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gameloader
228
exp/exp_imputation.py
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228
exp/exp_imputation.py
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from data_provider.data_factory import data_provider
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from exp.exp_basic import Exp_Basic
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from utils.tools import EarlyStopping, adjust_learning_rate, visual
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from utils.metrics import metric
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import torch
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import torch.nn as nn
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from torch import optim
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import os
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import time
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import warnings
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import numpy as np
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warnings.filterwarnings('ignore')
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class Exp_Imputation(Exp_Basic):
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def __init__(self, args):
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super(Exp_Imputation, self).__init__(args)
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def _build_model(self):
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model = self.model_dict[self.args.model].Model(self.args).float()
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if self.args.use_multi_gpu and self.args.use_gpu:
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model = nn.DataParallel(model, device_ids=self.args.device_ids)
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return model
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def _get_data(self, flag):
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data_set, data_loader = data_provider(self.args, flag)
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return data_set, data_loader
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def _select_optimizer(self):
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model_optim = optim.Adam(self.model.parameters(), lr=self.args.learning_rate)
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return model_optim
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def _select_criterion(self):
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criterion = nn.MSELoss()
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return criterion
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def vali(self, vali_data, vali_loader, criterion):
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total_loss = []
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self.model.eval()
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with torch.no_grad():
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for i, (batch_x, batch_y, batch_x_mark, batch_y_mark) in enumerate(vali_loader):
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batch_x = batch_x.float().to(self.device)
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batch_x_mark = batch_x_mark.float().to(self.device)
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# random mask
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B, T, N = batch_x.shape
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"""
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B = batch size
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T = seq len
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N = number of features
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"""
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mask = torch.rand((B, T, N)).to(self.device)
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mask[mask <= self.args.mask_rate] = 0 # masked
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mask[mask > self.args.mask_rate] = 1 # remained
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inp = batch_x.masked_fill(mask == 0, 0)
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outputs = self.model(inp, batch_x_mark, None, None, mask)
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f_dim = -1 if self.args.features == 'MS' else 0
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outputs = outputs[:, :, f_dim:]
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# add support for MS
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batch_x = batch_x[:, :, f_dim:]
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mask = mask[:, :, f_dim:]
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pred = outputs.detach()
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true = batch_x.detach()
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mask = mask.detach()
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loss = criterion(pred[mask == 0], true[mask == 0])
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total_loss.append(loss.item())
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total_loss = np.average(total_loss)
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self.model.train()
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return total_loss
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def train(self, setting):
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train_data, train_loader = self._get_data(flag='train')
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vali_data, vali_loader = self._get_data(flag='val')
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test_data, test_loader = self._get_data(flag='test')
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path = os.path.join(self.args.checkpoints, setting)
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if not os.path.exists(path):
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os.makedirs(path)
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time_now = time.time()
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train_steps = len(train_loader)
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early_stopping = EarlyStopping(patience=self.args.patience, verbose=True)
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model_optim = self._select_optimizer()
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criterion = self._select_criterion()
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for epoch in range(self.args.train_epochs):
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iter_count = 0
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train_loss = []
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self.model.train()
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epoch_time = time.time()
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for i, (batch_x, batch_y, batch_x_mark, batch_y_mark) in enumerate(train_loader):
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iter_count += 1
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model_optim.zero_grad()
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batch_x = batch_x.float().to(self.device)
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batch_x_mark = batch_x_mark.float().to(self.device)
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# random mask
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B, T, N = batch_x.shape
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mask = torch.rand((B, T, N)).to(self.device)
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mask[mask <= self.args.mask_rate] = 0 # masked
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mask[mask > self.args.mask_rate] = 1 # remained
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inp = batch_x.masked_fill(mask == 0, 0)
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outputs = self.model(inp, batch_x_mark, None, None, mask)
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f_dim = -1 if self.args.features == 'MS' else 0
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outputs = outputs[:, :, f_dim:]
