mcar_mask_generator: Missing Completely at Random (MCAR) Mask Generator

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

Missing Completely at Random (MCAR) Mask Generator

Description

A mask generator which masks the entries in the input completely at random.

Usage

mcar_mask_generator(masking_ratio = 0.5, paired_sampling = FALSE)

Arguments

masking_ratio	Numeric between 0 and 1. The probability for an entry in the generated mask to be 1 (masked).
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 ensures that the batch contains two instances for each original observation. That is, batch = [X_1, X_1, X_2, X_2, X_3, X_3, ...], where each entry X_j is a row of dimension p (i.e., the number of features).

Details

The mask generator mask each element in the batch (N x p) using a component-wise independent Bernoulli distribution with probability masking_ratio. Default values for masking_ratio is 0.5, so all masks are equally likely to be generated, including the empty and full masks. The function returns a mask of the same shape as the input batch, and the batch can contain missing values, indicated by the "NaN" token, which will always be masked.

Shape

• Input: (N, p) where N is the number of observations in the batch and p is the number of features.

• Output: (N, p), same shape as the input

Author(s)

Lars Henry Berge Olsen

Examples

## Not run: 
mask_gen <- mcar_mask_generator(masking_ratio = 0.5, paired_sampling = FALSE)
batch <- torch::torch_randn(c(5, 3))
mask_gen(batch)

## End(Not run)

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