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exp_polla_dense.py
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from data.dataloader import Dataset_ST
from exp.exp_basic import Exp_Basic
from models.modeldense import POLLA_diff_Score, POLLA_gcn_Score, POLLA_gcn, POLLA_diff, POLLA_adpadj
from util.tools import EarlyStopping, adjust_learning_rate, load_adj
from util.metrics import masked_mae, masked_rmse, masked_mape, metric
import numpy as np
import torch
import torch.nn as nn
from torch import optim
from torch.utils.data import DataLoader
import os
import time
from sklearn.metrics import mean_squared_error, mean_absolute_error
import warnings
warnings.filterwarnings('ignore')
class Exp_POLLA(Exp_Basic):
def __init__(self, args):
super(Exp_POLLA, self).__init__(args)
def _build_model(self):
model_dict = {
'pollagcn':POLLA_gcn,
'polladiff':POLLA_diff,
'pollaadp':POLLA_adpadj,
'polladiffscore': POLLA_diff_Score,
'pollagcnscore': POLLA_gcn_Score,
}
supports, adjinit = self._get_adj()
if self.args.model=='polladiff' or self.args.model=='pollaadp':
self.support = supports
model = model_dict[self.args.model](
self.args.c_in,
self.args.c_out,
self.args.seq_len,
self.args.pred_len,
self.args.d_model,
self.args.n_heads,
self.args.n_layers,
self.args.d_ff,
self.args.nodes,
adjinit,
supports,
self.args.order,
self.args.dropout,
self.args.activation,
self.device
)
elif self.args.model=='polladiffscore':
self.support = supports
model = model_dict[self.args.model](
self.args.c_in,
self.args.c_out,
self.args.seq_len,
self.args.pred_len,
self.args.d_model,
self.args.n_heads,
self.args.n_layers,
self.args.d_ff,
self.args.nodes,
adjinit,
supports,
self.args.order,
self.args.dropout,
self.args.activation,
self.device,
self.args.attn_type
)
elif self.args.model=='pollagcnscore':
self.support = supports[0]
model = model_dict[self.args.model](
self.args.c_in,
self.args.c_out,
self.args.seq_len,
self.args.pred_len,
self.args.d_model,
self.args.n_heads,
self.args.n_layers,
self.args.d_ff,
self.args.nodes,
self.args.dropout,
self.args.activation,
self.device,
self.args.attn_type
)
elif self.args.model=='pollagcn':
self.support = supports[0]
model = model_dict[self.args.model](
self.args.c_in,
self.args.c_out,
self.args.seq_len,
self.args.pred_len,
self.args.d_model,
self.args.n_heads,
self.args.n_layers,
self.args.d_ff,
self.args.nodes,
self.args.dropout,
self.args.activation,
self.device
)
return model.double()
def _get_data(self, flag):
args = self.args
if flag == 'test':
shuffle_flag = False
else:
shuffle_flag = True
data_set = Dataset_ST(
root_path=args.root_path,
data_path=args.data_path,
flag=flag,
seq_len=args.seq_len,
pred_len=args.pred_len
)
print(flag, len(data_set))
data_loader = DataLoader(
data_set,
batch_size=args.batch_size,
shuffle=shuffle_flag,
num_workers=args.num_workers,
drop_last=True)
return data_set, data_loader
def _get_adj(self):
sensor_ids, sensor_id_to_ind, adj_mx = load_adj(self.args.adjdata, self.args.adjtype)
supports = [torch.DoubleTensor(i).double().to(self.device) for i in adj_mx]
adjinit = None if self.args.randomadj else supports[0]
print('adjinit shape:', adjinit.shape)
return supports, adjinit
def vali(self, vali_data, vali_loader, criterion):
self.model.eval()
total_loss = []
for i, (batch_x,batch_y,batch_x_mark,batch_y_mark) in enumerate(vali_loader):
batch_x = batch_x.double().to(self.device)
batch_y = batch_y.double().to(self.device)
batch_x_mark = batch_x_mark.double().to(self.device)
