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main.py
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main.py
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import warnings
warnings.simplefilter('ignore')
from sklearn.metrics import confusion_matrix
from net.model import *
import util
from torch.utils.data import DataLoader
import torch
import numpy as np
from random import randrange
argv = util.option.parse()
from data_pre import DATASET
full_dataset = DATASET()
T=0
ACC=[]
SEN=[]
SPE=[]
BAC=[]
PPV=[]
NPV=[]
PRE=[]
REC=[]
F1_SCORE=[]
AUC=[]
for seed in [1,2,3,4,5]:
print(seed)
from sklearn.model_selection import KFold
k = argv.k_fold
kfold = KFold(n_splits=k, random_state=seed, shuffle=True)
Acc2 = []
Sen2 = []
Spe2 = []
Bac2 = []
Ppv2 = []
Npv2 = []
Pre2 = []
Rec2 = []
F1_score2 = []
Auc2 = []
for fold, (train_idx, test_idx) in enumerate(kfold.split(full_dataset)):
print('------------fold no---------{}----------------------'.format(fold))
train_subsampler = torch.utils.data.SubsetRandomSampler(train_idx)
test_subsampler = torch.utils.data.SubsetRandomSampler(test_idx)
train_loader = torch.utils.data.DataLoader(
full_dataset,
batch_size=argv.minibatch_size, sampler=train_subsampler) # 16,64
test_loader = torch.utils.data.DataLoader(
full_dataset,
batch_size=argv.minibatch_size, sampler=test_subsampler)
def calculate_metric(gt, pred):
pred[pred > 0.5] = 1
pred[pred < 1] = 0
confusion = confusion_matrix(gt, pred)
TP = confusion[1, 1]
TN = confusion[0, 0]
FP = confusion[0, 1]
FN = confusion[1, 0]
acc = (TP + TN) / float(TP + TN + FP + FN)
sen = TP / float(TP + FN)
spe = TN / float(TN + FP)
bac = (sen + spe) / 2
ppv = TP / float(TP + FP)
npv = TN / float(TN + FN)
pre = TP / float(TP + FP)
rec = TP / float(TP + FN)
f1_score = 2 * pre * rec / (pre + rec)
return acc, sen, spe, bac, ppv, npv, pre, rec, f1_score
model = MDRL(
input_dim=116,
hidden_dim=argv.hidden_dim,
num_classes=2,
num_heads=argv.num_heads,
num_layers=argv.num_layers,
sparsity=argv.sparsity,
dropout=argv.dropout,
cls_token=argv.cls_token,
readout=argv.readout)
def get_device():
return 'cuda' if torch.cuda.is_available() else 'cpu'
device=get_device()
print(device)
model.to(device)
Ep=argv.num_epochs
Lr=argv.lr
criterion = torch.nn.CrossEntropyLoss()
optimizer = torch.optim.Adam(model.parameters(), lr= Lr)
scheduler = torch.optim.lr_scheduler.OneCycleLR(optimizer, max_lr=argv.max_lr, epochs=Ep,
steps_per_epoch=len(train_loader), pct_start=0.2,
div_factor=argv.max_lr / Lr, final_div_factor=1000)
for epoch in range(Ep):
train_acc = 0.0
train_loss = 0.0
test_acc = 0.0
test_loss = 0.0
model.train()
for i,(timeseries,label) in enumerate(train_loader):
clip_grad=0.0
dyn_a, sampling_points=util.bold.process_dynamic_fc(timeseries, argv.window_size, argv.window_stride, argv.dynamic_length)
sampling_endpoints = [p+argv.window_size for p in sampling_points]
dyn_v=torch.nan_to_num(dyn_a.float())
t = timeseries.permute(1,0,2)
label = label.long().to(device)
t = t.to(torch.float32)
optimizer.zero_grad()
logit, reconstruct_loss, modularityloss,attention, latent, reg_ortho = model(dyn_v.to(device), dyn_a.to(device))
pred = logit.argmax(1).to(device)
prob = logit.softmax(1)
batch_loss = criterion(logit, label.to(device))+argv.lambda1*reconstruct_loss+argv.lambda2*modularityloss
_, train_pred = torch.max(logit, 1)
if optimizer is not None:
optimizer.zero_grad()
batch_loss.backward()
if clip_grad > 0.0: torch.nn.utils.clip_grad_value_(model.parameters(), clip_grad)
optimizer.step()
