tabnet (version 0.4.0)

autoplot.tabnet_explain: Plot tabnet_explain mask importance heatmap

Description

Plot tabnet_explain mask importance heatmap

Usage

autoplot.tabnet_explain(
  object,
  type = c("mask_agg", "steps"),
  quantile = 1,
  ...
)

Value

A ggplot object.

Arguments

object

A tabnet_explain object as a result of tabnet_explain().

type

a character value. Either "mask_agg" the default, for a single heatmap of aggregated mask importance per predictor along the dataset, or "steps" for one heatmap at each mask step.

quantile

numerical value between 0 and 1. Provides quantile clipping of the mask values

...

not used.

Details

Plot the tabnet_explain object mask importance per variable along the predicted dataset. type="mask_agg" output a single heatmap of mask aggregated values, type="steps" provides a plot faceted along the n_steps mask present in the model. quantile=.995 may be used for strong outlier clipping, in order to better highlight low values. quantile=1, the default, do not clip any values.

Examples

# \donttest{
if (torch::torch_is_installed()) {
library(ggplot2)
data("attrition", package = "modeldata")
attrition_fit <- tabnet_fit(Attrition ~. , data=attrition, epoch=11)
attrition_explain <- tabnet_explain(attrition_fit, attrition)
# Plot the model aggregated mask interpretation heatmap
autoplot(attrition_explain)
}
# }