# 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_abbr = model_abbr_from_cfg(self.cfg['judge_model']) 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]]) judge_model = self.judge_abbr fout_list = [] for dataset in dataset_cfgs: dataset_abbr = dataset_abbr_from_cfg(dataset) 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'], 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)