if (FALSE) {
# load the required libraries for building the base-learners and the ensemble models
library(h2o) #shapley supports h2o models
library(autoEnsemble) #autoEnsemble models, particularly useful under severe class imbalance
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)
#######################################################
### Significance testing of contributions of two features
#######################################################
shapley.test(result, features = c("GLEASON", "PSA"), n=5000)
}
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