context("multilabel_randomForestSRC")
test_that("multilabel_randomForestSRC", {
requirePackagesOrSkip("randomForestSRC", default.method = "load")
parset.list = list(
list(),
list(ntree = 100),
list(ntree = 250, mtry = 3),
list(ntree = 250, nodesize = 2, na.action = "na.impute", importance = "permute", proximity = FALSE)
)
old.predicts.list = list()
old.probs.list = list()
for (i in seq_along(parset.list)) {
parset = parset.list[[i]]
for (j in multilabel.target) {
multilabel.train[j] = factor(multilabel.train[[j]], levels = c("TRUE", "FALSE"))
multilabel.test[j] = factor(multilabel.test[[j]], levels = c("TRUE", "FALSE"))
}
parset = c(parset, list(data = multilabel.train, formula = multilabel.formula.cbind, forest = TRUE))
set.seed(getOption("mlr.debug.seed"))
m = do.call(randomForestSRC::rfsrc, parset)
set.seed(getOption("mlr.debug.seed"))
p = predict(m, newdata = multilabel.test, membership = FALSE, na.action = "na.impute")
old.predicts.list[[i]] = as.data.frame(lapply(p$classOutput, function(x) as.logical(x$class)))
old.probs.list[[i]] = as.data.frame(lapply(p$classOutput, function(x) x$predicted[, 1]))
}
testSimpleParsets("multilabel.randomForestSRC", multilabel.df, multilabel.target, multilabel.train.inds, old.predicts.list, parset.list)
testProbParsets("multilabel.randomForestSRC", multilabel.df, multilabel.target, multilabel.train.inds, old.probs.list, parset.list)
})
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