gauss_cat_loss: A 'torch::nn_module()' Representing a 'gauss_cat_loss'
In shapr: Prediction Explanation with Dependence-Aware Shapley Values

View source: R/approach_vaeac_torch_modules.R

gauss_cat_loss

R Documentation

A `torch::nn_module()` Representing a `gauss_cat_loss`

Description

The ⁠gauss_cat_loss module⁠ layer computes the log probability of the groundtruth for each object given the mask and the distribution parameters. That is, the log-likelihoods of the true/full training observations based on the generative distributions parameters distr_params inferred by the masked versions of the observations.

Usage

gauss_cat_loss(one_hot_max_sizes, min_sigma = 1e-04, min_prob = 1e-04)

Arguments

`one_hot_max_sizes`	A torch tensor of dimension `n_features` containing the one hot sizes of the `n_features` features. That is, if the `i`th feature is a categorical feature with 5 levels, then `one_hot_max_sizes[i] = 5`. While the size for continuous features can either be `0` or `1`.
`min_sigma`	For stability it might be desirable that the minimal sigma is not too close to zero.
`min_prob`	For stability it might be desirable that the minimal probability is not too close to zero.

Details

Note that the module works with mixed data represented as 2-dimensional inputs and it works correctly with missing values in groundtruth as long as they are represented by NaNs.