# flake8: noqa # yapf: disable import csv import os import os.path as osp import re from collections import defaultdict from datetime import datetime from itertools import product import numpy as np from mmengine import ConfigDict from tabulate import tabulate from opencompass.partitioners.sub_naive import remove_duplicate_pairs from opencompass.utils import dataset_abbr_from_cfg, model_abbr_from_cfg from .compass_arena import (CompassArenaSummarizer, check_position_bias, model_abbr_from_cfg_used_in_summarizer) from .utils import get_judgeanswer_and_reference, get_outdir task_group_new = { 'Information seeking': 'Information/Advice seeking', 'Creative Writing': 'Creative Tasks', 'Coding & Debugging': 'Coding & Debugging', 'Reasoning': 'Planning & Reasoning', 'Editing': 'Creative Tasks', 'Math': 'Math & Data Analysis', 'Planning': 'Planning & Reasoning', 'Brainstorming': 'Creative Tasks', 'Role playing': 'Creative Tasks', 'Advice seeking': 'Information/Advice seeking', 'Data Analysis': 'Math & Data Analysis', 'Others': 'Creative Tasks'} def post_process_wildbench_pair(judgement: str): pattern = r'\"choice\": \"(.*?)\"' matched_result = re.findall(pattern, judgement) if matched_result: return matched_result[0] else: return None def post_process_wildbench_single(judgement: str): pattern = r'\"score\": \"(.*?)\"' matched_result = re.findall(pattern, judgement) try: score = float(matched_result[0]) return {'score': score} except (ValueError, IndexError) as e: return None # if matched_result: # score = float(matched_result[0]) # else: # return None # return {'score': score} def get_capability_results( judged_answers, references, fout, fout_flag, model_abbr, ): capability_ratings = defaultdict(float) capability_counts = defaultdict(float) for ans, ref in zip(judged_answers, references): # rescale capability_ratings['total'] += ans capability_counts['total'] += 1 tags = [ref['primary_tag']] + ref['secondary_tag'] for tag in tags: capability_ratings[task_group_new[tag]] += ans capability_counts[task_group_new[tag]] += 1 capability_avg_ratings = defaultdict(float) for capability, total_score in capability_ratings.items(): s = (total_score / capability_counts[capability] - 5) * 2 * 10 s = round(s, 2) capability_avg_ratings[capability] = s columns = list(capability_avg_ratings.keys()) columns.insert(0, columns.pop(columns.index('total'))) with open(fout, 'a+', newline='') as csvfile: writer = csv.writer(csvfile) if fout_flag == 0: writer.writerow(['model'] + columns) writer.writerow([model_abbr] + [capability_avg_ratings[column] for column in columns]) class WildBenchSingleSummarizer(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) -> None: self.judge_type = 'single' self.tasks = [] self.cfg = config self.eval_model_cfgs = self.cfg['eval']['partitioner']['models'] self.judge_abbr = model_abbr_from_cfg(self.cfg['judge_models'][0]) self.judge_function = post_process_wildbench_single 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. """ # self.judge_type == 'single' dataset_cfgs = self.cfg['datasets'] output_dir, results_folder = get_outdir(self.cfg, time_str) fout_flag = 0 for eval_model_cfg in self.eval_model_cfgs: eval_model_abbr = model_abbr_from_cfg(eval_model_cfg) show_model_abbr = model_abbr_from_cfg_used_in_summarizer(eval_model_cfg) subdir_path = os.path.join(results_folder, eval_model_abbr + '_judged-by--' + self.judge_abbr) if os.path.isdir(subdir_path): fout = osp.join(output_dir, 'judged-by--' + self.judge_abbr + '-capability.csv') overall_judged_answers, overall_references = [], [] for dataset in dataset_cfgs: judged_answers, references = get_judgeanswer_and_reference(dataset, subdir_path, self.judge_function) judged_answers = [item['score'] for item in judged_answers] overall_judged_answers += judged_answers overall_references += references get_capability_results(overall_judged_answers, overall_references, fout, fout_flag, show_model_abbr) fout_flag += 1 else: print(subdir_path + ' is not exist! please check!') class WildBenchPairSummarizer(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, check_pos_bias=False) -> 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_abbr = model_abbr_from_cfg(self.cfg['judge_models'][0]) self.judge_function = post_process_wildbench_pair self.check_pos_bias = check_pos_bias def get_score(self, time_str): 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]]) if self.meta_judge_model is not None: self.judge_models.append(self.meta_judge_model) scores = {} for idx, judge_model_cfg in enumerate(self.judge_models): judge_model = model_abbr_from_cfg(judge_model_cfg) for dataset in self.cfg['datasets']: dataset_abbr = dataset_abbr_from_cfg(dataset) for model_pair in unique_combinations: base_model = model_pair[0]['abbr'] compare_model = model_pair[1]['abbr'] if idx == len(self.judge_models): subdir = base_model + '_' + compare_model + '_summarized-by--' + judge_model else: subdir = base_model + '_' + compare_model + '_judged-by--' + judge_model subdir_path = os.path.join(results_folder, subdir) if not os.path.isdir(subdir_path): print(subdir_path + ' is not exist! please check!') continue 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_base_model = defaultdict(float) win_compare_model = defaultdict(float) categories = defaultdict(float) # base_model = references[0]['answer1'] # compare_model = references[0]['answer2'] score_mapping = {'A++': 1, 'A+': 0.5, 'A=B': 0, 'B+': -0.5, 'B++': -1} for prediction, reference in zip(judged_answers, references): if prediction not in score_mapping: continue categories[dataset_abbr] += 1 flag = 1 if reference['answer1'] == base_model else -1 score_1 = score_mapping[prediction]*flag score_2 = -score_1 tags = [reference['primary_tag']] + reference['secondary_tag'] for tag in tags: win_base_model[task_group_new[tag]] += score_1 win_compare_model[task_group_new[tag]] += score_2 categories[task_group_new[tag]] += 1 win_compare_model[dataset_abbr] += score_2 win_base_model[dataset_abbr] += score_1 for capability in categories: win_base_model[capability] = win_base_model[capability] / categories[capability] * 100 win_base_model[capability] = round(win_base_model[capability], 2) win_compare_model[capability] = win_compare_model[capability] / categories[capability] * 100 win_compare_model[capability] = round(win_compare_model[capability], 2) win_base_model['position_bias'] = bias_num win_compare_model['position_bias'] = bias_num if judge_model not in scores: scores[judge_model] = {} if dataset_abbr not in scores[judge_model]: scores[judge_model][dataset_abbr] = {} scores[judge_model][dataset_abbr][base_model + '/' + compare_model] = win_compare_model return scores 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. """ scores = self.get_score(time_str) output_dir, results_folder = get_outdir(self.cfg, time_str) for idx, judge_model in enumerate(self.judge_models): judge_abbr = model_abbr_from_cfg(judge_model) for dataset in self.cfg['datasets']: dataset_abbr = dataset_abbr_from_cfg(dataset) summarizer_model_abbrs = [model_abbr_from_cfg_used_in_summarizer(i) for i in self.compare_models] one_column = list(scores[judge_abbr][dataset_abbr].values())[0] row_headers = [i for i in one_column.keys() if i not in [dataset_abbr, 'position_bias']] row_headers = [dataset_abbr, 'position_bias'] + row_headers table = [] for row_header in row_headers: row = [row_header] headers = [''] for model_cfg in self.compare_models: model_abbr = model_abbr_from_cfg(model_cfg) avg = 0 for base_model_cfg in self.base_models: base_model_abbr = model_abbr_from_cfg(base_model_cfg) base_compare = base_model_abbr + '/' + model_abbr headers.append(base_compare) s = scores[judge_abbr][dataset_abbr][base_compare].get(row_header, '') if isinstance(s, float): avg += s s = f'{s:.2f}' if isinstance(s, int): s = str(s) row.append(s) avg = avg/len(self.base_models) row.append(f'{avg:.2f}') headers.append('Avg') table.append(row) txt = tabulate(table, headers=headers) print(txt) if idx == len(self.judge_models): output_filename = osp.join(output_dir, 'summarized-by--' + judge_abbr + '-' + dataset_abbr + '-report.csv') else: output_filename = osp.join(output_dir, 'judged-by--' + judge_abbr + '-' + dataset_abbr + '-report.csv') with open(output_filename, 'w') as f: f.write(','.join(headers) + '\n') for line in table: f.write(','.join(line) + '\n') print(output_filename)