vaeac_normalize_data

Compute the mean and std for each continuous feature, while the categorical features will have mean 0 and std 1.

internal

Complex machine learning models are often hard to interpret. However, in
many situations it is crucial to understand and explain why a model made a specific
prediction. Shapley values is the only method for such prediction explanation framework
with a solid theoretical foundation. Previously known methods for estimating the Shapley
values do, however, assume feature independence. This package implements methods which accounts for any feature
dependence, and thereby produces more accurate estimates of the true Shapley values.
An accompanying 'Python' wrapper ('shaprpy') is available through the GitHub repository.

Martin Jullum

shapr

Prediction Explanation with Dependence-Aware Shapley Values

Lars Henry Berge Olsen

Annabelle Redelmeier

Jon Lachmann

Nikolai Sellereite

Anders Løland

Jens Christian Wahl

Camilla Lingjærde

Norsk Regnesentral 

vaeac_normalize_data function

<dl><dt>one_hot_max_sizes</dt>
<dd>A torch tensor of dimension <code>n_features</code> containing the one hot sizes of the <code>n_features</code>
features. That is, if the <code>i</code>th feature is a categorical feature with 5 levels, then <code>one_hot_max_sizes[i] = 5</code>.
While the size for continuous features can either be <code>0</code> or <code>1</code>.</dd>
<dt>norm_mean</dt>
<dd>Torch tensor (optional). A 1D array containing the means of the columns of <code>x_torch</code>.</dd>
<dt>norm_std</dt>
<dd>Torch tensor (optional). A 1D array containing the stds of the columns of <code>x_torch</code>.</dd></dl>

Arguments

Author

Normalize mixed data for vaeac — vaeac_normalize_data

<dl>

<dt>one_hot_max_sizes</dt>
<dd>A torch tensor of dimension <code>n_features</code> containing the one hot sizes of the <code>n_features</code>
features. That is, if the <code>i</code>th feature is a categorical feature with 5 levels, then <code>one_hot_max_sizes[i] = 5</code>.
While the size for continuous features can either be <code>0</code> or <code>1</code>.</dd>


<dt>norm_mean</dt>
<dd>Torch tensor (optional). A 1D array containing the means of the columns of <code>x_torch</code>.</dd>


<dt>norm_std</dt>
<dd>Torch tensor (optional). A 1D array containing the stds of the columns of <code>x_torch</code>.</dd>

</dl>

Normalize mixed data for <code>vaeac</code>

vaeac_normalize_data: Normalize mixed data for `vaeac`

Description

Usage

Value

Arguments

Author