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#' @export
makeRLearner.classif.ksvm = function() {
makeRLearnerClassif(
cl = "classif.ksvm",
package = "kernlab",
# FIXME: stringdot pars and check order, scale and offset limits
par.set = makeParamSet(
makeLogicalLearnerParam(id = "scaled", default = TRUE),
makeDiscreteLearnerParam(id = "type", default = "C-svc", values = c("C-svc", "nu-svc", "C-bsvc", "spoc-svc", "kbb-svc")),
makeDiscreteLearnerParam(id = "kernel", default = "rbfdot",
values = c("vanilladot", "polydot", "rbfdot", "tanhdot", "laplacedot", "besseldot", "anovadot", "splinedot")),
makeNumericLearnerParam(id = "C",
lower = 0, default = 1, requires = quote(type %in% c("C-svc", "C-bsvc", "spoc-svc", "kbb-svc"))),
makeNumericLearnerParam(id = "nu",
lower = 0, default = 0.2, requires = quote(type == "nu-svc")),
makeNumericLearnerParam(id = "epsilon", default = 0.1,
requires = quote(type %in% c("eps-svr", "nu-svr", "eps-bsvm"))),
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 = "shrinking", default = TRUE),
makeNumericVectorLearnerParam(id = "class.weights", len = NA_integer_, lower = 0),
makeLogicalLearnerParam(id = "fit", default = TRUE, tunable = FALSE),
makeIntegerLearnerParam(id = "cache", default = 40L, lower = 1L)
),
par.vals = list(fit = FALSE),
properties = c("twoclass", "multiclass", "numerics", "factors", "prob", "class.weights"),
class.weights.param = "class.weights",
name = "Support Vector Machines",
short.name = "ksvm",
note = "Kernel parameters have to be passed directly and not by using the `kpar` list in `ksvm`. Note that `fit` has been set to `FALSE` by default for speed.",
callees = "ksvm"
)
}
#' @export
trainLearner.classif.ksvm = function(.learner, .task, .subset, .weights = NULL, degree, offset, scale, sigma, order, length, lambda, normalized, ...) {
# FIXME: custom kernel. freezes? check mailing list
# FIXME: unify cla + regr, test all sigma stuff
# # there's a strange behaviour in r semantics here wgich forces this, see do.call and the comment about substitute
# if (!is.null(args$kernel) && is.function(args$kernel) && !is(args$kernel,"kernel")) {
# args$kernel = do.call(args$kernel, kpar)
# }
kpar = learnerArgsToControl(list, degree, offset, scale, sigma, order, length, lambda, normalized)
f = getTaskFormula(.task)
pm = .learner$predict.type == "prob"
if (base::length(kpar) > 0L) {
kernlab::ksvm(f, data = getTaskData(.task, .subset), kpar = kpar, prob.model = pm, ...)
} else {
kernlab::ksvm(f, data = getTaskData(.task, .subset), prob.model = pm, ...)
}
}
#' @export
predictLearner.classif.ksvm = 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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