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context("classif_ksvm")
test_that("classif_ksvm", {
requirePackagesOrSkip("kernlab", default.method = "load")
parset.list1 = list(
list(fit = FALSE),
list(kpar = list(sigma = 20), fit = FALSE),
list(kernel = "laplacedot", kpar = list(sigma = 10), fit = FALSE),
list(kernel = "polydot", kpar = list(degree = 3, offset = 2, scale = 1.5))
)
parset.list2 = list(
list(),
list(sigma = 20),
list(kernel = "laplacedot", sigma = 10),
list(kernel = "polydot", degree = 3, offset = 2, scale = 1.5)
)
old.predicts.list = list()
old.probs.list = list()
for (i in seq_along(parset.list1)) {
parset = parset.list1[[i]]
pars = list(x = multiclass.formula, data = multiclass.train)
pars = c(pars, parset)
pars$prob.model = TRUE
set.seed(getOption("mlr.debug.seed"))
m = do.call(kernlab::ksvm, pars)
old.predicts.list[[i]] = kernlab::predict(m, newdata = multiclass.test)
old.probs.list[[i]] = kernlab::predict(m, newdata = multiclass.test, type = "prob")
}
testSimpleParsets("classif.ksvm", multiclass.df, multiclass.target,
multiclass.train.inds, old.predicts.list, parset.list2)
testProbParsets("classif.ksvm", multiclass.df, multiclass.target,
multiclass.train.inds, old.probs.list, parset.list2)
tt = function(formula, data, subset = 1:150, ...) {
kernlab::ksvm(x = formula, data = data[subset, ], kernel = "polydot",
kpar = list(degree = 3, offset = 2, scale = 1.5))
}
tp = function(model, newdata, ...) {
kernlab::predict(model, newdata = newdata)
}
testCV("classif.ksvm", multiclass.df, multiclass.target, tune.train = tt, tune.predict = tp,
parset = list(kernel = "polydot", degree = 3, offset = 2, scale = 1.5))
})
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