medrax/llava/eval/summarize_gpt_review.py

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)