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test_that("regr_fnn", {
requirePackagesOrSkip("FNN", default.method = "load")
parset.list = list(
list(),
list(k = 1),
list(k = 4),
list(k = 10)
)
rdf = regr.df[, -4]
rtrain = regr.train[, -4]
rtest = regr.test[, -4]
rtask = makeRegrTask("regrtask", data = rdf, target = "medv")
old.predicts.list1 = list()
for (i in seq_along(parset.list)) {
parset = parset.list[[i]]
j = which(colnames(rtrain) == regr.target)
pars = list(train = rtrain[, -j], test = rtest[, -j], y = rtrain[, j])
pars = c(pars, parset)
old.predicts.list1[[i]] = do.call(FNN::knn.reg, pars)$pred
}
testSimpleParsets("regr.fnn", rdf, regr.target, regr.train.inds,
old.predicts.list1, parset.list)
tt = function(formula, data, k = 3) {
j = which(colnames(data) == as.character(formula)[2])
list(train = data[, -j], y = data[, j], k = k, target = j)
}
tp = function(model, newdata) {
newdata = newdata[, -model$target]
FNN::knn.reg(train = model$train, test = newdata, y = model$y,
k = model$k)$pred
}
testCVParsets("regr.fnn", rdf, regr.target, tune.train = tt,
tune.predict = tp, parset.list = parset.list)
})
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