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 warnings.filterwarnings('ignore') class Exp_Imputation(Exp_Basic): def __init__(self, args): super(Exp_Imputation, 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_x_mark = batch_x_mark.float().to(self.device) # random mask B, T, N = batch_x.shape """ B = batch size T = seq len N = number of features """ mask = torch.rand((B, T, N)).to(self.device) mask[mask <= self.args.mask_rate] = 0 # masked mask[mask > self.args.mask_rate] = 1 # remained inp = batch_x.masked_fill(mask == 0, 0) outputs = self.model(inp, batch_x_mark, None, None, mask) f_dim = -1 if self.args.features == 'MS' else 0 outputs = outputs[:, :, f_dim:] # add support for MS batch_x = batch_x[:, :, f_dim:] mask = mask[:, :, f_dim:] pred = outputs.detach() true = batch_x.detach() mask = mask.detach() loss = criterion(pred[mask == 0], true[mask == 0]) total_loss.append(loss.item()) 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() 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_x_mark = batch_x_mark.float().to(self.device) # random mask B, T, N = batch_x.shape mask = torch.rand((B, T, N)).to(self.device) mask[mask <= self.args.mask_rate] = 0 # masked mask[mask > self.args.mask_rate] = 1 # remained inp = batch_x.masked_fill(mask == 0, 0) outputs = self.model(inp, batch_x_mark, None, None, mask) f_dim = -1 if self.args.features == 'MS' else 0 outputs = outputs[:, :, f_dim:] # add support for MS batch_x = batch_x[:, :, f_dim:] mask = mask[:, :, f_dim:] loss = criterion(outputs[mask == 0], batch_x[mask == 0]) 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(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 = [] masks = [] 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_x_mark = batch_x_mark.float().to(self.device) # random mask B, T, N = batch_x.shape mask = torch.rand((B, T, N)).to(self.device) mask[mask <= self.args.mask_rate] = 0 # masked mask[mask > self.args.mask_rate] = 1 # remained inp = batch_x.masked_fill(mask == 0, 0) # imputation outputs = self.model(inp, batch_x_mark, None, None, mask) # eval f_dim = -1 if self.args.features == 'MS' else 0 outputs = outputs[:, :, f_dim:] # add support for MS batch_x = batch_x[:, :, f_dim:] mask = mask[:, :, f_dim:] outputs = outputs.detach().cpu().numpy() pred = outputs true = batch_x.detach().cpu().numpy() preds.append(pred) trues.append(true) masks.append(mask.detach().cpu()) if i % 20 == 0: filled = true[0, :, -1].copy() filled = filled * mask[0, :, -1].detach().cpu().numpy() + \ pred[0, :, -1] * (1 - mask[0, :, -1].detach().cpu().numpy()) visual(true[0, :, -1], filled, os.path.join(folder_path, str(i) + '.pdf')) preds = np.concatenate(preds, 0) trues = np.concatenate(trues, 0) masks = np.concatenate(masks, 0) 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[masks == 0], trues[masks == 0]) print('mse:{}, mae:{}'.format(mse, mae)) f = open("result_imputation.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