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# -*- coding:utf-8 -*-
import numpy as np
import torch
import torch.nn.functional as F
def masked_mape_np(y_true, y_pred, null_val=np.nan):
with np.errstate(divide='ignore', invalid='ignore'):
if np.isnan(null_val):
mask = ~np.isnan(y_true)
else:
mask = np.not_equal(y_true, null_val)
mask = mask.astype('float32')
mask /= np.mean(mask)
mape = np.abs(np.divide(np.subtract(y_pred, y_true).astype('float32'),
y_true))
mape = np.nan_to_num(mask * mape)
return np.mean(mape)
def masked_mse(preds, labels, null_val=np.nan):
if np.isnan(null_val):
mask = ~torch.isnan(labels)
else:
mask = (labels != null_val)
mask = mask.float()
# print(mask.sum())
# print(mask.shape[0]*mask.shape[1]*mask.shape[2])
mask /= torch.mean((mask))
mask = torch.where(torch.isnan(mask), torch.zeros_like(mask), mask)
loss = (preds - labels) ** 2
loss = loss * mask
loss = torch.where(torch.isnan(loss), torch.zeros_like(loss), loss)
return torch.mean(loss)
def masked_rmse(preds, labels, null_val=np.nan):
return torch.sqrt(masked_mse(preds=preds, labels=labels,
null_val=null_val))
def masked_mae(preds, labels, null_val=np.nan):
if np.isnan(null_val):
mask = ~torch.isnan(labels)
else:
mask = (labels != null_val)
mask = mask.float()
mask /= torch.mean((mask))
mask = torch.where(torch.isnan(mask), torch.zeros_like(mask), mask)
loss = torch.abs(preds - labels)
loss = loss * mask
loss = torch.where(torch.isnan(loss), torch.zeros_like(loss), loss)
return torch.mean(loss)
def masked_mae_test(y_true, y_pred, null_val=np.nan):
with np.errstate(divide='ignore', invalid='ignore'):
if np.isnan(null_val):
mask = ~np.isnan(y_true)
else:
mask = np.not_equal(y_true, null_val)
mask = mask.astype('float32')
mask /= np.mean(mask)
mae = np.abs(np.subtract(y_pred, y_true).astype('float32'),
)
mae = np.nan_to_num(mask * mae)
return np.mean(mae)
def masked_rmse_test(y_true, y_pred, null_val=np.nan):
with np.errstate(divide='ignore', invalid='ignore'):
if np.isnan(null_val):
mask = ~np.isnan(y_true)
else:
# null_val=null_val
mask = np.not_equal(y_true, null_val)
mask = mask.astype('float32')
mask /= np.mean(mask)
mse = ((y_pred - y_true) ** 2)
mse = np.nan_to_num(mask * mse)
return np.sqrt(np.mean(mse))
def sce_loss(x, y, alpha=3):
x = F.normalize(x, p=2, dim=-1)
y = F.normalize(y, p=2, dim=-1)
# loss = - (x * y).sum(dim=-1)
# loss = (x_h - y_h).norm(dim=1).pow(alpha)
loss = (1 - (x * y).sum(dim=-1)).pow_(alpha)
loss = loss.mean()
return loss
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