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exp_main.py
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from data_provider.data_factory import data_provider
from exp.exp_basic import Exp_Basic
from models import Informer, Autoformer, Transformer, SCformer, Preformer, Preformer_wo_MS, Decom_FullAttention, Decom_ProbAttention, Decom_LogAttention, SCformer_de2, LogTrans
from utils.tools import EarlyStopping, adjust_learning_rate, visual
from utils.metrics import metric
import numpy as np
import torch
import torch.nn as nn
from torch import optim
import os
import time
import warnings
import matplotlib.pyplot as plt
import numpy as np
warnings.filterwarnings('ignore')
class Exp_Main(Exp_Basic):
def __init__(self, args):
super(Exp_Main, self).__init__(args)
def _build_model(self):
model_dict = {
'Preformer': Preformer,
'Preformer_wo_MS': Preformer_wo_MS,
'SCformer': SCformer,
'Autoformer': Autoformer,
'Transformer': Transformer,
'Informer': Informer,
'LogTrans': LogTrans,
'Decom_Log': Decom_LogAttention,
'Decom_Prob': Decom_ProbAttention,
'Decom_Full': Decom_FullAttention,
'SCformer_de2': SCformer_de2,
}
model = model_dict[self.args.model].Model(self.args).float()
if self.args.use_multi_gpu and self.args.use_gpu:
model = nn.DataParallel(model, device_ids=self.args.device_ids)
return model
def _get_data(self, flag):
data_set, data_loader = data_provider(self.args, flag)
return data_set, data_loader
def _select_optimizer(self):
model_optim = optim.Adam(self.model.parameters(), lr=self.args.learning_rate)
return model_optim
def _select_criterion(self):
criterion = nn.MSELoss()
return criterion
def vali(self, vali_data, vali_loader, criterion):
total_loss = []
self.model.eval()
with torch.no_grad():
for i, (batch_x, batch_y, batch_x_mark, batch_y_mark) in enumerate(vali_loader):
batch_x = batch_x.float().to(self.device)
batch_y = batch_y.float()
batch_x_mark = batch_x_mark.float().to(self.device)
batch_y_mark = batch_y_mark.float().to(self.device)
# decoder input
dec_inp = torch.zeros_like(batch_y[:, -self.args.pred_len:, :]).float()
dec_inp = torch.cat([batch_y[:, :self.args.label_len, :], dec_inp], dim=1).float().to(self.device)
# encoder - decoder
if self.args.use_amp:
with torch.cuda.amp.autocast():
if self.args.output_attention:
outputs = self.model(batch_x, batch_x_mark, dec_inp, batch_y_mark)[0]
else:
outputs = self.model(batch_x, batch_x_mark, dec_inp, batch_y_mark)
else:
if self.args.output_attention:
outputs = self.model(batch_x, batch_x_mark, dec_inp, batch_y_mark)[0]
else:
outputs = self.model(batch_x, batch_x_mark, dec_inp, batch_y_mark)
f_dim = -1 if self.args.features == 'MS' else 0
batch_y = batch_y[:, -self.args.pred_len:, f_dim:].to(self.device)
pred = outputs.detach().cpu()
true = batch_y.detach().cpu()
loss = criterion(pred, true)
total_loss.append(loss)
total_loss = np.average(total_loss)
self.model.train()
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')
test_data, test_loader = self._get_data(flag='test')
path = os.path.join(self.args.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 = self._select_optimizer()
criterion = self._select_criterion()
if self.args.use_amp:
scaler = torch.cuda.amp.GradScaler()
for epoch in range(self.args.train_epochs):
iter_count = 0
train_loss = []
self.model.train()
epoch_time = time.time()
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.float().to(self.device)
batch_y = batch_y.float().to(self.device)
batch_x_mark = batch_x_mark.float().to(self.device)
batch_y_mark = batch_y_mark.float().to(self.device)
# decoder input
dec_inp = torch.zeros_like(batch_y[:, -self.args.pred_len:, :]).float()
dec_inp = torch.cat([batch_y[:, :self.args.label_len, :], dec_inp], dim=1).float().to(self.device)
