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import torch.nn as nn | ||
from typing import List | ||
import torch.distributions as distributions | ||
import torch | ||
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class DistributionLoss(nn.Module): | ||
""" | ||
DistributionLoss base class. | ||
Class should be inherited for all distribution losses, i.e. if a network predicts | ||
the parameters of a probability distribution, DistributionLoss can be used to | ||
score those parameters and calculate loss for given true values. | ||
Define two class attributes in a child class: | ||
Attributes: | ||
distribution_class (distributions.Distribution): torch probability distribution | ||
distribution_arguments (List[str]): list of parameter names for the distribution | ||
Further, implement the methods :py:meth:`~map_x_to_distribution` and :py:meth:`~rescale_parameters`. | ||
""" | ||
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distribution_class: distributions.Distribution | ||
distribution_arguments: List[str] | ||
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def __init__( | ||
self, quantiles: List[float] = [.05, .25, .5, .75, .95], reduction="mean" | ||
): | ||
""" | ||
Initialize loss | ||
Args: | ||
quantiles (List[float], optional): quantiles for probability range. | ||
Defaults to [0.02, 0.1, 0.25, 0.5, 0.75, 0.9, 0.98]. | ||
reduction (str, optional): Reduction, "none", "mean" or "sqrt-mean". Defaults to "mean". | ||
""" | ||
super().__init__() | ||
self.quantiles = quantiles | ||
self.reduction = getattr(torch, reduction) | ||
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# def rescale_parameters( | ||
# self, parameters: torch.Tensor, target_scale: torch.Tensor, encoder: RevIN | ||
# ) -> torch.Tensor: | ||
# """ | ||
# Rescale normalized parameters into the scale required for the output. | ||
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# Args: | ||
# parameters (torch.Tensor): normalized parameters (indexed by last dimension) | ||
# target_scale (torch.Tensor): scale of parameters (n_batch_samples x (center, scale)) | ||
# encoder (BaseEstimator): original encoder that normalized the target in the first place | ||
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# Returns: | ||
# torch.Tensor: parameters in real/not normalized space | ||
# """ | ||
# return encoder(dict(prediction=parameters, target_scale=target_scale)) | ||
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def map_x_to_distribution(self, x: torch.Tensor) -> distributions.Distribution: | ||
""" | ||
Map the a tensor of parameters to a probability distribution. | ||
Args: | ||
x (torch.Tensor): parameters for probability distribution. Last dimension will index the parameters | ||
Returns: | ||
distributions.Distribution: torch probability distribution as defined in the | ||
class attribute ``distribution_class`` | ||
""" | ||
raise NotImplementedError("implement this method") | ||
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def forward(self, y_pred: torch.Tensor, y_actual: torch.Tensor) -> torch.Tensor: | ||
""" | ||
Calculate negative likelihood | ||
Args: | ||
y_pred: network output | ||
y_actual: actual values | ||
Returns: | ||
torch.Tensor: metric value on which backpropagation can be applied | ||
""" | ||
distribution = self.map_x_to_distribution(y_pred) | ||
loss = -distribution.log_prob(y_actual) | ||
loss = self.reduction(loss) | ||
return loss | ||
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class NormalDistributionLoss(DistributionLoss): | ||
""" | ||
Normal distribution loss. | ||
""" | ||
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distribution_class = distributions.Normal | ||
distribution_arguments = ["affine_loc", "affine_scale", "loc", "scale"] | ||
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def map_x_to_distribution(self, x: torch.Tensor) -> distributions.Normal: | ||
loc = x[..., 2] | ||
scale = x[..., 3] | ||
distr = self.distribution_class(loc=loc, scale=scale) | ||
scaler = distributions.AffineTransform(loc=x[..., 0], scale=x[..., 1]) | ||
return distributions.TransformedDistribution(distr, [scaler]) | ||
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# def rescale_parameters( | ||
# self, parameters: torch.Tensor, target_scale: torch.Tensor, encoder: BaseEstimator | ||
# ) -> torch.Tensor: | ||
# self._transformation = encoder.transformation | ||
# loc = parameters[..., 0] | ||
# scale = F.softplus(parameters[..., 1]) | ||
# return torch.concat( | ||
# [target_scale.unsqueeze(1).expand(-1, loc.size(1), -1), loc.unsqueeze(-1), scale.unsqueeze(-1)], dim=-1 | ||
# ) |