256 lines
7.4 KiB
Python
256 lines
7.4 KiB
Python
import numpy as np
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import torch
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import torch.nn as nn
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import matplotlib.pyplot as plt
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from tqdm import tqdm
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from datetime import datetime
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from distutils.util import strtobool
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import pandas as pd
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from utils.metrics import metric
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plt.switch_backend('agg')
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def adjust_learning_rate(optimizer, epoch, args):
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# lr = args.learning_rate * (0.2 ** (epoch // 2))
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# if args.decay_fac is None:
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# args.decay_fac = 0.5
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# if args.lradj == 'type1':
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# lr_adjust = {epoch: args.learning_rate * (args.decay_fac ** ((epoch - 1) // 1))}
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# elif args.lradj == 'type2':
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# lr_adjust = {
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# 2: 5e-5, 4: 1e-5, 6: 5e-6, 8: 1e-6,
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# 10: 5e-7, 15: 1e-7, 20: 5e-8
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# }
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if args.lradj =='type1':
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lr_adjust = {epoch: args.learning_rate if epoch < 3 else args.learning_rate * (0.9 ** ((epoch - 3) // 1))}
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elif args.lradj =='type2':
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lr_adjust = {epoch: args.learning_rate * (args.decay_fac ** ((epoch - 1) // 1))}
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elif args.lradj =='type4':
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lr_adjust = {epoch: args.learning_rate * (args.decay_fac ** ((epoch) // 1))}
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else:
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args.learning_rate = 1e-4
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lr_adjust = {epoch: args.learning_rate if epoch < 3 else args.learning_rate * (0.9 ** ((epoch - 3) // 1))}
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print("lr_adjust = {}".format(lr_adjust))
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if epoch in lr_adjust.keys():
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lr = lr_adjust[epoch]
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for param_group in optimizer.param_groups:
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param_group['lr'] = lr
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print('Updating learning rate to {}'.format(lr))
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class EarlyStopping:
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def __init__(self, patience=7, verbose=False, delta=0):
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self.patience = patience
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self.verbose = verbose
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self.counter = 0
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self.best_score = None
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self.early_stop = False
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self.val_loss_min = np.Inf
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self.delta = delta
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def __call__(self, val_loss, model, path):
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score = -val_loss
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if self.best_score is None:
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self.best_score = score
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self.save_checkpoint(val_loss, model, path)
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elif score < self.best_score + self.delta:
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self.counter += 1
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print(f'EarlyStopping counter: {self.counter} out of {self.patience}')
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if self.counter >= self.patience:
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self.early_stop = True
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else:
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self.best_score = score
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self.save_checkpoint(val_loss, model, path)
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self.counter = 0
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def save_checkpoint(self, val_loss, model, path):
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if self.verbose:
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print(f'Validation loss decreased ({self.val_loss_min:.6f} --> {val_loss:.6f}). Saving model ...')
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torch.save(model.state_dict(), path + '/' + 'checkpoint.pth')
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self.val_loss_min = val_loss
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class dotdict(dict):
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"""dot.notation access to dictionary attributes"""
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__getattr__ = dict.get
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__setattr__ = dict.__setitem__
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__delattr__ = dict.__delitem__
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class StandardScaler():
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def __init__(self, mean, std):
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self.mean = mean
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self.std = std
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def transform(self, data):
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return (data - self.mean) / self.std
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def inverse_transform(self, data):
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return (data * self.std) + self.mean
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def vali(model, vali_loader, criterion, args, device):
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total_loss = []
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model.eval()
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with torch.no_grad():
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for i, (batch_x, batch_y) in enumerate(vali_loader.get_iterator()):
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# batch_x = torch.squeeze(batch_x)
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# batch_y = torch.squeeze(batch_y)
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outputs = model(batch_x)
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# encoder - decoder
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outputs = outputs[..., 0]
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batch_y = batch_y[..., 0]
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# pred = outputs.detach().cpu()
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# true = batch_y.detach().cpu()
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pred = outputs
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true = batch_y
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# loss = criterion(pred, true)
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loss = masked_mae(pred, true, 0.0)
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total_loss.append(loss)
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# total_loss = np.average(total_loss)
