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gather_best_mm_results.py
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gather_best_mm_results.py
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import os
import os.path as op
fout = open('best_mm_results.txt', 'w', encoding='utf-8')
root = 'out'
datasets = ['mvsa-s', 'mvsa-s+'] # Options: ['t2015', 't2015+', ... ,'tumemo', 'tumemo+']
for dataset in datasets:
fout.write(dataset + '\n')
dir_1 = op.join(root, dataset)
templates = os.listdir(dir_1)
templates.sort(key=lambda x: (x[:12], x[-6:])) # [s1/2][d1/2][t1/2], [lp11/22/.../77]
for template in templates:
if template[-6:] not in ['[lp11]', '[lp22]', '[lp33]', '[lp44]', '[lp55]', '[lp66]', '[lp77]']:
continue
fout.write(template + '\n')
dir_2 = op.join(dir_1, template)
if '+' not in dataset:
files = os.listdir(dir_2)
files = [f for f in files if f[-4:] == '.txt']
for file in files: # only one file here actually
fout.write(file[:-4] + '\n')
continue
loads = os.listdir(dir_2)
loads.sort()
result_files = []
for load in loads:
dir_3 = op.join(dir_2, load)
files = os.listdir(dir_3)
files = [f for f in files if f[-4:] == '.txt']
result_files += files
idx = 0
best_idx = 0
best_value = 0
for f in result_files:
values = f.split('_')[4:7] # mean values (Acc, Mac-F1, Wtd-F1)
value = sum([float(i) for i in values])
if value > best_value:
best_value = value
best_idx = idx
idx += 1
fout.write(result_files[best_idx][:-4] + '\n')
fout.write('\n\n')
fout.close()