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https://github.com/wassname/pytorch-transformer-ts.git
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Hstellar
94 lines
2.6 KiB
Python
94 lines
2.6 KiB
Python
from torch.nn.modules import loss
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import torch
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import numpy as np
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def MAE(pred, true):
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return np.mean(np.abs(pred-true))
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def MSE(pred, true):
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return np.mean((pred-true)**2)
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def RMSE(pred, true):
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return np.sqrt(MSE(pred, true))
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def MAPE(pred, true):
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return np.mean(np.abs((pred - true) / true))
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def MSPE(pred, true):
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return np.mean(np.square((pred - true) / true))
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def metric(pred, true):
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mae = MAE(pred, true)
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mse = MSE(pred, true)
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rmse = RMSE(pred, true)
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mape = MAPE(pred, true)
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mspe = MSPE(pred, true)
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return mae,mse,rmse,mape,mspe
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class StandardScaler():
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def __init__(self):
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self.mean = 0.
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self.std = 1.
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def fit(self, data):
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self.mean = data.mean(0)
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self.std = data.std(0)
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def transform(self, data):
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mean = torch.from_numpy(self.mean).type_as(data).to(data.device) if torch.is_tensor(data) else self.mean
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std = torch.from_numpy(self.std).type_as(data).to(data.device) if torch.is_tensor(data) else self.std
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return (data - mean) / std
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def inverse_transform(self, data):
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mean = torch.from_numpy(self.mean).type_as(data).to(data.device) if torch.is_tensor(data) else self.mean
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std = torch.from_numpy(self.std).type_as(data).to(data.device) if torch.is_tensor(data) else self.std
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return (data * std) + mean
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class TopkMSELoss(torch.nn.Module):
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def __init__(self, topk) -> None:
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super().__init__()
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self.topk = topk
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self.criterion = torch.nn.MSELoss(reduction='none')
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def forward(self, output, label):
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losses = self.criterion(output, label).mean(2).mean(1)
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losses = torch.topk(losses, self.topk)[0]
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return losses
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class SingleStepLoss(torch.nn.Module):
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""" Compute top-k log-likelihood and mse. """
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def __init__(self, ignore_zero):
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super().__init__()
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self.ignore_zero = ignore_zero
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def forward(self, mu, sigma, labels, topk=0):
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if self.ignore_zero:
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indexes = (labels != 0)
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else:
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indexes = (labels >= 0)
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distribution = torch.distributions.normal.Normal(mu[indexes], sigma[indexes])
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likelihood = -distribution.log_prob(labels[indexes])
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diff = labels[indexes] - mu[indexes]
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se = diff * diff
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if 0 < topk < len(likelihood):
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likelihood = torch.topk(likelihood, topk)[0]
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se = torch.topk(se, topk)[0]
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return likelihood, se
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def AE_loss(mu, labels, ignore_zero):
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if ignore_zero:
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indexes = (labels != 0)
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else:
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indexes = (labels >= 0)
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ae = torch.abs(labels[indexes] - mu[indexes])
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return ae
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