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total_loss = torch.mean(torch.tensor(total_loss))
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model.train()
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return total_loss
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def MASE(x, freq, pred, true):
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masep = np.mean(np.abs(x[:, freq:] - x[:, :-freq]))
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return np.mean(np.abs(pred - true) / (masep + 1e-8))
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def test(model, test_loader, args, device):
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preds = []
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trues = []
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# mases = []
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model.eval()
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with torch.no_grad():
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for i, (batch_x, batch_y) in enumerate(test_loader.get_iterator()):
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outputs = model(batch_x)
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# encoder - decoder
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outputs = outputs[... , 0]
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batch_y = batch_y[... , 0]
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# pred = outputs.detach().cpu().numpy()
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# true = batch_y.detach().cpu().numpy()
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pred = outputs
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true = batch_y
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preds.append(pred)
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trues.append(true)
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# preds = torch.Tensor(preds)
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# trues = torch.Tensor(trues)
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preds = torch.stack(preds[:-1])
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trues = torch.stack(trues[:-1])
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amae = []
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amape = []
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armse = []
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for i in range(args.pred_len):
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pred = preds[..., i]
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real = trues[..., i]
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metric = metrics(pred,real)
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log = 'Evaluate best model on test data for horizon {:d}, Test MAE: {:.4f}, Test MAPE: {:.4f}, Test RMSE: {:.4f}'
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print(log.format(i+1, metric[0], metric[1], metric[2]))
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amae.append(metric[0])
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amape.append(metric[1])
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armse.append(metric[2])
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# return np.mean(amae),np.mean(amape),np.mean(armse)
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return torch.mean(torch.tensor(amae)), torch.mean(torch.tensor(amape)), torch.mean(torch.tensor(armse))
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def masked_mse(preds, labels, null_val=np.nan):
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if np.isnan(null_val):
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mask = ~torch.isnan(labels)
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else:
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mask = (labels!=null_val)
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mask = mask.float()
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mask /= torch.mean((mask))
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mask = torch.where(torch.isnan(mask), torch.zeros_like(mask), mask)
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loss = (preds-labels)**2
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loss = loss * mask
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loss = torch.where(torch.isnan(loss), torch.zeros_like(loss), loss)
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loss = (preds-labels)**2
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return torch.mean(loss)
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def masked_rmse(preds, labels, null_val=np.nan):
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return torch.sqrt(masked_mse(preds=preds, labels=labels, null_val=null_val))
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def masked_mae(preds, labels, null_val=np.nan):
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if np.isnan(null_val):
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mask = ~torch.isnan(labels)
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else:
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mask = (labels!=null_val)
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mask = mask.float()
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mask /= torch.mean((mask))
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mask = torch.where(torch.isnan(mask), torch.zeros_like(mask), mask)
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loss = torch.abs(preds-labels)
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loss = loss * mask
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loss = torch.where(torch.isnan(loss), torch.zeros_like(loss), loss)
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loss = torch.abs(preds-labels)
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return torch.mean(loss)
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def masked_mape(preds, labels, null_val=np.nan):
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if np.isnan(null_val):
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mask = ~torch.isnan(labels)
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else:
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mask = (labels!=null_val)
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mask = mask.float()
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mask /= torch.mean((mask))
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mask = torch.where(torch.isnan(mask), torch.zeros_like(mask), mask)
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loss = torch.abs(preds-labels)/labels
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loss = loss * mask
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loss = torch.where(torch.isnan(loss), torch.zeros_like(loss), loss)
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loss = torch.abs(preds-labels)/labels
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return torch.mean(loss)
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def metrics(pred, real):
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mae = masked_mae(pred,real,0.0).item()
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mape = masked_mape(pred,real,0.0).item()
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rmse = masked_rmse(pred,real,0.0).item()
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return mae,mape,rmse
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# # import numpy as np
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# def cal_metrics(y_true, y_pred):
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# mse = torch.square(y_pred - y_true)
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# mse = torch.mean(mse)
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# # rmse = torch.square(np.abs(y_pred - y_true))
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# rmse = torch.sqrt(mse)
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# mae = torch.abs(y_pred - y_true)
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# mae = torch.mean(mae)
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# return rmse, 0, mae
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