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#' @export
makeRLearner.regr.h2o.gbm = function() {
makeRLearnerRegr(
cl = "regr.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 = 1024),
makeIntegerLearnerParam("seed", tunable = FALSE)
),
properties = c("numerics", "factors", "missings"),
name = "h2o.gbm",
short.name = "h2o.gbm",
note = "'distribution' is set automatically to 'gaussian'.",
callees = "h2o.gbm"
)
}
#' @export
trainLearner.regr.h2o.gbm = function(.learner, .task, .subset, .weights = NULL, ...) {
# 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()
}
y = getTaskTargetNames(.task)
x = getTaskFeatureNames(.task)
d = getTaskData(.task, subset = .subset)
h2of = h2o::as.h2o(d)
h2o::h2o.gbm(y = y, x = x, training_frame = h2of, distribution = "gaussian", ...)
}
#' @export
predictLearner.regr.h2o.gbm = function(.learner, .model, .newdata, ...) {
m = .model$learner.model
h2of = h2o::as.h2o(.newdata)
p = h2o::h2o.predict(m, newdata = h2of, ...)
p.df = as.data.frame(p)
return(p.df$predict)
}
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