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import argparse
import util
from collections import defaultdict
import pandas as pd
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)
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