if scheduler is not None:
scheduler.step()
train_acc += (train_pred.cpu() == label.cpu()).sum().item()
train_loss += batch_loss.item()
Labels = []
Test_pred = []
Pre_score = []
model.eval()
with torch.no_grad():
for i,(timeseries,label) in enumerate(test_loader):
Labels.append(label)
dyn_a, sampling_points = util.bold.process_dynamic_fc(timeseries,argv.window_size, argv.window_stride, argv.dynamic_length)
sampling_endpoints = [p+argv.window_size for p in sampling_points]
dyn_v = torch.nan_to_num(dyn_a.float())
t = timeseries.permute(1, 0, 2)
label = label.long().to(device)
t = t.to(torch.float32)
logit, reconstruct_loss, modularityloss,attention, latent, reg_ortho = model(dyn_v.to(device), dyn_a.to(device))
batch_loss = criterion(logit, label.to(device))+argv.lambda1*reconstruct_loss+argv.lambda2*modularityloss
pre_socre = logit[:, 1]
Pre_score.append(pre_socre)
_, test_pred = torch.max(logit, 1)
Test_pred.append(test_pred)
test_acc += (
test_pred.cpu() == label.cpu()).sum().item()
test_loss += batch_loss.item()
print('[{:03d}/{:03d}] Train Acc: {:3.6f} Loss: {:3.6f} | Test Acc: {:3.6f} loss: {:3.6f}'.format(
epoch + 1, Ep, train_acc / len(train_idx), train_loss / len(train_loader),
test_acc / len(test_idx), test_loss / len(test_loader)
))
y_true = torch.cat(Labels, -1).cpu()
y_pred = torch.cat(Test_pred, -1).cpu()
PPre_score = torch.cat(Pre_score, -1).cpu()
acc, sen, spe, bac, ppv, npv, pre, rec, f1_score = calculate_metric(y_true, y_pred)
from sklearn import metrics
fpr, tpr, threshold = metrics.roc_curve(y_true, PPre_score)
auc = metrics.auc(fpr, tpr)
Acc2.append(acc)
Sen2.append(sen)
Spe2.append(spe)
Bac2.append(bac)
Ppv2.append(ppv)
Npv2.append(npv)
Pre2.append(pre)
Rec2.append(rec)
F1_score2.append(f1_score)
Auc2.append(auc)
avg_Acc = sum(Acc2) / k
print(avg_Acc)
print('Acc2std', np.std(Acc2, ddof=1))
avg_Sen = sum(Sen2) / k
print(avg_Sen)
print('Sen2std', np.std(Sen2, ddof=1))
avg_Spe = sum(Spe2) / k
print(avg_Spe)
print('Spe2std', np.std(Spe2, ddof=1))
avg_Bac = sum(Bac2) / k
print(avg_Bac)
print('Bac2std', np.std(Bac2, ddof=1))
avg_Ppv = sum(Ppv2) / k
print(avg_Ppv)
print('Ppv2std', np.std(Ppv2, ddof=1))
avg_Npv = sum(Npv2) / k
print(avg_Npv)
print('Npv2std', np.std(Npv2, ddof=1))
avg_Pre = sum(Pre2) / k
print(avg_Pre)
print('Pre2std', np.std(Pre2, ddof=1))
avg_Rec = sum(Rec2) / k
print(avg_Rec)
print('Rec2std', np.std(Rec2, ddof=1))
avg_F1_score = sum(F1_score2) / k
print(avg_F1_score)
print('F1_score2std', np.std(F1_score2, ddof=1))
avg_Auc = sum(Auc2) / k
print(avg_Auc)
print('Auc2std', np.std(Auc2, ddof=1))
ACC.extend([avg_Acc])
SEN.extend([avg_Sen])
SPE.extend([avg_Spe])
BAC.extend([avg_Bac])
PPV.extend([avg_Ppv])
NPV.extend([avg_Npv])
PRE.extend([avg_Pre])
REC.extend([avg_Rec])
F1_SCORE.extend([avg_F1_score])
AUC.extend([avg_Auc])
print('ACCmean', np.mean(ACC))
print('ACCstd', np.std(ACC, ddof=1))
print('SENmean', np.mean(SEN))
print('SENstd', np.std(SEN, ddof=1))
print('SPEmean', np.mean(SPE))
print('SPEstd', np.std(SPE, ddof=1))
print('BACmean', np.mean(BAC))
print('BACstd', np.std(BAC, ddof=1))
print('PPVmean', np.mean(PPV))
print('PPVstd', np.std(PPV, ddof=1))
print('NPVmean', np.mean(NPV))
print('NPVstd', np.std(NPV, ddof=1))
print('PREmean', np.mean(PRE))
print('PREstd', np.std(PRE, ddof=1))
print('RECmean', np.mean(REC))
print('RECstd', np.std(REC, ddof=1))
print('F1_SCOREmean', np.mean(F1_SCORE))
print('F1_SCOREstd', np.std(F1_SCORE, ddof=1))
print('AUCmean', np.mean(AUC))
print('AUCstd', np.std(AUC, ddof=1))