Source code for gluonts.torch.modules.loss

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import torch
from pydantic import BaseModel


[docs]class DistributionLoss(BaseModel): """ A ``torch.nn.Module`` extensions that computes loss values by comparing a ``Distribution`` (prediction) to a ``Tensor`` (ground-truth). """ def __call__( self, input: torch.distributions.Distribution, target: torch.Tensor ) -> torch.Tensor: """ Compute the loss of predicting ``target`` with the ``input`` distribution. Parameters ---------- input Distribution object representing the prediction. target Tensor containing the ground truth. Returns ------- torch.Tensor Tensor containing loss values, with the same shape as ``target``. Raises ------ NotImplementedError [description] """ raise NotImplementedError
[docs]class NegativeLogLikelihood(DistributionLoss): """ Compute the negative log likelihood loss. Parameters ---------- beta: float in range (0, 1) beta parameter from the paper: "On the Pitfalls of Heteroscedastic Uncertainty Estimation with Probabilistic Neural Networks" by Seitzer et al. 2022 https://openreview.net/forum?id=aPOpXlnV1T """ beta: float = 0.0 def __call__( self, input: torch.distributions.Distribution, target: torch.Tensor ) -> torch.Tensor: nll = -input.log_prob(target) if self.beta > 0.0: variance = input.variance nll = nll * (variance.detach() ** self.beta) return nll
[docs]class CRPS(DistributionLoss): def __call__( self, input: torch.distributions.Distribution, target: torch.Tensor ) -> torch.Tensor: return input.crps(target)
[docs]class EnergyScore(DistributionLoss): def __call__(self, input, target: torch.Tensor) -> torch.Tensor: return input.energy_score(target)