OpenCompass/opencompass/summarizers/subjective/compass_arena.py

# flake8: noqa: E501
import ast
import csv
import os
import os.path as osp
import re
from collections import defaultdict
from datetime import datetime
from itertools import product

import mmengine
from mmengine import ConfigDict
from prettytable import from_csv

from opencompass.partitioners.sub_naive import remove_duplicate_pairs
from opencompass.utils import dataset_abbr_from_cfg, model_abbr_from_cfg

from .utils import get_judgeanswer_and_reference, get_outdir


def post_process_compass_arena(s):
    if result := re.findall('(?:选择：|Choice: )([ABC])', s):
        return result[0]
    else:
        return None


def check_position_bias(judged_answers, references, banned_choice=['C']):
    """Check position bias for judgellm's judgement.

    Args:
        judged_answers: The successfully extracted judgement.
        references: The references contains original question, which is used to located the same question for different position judgement.
    """
    position_bias_flag = 0
    position_bias_dict = {}
    for judge, ref in zip(judged_answers, references):
        question = ref['question']
        question_hash = hash(question)
        if question_hash not in position_bias_dict:
            position_bias_dict[question_hash] = {
                'question': question,
                'judge': judge
            }
        else:
            first_judge = position_bias_dict[question_hash]['judge']
            if judge == first_judge and first_judge not in banned_choice and judge not in banned_choice:
                # If second choice is same with first choice, there has position bias.
                position_bias_flag += 1
    return position_bias_flag


class CompassArenaSummarizer:
    """Do the subjectivity analyze based on evaluation results.

    Args:
        config (ConfigDict): The configuration object of the evaluation task.
            It's expected to be filled out at runtime.
    """

    def __init__(self,
                 config: ConfigDict,
                 judge_type='general',
                 check_pos_bias=True,
                 summary_type='single') -> None:
        self.tasks = []
        self.cfg = config
        self.base_models = self.cfg['eval']['partitioner']['base_models']
        self.compare_models = self.cfg['eval']['partitioner']['compare_models']
        self.judge_models = self.cfg.get('judge_models', None)
        self.meta_judge_model = self.cfg.eval.partitioner.get(
            'meta_judge_model', None)
        self.judge_type = judge_type
        assert self.judge_type in ['general']
        self.judge_map = {
            'general': post_process_compass_arena,
        }
        self.judge_function = self.judge_map[self.judge_type]
        self.check_pos_bias = check_pos_bias
        self.summary_type = summary_type

    def summarize(
            self,
            time_str: str = datetime.now().strftime('%Y%m%d_%H%M%S'),
    ):
        """Summarize the subjectivity analysis based on evaluation results.

        Args:
            time_str (str): Timestamp for file naming.

        Returns:
            pd.DataFrame: The summary results.
        """
        dataset_cfgs = self.cfg['datasets']
        output_dir, results_folder = get_outdir(self.cfg, time_str)
        model_combinations = list(
            product(self.base_models, self.compare_models))
        unique_combinations = remove_duplicate_pairs(
            [combo for combo in model_combinations if combo[0] != combo[1]])

