dict:
        """Postprocess the final accuracy score.

        Args:
            scores (dict): Dictionary of calculated accuracy score.

        Returns:
            dict: Postprocessed accuracy score.
        """
        return scores

    def score(self, predictions: List, references: List) -> dict:
        """Calculate accuracy score.

        Args:
            predictions (List): List of predicted strings for each sample.
            references (List): List of reference strings for each sample.

        Returns:
            dict: Calculated accuracy score.
        """
        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 {
                'error': 'predictions and references have different '
                         f'length. len(predictions): {len(predictions)}, '
                         f'len(references): {len(references)}'
            }

        preprocessed_data = self._preprocess(predictions, references)

        correct = 0
        for hyp, ref in zip(preprocessed_data['predictions'], preprocessed_data['references']):
            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)
        result = {
            "ACCStrScore": accuracy * 100,
            "details": details
        }
        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__()