sv_importance: SHAP Importance Plots

In shapviz: SHAP Visualizations

SHAP Importance Plots

Description

This function provides two types of SHAP importance plots: a bar plot and a beeswarm plot (sometimes called "SHAP summary plot"). The two types of plots can also be combined.

Usage

sv_importance(object, ...)

## Default S3 method:
sv_importance(object, ...)

## S3 method for class 'shapviz'
sv_importance(
  object,
  kind = c("bar", "beeswarm", "both", "no"),
  max_display = 15L,
  fill = "#fca50a",
  bar_width = 2/3,
  bee_width = 0.4,
  bee_adjust = 0.5,
  viridis_args = getOption("shapviz.viridis_args"),
  color_bar_title = "Feature value",
  show_numbers = FALSE,
  format_fun = format_max,
  number_size = 3.2,
  sort_features = TRUE,
  ...
)

## S3 method for class 'mshapviz'
sv_importance(
  object,
  kind = c("bar", "beeswarm", "both", "no"),
  max_display = 15L,
  fill = "#fca50a",
  bar_width = 2/3,
  bar_type = c("dodge", "stack", "facets", "separate"),
  bee_width = 0.4,
  bee_adjust = 0.5,
  viridis_args = getOption("shapviz.viridis_args"),
  color_bar_title = "Feature value",
  show_numbers = FALSE,
  format_fun = format_max,
  number_size = 3.2,
  sort_features = TRUE,
  ...
)

Arguments

object	An object of class "(m)shapviz".
...	Arguments passed to ggplot2::geom_bar() (if kind = "bar") or to ggplot2::geom_point() otherwise. For instance, passing alpha = 0.2 will produce semi-transparent beeswarms, and setting size = 3 will produce larger dots.
kind	Should a "bar" plot (the default), a "beeswarm" plot, or "both" be shown? Set to "no" in order to suppress plotting. In that case, the sorted SHAP feature importances of all variables are returned.
max_display	How many features should be plotted? Set to Inf to show all features. Has no effect if kind = "no".
fill	Color used to fill the bars (only used if bars are shown).
bar_width	Relative width of the bars (only used if bars are shown).
bee_width	Relative width of the beeswarms.
bee_adjust	Relative bandwidth adjustment factor used in estimating the density of the beeswarms.
viridis_args	List of viridis color scale arguments. The default points to the global option shapviz.viridis_args, which corresponds to list(begin = 0.25, end = 0.85, option = "inferno"). These values are passed to ggplot2::scale_color_viridis_c(). For example, to switch to standard viridis, either change the default with options(shapviz.viridis_args = list()) or set viridis_args = list().
color_bar_title	Title of color bar of the beeswarm plot. Set to NULL to hide the color bar altogether.
show_numbers	Should SHAP feature importances be printed? Default is FALSE.
format_fun	Function used to format SHAP feature importances (only if show_numbers = TRUE). To change to scientific notation, use ⁠function(x) = prettyNum(x, scientific = TRUE)⁠.
number_size	Text size of the numbers (if show_numbers = TRUE).
sort_features	Should features be sorted or not? The default is TRUE.
bar_type	For "mshapviz" objects with kind = "bar": How should bars be represented? The default is "dodge" for dodged bars. Other options are "stack", "wrap", or "separate" (via "patchwork"). Note that "separate" is currently the only option that supports show_numbers = TRUE.

Details

The bar plot shows SHAP feature importances, calculated as the average absolute SHAP value per feature. The beeswarm plot displays SHAP values per feature, using min-max scaled feature values on the color axis. Non-numeric features are transformed to numeric by calling data.matrix() first. For both types of plots, the features are sorted in decreasing order of importance.

Value

A "ggplot" (or "patchwork") object representing an importance plot, or - if kind = "no" - a named numeric vector of sorted SHAP feature importances (or a matrix in case of an object of class "mshapviz").

Methods (by class)

• sv_importance(default): Default method.

• sv_importance(shapviz): SHAP importance plot for an object of class "shapviz".

• sv_importance(mshapviz): SHAP importance plot for an object of class "mshapviz".

See Also

sv_interaction

Examples

X_train <- data.matrix(iris[, -1])
dtrain <- xgboost::xgb.DMatrix(X_train, label = iris[, 1], nthread = 1)
fit <- xgboost::xgb.train(data = dtrain, nrounds = 10, nthread = 1)
x <- shapviz(fit, X_pred = X_train)
sv_importance(x)
sv_importance(x, kind = "no")
sv_importance(x, kind = "beeswarm", show_numbers = TRUE)

shapviz documentation built on Sept. 14, 2024, 5:07 p.m.