shapley (version 0.6.0)

shapley.domain: Compute and plot weighted mean SHAP contributions at group level (factors or domains)

Description

Aggregates SHAP contributions across user-defined domains (groups of features), computes weighted mean and an 95 returns a plot plus summary tables.

Usage

shapley.domain(
  shapley,
  domains,
  plot = TRUE,
  print = FALSE,
  colorcode = NULL,
  xlab = "Domains"
)

Value

A list with:

domainSummary

Data frame with WMSHAP domain contributions and CI.

domainRatio

Data frame with per-model WMSHAP domain contribution ratios.

plot

A ggplot object (or NULL if plotting not requested/implemented).

Arguments

shapley

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

domains

Named list of character vectors. Each element name is a domain name; each element value is a character vector of feature names assigned to that domain.

plot

Logical. If TRUE, a bar plot of domain WMSHAP contributions is created.

print

Logical. If TRUE, prints the domain WMSHAP summary table.

colorcode

Character vector for specifying the color names for each domain in the plot.

xlab

Character. Specify the ggplot 'xlab' label in the plot (default is "Domains")

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")

#######################################################
### DEFINE DOMAINS (GROUPS OF FEATURES OR FACTORS)
#######################################################
shapley.domain(shapley = result, plot = TRUE,
               domains = list(Demographic = c("RACE", "AGE"),
                              Cancer = c("VOL", "PSA", "GLEASON"),
                              Tests = c("DPROS", "DCAPS")),
                              print = TRUE)
}