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test_that("classif_kknn", {
requirePackagesOrSkip("kknn", default.method = "load")
parset.list = list(
list(),
list(k = 1),
list(k = 4),
list(k = 10)
)
old.predicts.list = list()
old.probs.list = list()
for (i in seq_along(parset.list)) {
parset = parset.list[[i]]
pars = list(formula = multiclass.formula, train = multiclass.train, test = multiclass.test)
pars = c(pars, parset)
m = do.call(kknn::kknn, pars)
p = predict(m, newdata = multiclass.test)
old.predicts.list[[i]] = p
old.probs.list[[i]] = m$prob
}
testSimpleParsets("classif.kknn", multiclass.df, multiclass.target, multiclass.train.inds,
old.predicts.list, parset.list)
testProbParsets("classif.kknn", multiclass.df, multiclass.target, multiclass.train.inds,
old.probs.list, parset.list)
tt = function(formula, data, k = 7) {
return(list(formula = formula, data = data, k = k))
}
tp = function(model, newdata) {
kknn::kknn(model$formula, train = model$data, test = newdata, k = model$k)$fitted
}
testCVParsets("classif.kknn", multiclass.df, multiclass.target, tune.train = tt, tune.predict = tp,
parset.list = parset.list)
})
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