sv_dependence2D: 2D SHAP Dependence Plot

Description

Scatterplot of two features, showing the sum of their SHAP values on the color scale. This allows to visualize the combined effect of two features, including interactions. A typical application are models with latitude and longitude as features (plus maybe other regional features that can be passed via add_vars).

If SHAP interaction values are available, setting interactions = TRUE allows to focus on pure interaction effects (multiplied by two). In this case, add_vars has no effect.

Usage

sv_dependence2D(object, ...)
# S3 method for default
sv_dependence2D(object, ...)
# S3 method for shapviz
sv_dependence2D(
  object,
  x,
  y,
  viridis_args = getOption("shapviz.viridis_args"),
  jitter_width = NULL,
  jitter_height = NULL,
  interactions = FALSE,
  add_vars = NULL,
  ...
)
# S3 method for mshapviz
sv_dependence2D(
  object,
  x,
  y,
  viridis_args = getOption("shapviz.viridis_args"),
  jitter_width = NULL,
  jitter_height = NULL,
  interactions = FALSE,
  add_vars = NULL,
  ...
)

Value

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

Arguments

object: An object of class "(m)shapviz".
...: Arguments passed to ggplot2::geom_jitter().
x: Feature name for x axis. Can be a vector/list if object is of class "shapviz".
y: Feature name for y axis. Can be a vector/list if object is of class "shapviz".
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.
jitter_height: Similar to jitter_width for vertical scatter.
interactions: Should SHAP interaction values be plotted? The default (FALSE) will show the rowwise sum of the SHAP values of x and y. If TRUE, will use twice the SHAP interaction value (requires SHAP interactions).
add_vars: Optional vector of feature names, whose SHAP values should be added to the sum of the SHAP values of x and y (only if interactions = FALSE). A use case would be a model with geographic x and y coordinates, along with some additional locational features like distance to the next train station.

Methods (by class)

sv_dependence2D(default): Default method.
sv_dependence2D(shapviz): 2D SHAP dependence plot for "shapviz" object.
sv_dependence2D(mshapviz): 2D SHAP dependence plot for "mshapviz" object.

Examples

Run this code

dtrain <- xgboost::xgb.DMatrix(
  data.matrix(iris[, -1]), label = iris[, 1], nthread = 1
)
fit <- xgboost::xgb.train(data = dtrain, nrounds = 10, nthread = 1)
sv <- shapviz(fit, X_pred = dtrain, X = iris)
sv_dependence2D(sv, x = "Petal.Length", y = "Species")
sv_dependence2D(sv, x = c("Petal.Length", "Species"), y = "Sepal.Width")

# SHAP interaction values
sv2 <- shapviz(fit, X_pred = dtrain, X = iris, interactions = TRUE)
sv_dependence2D(sv2, x = "Petal.Length", y = "Species", interactions = TRUE)
sv_dependence2D(
  sv2, x = "Petal.Length", y = c("Species", "Petal.Width"), interactions = TRUE
)

# mshapviz object
mx <- split(sv, f = iris$Species)
sv_dependence2D(mx, x = "Petal.Length", y = "Sepal.Width")