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#' @export
makeRLearner.classif.dcSVM = function() {
makeRLearnerClassif(
cl = "classif.dcSVM",
package = c("SwarmSVM", "e1071"),
par.set = makeParamSet(
makeIntegerLearnerParam(id = "k", default = 4, lower = 1),
makeIntegerLearnerParam(id = "m", lower = 1),
makeDiscreteLearnerParam(id = "kernel", default = 3, values = c(1, 2, 3)),
makeIntegerLearnerParam(id = "max.levels", lower = 1),
makeIntegerLearnerParam(id = "early", default = 0, lower = 0),
makeLogicalLearnerParam(id = "final.training", default = FALSE),
makeLogicalLearnerParam(id = "pre.scale", default = FALSE),
makeNumericLearnerParam(id = "seed"),
makeLogicalLearnerParam(id = "verbose", default = TRUE),
makeUntypedLearnerParam(id = "valid.x", default = NULL),
makeUntypedLearnerParam(id = "valid.y", default = NULL),
makeUntypedLearnerParam(id = "valid.metric", default = NULL),
makeDiscreteLearnerParam(id = "cluster.method", default = "kmeans", values = c("kmeans", "kernkmeans")),
makeFunctionLearnerParam(id = "cluster.fun"),
makeFunctionLearnerParam(id = "cluster.predict")
),
properties = c("twoclass", "numerics"),
name = "Divided-Conquer Support Vector Machines",
short.name = "dcSVM",
callees = c("dcSVM", "svm")
)
}
#' @export
trainLearner.classif.dcSVM = function(.learner, .task, .subset, .weights = NULL, ...) {
d = getTaskData(.task, .subset, target.extra = TRUE)
pars = list(...)
m.flag = FALSE
max.levels.flag = FALSE
if (!any(stri_detect_regex(names(pars), "m"))) {
m = 800
m.flag = TRUE
} else {
m = pars$m
}
if (!any(stri_detect_regex(names(pars), "max.levels"))) {
max.levels = 1
max.levels.flag = TRUE
} else {
max.levels = pars$max.levels
}
if (!any(stri_detect_regex(names(pars), "k"))) {
k = 4
} else {
k = pars$k
}
m = min(nrow(d$data), m)
min.cluster = ceiling(5 * m / (k^max.levels))
if (min.cluster > m) {
f = getTaskFormula(.task)
# map kernel to corresponding e1071 kernel
if (!is.null(pars$kernel)) {
kernel = c("linear", "polynomial", "radial")[pars$kernel]
} else {
kernel = "radial"
}
pars$kernel = kernel
result = do.call(e1071::svm, c(f, list(data = getTaskData(.task, .subset), probability = FALSE), pars))
return(result)
}
if (m.flag && max.levels.flag) {
pname = "SwarmSVM"
asNamespace(pname)$dcSVM(x = d$data, y = d$target, m = m, max.levels = max.levels, ...)
} else if (!m.flag && max.levels.flag) {
pname = "SwarmSVM"
asNamespace(pname)$dcSVM(x = d$data, y = d$target, max.levels = max.levels, ...)
} else if (m.flag && !max.levels.flag) {
pname = "SwarmSVM"
asNamespace(pname)$dcSVM(x = d$data, y = d$target, m = m, ...)
} else {
pname = "SwarmSVM"
asNamespace(pname)$dcSVM(x = d$data, y = d$target, ...)
}
}
#' @export
predictLearner.classif.dcSVM = function(.learner, .model, .newdata, ...) {
prediction = predict(.model$learner.model, newdata = .newdata, ...)
if (!is.factor(prediction)) { # depends on parameters AND data
prediction = factor(prediction, levels = c(1, 2), labels = .model$factor.levels[[1]])
}
prediction
}
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