from data_provider.data_factory import data_provider from data_provider.m4 import M4Meta from exp.exp_basic import Exp_Basic from utils.tools import EarlyStopping, adjust_learning_rate, visual from utils.losses import mape_loss, mase_loss, smape_loss from utils.m4_summary import M4Summary import torch import torch.nn as nn from torch import optim import os import time import warnings import numpy as np import pandas warnings.filterwarnings('ignore') class Exp_Short_Term_Forecast(Exp_Basic): def __init__(self, args): super(Exp_Short_Term_Forecast, self).__init__(args) def _build_model(self): if self.args.data == 'm4': self.args.pred_len = M4Meta.horizons_map[self.args.seasonal_patterns] # Up to M4 config self.args.seq_len = 2 * self.args.pred_len # input_len = 2*pred_len self.args.label_len = self.args.pred_len self.args.frequency_map = M4Meta.frequency_map[self.args.seasonal_patterns] model = self.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, loss_name='MSE'): if loss_name == 'MSE': return nn.MSELoss() elif loss_name == 'MAPE': return mape_loss() elif loss_name == 'MASE': return mase_loss() elif loss_name == 'SMAPE': return smape_loss() def train(self, setting): train_data, train_loader = self._get_data(flag='train') vali_data, vali_loader = self._get_data(flag='val') 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(self.args.loss) mse = nn.MSELoss() 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_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) outputs = self.model(batch_x, None, dec_inp, None) f_dim = -1 if self.args.features == 'MS' else 0 outputs = outputs[:, -self.args.pred_len:, f_dim:] batch_y = batch_y[:, -self.args.pred_len:, f_dim:].to(self.device) batch_y_mark = batch_y_mark[:, -self.args.pred_len:, f_dim:].to(self.device) loss_value = criterion(batch_x, self.args.frequency_map, outputs, batch_y, batch_y_mark) loss_sharpness = mse((outputs[:, 1:, :] - outputs[:, :-1, :]), (batch_y[:, 1:, :] - batch_y[:, :-1, :])) loss = loss_value # + loss_sharpness * 1e-5 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() print("Epoch: {} cost time: {}".format(epoch + 1, time.time() - epoch_time)) train_loss = np.average(train_loss) vali_loss = self.vali(train_loader, vali_loader, criterion) test_loss = vali_loss 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 vali(self, train_loader, vali_loader, criterion): x, _ = train_loader.dataset.last_insample_window() y = vali_loader.dataset.timeseries x = torch.tensor(x, dtype=torch.float32).to(self.device) x = x.unsqueeze(-1) self.model.eval() with torch.no_grad(): # decoder input B, _, C = x.shape dec_inp = torch.zeros((B, self.args.pred_len, C)).float().to(self.device) dec_inp = torch.cat([x[:, -self.args.label_len:, :], dec_inp], dim=1).float() # encoder - decoder outputs = torch.zeros((B, self.args.pred_len, C)).float() # .to(self.device) id_list = np.arange(0, B, 500) # validation set size id_list = np.append(id_list, B) for i in range(len(id_list) - 1): outputs[id_list[i]:id_list[i + 1], :, :] = self.model(x[id_list[i]:id_list[i + 1]], None, dec_inp[id_list[i]:id_list[i + 1]], None).detach().cpu() f_dim = -1 if self.args.features == 'MS' else 0 outputs = outputs[:, -self.args.pred_len:, f_dim:] pred = outputs true = torch.from_numpy(np.array(y)) batch_y_mark = torch.ones(true.shape) loss = criterion(x.detach().cpu()[:, :, 0], self.args.frequency_map, pred[:, :, 0], true, batch_y_mark) self.model.train() return loss def test(self, setting, test=0): _, train_loader = self._get_data(flag='train') _, test_loader = self._get_data(flag='test') x, _ = train_loader.dataset.last_insample_window() y = test_loader.dataset.timeseries x = torch.tensor(x, dtype=torch.float32).to(self.device) x = x.unsqueeze(-1) if test: print('loading model') self.model.load_state_dict(torch.load(os.path.join('./checkpoints/' + setting, 'checkpoint.pth'))) folder_path = './test_results/' + setting + '/' if not os.path.exists(folder_path): os.makedirs(folder_path) self.model.eval() with torch.no_grad(): B, _, C = x.shape dec_inp = torch.zeros((B, self.args.pred_len, C)).float().to(self.device) dec_inp = torch.cat([x[:, -self.args.label_len:, :], dec_inp], dim=1).float() # encoder - decoder outputs = torch.zeros((B, self.args.pred_len, C)).float().to(self.device) id_list = np.arange(0, B, 1) id_list = np.append(id_list, B) for i in range(len(id_list) - 1): outputs[id_list[i]:id_list[i + 1], :, :] = self.model(x[id_list[i]:id_list[i + 1]], None, dec_inp[id_list[i]:id_list[i + 1]], None) if id_list[i] % 1000 == 0: print(id_list[i]) f_dim = -1 if self.args.features == 'MS' else 0 outputs = outputs[:, -self.args.pred_len:, f_dim:] outputs = outputs.detach().cpu().numpy() preds = outputs trues = y x = x.detach().cpu().numpy() for i in range(0, preds.shape[0], preds.shape[0] // 10): gt = np.concatenate((x[i, :, 0], trues[i]), axis=0) pd = np.concatenate((x[i, :, 0], preds[i, :, 0]), axis=0) visual(gt, pd, os.path.join(folder_path, str(i) + '.pdf')) print('test shape:', preds.shape) # result save folder_path = './m4_results/' + self.args.model + '/' if not os.path.exists(folder_path): os.makedirs(folder_path) forecasts_df = pandas.DataFrame(preds[:, :, 0], columns=[f'V{i + 1}' for i in range(self.args.pred_len)]) forecasts_df.index = test_loader.dataset.ids[:preds.shape[0]] forecasts_df.index.name = 'id' forecasts_df.set_index(forecasts_df.columns[0], inplace=True) forecasts_df.to_csv(folder_path + self.args.seasonal_patterns + '_forecast.csv') print(self.args.model) file_path = './m4_results/' + self.args.model + '/' if 'Weekly_forecast.csv' in os.listdir(file_path) \ and 'Monthly_forecast.csv' in os.listdir(file_path) \ and 'Yearly_forecast.csv' in os.listdir(file_path) \ and 'Daily_forecast.csv' in os.listdir(file_path) \ and 'Hourly_forecast.csv' in os.listdir(file_path) \ and 'Quarterly_forecast.csv' in os.listdir(file_path): m4_summary = M4Summary(file_path, self.args.root_path) # m4_forecast.set_index(m4_winner_forecast.columns[0], inplace=True) smape_results, owa_results, mape, mase = m4_summary.evaluate() print('smape:', smape_results) print('mape:', mape) print('mase:', mase) print('owa:', owa_results) else: print('After all 6 tasks are finished, you can calculate the averaged index') return