File size: 1,836 Bytes
f268251 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 |
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) |