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context("classif_rknn")
test_that("classif_rknn", {
requirePackagesOrSkip("rknn", default.method = "load")
k = c(2L, 4L)
r = c(100L, 100L)
mtry = c(2L, 3L)
parset.grid = expand.grid(k = k, r = r, mtry = mtry)
parset.list = apply(parset.grid, MARGIN = 1L, as.list)
# rknn needs integer seed for reproducibility
parset.list = lapply(parset.list, function(x) c(x, seed = 2015L))
parset.list = c(parset.list, list(list(seed = 2015L)))
old.predicts.list = list()
for (i in seq_along(parset.list)) {
parset = parset.list[[i]]
train = multiclass.train
target = train[, multiclass.target]
train[, multiclass.target] = NULL
test = multiclass.test
test[, multiclass.target] = NULL
pars = list(data = train, y = target, newdata = test)
pars = c(pars, parset)
p = do.call(rknn::rknn, pars)$pred
old.predicts.list[[i]] = p
}
testSimpleParsets("classif.rknn", multiclass.df, multiclass.target,
multiclass.train.inds,
old.predicts.list, parset.list)
tt = function(formula, data, k = 1L, r = 500L, mtry = 2L, seed = 2015L,
cluster = NULL) {
return(list(formula = formula, data = data, k = k, r = r, mtry = mtry,
seed = seed, cluster = cluster))
}
tp = function(model, newdata) {
target = as.character(model$formula)[2L]
train = model$data
y = train[, target]
train[, target] = NULL
newdata[, target] = NULL
rknn::rknn(data = train, y = y, newdata = newdata, k = model$k, r = model$r,
mtry = model$mtry, seed = model$seed, cluster = model$cluster)$pred
}
testCVParsets(t.name = "classif.rknn", df = multiclass.df,
target = multiclass.target, tune.train = tt, tune.predict = tp,
parset.list = parset.list)
})
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