#' @export
makeRLearner.classif.bst = function() {
makeRLearnerClassif(
cl = "classif.bst",
package = c("bst", "rpart"),
par.set = makeParamSet(
makeNumericLearnerParam(id = "cost", default = 0.5, lower = 0, upper = 1),
makeDiscreteLearnerParam(id = "family", values = c("gaussian", "hinge")),
makeIntegerLearnerParam(id = "mstop", default = 50L, lower = 1L, when = "both"),
makeNumericLearnerParam(id = "nu", default = 0.1),
makeLogicalLearnerParam(id = "twinboost", default = FALSE),
makeUntypedLearnerParam(id = "f.init", default = NULL),
makeUntypedLearnerParam(id = "xselect.init", default = NULL),
makeLogicalLearnerParam(id = "center", default = FALSE),
makeLogicalLearnerParam(id = "trace", default = FALSE, tunable = FALSE),
makeIntegerLearnerParam(id = "numsample", default = 50L, lower = 1L),
makeIntegerLearnerParam(id = "df", default = 4L, lower = 1L),
makeIntegerLearnerParam(id = "minsplit", default = 20L, lower = 1L),
makeIntegerLearnerParam(id = "minbucket", lower = 1L),
makeNumericLearnerParam(id = "cp", default = 0.01, lower = 0, upper = 1),
makeIntegerLearnerParam(id = "maxcompete", default = 4L, lower = 0L),
makeIntegerLearnerParam(id = "maxsurrogate", default = 5L, lower = 0L),
makeDiscreteLearnerParam(id = "usesurrogate", default = 2L, values = 0:2),
makeIntegerLearnerParam(id = "xval", default = 10L, lower = 0L, tunable = FALSE),
makeDiscreteLearnerParam(id = "surrogatestyle", default = 0L, values = 0:1),
makeIntegerLearnerParam(id = "maxdepth", default = 1L, lower = 1L, upper = 30L),
makeDiscreteLearnerParam(id = "Learner", default = "ls", values = c("ls", "sm", "tree"))
# FIXME bst() seems to have no default for Learner. But if "ls" is correct, it is not needed in par.vals
),
par.vals = list(Learner = "ls", maxdepth = 1L, xval = 0L),
# FIXME par.vals default for maxdepth is the same as the default of LearnerParam and the function
properties = c("numerics", "twoclass"),
name = "Gradient Boosting",
short.name = "bst",
note = 'Renamed parameter `learner` to `Learner` due to nameclash with `setHyperPars`. Default changes: `Learner = "ls"`, `xval = 0`, and `maxdepth = 1`.',
callees = c("bst", "bst_control", "rpart.control")
)
}
#' @export
trainLearner.classif.bst = function(.learner, .task, .subset, .weights = NULL, mstop, nu, twinboost,
f.init, xselect.init, center, trace, numsample, df, minsplit, minbucket, cp, maxsurrogate,
usesurrogate, surrogatestyle, maxdepth, xval, Learner, ...) {
d = getTaskData(.task, .subset, target.extra = TRUE, recode.target = "-1+1")
ctrl = learnerArgsToControl(bst::bst_control, mstop, nu, twinboost, f.init, xselect.init, center, trace, numsample, df)
control.tree = learnerArgsToControl(list, minsplit, minbucket, cp, maxsurrogate, usesurrogate, surrogatestyle, maxdepth, xval)
bst::bst(x = d$data, y = d$target, ctrl = ctrl, control.tree = control.tree, learner = Learner, ...)
}
#' @export
predictLearner.classif.bst = function(.learner, .model, .newdata, ...) {
levs = c(.model$task.desc$negative, .model$task.desc$positive)
p = predict(.model$learner.model, .newdata, ...)
as.factor(ifelse(p > 0, levs[2L], levs[1L]))
}
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