gauss_cat_parameters: A 'torch::nn_module()' Representing a 'gauss_cat_parameters'

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

A torch::nn_module() Representing a gauss_cat_parameters

Description

The gauss_cat_parameters module extracts the parameters from the inferred generative Gaussian and categorical distributions for the continuous and categorical features, respectively.

If one_hot_max_sizes is [4, 1, 1, 2], then the inferred distribution parameters for one observation is the vector [p_{00}, p_{01}, p_{02}, p_{03}, \mu_1, \sigma_1, \mu_2, \sigma_2, p_{30}, p_{31}], where \operatorname{Softmax}([p_{00}, p_{01}, p_{02}, p_{03}]) and \operatorname{Softmax}([p_{30}, p_{31}]) are probabilities of the first and the fourth feature categories respectively in the model generative distribution, and Gaussian(\mu_1, \sigma_1^2) and Gaussian(\mu_2, \sigma_2^2) are the model generative distributions on the second and the third features.

Usage

gauss_cat_parameters(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 ith 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.

Author(s)

Lars Henry Berge Olsen

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