import numpy as np
import pandas as pd
import sys, os
from random import shuffle
import torch
import torch.nn as nn
from models.gat import GATNet
from models.gat_gcn import GAT_GCN
from models.gcn import GCNNet
from models.ginconv import GINConvNet
from utils import *
# training function at each epoch
def train(model, device, train_loader, optimizer, epoch):
print('Training on {} samples...'.format(len(train_loader.dataset)))
model.train()
for batch_idx, data in enumerate(train_loader):
data = data.to(device)
optimizer.zero_grad()
output = model(data)
loss = loss_fn(output, data.y.view(-1, 1).float().to(device))
loss.backward()
optimizer.step()
if batch_idx % LOG_INTERVAL == 0:
print('Train epoch: {} [{}/{} ({:.0f}%)]\tLoss: {:.6f}'.format(epoch,
batch_idx * len(data.x),
len(train_loader.dataset),
100. * batch_idx / len(train_loader),
loss.item()))
def predicting(model, device, loader):
model.eval()
total_preds = torch.Tensor()
total_labels = torch.Tensor()
print('Make prediction for {} samples...'.format(len(loader.dataset)))
with torch.no_grad():
for data in loader:
data = data.to(device)
output = model(data)
total_preds = torch.cat((total_preds, output.cpu()), 0)
total_labels = torch.cat((total_labels, data.y.view(-1, 1).cpu()), 0)
return total_labels.numpy().flatten(),total_preds.numpy().flatten()
datasets = [['davis','kiba'][int(sys.argv[1])]]
modeling = [GINConvNet, GATNet, GAT_GCN, GCNNet][int(sys.argv[2])]
model_st = modeling.__name__
cuda_name = "cuda:0"
if len(sys.argv)>3:
cuda_name = "cuda:" + str(int(sys.argv[3]))
print('cuda_name:', cuda_name)
TRAIN_BATCH_SIZE = 512
TEST_BATCH_SIZE = 512
LR = 0.0005
LOG_INTERVAL = 20
NUM_EPOCHS = 1000
print('Learning rate: ', LR)
print('Epochs: ', NUM_EPOCHS)
# Main program: iterate over different datasets
for dataset in datasets:
print('\nrunning on ', model_st + '_' + dataset )
processed_data_file_train = 'data/processed/' + dataset + '_train.pt'
processed_data_file_test = 'data/processed/' + dataset + '_test.pt'
if ((not os.path.isfile(processed_data_file_train)) or (not os.path.isfile(processed_data_file_test))):
print('please run create_data.py to prepare data in pytorch format!')
else:
train_data = TestbedDataset(root='data', dataset=dataset+'_train')
test_data = TestbedDataset(root='data', dataset=dataset+'_test')
# make data PyTorch mini-batch processing ready
train_loader = DataLoader(train_data, batch_size=TRAIN_BATCH_SIZE, shuffle=True)
test_loader = DataLoader(test_data, batch_size=TEST_BATCH_SIZE, shuffle=False)
# training the model
device = torch.device(cuda_name if torch.cuda.is_available() else "cpu")
model = modeling().to(device)
loss_fn = nn.MSELoss()
optimizer = torch.optim.Adam(model.parameters(), lr=LR)
best_mse = 1000
best_ci = 0
best_epoch = -1
model_file_name = 'model_' + model_st + '_' + dataset + '.model'
result_file_name = 'result_' + model_st + '_' + dataset + '.csv'
for epoch in range(NUM_EPOCHS):
train(model, device, train_loader, optimizer, epoch+1)
G,P = predicting(model, device, test_loader)
ret = [rmse(G,P),mse(G,P),pearson(G,P),spearman(G,P),ci(G,P)]
if ret[1]<best_mse:
torch.save(model.state_dict(), model_file_name)
with open(result_file_name,'w') as f:
f.write(','.join(map(str,ret)))
best_epoch = epoch+1
best_mse = ret[1]
best_ci = ret[-1]
print('rmse improved at epoch ', best_epoch, '; best_mse,best_ci:', best_mse,best_ci,model_st,dataset)
else:
print(ret[1],'No improvement since epoch ', best_epoch, '; best_mse,best_ci:', best_mse,best_ci,model_st,dataset)
Datasets
Models