        fout_list = []
        pre_len = len(self.judge_models)
        if self.meta_judge_model is not None:
            self.judge_models.append(self.meta_judge_model)
            meta_judge_model_abbr = model_abbr_from_cfg(self.meta_judge_model)
        else:
            meta_judge_model_abbr = None
        for idx, judge_model in enumerate(self.judge_models):
            judge_model = model_abbr_from_cfg(judge_model)
            for dataset in dataset_cfgs:
                dataset_abbr = dataset_abbr_from_cfg(dataset)
                if idx == pre_len:
                    fout = osp.join(
                        output_dir, 'summarized-by--' + judge_model + '-' +
                        dataset_abbr + '-report.csv')
                else:
                    fout = osp.join(
                        output_dir, 'judged-by--' + judge_model + '-' +
                        dataset_abbr + '-report.csv')
                fout_list.append(fout)
                for model_pair in unique_combinations:
                    model1, model2, = model_pair[0]['abbr'], model_pair[1][
                        'abbr'],
                    if idx == pre_len:
                        subdir = model1 + '_' + model2 + '_summarized-by--' + judge_model
                    else:
                        subdir = model1 + '_' + model2 + '_judged-by--' + judge_model
                    subdir_path = os.path.join(results_folder, subdir)
                    if os.path.isdir(subdir_path):
                        judged_answers, references = get_judgeanswer_and_reference(
                            dataset,
                            subdir_path,
                            self.judge_function,
                        )
                        if self.check_pos_bias:
                            bias_num = check_position_bias(
                                judged_answers, references)
                        else:
                            bias_num = 0
                        win_model1, win_model2, categories = defaultdict(
                            float), defaultdict(float), defaultdict(float)
                        model1, model2 = references[0]['answer1'], references[
                            0]['answer2']
                        for prediction, reference in zip(
                                judged_answers, references):
                            if self.summary_type == 'single':
                                if prediction == 'A':
                                    categories['total'] += 1
                                    categories[reference['capability']] += 1
                                    if reference['answer1'] == model1:
                                        win_model1[
                                            reference['capability']] += 1
                                        win_model1['total'] += 1
                                    else:
                                        win_model2[
                                            reference['capability']] += 1
                                        win_model2['total'] += 1
                                elif prediction == 'B':
                                    categories['total'] += 1
                                    categories[reference['capability']] += 1
                                    if reference['answer1'] == model1:
                                        win_model2[
                                            reference['capability']] += 1
                                        win_model2['total'] += 1
                                    else:
                                        win_model1[
                                            reference['capability']] += 1
                                        win_model1['total'] += 1
                            elif self.summary_type == 'half_add':
                                categories['total'] += 1
                                categories[reference['capability']] += 1
                                if prediction == 'A':
                                    if reference['answer1'] == model1:
                                        win_model1[
                                            reference['capability']] += 1
                                        win_model1['total'] += 1
                                    else:
                                        win_model2[
                                            reference['capability']] += 1
                                        win_model2['total'] += 1
                                elif prediction == 'B':
                                    if reference['answer1'] == model1:
                                        win_model2[
                                            reference['capability']] += 1
                                        win_model2['total'] += 1
                                    else:
                                        win_model1[
                                            reference['capability']] += 1
                                        win_model1['total'] += 1
                                elif prediction == 'C':
                                    win_model1[reference['capability']] += 0.5
                                    win_model1['total'] += 0.5
                                    win_model2[reference['capability']] += 0.5
                                    win_model2['total'] += 0.5
                        for capability in categories:
                            if capability not in win_model1:
                                win_model1[capability] = 0.0
                            else:
                                win_model1[capability] = round(
                                    (win_model1[capability] /
                                     categories[capability]) * 100, 2)
                            if capability not in win_model2:
                                win_model2[capability] = 0.0
                            else:
                                win_model2[capability] = round(
                                    (win_model2[capability] /
                                     categories[capability]) * 100, 2)
                        win_model1['position_bias'] = bias_num
                        win_model2['position_bias'] = bias_num
                        scores = {
                            'win_' + model1: win_model1,
                            'win_' + model2: win_model2
                        }
                        rows = list(scores.keys())
                        columns = list(scores[rows[0]].keys())
                        columns.insert(0, columns.pop(columns.index('total')))
                        columns.insert(
                            1, columns.pop(columns.index('position_bias')))
                        with open(fout, 'a+', newline='') as csvfile:
                            writer = csv.writer(csvfile)
                            writer.writerow([model1 + '_vs_' + model2] +
                                            columns)
                            for row in rows:
                                writer.writerow([row] + [
                                    scores[row][column] for column in columns
                                ])
                    else:
                        print(subdir_path + ' is not exist! please check!')
        for fout in fout_list:
            with open(fout, 'r') as f:
                x = from_csv(f)
            print(x)