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#' @export
makeRLearner.classif.gausspr = function() {
makeRLearnerClassif(
cl = "classif.gausspr",
package = "kernlab",
# FIXME: stringdot pars and check order, scale and offset limits
par.set = makeParamSet(
makeLogicalLearnerParam(id = "scaled", default = TRUE),
makeDiscreteLearnerParam(id = "kernel", default = "rbfdot",
values = c("vanilladot", "polydot", "rbfdot", "tanhdot", "laplacedot",
"besseldot", "anovadot", "splinedot")),
makeNumericLearnerParam(id = "sigma",
lower = 0, requires = quote(kernel %in% c("rbfdot", "anovadot", "besseldot", "laplacedot"))),
makeIntegerLearnerParam(id = "degree", default = 3L, lower = 1L,
requires = quote(kernel %in% c("polydot", "anovadot", "besseldot"))),
makeNumericLearnerParam(id = "scale", default = 1, lower = 0,
requires = quote(kernel %in% c("polydot", "tanhdot"))),
makeNumericLearnerParam(id = "offset", default = 1,
requires = quote(kernel %in% c("polydot", "tanhdot"))),
makeIntegerLearnerParam(id = "order", default = 1L,
requires = quote(kernel == "besseldot")),
makeNumericLearnerParam(id = "tol", default = 0.001, lower = 0),
makeLogicalLearnerParam(id = "fit", default = TRUE)
),
par.vals = list(fit = FALSE),
properties = c("twoclass", "multiclass", "numerics", "factors", "prob"),
name = "Gaussian Processes",
short.name = "gausspr",
note = "Kernel parameters have to be passed directly and not by using the `kpar` list in `gausspr`.
Note that `fit` has been set to `FALSE` by default for speed.",
callees = "gausspr"
)
}
#' @export
trainLearner.classif.gausspr = function(.learner, .task, .subset, .weights = NULL,
degree, offset, scale, sigma, order, length, lambda, normalized, ...) {
kpar = learnerArgsToControl(list, degree, offset, scale, sigma, order, length, lambda, normalized)
f = getTaskFormula(.task)
pm = .learner$predict.type == "prob"
if (base::length(kpar) > 0L) {
kernlab::gausspr(f, data = getTaskData(.task, .subset), kpar = kpar, prob.model = pm, ...)
} else {
kernlab::gausspr(f, data = getTaskData(.task, .subset), prob.model = pm, ...)
}
}
#' @export
predictLearner.classif.gausspr = function(.learner, .model, .newdata, ...) {
type = switch(.learner$predict.type, prob = "probabilities", "response")
kernlab::predict(.model$learner.model, newdata = .newdata, type = type, ...)
}
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