SHAP Importance Plots

Source: R/sv_importance.R

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.

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.

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").

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.

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")
#> Petal.Length  Sepal.Width  Petal.Width      Species 
#>   0.62123659   0.08254966   0.06248401   0.02103343 
sv_importance(x, kind = "beeswarm", show_numbers = TRUE)