# print(batch_x.shape)
# print(batch_x_mark.shape)
# print(dec_inp.shape)
# print(batch_y_mark.shape)
# encoder - decoder
if self.args.use_amp:
with torch.cuda.amp.autocast():
if self.args.output_attention:
outputs = self.model(batch_x, batch_x_mark, dec_inp, batch_y_mark)[0]
else:
outputs = self.model(batch_x, batch_x_mark, dec_inp, batch_y_mark)
f_dim = -1 if self.args.features == 'MS' else 0
batch_y = batch_y[:, -self.args.pred_len:, f_dim:].to(self.device)
loss = criterion(outputs, batch_y)
train_loss.append(loss.item())
else:
if self.args.output_attention:
outputs = self.model(batch_x, batch_x_mark, dec_inp, batch_y_mark)[0]
else:
outputs = self.model(batch_x, batch_x_mark, dec_inp, batch_y_mark, batch_y)
f_dim = -1 if self.args.features == 'MS' else 0
batch_y = batch_y[:, -self.args.pred_len:, f_dim:].to(self.device)
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()
if self.args.use_amp:
scaler.scale(loss).backward()
scaler.step(model_optim)
scaler.update()
else:
loss.backward()
model_optim.step()
print("Epoch: {} cost time: {}".format(epoch + 1, time.time() - epoch_time))
train_loss = np.average(train_loss)
vali_loss = self.vali(vali_data, vali_loader, criterion)
test_loss = self.vali(test_data, test_loader, criterion)
print("Epoch: {0}, Steps: {1} | Train Loss: {2:.7f} Vali Loss: {3:.7f} Test Loss: {4:.7f}".format(
epoch + 1, train_steps, train_loss, vali_loss, test_loss))
early_stopping(vali_loss, self.model, path)
if early_stopping.early_stop:
print("Early stopping")
break
adjust_learning_rate(model_optim, epoch + 1, 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, test=0):
test_data, test_loader = self._get_data(flag='test')
if test:
print('loading model')
self.model.load_state_dict(torch.load(os.path.join('./checkpoints/' + setting, 'checkpoint.pth')))
preds = []
trues = []
folder_path = './test_results/' + setting + '/'
if not os.path.exists(folder_path):
os.makedirs(folder_path)
self.model.eval()
all_mse_min = 100
all_index_min = 0
with torch.no_grad():
for i, (batch_x, batch_y, batch_x_mark, batch_y_mark) in enumerate(test_loader):
batch_x = batch_x.float().to(self.device)
batch_y = batch_y.float().to(self.device)
batch_x_mark = batch_x_mark.float().to(self.device)
batch_y_mark = batch_y_mark.float().to(self.device)
# decoder input
dec_inp = torch.zeros_like(batch_y[:, -self.args.pred_len:, :]).float()
dec_inp = torch.cat([batch_y[:, :self.args.label_len, :], dec_inp], dim=1).float().to(self.device)
# encoder - decoder
if self.args.use_amp:
with torch.cuda.amp.autocast():
if self.args.output_attention:
outputs = self.model(batch_x, batch_x_mark, dec_inp, batch_y_mark)[0]
else:
outputs = self.model(batch_x, batch_x_mark, dec_inp, batch_y_mark)
else:
if self.args.output_attention:
outputs = self.model(batch_x, batch_x_mark, dec_inp, batch_y_mark)[0]
else:
outputs = self.model(batch_x, batch_x_mark, dec_inp, batch_y_mark)
f_dim = -1 if self.args.features == 'MS' else 0
batch_y = batch_y[:, -self.args.pred_len:, f_dim:].to(self.device)
outputs = outputs.detach().cpu().numpy()
batch_y = batch_y.detach().cpu().numpy()
pred = outputs # outputs.detach().cpu().numpy() # .squeeze()
true = batch_y # batch_y.detach().cpu().numpy() # .squeeze()
preds.append(pred)
trues.append(true)
if i % 1 == 0:
print(i)
mse_min = 100
index_min = 0
for k in range(true.shape[0]):
current_mse = np.mean((pred[k, :, -1] - true[k, :, -1]) ** 2)
if current_mse < mse_min:
index_min = k
mse_min = current_mse
print(index_min)
if mse_min < all_mse_min:
all_mse_min = mse_min
all_index_min = i
input = batch_x.detach().cpu().numpy()
gt = np.concatenate((input[index_min, :, -1], true[index_min, :, -1]), axis=0)
pd = np.concatenate((input[index_min, :, -1], pred[index_min, :, -1]), axis=0)
visual(gt, pd, os.path.join(folder_path, str(i) + '.pdf'))
