dqs.torch.loss.NegativeLogLikelihood

This class is used to compute the negative log-likelihood, which is one of the measures of the accuracy of probabilistic predictions. It is calculated by comparing the predicted probability distribution to the actual outcome. It quantifies the negative log-likelihood between the predicted probability distribution and the corresponding actual outcome.

Suppose that the predicted probability distribution is represented as a CDF with \(n\) knots, and the probability that the target value is contained in the \(i\)-th bin can be represented as f[i]. Then the negative log-likelihood for an actual outcome \(y\) can be calculated as

\[- \log f[i],\]

where \(i\) is the index of the bin \(y\) is contained.

class NegativeLogLikelihood(
    distribution,
    loss_boundaries)

Parameters:

Args	Type	Description
distribution	dqs.distribution	Object from dqs.distribution package to represent probability distribution.
boundaries	list (float)	Boundaries used in negative log-likelihood.

loss(pred, y, e=None)

Parameters:

Args	Type	Description
pred	Tensor (float)	Estimated probability distribution to be evaluated.
y	Tensor (float)	One-dimensional tensor to represent labels from a dataset.
e	Tensor (bool)	Optional parameter for survival analysis. One-dimensional tensor to represent censored (False) or uncensored (True).

Return type: Tensor representing a single float.

Example

The following code computes negative log-likelihood based on pred and y.

boundaries = torch.linspace(0.0, 10.0, 11)
dist = dqs.distribution.DistributionLinear(boundaries)
loss_fn = dqs.loss.NegativeLogLikelihood(dist, boundaries)
pred = torch.Tensor([[0.4,0.6],[0.2,0.8]])
y = torch.Tensor([5.0,5.0])
loss = loss_fn.loss(pred, y)