Hugging Face's logo

auralray
/
DAminoMuta

Joblib
Model card Files
xet
Community
DAminoMuta
File size: 4,043 Bytes 
acbef3a
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
 
import os
import csv
import argparse
from natsort import natsorted

parser = argparse.ArgumentParser()
parser.add_argument('--dir', type=str, default='./run-reg')
parser.add_argument('--output', type=str, default='summary.csv')
args = parser.parse_args()

# 设置目录路径和输出CSV文件名
directory_path = args.dir
output_csv = args.output

# 创建CSV文件并写入数据
with open(output_csv, mode='w', newline='') as file:
    writer = csv.writer(file)

    # 写入第一个子表的标题和表头
    writer.writerow(['Valid'])
    writer.writerow(['Run', 'MAE', 'RSE', 'PCC', 'KCC'])

    # 遍历指定目录中的子目录
    for subdirectory in natsorted(os.listdir(directory_path)):
        subdirectory_path = os.path.join(directory_path, subdirectory)
        if os.path.isdir(subdirectory_path):
            result_file_path = os.path.join(subdirectory_path, 'result.txt')
            if os.path.isfile(result_file_path):
                with open(result_file_path, 'r') as result_file:
                    lines = result_file.readlines()
                    if len(lines) >= 3:
                        means_5_6 = lines[1].strip().split(',')
                        std_devs_5_6 = lines[2].strip().split(',')
                        if len(means_5_6) == 4 and len(std_devs_5_6) == 4:
                            values_5_6 = [f'{float(mean.strip()) * 100:.2f}$_{{\\pm{{{float(std_dev.strip()) * 100:.2f}}}}}$' 
                                          for mean, std_dev in zip(means_5_6, std_devs_5_6)]
                            writer.writerow([subdirectory] + values_5_6)

    # 加入空行以分隔两个子表
    writer.writerow([])

    # 写入第一个子表的标题和表头
    writer.writerow(['QLX'])
    writer.writerow(['Run', 'MAE', 'RSE', 'PCC', 'KCC'])

    # 遍历指定目录中的子目录
    for subdirectory in natsorted(os.listdir(directory_path)):
        subdirectory_path = os.path.join(directory_path, subdirectory)
        if os.path.isdir(subdirectory_path):
            result_file_path = os.path.join(subdirectory_path, 'result.txt')
            if os.path.isfile(result_file_path):
                with open(result_file_path, 'r') as result_file:
                    lines = result_file.readlines()
                    if len(lines) >= 6:
                        means_5_6 = lines[4].strip().split(',')
                        std_devs_5_6 = lines[5].strip().split(',')
                        if len(means_5_6) == 4 and len(std_devs_5_6) == 4:
                            values_5_6 = [f'{float(mean.strip()) * 100:.2f}$_{{\\pm{{{float(std_dev.strip()) * 100:.2f}}}}}$' 
                                          for mean, std_dev in zip(means_5_6, std_devs_5_6)]
                            writer.writerow([subdirectory] + values_5_6)

    # # 加入空行以分隔两个子表
    # writer.writerow([])

    # # 写入第二个子表的标题和表头
    # writer.writerow(['SAAP'])
    # writer.writerow(['Run', 'AP', 'F1', 'ACC', 'AUC'])

    # # 再次遍历指定目录中的子目录
    # for subdirectory in natsorted(os.listdir(directory_path)):
    #     subdirectory_path = os.path.join(directory_path, subdirectory)
    #     if os.path.isdir(subdirectory_path):
    #         result_file_path = os.path.join(subdirectory_path, 'result.txt')
    #         if os.path.isfile(result_file_path):
    #             with open(result_file_path, 'r') as result_file:
    #                 lines = result_file.readlines()
    #                 if len(lines) >= 9:
    #                     means_8_9 = lines[7].strip().split(',')
    #                     std_devs_8_9 = lines[8].strip().split(',')
    #                     if len(means_8_9) == 4 and len(std_devs_8_9) == 4:
    #                         values_8_9 = [f'{float(mean.strip()) * 100:.2f}$_{{\\pm{{{float(std_dev.strip()) * 100:.2f}}}}}$' 
    #                                   for mean, std_dev in zip(means_8_9, std_devs_8_9)]
    #                         writer.writerow([subdirectory] + values_8_9)