# if i == 130:
# k = 1
# input = batch_x.detach().cpu().numpy()
# gt = np.concatenate((input[k, :, -1], true[k, :, -1]), axis=0)
# pd = np.concatenate((input[k, :, -1], pred[k, :, -1]), axis=0)
# visual(gt, pd, os.path.join(folder_path, str(i) + "_" + str(k) + '.pdf'))
#
# if i == 151:
# k = 11
# input = batch_x.detach().cpu().numpy()
# gt = np.concatenate((input[k, :, -1], true[k, :, -1]), axis=0)
# pd = np.concatenate((input[k, :, -1], pred[k, :, -1]), axis=0)
# visual(gt, pd, os.path.join(folder_path, str(i) + "_" + str(k) + '.pdf'))
#
# if i == 123:
# k = 29
# input = batch_x.detach().cpu().numpy()
# gt = np.concatenate((input[k, :, -1], true[k, :, -1]), axis=0)
# pd = np.concatenate((input[k, :, -1], pred[k, :, -1]), axis=0)
# visual(gt, pd, os.path.join(folder_path, str(i) + "_" + str(k) + '.pdf'))
#
# if i == 124:
# k = 7
# input = batch_x.detach().cpu().numpy()
# gt = np.concatenate((input[k, :, -1], true[k, :, -1]), axis=0)
# pd = np.concatenate((input[k, :, -1], pred[k, :, -1]), axis=0)
# visual(gt, pd, os.path.join(folder_path, str(i) + "_" + str(k) + '.pdf'))
# if i == 228:
# k = 14
# input = batch_x.detach().cpu().numpy()
# gt = np.concatenate((input[k, :, -1], true[k, :, -1]), axis=0)
# pd = np.concatenate((input[k, :, -1], pred[k, :, -1]), axis=0)
# visual_one(gt, pd, os.path.join(folder_path, str(i) + "_" + str(k) + '.pdf'), -2, -1.2, 0.1)
print("最小mse对应的i为" + str(all_index_min))
preds = np.array(preds)
trues = np.array(trues)
print('test shape:', preds.shape, trues.shape)
preds = preds.reshape(-1, preds.shape[-2], preds.shape[-1])
trues = trues.reshape(-1, trues.shape[-2], trues.shape[-1])
print('test shape:', preds.shape, trues.shape)
# result save
# folder_path = './results/' + setting + '/'
# if not os.path.exists(folder_path):
# os.makedirs(folder_path)
# mae, mse, rmse, mape, mspe = metric(preds, trues)
# print('mse:{}, mae:{}'.format(mse, mae))
# f = open("result.txt", 'a')
# f.write(setting + " \n")
# f.write('mse:{}, mae:{}'.format(mse, mae))
# f.write('\n')
# f.write('\n')
# f.close()
# np.save(folder_path + 'metrics.npy', np.array([mae, mse, rmse, mape, mspe]))
# np.save(folder_path + 'pred.npy', preds)
# np.save(folder_path + 'true.npy', trues)
return
def predict(self, setting, load=False):
pred_data, pred_loader = self._get_data(flag='pred')
if load:
path = os.path.join(self.args.checkpoints, setting)
best_model_path = path + '/' + 'checkpoint.pth'
self.model.load_state_dict(torch.load(best_model_path))
preds = []
self.model.eval()
with torch.no_grad():
for i, (batch_x, batch_y, batch_x_mark, batch_y_mark) in enumerate(pred_loader):
batch_x = batch_x.float().to(self.device)
batch_y = batch_y.float()
batch_x_mark = batch_x_mark.float().to(self.device)
batch_y_mark = batch_y_mark.float().to(self.device)
# decoder input
dec_inp = torch.zeros_like(batch_y[:, -self.args.pred_len:, :]).float()
dec_inp = torch.cat([batch_y[:, :self.args.label_len, :], dec_inp], dim=1).float().to(self.device)
# encoder - decoder
if self.args.use_amp:
with torch.cuda.amp.autocast():
if self.args.output_attention:
outputs = self.model(batch_x, batch_x_mark, dec_inp, batch_y_mark)[0]
else:
outputs = self.model(batch_x, batch_x_mark, dec_inp, batch_y_mark)
else:
if self.args.output_attention:
outputs = self.model(batch_x, batch_x_mark, dec_inp, batch_y_mark)[0]
else:
outputs = self.model(batch_x, batch_x_mark, dec_inp, batch_y_mark)
pred = outputs.detach().cpu().numpy() # .squeeze()
preds.append(pred)
preds = np.array(preds)
preds = preds.reshape(-1, preds.shape[-2], preds.shape[-1])
# result save
folder_path = './results/' + setting + '/'
if not os.path.exists(folder_path):
os.makedirs(folder_path)
np.save(folder_path + 'real_prediction.npy', preds)
return