sv_dependence: SHAP Dependence Plot

In shapviz: SHAP Visualizations

SHAP Dependence Plot

Description

Scatterplot of the SHAP values of a feature against its feature values. If SHAP interaction values are available, setting interactions = TRUE allows to focus on pure interaction effects (multiplied by two) or on pure main effects. By default, the feature on the color scale is selected via SHAP interactions (if available) or an interaction heuristic, see potential_interactions().

Usage

sv_dependence(object, ...)

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

## S3 method for class 'shapviz'
sv_dependence(
  object,
  v,
  color_var = "auto",
  color = "#3b528b",
  viridis_args = getOption("shapviz.viridis_args"),
  jitter_width = NULL,
  interactions = FALSE,
  ih_nbins = NULL,
  ih_color_num = TRUE,
  ih_scale = FALSE,
  ih_adjusted = FALSE,
  ...
)

## S3 method for class 'mshapviz'
sv_dependence(
  object,
  v,
  color_var = "auto",
  color = "#3b528b",
  viridis_args = getOption("shapviz.viridis_args"),
  jitter_width = NULL,
  interactions = FALSE,
  ih_nbins = NULL,
  ih_color_num = TRUE,
  ih_scale = FALSE,
  ih_adjusted = FALSE,
  ...
)

Arguments

object	An object of class "(m)shapviz".
...	Arguments passed to ggplot2::geom_jitter().
v	Column name of feature to be plotted. Can be a vector/list if object is of class "shapviz".
color_var	Feature name to be used on the color scale to investigate interactions. The default ("auto") uses SHAP interaction values (if available), or a heuristic to select the strongest interacting feature. Set to NULL to not use the color axis. Can be a vector/list if object is of class "shapviz".
color	Color to be used if color_var = NULL. Can be a vector/list if v is a vector.
viridis_args	List of viridis color scale arguments, see ?ggplot2::scale_color_viridis_c. 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_*()⁠. For example, to switch to a standard viridis scale, you can either change the default via options(shapviz.viridis_args = list()), or set viridis_args = list(). Only relevant if color_var is not NULL.
jitter_width	The amount of horizontal jitter. The default (NULL) will use a value of 0.2 in case v is discrete, and no jitter otherwise. (Numeric variables are considered discrete if they have at most 7 unique values.) Can be a vector/list if v is a vector.
interactions	Should SHAP interaction values be plotted? Default is FALSE. Requires SHAP interaction values. If color_var = NULL (or it is equal to v), the pure main effect of v is visualized. Otherwise, twice the SHAP interaction values between v and the color_var are plotted.
ih_nbins, ih_color_num, ih_scale, ih_adjusted	Interaction heuristic (ih) parameters used to select the color variable, see potential_interactions(). Only used if color_var = "auto" and if there are no SHAP interaction values.

Value

An object of class "ggplot" (or "patchwork") representing a dependence plot.

Methods (by class)

• sv_dependence(default): Default method.

• sv_dependence(shapviz): SHAP dependence plot for "shapviz" object.

• sv_dependence(mshapviz): SHAP dependence plot for "mshapviz" object.

See Also

potential_interactions()

Examples

dtrain <- xgboost::xgb.DMatrix(
  data.matrix(iris[, -1]), label = iris[, 1], nthread = 1
)
fit <- xgboost::xgb.train(data = dtrain, nrounds = 10, nthread = 1)
x <- shapviz(fit, X_pred = dtrain, X = iris)
sv_dependence(x, "Petal.Length")
sv_dependence(x, "Petal.Length", color_var = "Species")
sv_dependence(x, "Petal.Length", color_var = NULL)
sv_dependence(x, c("Species", "Petal.Length"))
sv_dependence(x, "Petal.Width", color_var = c("Species", "Petal.Length"))

# SHAP interaction values/main effects
x2 <- shapviz(fit, X_pred = dtrain, X = iris, interactions = TRUE)
sv_dependence(x2, "Petal.Length", interactions = TRUE)
sv_dependence(
  x2, c("Petal.Length", "Species"), color_var = NULL, interactions = TRUE
)

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