fNIRSNet/LOSO_Result.py at main · wzhlearning/fNIRSNet · GitHub

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import numpy as np
import os

# Select dataset through task_id
task = ['UFFT', 'MA']
task_id = 0
print(task[task_id])

if task_id == 0:
    # UFFT
    all_sub = 30
elif task_id == 1:
    # MA
    all_sub = 29


all_acc = []
all_pre = []
all_rec = []
all_f1 = []
all_kap = []
for n_sub in range(1, all_sub+1):
    path = os.path.join('save',  task[task_id], 'LOSO', str(n_sub))
    val_acc = open(path + '/metrics.txt', "r")
    string = val_acc.readlines()[-1]

    acc = string.split('acc=')[1].split(', pre')[0]
    pre = string.split('pre=')[1].split(', rec')[0]
    rec = string.split('rec=')[1].split(', f1')[0]
    f1 = string.split('f1=')[1].split(', kap')[0]
    kappa = string.split('kap=')[1]

    acc = float(acc)
    pre = float(pre)
    rec = float(rec)
    f1 = float(f1) * 100
    kappa = float(kappa)

    all_acc.append(acc)
    all_pre.append(pre)
    all_rec.append(rec)
    all_f1.append(f1)
    all_kap.append(kappa)

    print('\nacc = %d : %.2f' % (n_sub, acc))
    print('pre = %d : %.2f' % (n_sub, pre))
    print('rec = %d : %.2f' % (n_sub, rec))
    print('f1  = %d : %.2f' % (n_sub, f1))
    print('kap = %d : %.2f' % (n_sub, kappa))


print('\n=======> LOSO-CV results of all subjects on ' + task[task_id])
all_acc = np.array(all_acc)
all_pre = np.array(all_pre)
all_rec = np.array(all_rec)
all_f1 = np.array(all_f1)
all_kap = np.array(all_kap)
print('acc = %.2f ± %.2f' % (np.mean(all_acc), np.std(all_acc)))
print('pre = %.2f ± %.2f' % (np.mean(all_pre), np.std(all_pre)))
print('rec = %.2f ± %.2f' % (np.mean(all_rec), np.std(all_rec)))
print('f1  = %.2f ± %.2f' % (np.mean(all_f1), np.std(all_f1)))
print('kap = %.2f ± %.2f' % (np.mean(all_kap), np.std(all_kap)))