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#' @export
makeRLearner.classif.h2o.gbm = function() {
makeRLearnerClassif(
cl = "classif.h2o.gbm",
package = "h2o",
par.set = makeParamSet(
makeIntegerLearnerParam("ntrees", lower = 1L, default = 50L),
makeIntegerLearnerParam("max_depth", lower = 1L, default = 5L),
makeIntegerLearnerParam("min_rows", lower = 1L, default = 10L),
makeNumericLearnerParam("learn_rate", lower = 0, upper = 1, default = 0.1),
makeIntegerLearnerParam("nbins", lower = 1L, default = 20L),
makeIntegerLearnerParam("nbins_cats", lower = 1L, default = 1024L),
makeLogicalLearnerParam("balance_classes", default = FALSE),
makeIntegerLearnerParam("max_after_balance_size", lower = 0L, default = 1L),
makeIntegerLearnerParam("seed", tunable = FALSE)
),
properties = c("twoclass", "multiclass", "numerics", "factors", "prob", "missings", "featimp"),
name = "h2o.gbm",
short.name = "h2o.gbm",
note = "'distribution' is set automatically to 'gaussian'.",
callees = "h2o.gbm"
)
}
#' @export
trainLearner.classif.h2o.gbm = function(.learner, .task, .subset, .weights = NULL, ...) {
params = list(...)
# check if h2o connection already exists, otherwise start one
conn.up = tryCatch(h2o::h2o.getConnection(), error = function(err) {
return(FALSE)
})
if (!inherits(conn.up, "H2OConnection")) {
h2o::h2o.init()
}
params$y = getTaskTargetNames(.task)
params$x = getTaskFeatureNames(.task)
params$training_frame = getTaskData(.task, subset = .subset)
if (!is.null(.weights)) {
params$weights_column = .weights
}
params$training_frame = h2o::as.h2o(params$training_frame)
params$distribution = ifelse(length(getTaskDesc(.task)$class.levels) == 2L, "bernoulli", "multinomial")
model = do.call(h2o::h2o.gbm, params)
return(model)
}
#' @export
predictLearner.classif.h2o.gbm = function(.learner, .model, .newdata, ...) {
m = .model$learner.model
h2of = h2o::as.h2o(.newdata)
p = h2o::h2o.predict(m, newdata = h2of, ...)
p.df = as.data.frame(p)
# check if class names are integers. if yes, colnames of p.df need to be adapted
int = stri_detect_regex(p.df$predict, "^[[:digit:]]+$")
pcol = stri_detect_regex(colnames(p.df), "^p[[:digit:]]+$")
if (any(int) && any(pcol)) {
colnames(p.df)[pcol] = stri_sub(colnames(p.df)[pcol], 2L)
}
if (.learner$predict.type == "response") {
return(p.df$predict)
} else {
p.df$predict = NULL
return(as.matrix(p.df))
}
}
#' @export
getFeatureImportanceLearner.classif.h2o.gbm = function(.learner, .model, ...) {
mod = getLearnerModel(.model, more.unwrap = TRUE)
extractH2OVarImp(mod, ...)
}
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