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train.py
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# -*- coding: utf-8 -*-
"""
Created on Thu Jan 26 20:58:25 2023
@author: Haiyang Jiang
"""
from networks import DAT_Net
from torch import tensor
import torch
from torch_geometric.datasets import TUDataset
from torch_geometric.data import DataLoader
import torch.nn.functional as F
import argparse
import os
from torch.utils.data import random_split, WeightedRandomSampler
import time
from sklearn.metrics import roc_auc_score
import numpy as np
def get_labels_numbers(dataset):
I,J = 0,0
for data in dataset:
#print(data.y.shape)
if data.y==1:
I+=1
if data.y==0:
J+=1
print("@@@@@@@@@@@@@@@@")
print("number of y=1:", I)
print("number of y=0:", J)
def labels_weights(dataset):
I,J = 0,0
for data in dataset:
if data.y==1:
#print("label y=1")
I+=1
if data.y==0:
#print("label y=0")
J+=1
return (I+J)/I,(I+J)/J
def get_sample_weight(dataset):
weight = [labels_weights(dataset)[1],labels_weights(dataset)[0]]
sample_weight = [weight[t.y] for t in dataset]
return sample_weight
patience = 0
parser = argparse.ArgumentParser()
parser.add_argument('--seed', type=int, default=777,
help='seed')
parser.add_argument('--batch_size', type=int, default=128,
help='batch size')
parser.add_argument('--lr', type=float, default=0.005, #0.0005
help='learning rate')
parser.add_argument('--weight_decay', type=float, default=0.0001,
help='weight decay')
parser.add_argument('--nhid', type=int, default=128,
help='hidden size')
parser.add_argument('--dropout', type=float, default=0.,
help='dropout ratio on GNN')
parser.add_argument('--dropout_ratio1', type=float, default=0.1,
help='dropout ratio on GNN')
parser.add_argument('--dropout_ratio2', type=float, default=0.3,
help='dropout ratio on MLP')
parser.add_argument('--pooling_ratio', type=float, default=0.5,
help='dropout ratio on MLP')
parser.add_argument('--dataset', type=str, default='DD',
help='DD/PROTEINS/NCI1/NCI109/Mutagenicity/MOLT-4H/SF-295H')
parser.add_argument('--epochs', type=int, default=1000,
help='maximum number of epochs')
parser.add_argument('--patience', type=int, default=50,
help='patience for earlystopping')
parser.add_argument('--K', type=int, default=2)
parser.add_argument('--alfa', type=float, default=0.8)
parser.add_argument('--random', type=str, default='uniform',
help='uniform, norm, poisson')
parser.add_argument('--runs', type=int, default=5)
parser.add_argument('--heads', type=int, default=4)
parser.add_argument('--model', type=str, default='DAT',
help='SAGPool, DAT, DAGNN')
def test(model,loader):
model.eval()
correct = 0.
loss = 0.
i=0
for data in loader:
data = data.to(args.device)
out = model(data)
pred = out.max(dim=1)[1]
correct += pred.eq(data.y).sum().item()
loss += F.nll_loss(out,data.y,reduction='sum').item()
return correct / (len(loader.dataset)-i),loss / (len(loader.dataset)-i)
def train(model):
val_list = []
optimizer = torch.optim.Adam(model.parameters(), lr=args.lr, weight_decay=args.weight_decay)
min_loss = 1e10
patience = 0
train_time = []
for epoch in range(args.epochs):
time_0 = time.time()
model.train()
for i, data in enumerate(train_loader):
data = data.to(args.device)
out = model(data)
loss = F.nll_loss(out, data.y)
loss.backward()
optimizer.step()
optimizer.zero_grad()
time_1 = time.time()
train_time.append(time_1-time_0)
val_acc,val_loss = test(model,val_loader)
val_list.append(val_acc)
#print("Validation loss:{}\taccuracy:{}".format(val_loss,val_acc))
if val_loss < min_loss:
torch.save(model.state_dict(),'latest2.pth')
#print("Model saved at epoch{}".format(epoch))
test_acc,test_loss = test(model,test_loader)
#print("################Test accuarcy:{}".format(test_acc))
min_loss = val_loss
patience = 0
else:
patience += 1
if patience > args.patience:
#print("Early stop!")
break
#
return test_acc, epoch, train_time
def ROC_AUC(model,loader):
model.eval()
True_ = []
Pred = []
for data in loader:
data = data.to(args.device)
out = model(data)
#print("out", out.shape)
true = data.y
Pred.append(np.array(out.cpu().detach()).reshape(-1)[-1])
True_.append(np.array(true.cpu().detach()))
Pred = np.array(Pred)
True_ = np.array(True_)
#print(Pred.shape, True_.shape)
auc = roc_auc_score(True_, Pred)
#print('AUC', auc)
return auc
args = parser.parse_args()
args.device = 'cpu'
torch.manual_seed(args.seed)
if torch.cuda.is_available():
torch.cuda.manual_seed(args.seed)
args.device = 'cuda:0'
dataset = TUDataset(os.path.join('data',args.dataset),name=args.dataset)
args.num_classes = dataset.num_classes
args.num_features = dataset.num_features
print(args)
num_training = int(len(dataset)*0.8)
num_val = int(len(dataset)*0.1)
num_test = len(dataset) - (num_training+num_val)
training_set,validation_set,test_set = random_split(dataset,[num_training,num_val,num_test])
train_loader = DataLoader(training_set, batch_size=args.batch_size, shuffle=True)
val_loader = DataLoader(validation_set,batch_size=args.batch_size,shuffle=False)
test_loader = DataLoader(test_set,batch_size=1,shuffle=False)
model2 = DAT_Net(args).to(args.device)
print(model2)
t1 = time.time()
model2.reset_parameters()
test_acc, epoch, train_time = train(model2)
auc = ROC_AUC(model2, test_loader)
print("################Test accuarcy:{:.4f}, AUC:{:.3f}".
format(test_acc*100, auc))