| import argparse | |
| from copy import deepcopy | |
| import util | |
| from pprint import pprint | |
| from collections import defaultdict | |
| import pandas as pd | |
| import json | |
| def get_domain(x): | |
| for domain in ['chest_xray', 'mri', 'histology', 'gross', 'ct_scan']: | |
| in_domain = x['domain'][domain] | |
| if in_domain: | |
| return domain | |
| def main(args): | |
| scores_data = util.load_file_jsonl(args.scores_file) | |
| predictions = [(x['question_id'], x['type'], get_domain(x), x['gpt_eval'].split('\n')[0].split(' ')) for x in scores_data] | |
| score_type_dict = defaultdict(lambda: defaultdict(list)) | |
| for q_id, q_type, domain, (a1_score, a2_score) in predictions: | |
| score_type_dict[q_type][1].append(a1_score) | |
| score_type_dict[q_type][2].append(a2_score) | |
| score_type_dict['overall'][1].append(a1_score) | |
| score_type_dict['overall'][2].append(a2_score) | |
| score_type_dict[domain][1].append(a1_score) | |
| score_type_dict[domain][2].append(a2_score) | |
| result = defaultdict(dict) | |
| for q_type, score_dict in score_type_dict.items(): | |
| result[q_type]['gpt4_score'] = util.get_avg(score_dict[1]) | |
| result[q_type]['pred_score'] = util.get_avg(score_dict[2]) | |
| result[q_type]['pred_relative_score'] = util.get_avg([float(s2)/float(s1) for s1, s2 in zip(score_dict[1], score_dict[2])])*100 | |
| result[q_type]['data_size'] = len(score_dict[1]) | |
| df = pd.DataFrame.from_dict(result).filter(['conversation', 'detailed_description', 'chest_xray', 'mri', 'histology', 'gross', 'ct_scan', 'overall']) | |
| print(df) | |
| if __name__ == '__main__': | |
| parser = argparse.ArgumentParser("GPT-4 Multimodal Chat Eval Postprocessing", add_help=True) | |
| parser.add_argument("--scores-file", default="", metavar="FILE", help="input path to gpt-4 score file") | |
| args = parser.parse_args() | |
| main(args) |