shapley (version 0.6.0)

shapley.plot: Plot weighted mean SHAP (WMSHAP) contributions

Description

Visualizes WMSHAP summaries from a shapley object. Features can be selected using method and method/cutoff, top_n_features, or explicit features to specify feature selection method.

Usage

shapley.plot(
  shapley,
  plot = "bar",
  method = "mean",
  cutoff = 0.01,
  top_n_features = NULL,
  features = NULL,
  legendstyle = "continuous",
  scale_colour_gradient = NULL,
  labels = NULL
)

Value

A ggplot object

Arguments

shapley

object of class "shapley", as returned by the 'shapley' function

plot

Character. One of "bar" or "wmshap".

method

Character. One of "mean" or "lowerCI"; used by feature.selection() for feature selection when top_n_features or features are not set.

cutoff

Numeric cutoff for method selection.

top_n_features

Integer. If set, selects top N features by WMSHAP (overrides cutoff and method arguments).

features

Character vector, specifying the feature to be plotted (overrides cutoff and method arguments).

legendstyle

Character. For plot = "wmshap" only: "continuous" (default) or "discrete".

scale_colour_gradient

Optional character vector of length 3, specifying color names: c(low, mid, high). Used only when plot = "wmshap".

labels

Optional named character vector mapping feature names to display labels. To specify the labels, use the c function and for each feature, provide a label. For example, c(feature1 = label2, feature2 = label2, ...).

Author

E. F. Haghish

Examples

if (FALSE) {
# load the required libraries for building the base-learners and the ensemble models
library(h2o)            #shapley supports h2o models
library(shapley)

# initiate the h2o server
h2o.init(ignore_config = TRUE, nthreads = 2, bind_to_localhost = FALSE, insecure = TRUE)

# upload data to h2o cloud
prostate_path <- system.file("extdata", "prostate.csv", package = "h2o")
prostate <- h2o.importFile(path = prostate_path, header = TRUE)

### H2O provides 2 types of grid search for tuning the models, which are
### AutoML and Grid. Below, I demonstrate how weighted mean shapley values
### can be computed for both types.

set.seed(10)

#######################################################
### PREPARE AutoML Grid (takes a couple of minutes)
#######################################################
# run AutoML to tune various models (GBM) for 60 seconds
y <- "CAPSULE"
prostate[,y] <- as.factor(prostate[,y])  #convert to factor for classification
aml <- h2o.automl(y = y, training_frame = prostate, max_runtime_secs = 120,
                 include_algos=c("GBM"),

                 # this setting ensures the models are comparable for building a meta learner
                 seed = 2023, nfolds = 10,
                 keep_cross_validation_predictions = TRUE)

### call 'shapley' function to compute the weighted mean and weighted confidence intervals
### of SHAP values across all trained models.
### Note that the 'newdata' should be the testing dataset!
result <- shapley(models = aml, newdata = prostate, plot = TRUE)

#######################################################
### PLOT THE WEIGHTED MEAN SHAP VALUES
#######################################################

shapley.plot(result, plot = "bar")
shapley.plot(result, plot = "wmshap")
}