#' @export
makeRLearner.regr.h2o.gbm = function() {
makeRLearnerRegr(
cl = "regr.h2o.gbm",
package = "h2o",
par.set = makeParamSet(
# See http://docs.h2o.ai/h2o/latest-stable/h2o-docs/data-science/gbm.html
makeIntegerLearnerParam("ntrees", lower = 1L, default = 50L),
makeIntegerLearnerParam("max_depth", lower = 1L, default = 5L),
makeIntegerLearnerParam("min_rows", lower = 1L, default = 10L),
makeIntegerLearnerParam("nbins", lower = 1L, default = 20L),
makeIntegerLearnerParam("nbins_cats", lower = 1L, default = 1024),
makeIntegerLearnerParam("nbins_top_level", lower = 1L, default = 1024),
makeIntegerLearnerParam("seed", default = -1L, tunable = FALSE),
makeNumericLearnerParam("learn_rate", lower = 0, upper = 1, default = 0.1),
makeNumericLearnerParam("learn_rate_annealing", lower = 0, upper = 1, default = 1),
makeDiscreteLearnerParam("distribution",
values = c("poisson", "laplace", "tweedie", "gaussian", "huber", "gamma", "quantile"),
default = "gaussian"),
makeNumericLearnerParam("sample_rate", lower = 0, upper = 1, default = 1),
# makeNumericLearnerParam("sample_rate_per_class", lower = 0, upper = 1, default = NULL, special.vals = list(NULL)),
makeNumericLearnerParam("col_sample_rate", lower = 0, upper = 1, default = 1),
makeNumericLearnerParam("col_sample_rate_change_per_level", lower = 0, upper = 1, default = 1),
makeNumericLearnerParam("col_sample_rate_per_tree", lower = 0, upper = 1, default = 1),
makeNumericLearnerParam("max_abs_leafnode_pred", lower = 0, default = Inf, allow.inf = TRUE),
makeNumericLearnerParam("pred_noise_bandwidth", lower = 0, default = 0),
makeDiscreteLearnerParam("categorical_encoding",
values = c("AUTO", "Enum", "OneHotInternal", "OneHotExplicit", "Binary",
"Eigen", "LabelEncoder", "SortByResponse"),
default = "AUTO"),
makeNumericLearnerParam("min_split_improvement", lower = 0, default = 1e-05),
makeDiscreteLearnerParam("histogram_type",
values = c("AUTO", "UniformAdaptive", "Random", "QuantilesGlobal", "RoundRobin"),
default = "AUTO"),
makeLogicalLearnerParam("score_each_iteration", default = FALSE, tunable = FALSE),
makeIntegerLearnerParam("score_tree_interval", lower = 0L, default = 0L, tunable = FALSE),
makeIntegerLearnerParam("stopping_rounds", lower = 0L, default = 0L, tunable = FALSE),
makeDiscreteLearnerParam("stopping_metric",
values = c("AUTO", "deviance", "logloss", "MSE", "RMSE", "MAE", "RMSLE"),
default = "AUTO", tunable = FALSE),
makeNumericLearnerParam("stopping_tolerance", lower = 0, upper = Inf, default = 0.001, tunable = FALSE),
makeNumericLearnerParam("quantile_alpha", lower = 0, upper = 100, default = 0.5,
requires = expression(distribution == "quantile")),
makeNumericLearnerParam("tweedie_power", lower = 1, upper = 2, default = 1.5, special.vals = list(0),
requires = expression(distribution == "tweedie")),
makeNumericLearnerParam("huber_alpha", lower = 0, upper = 1, default = 0.9,
requires = expression(distribution == "huber"))
),
properties = c("numerics", "factors", "missings"),
name = "h2o.gbm",
short.name = "h2o.gbm",
note = "",
callees = "h2o.gbm"
)
}
#' @export
trainLearner.regr.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()
options("h2o.use.data.table" = TRUE)
}
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)
model = do.call(h2o::h2o.gbm, params)
return(model)
}
#' @export
predictLearner.regr.h2o.gbm = function(.learner, .model, .newdata, ...) {
m = .model$learner.model
h2of = h2o::as.h2o(.newdata)
p = h2o::h2o.predict(m, newdata = h2of, ...)
p.df = as.data.frame(p)
h2o::h2o.rm(h2of)
h2o::h2o.rm(p)
return(p.df$predict)
}
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