outputs = self.model(batch_x, batch_x_mark, support=self.support)
pred = vali_data.scaler.inverse_transform(outputs.detach().cpu().numpy().squeeze())
true = batch_y.detach().cpu().numpy().squeeze()
pred = torch.from_numpy(pred); true = torch.from_numpy(true)
loss = masked_mae(pred, true, 0.0).item()
total_loss.append(loss)
total_loss = np.average(total_loss)
return total_loss
def train(self, setting):
train_data, train_loader = self._get_data(flag = 'train')
vali_data, vali_loader = self._get_data(flag = 'val')
path = './checkpoints/'+setting
if not os.path.exists(path):
os.makedirs(path)
time_now = time.time()
train_steps = len(train_loader)
early_stopping = EarlyStopping(patience=self.args.patience, verbose=True)
model_optim = optim.Adam(self.model.parameters(), lr=self.args.learning_rate)
if self.args.loss=='huber':
criterion = nn.SmoothL1Loss().to(self.device)
else:
criterion = masked_mae
for epoch in range(self.args.train_epochs):
iter_count = 0
train_loss = []
self.model.train()
for i, (batch_x,batch_y,batch_x_mark,batch_y_mark) in enumerate(train_loader):
iter_count += 1
model_optim.zero_grad()
batch_x = batch_x.double().to(self.device)
batch_y = batch_y.double().to(self.device)
batch_x_mark = batch_x_mark.double().to(self.device)
outputs = self.model(batch_x, batch_x_mark, support=self.support)
outputs = train_data.scaler.inverse_transform(outputs)
loss = criterion(outputs, batch_y)
train_loss.append(loss.item())
if (i+1) % 100==0:
print("\titers: {0}, epoch: {1} | loss: {2:.7f}".format(i + 1, epoch + 1, loss.item()))
speed = (time.time()-time_now)/iter_count
left_time = speed*((self.args.train_epochs - epoch)*train_steps - i)
print('\tspeed: {:.4f}s/iter; left time: {:.4f}s'.format(speed, left_time))
iter_count = 0
time_now = time.time()
loss.backward()
model_optim.step()
train_loss = np.average(train_loss)
vali_loss = self.vali(vali_data, vali_loader, criterion)
print("Epoch: {0}, Steps: {1} | Train Loss: {2:.7f} Vali Loss: {3:.7f}".format(
epoch + 1, train_steps, train_loss, vali_loss))
early_stopping(vali_loss, self.model, path)
if early_stopping.early_stop:
print("Early stopping")
break
adjust_learning_rate(model_optim, epoch, self.args)
best_model_path = path+'/'+'checkpoint.pth'
self.model.load_state_dict(torch.load(best_model_path))
return self.model
def test(self, setting): # args.mode=='direct'
test_data, test_loader = self._get_data(flag='test')
self.model.eval()
preds = []
trues = []
for i, (batch_x,batch_y,batch_x_mark,batch_y_mark) in enumerate(test_loader):
batch_x = batch_x.double().to(self.device)
batch_y = batch_y.double().to(self.device)
batch_x_mark = batch_x_mark.double().to(self.device)
outputs = self.model(batch_x, batch_x_mark, support=self.support)
pred = test_data.scaler.inverse_transform(outputs.detach().cpu().numpy().squeeze())
true = batch_y.detach().cpu().numpy().squeeze()
preds.append(pred.squeeze())
trues.append(true.squeeze())
preds = np.array(preds)
trues = np.array(trues)
print('test shape:', preds.shape, trues.shape)
# result save
folder_path = './results/' + setting +'/'
if not os.path.exists(folder_path):
os.makedirs(folder_path)
preds = torch.from_numpy(preds.reshape(-1, preds.shape[-2], preds.shape[-1]))
trues = torch.from_numpy(trues.reshape(-1, trues.shape[-2], trues.shape[-1]))
mae,mape,rmse = metric(preds, trues)
print('mae:{}, mape:{}, rmse:{}'.format(mae, mape, rmse))
np.save(folder_path+'metrics.npy', np.array([mae,rmse,mape]))
np.save(folder_path+'pred.npy', preds)
np.save(folder_path+'true.npy', trues)
return