def eval(self, input, real_val):
self.model.eval()
#input = nn.functional.pad(input,(1,0,0,0))
output, _, _ = self.model(input)
output = output.transpose(1, 3)
#output = [batch_size,12,num_nodes,1]
real = torch.unsqueeze(real_val, dim=1)
predict = output
loss = self.loss(predict, real, 0.0)
mae = util.masked_mae(predict, real, 0.0).item()
mape = util.masked_mape(predict, real, 0.0).item()
rmse = util.masked_rmse(predict, real, 0.0).item()
return loss.item(), mae, mape, rmse
def train(self, input, real_val):
self.model.train()
self.optimizer.zero_grad()
#input = nn.functional.pad(input,(1,0,0,0))
output, _, _ = self.model(input)
output = output.transpose(1, 3)
#output = [batch_size,12,num_nodes,1]
real = torch.unsqueeze(real_val, dim=1)
predict = output
loss = self.loss(predict, real, 0.0)
loss.backward()
if self.clip is not None:
torch.nn.utils.clip_grad_norm_(self.model.parameters(), self.clip)
self.optimizer.step()
mae = util.masked_mae(predict, real, 0.0).item()
mape = util.masked_mape(predict, real, 0.0).item()
rmse = util.masked_rmse(predict, real, 0.0).item()
return loss.item(), mae, mape, rmse
def train(self, input, input_cluster, real_val, real_val_cluster):
self.model.train()
self.optimizer.zero_grad()
#input = nn.functional.pad(input,(1,0,0,0))
output, output_cluster, tran2 = self.model(input, input_cluster)
output = output.transpose(1, 3)
#output_cluster = output_cluster.transpose(1,3)
#output = [batch_size,1,num_nodes,12]
real = torch.unsqueeze(real_val, dim=1)
#real_cluster = real_val_cluster[:,1,:,:]
#real_cluster = torch.unsqueeze(real_cluster,dim=1)
predict = output
loss = self.loss(predict, real, 0.0) #+energy
#loss1 =self.loss(output_cluster, real_cluster,0.0)
#print(loss)
(loss).backward()
if self.clip is not None:
torch.nn.utils.clip_grad_norm_(self.model.parameters(), self.clip)
self.optimizer.step()
mae = util.masked_mae(predict, real, 0.0).item()
mape = util.masked_mape(predict, real, 0.0).item()
rmse = util.masked_rmse(predict, real, 0.0).item()
return loss.item(), mae, mape, rmse
def evaluate_all(pred, target):
mape = util.masked_mape(pred, target, 0.0).item()
rmse = util.masked_rmse(pred, target, 0.0).item()
mae = util.masked_mae(pred, target, 0.0).item()
return mape, rmse, mae
