Plot tabnet_explain mask importance heatmap

Usage

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

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.

Value

A ggplot object.

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

 if (FALSE) { # \dontrun{
library(ggplot2)
data("attrition", package = "modeldata")

## Single-outcome binary classification of `Attrition` in `attrition` dataset
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)

## Multi-outcome regression on `Sale_Price` and `Pool_Area` in `ames` dataset,
data("ames", package = "modeldata")
x <- ames[,-which(names(ames) %in% c("Sale_Price", "Pool_Area"))]
y <- ames[, c("Sale_Price", "Pool_Area")]
ames_fit <- tabnet_fit(x, y, epochs = 1, verbose=TRUE)
ames_explain <- tabnet_explain(ames_fit, x)
autoplot(ames_explain, quantile = 0.99)
} # }