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- from data_provider.data_factory import data_provider
- from exp.exp_basic import Exp_Basic
- from models import Priceformer
- 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 = {
- 'Priceformer': Priceformer
- }
- 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)
-
- # 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()
- 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 % 20 == 0:
- input = batch_x.detach().cpu().numpy()
- gt = np.concatenate((input[0, :, -1], true[0, :, -1]), axis=0)
- pd = np.concatenate((input[0, :, -1], pred[0, :, -1]), axis=0)
- visual(gt, pd, os.path.join(folder_path, str(i) + '.pdf'))
-
- 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, wmape = metric(preds, trues)
- print('mse:{}, mae:{}, mape:{}, wmape:{}'.format(mse, mae, mape, wmape))
- 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, wmape]))
- 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
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