specified_prob_mask_generator: A 'torch::nn_module()' Representing a...

In NorskRegnesentral/shapr: Prediction Explanation with Dependence-Aware Shapley Values

A torch::nn_module() Representing a specified_prob_mask_generator

Description

A mask generator which masks the entries based on specified probabilities.

Usage

specified_prob_mask_generator(masking_probs, paired_sampling = FALSE)

Arguments

masking_probs	An M+1 numerics containing the probabilities masking 'd' of the (0,...M) entries for each observation.
paired_sampling	Boolean. If we are doing paired sampling. So include both S and \bar{S}. If TRUE, then batch must be sampled using 'paired_sampler' which creates batches where the first half and second half of the rows are duplicates of each other. That is, ⁠batch = [row1, row1, row2, row2, row3, row3, ...]⁠.

Details

A class that takes in the probabilities of having d masked observations. I.e., for M dimensional data, masking_probs is of length M+1, where the d'th entry is the probability of having d-1 masked values.

A mask generator that first samples the number of entries 'd' to be masked in the 'M'-dimensional observation 'x' in the batch based on the given M+1 probabilities. The 'd' masked are uniformly sampled from the 'M' possible feature indices. The d'th entry of the probability of having d-1 masked values.

Note that mcar_mask_generator with p = 0.5 is the same as using specified_prob_mask_generator() with masking_ratio = choose(M, 0:M), where M is the number of features. This function was initially created to check if increasing the probability of having a masks with many masked features improved vaeac's performance by focusing more on these situations during training.

Examples

## Not run: 
probs <- c(1, 8, 6, 3, 2)
mask_gen <- specified_prob_mask_generator(probs)
masks <- mask_gen(torch::torch_randn(c(10000, length(probs)) - 1))
empirical_prob <- table(as.array(masks$sum(2)))
empirical_prob / sum(empirical_prob)
probs / sum(probs)

## End(Not run)

NorskRegnesentral/shapr documentation built on April 19, 2024, 1:19 p.m.