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test_that("classif_fnn", {
requirePackagesOrSkip("FNN", default.method = "load")
parset.list = list(
list(),
list(k = 1),
list(k = 4),
list(k = 10)
)
old.predicts.list1 = list()
old.predicts.list2 = list()
for (i in seq_along(parset.list)) {
parset = parset.list[[i]]
j = which(colnames(multiclass.train) == multiclass.target)
pars = list(train = multiclass.train[, -j], test = multiclass.test[, -j],
cl = multiclass.train[, j])
pars = c(pars, parset)
old.predicts.list1[[i]] = do.call(FNN::knn, pars)
j = which(colnames(binaryclass.train) == binaryclass.target)
pars = list(train = binaryclass.train[, -j], test = binaryclass.test[, -j],
cl = binaryclass.train[, j])
pars = c(pars, parset)
old.predicts.list2[[i]] = do.call(FNN::knn, pars)
}
testSimpleParsets("classif.fnn", multiclass.df, multiclass.target,
multiclass.train.inds, old.predicts.list1, parset.list)
testSimpleParsets("classif.fnn", binaryclass.df, binaryclass.target,
binaryclass.train.inds, old.predicts.list2, parset.list)
tt = function(formula, data, k = 1) {
j = which(colnames(data) == as.character(formula)[2])
list(train = data[, -j], cl = data[, j], k = k, target = j)
}
tp = function(model, newdata) {
newdata = newdata[, -model$target]
FNN::knn(train = model$train, test = newdata, cl = model$cl, k = model$k)
}
testCVParsets("classif.fnn", multiclass.df, multiclass.target, tune.train = tt,
tune.predict = tp, parset.list = parset.list)
testCVParsets("classif.fnn", binaryclass.df, binaryclass.target, tune.train = tt,
tune.predict = tp, parset.list = parset.list)
})
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