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# add support for MS
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batch_x = batch_x[:, :, f_dim:]
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mask = mask[:, :, f_dim:]
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loss = criterion(outputs[mask == 0], batch_x[mask == 0])
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train_loss.append(loss.item())
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if (i + 1) % 100 == 0:
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print("\titers: {0}, epoch: {1} | loss: {2:.7f}".format(i + 1, epoch + 1, loss.item()))
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speed = (time.time() - time_now) / iter_count
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left_time = speed * ((self.args.train_epochs - epoch) * train_steps - i)
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print('\tspeed: {:.4f}s/iter; left time: {:.4f}s'.format(speed, left_time))
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iter_count = 0
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time_now = time.time()
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loss.backward()
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model_optim.step()
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print("Epoch: {} cost time: {}".format(epoch + 1, time.time() - epoch_time))
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train_loss = np.average(train_loss)
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vali_loss = self.vali(vali_data, vali_loader, criterion)
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test_loss = self.vali(test_data, test_loader, criterion)
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print("Epoch: {0}, Steps: {1} | Train Loss: {2:.7f} Vali Loss: {3:.7f} Test Loss: {4:.7f}".format(
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epoch + 1, train_steps, train_loss, vali_loss, test_loss))
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early_stopping(vali_loss, self.model, path)
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if early_stopping.early_stop:
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print("Early stopping")
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break
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adjust_learning_rate(model_optim, epoch + 1, self.args)
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best_model_path = path + '/' + 'checkpoint.pth'
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self.model.load_state_dict(torch.load(best_model_path))
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return self.model
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def test(self, setting, test=0):
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test_data, test_loader = self._get_data(flag='test')
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if test:
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print('loading model')
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self.model.load_state_dict(torch.load(os.path.join('./checkpoints/' + setting, 'checkpoint.pth')))
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preds = []
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trues = []
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masks = []
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folder_path = './test_results/' + setting + '/'
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if not os.path.exists(folder_path):
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os.makedirs(folder_path)
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self.model.eval()
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with torch.no_grad():
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for i, (batch_x, batch_y, batch_x_mark, batch_y_mark) in enumerate(test_loader):
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batch_x = batch_x.float().to(self.device)
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batch_x_mark = batch_x_mark.float().to(self.device)
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# random mask
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B, T, N = batch_x.shape
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mask = torch.rand((B, T, N)).to(self.device)
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mask[mask <= self.args.mask_rate] = 0 # masked
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mask[mask > self.args.mask_rate] = 1 # remained
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inp = batch_x.masked_fill(mask == 0, 0)
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# imputation
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outputs = self.model(inp, batch_x_mark, None, None, mask)
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# eval
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f_dim = -1 if self.args.features == 'MS' else 0
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outputs = outputs[:, :, f_dim:]
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# add support for MS
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batch_x = batch_x[:, :, f_dim:]
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mask = mask[:, :, f_dim:]
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outputs = outputs.detach().cpu().numpy()
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pred = outputs
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true = batch_x.detach().cpu().numpy()
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preds.append(pred)
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trues.append(true)
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masks.append(mask.detach().cpu())
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if i % 20 == 0:
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filled = true[0, :, -1].copy()
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filled = filled * mask[0, :, -1].detach().cpu().numpy() + \
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pred[0, :, -1] * (1 - mask[0, :, -1].detach().cpu().numpy())
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visual(true[0, :, -1], filled, os.path.join(folder_path, str(i) + '.pdf'))
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preds = np.concatenate(preds, 0)
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trues = np.concatenate(trues, 0)
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masks = np.concatenate(masks, 0)
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print('test shape:', preds.shape, trues.shape)
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# result save
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folder_path = './results/' + setting + '/'
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if not os.path.exists(folder_path):
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os.makedirs(folder_path)
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mae, mse, rmse, mape, mspe = metric(preds[masks == 0], trues[masks == 0])
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print('mse:{}, mae:{}'.format(mse, mae))
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f = open("result_imputation.txt", 'a')
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f.write(setting + " \n")
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f.write('mse:{}, mae:{}'.format(mse, mae))
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f.write('\n')
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f.write('\n')
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f.close()
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np.save(folder_path + 'metrics.npy', np.array([mae, mse, rmse, mape, mspe]))
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np.save(folder_path + 'pred.npy', preds)
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np.save(folder_path + 'true.npy', trues)
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return
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