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