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| from data_provider.data_factory import data_provider | |
| from exp.exp_basic import Exp_Basic | |
| from utils.tools import EarlyStopping, adjust_learning_rate, visual | |
| from utils.metrics import metric | |
| import torch | |
| import torch.nn as nn | |
| from torch import optim | |
| import os | |
| import time | |
| import warnings | |
| import numpy as np | |
| import joblib | |
| warnings.filterwarnings('ignore') | |
| class Exp_Long_Term_Forecast(Exp_Basic): | |
| def __init__(self, args): | |
| super(Exp_Long_Term_Forecast, self).__init__(args) | |
| def _build_model(self): | |
| 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): | |
| 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) | |
| 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) | |
| 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) | |
| if outputs.dim() == 2: | |
| outputs = outputs.unsqueeze(-1) | |
| 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) | |
| 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) | |
| 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) | |
| 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) | |
| if outputs.dim() == 2: | |
| outputs = outputs.unsqueeze(-1) | |
| 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) | |
| 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) | |
| if outputs.dim() == 2: | |
| outputs = outputs.unsqueeze(-1) | |
| 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) | |
| 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)) | |
| # --- ★★★ 핵심 수정 사항 2 ★★★ --- | |
| # 학습 데이터의 스케일러를 파일로 저장 | |
| scaler_path = os.path.join(path, 'scaler.gz') | |
| joblib.dump(train_data.scaler, scaler_path) | |
| print(f"Scaler saved to {scaler_path}") | |
| # ---------------------------------- | |
| return self.model | |
| # exp_long_term_forecasting.py의 test 함수 | |
| 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 = [] | |
| attentions = [] | |
| # 1. 시각화(PDF) 저장을 위한 폴더 경로 설정 | |
| pdf_folder_path = './test_results/' + setting + '/' | |
| if not os.path.exists(pdf_folder_path): | |
| os.makedirs(pdf_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) | |
| 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) | |
| if self.args.output_attention: | |
| outputs, *attention = self.model(batch_x, batch_x_mark, dec_inp, batch_y_mark) | |
| else: | |
| outputs = self.model(batch_x, batch_x_mark, dec_inp, batch_y_mark) | |
| if outputs.dim() == 2: | |
| outputs = outputs.unsqueeze(-1) | |
| f_dim = -1 if self.args.features == 'MS' else 0 | |
| outputs = outputs[:, -self.args.pred_len:, :] | |
| batch_y = batch_y[:, -self.args.pred_len:, :].to(self.device) | |
| outputs = outputs.detach().cpu().numpy() | |
| batch_y = batch_y.detach().cpu().numpy() | |
| if self.args.output_attention: | |
| attentions.append(attention[0][0].detach().cpu().numpy()) | |
| if test_data.scale and self.args.inverse: | |
| unscaled_outputs_list = [] | |
| unscaled_batch_y_list = [] | |
| for j in range(outputs.shape[0]): | |
| output_sample = outputs[j] | |
| true_sample = batch_y[j] | |
| if self.args.features == 'MS': | |
| num_features = test_data.scaler.n_features_in_ | |
| output_sample = np.tile(output_sample, (1, num_features)) | |
| unscaled_output = test_data.inverse_transform(output_sample) | |
| unscaled_outputs_list.append(unscaled_output) | |
| unscaled_true = test_data.inverse_transform(true_sample) | |
| unscaled_batch_y_list.append(unscaled_true) | |
| outputs = np.stack(unscaled_outputs_list, axis=0) | |
| batch_y = np.stack(unscaled_batch_y_list, axis=0) | |
| outputs = outputs[:, :, f_dim:] | |
| batch_y = batch_y[:, :, f_dim:] | |
| pred = outputs | |
| true = batch_y | |
| preds.append(pred) | |
| trues.append(true) | |
| if i % 20 == 0: | |
| input = batch_x.detach().cpu().numpy() | |
| if test_data.scale and self.args.inverse: | |
| shape = input.shape | |
| input = test_data.inverse_transform(input.reshape(-1, input.shape[-1])).reshape(shape) | |
| gt = np.concatenate((input[0, :, -1], true[0, :, -1]), axis=0) | |
| pd = np.concatenate((input[0, :, -1], pred[0, :, -1]), axis=0) | |
| # PDF 저장 경로를 'pdf_folder_path'로 지정 | |
| visual(gt, pd, os.path.join(pdf_folder_path, str(i) + '.pdf')) | |
| preds = np.concatenate(preds, axis=0) | |
| trues = np.concatenate(trues, axis=0) | |
| print('test shape:', preds.shape, trues.shape) | |
| # 2. 최종 결과(NPY, TXT) 저장을 위한 폴더 경로 설정 | |
| results_folder_path = './results/' + setting + '/' | |
| if not os.path.exists(results_folder_path): | |
| os.makedirs(results_folder_path) | |
| if self.args.output_attention: | |
| attentions = np.concatenate(attentions, axis=0) | |
| print('attention shape:', attentions.shape) | |
| np.save(os.path.join(results_folder_path, 'attention.npy'), attentions) | |
| mae, mse, rmse, mape, mspe = metric(preds, trues) | |
| print('mse:{}, mae:{}'.format(mse, mae)) | |
| # 결과 저장 경로를 'results_folder_path'로 지정 | |
| with open(os.path.join(results_folder_path, "result_metrics.txt"), 'a') as f: | |
| f.write(setting + " \n") | |
| f.write('mse:{}, mae:{}'.format(mse, mae)) | |
| f.write('\n') | |
| f.write('\n') | |
| # 2. 공통 summary 파일에도 추가 저장 | |
| summary_path = './results/summary_result_metrics.txt' | |
| with open(summary_path, 'a') as f: | |
| f.write(setting + " \n") | |
| f.write('mse:{}, mae:{}'.format(mse, mae)) | |
| f.write('\n\n') | |
| np.save(os.path.join(results_folder_path, 'metrics.npy'), np.array([mae, mse, rmse, mape, mspe])) | |
| np.save(os.path.join(results_folder_path, 'pred.npy'), preds) | |
| np.save(os.path.join(results_folder_path, 'true.npy'), trues) | |
| return |