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

In shapr: Prediction Explanation with Dependence-Aware Shapley Values

A torch::nn_module() Representing a categorical_to_one_hot_layer

Description

The categorical_to_one_hot_layer module/layer expands categorical features into one-hot vectors, because multi-layer perceptrons are known to work better with this data representation. It also replaces NaNs with zeros in order so that further layers may work correctly.

Usage

categorical_to_one_hot_layer(
  one_hot_max_sizes,
  add_nans_map_for_columns = NULL
)

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.
add_nans_map_for_columns	Optional list which contains indices of columns which is_nan masks are to be appended to the result tensor. This option is necessary for the full encoder to distinguish whether value is to be reconstructed or not.

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.

Author(s)

Lars Henry Berge Olsen

shapr documentation built on April 4, 2025, 12:18 a.m.