vaeac_categorical_parse_params: Creates Categorical Distributions

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

Creates Categorical Distributions

Description

Function that takes in a tensor containing the logits for each of the K classes. Each row corresponds to an observations. Send each row through the softmax function to convert from logits to probabilities that sum 1 one. The function also clamps the probabilities between a minimum and maximum probability. Note that we still normalize them afterward, so the final probabilities can be marginally below or above the thresholds.

Usage

vaeac_categorical_parse_params(params, min_prob = 0, max_prob = 1)

Arguments

params	Tensor of dimension batch_size x K containing the logits for each of the K classes and batch_size observations.
min_prob	For stability it might be desirable that the minimal probability is not too close to zero.
max_prob	For stability it might be desirable that the maximal probability is not too close to one.

Details

Take a Tensor (e. g. a part of neural network output) and return torch::distr_categorical() distribution. The input tensor after applying softmax over the last axis contains a batch of the categorical probabilities. So there are no restrictions on the input tensor. Technically, this function treats the last axis as the categorical probabilities, but Categorical takes only 2D input where the first axis is the batch axis and the second one corresponds to the probabilities, so practically the function requires 2D input with the batch of probabilities for one categorical feature. min_prob is the minimal probability for each class. After clipping the probabilities from below and above they are renormalized in order to be a valid distribution. This regularization is required for the numerical stability and may be considered as a neural network architecture choice without any change to the probabilistic model.Note that the softmax function is given by \operatorname{Softmax}(x_i) = (\exp(x_i))/(\sum_{j} \exp(x_j)), where x_i are the logits and can take on any value, negative and positive. The output \operatorname{Softmax}(x_i) \in [0,1] and \sum_{j} Softmax(x_i) = 1.

Value

A torch::distr_categorical distributions with the provided probabilities for each class.

Author(s)

Lars Henry Berge Olsen

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