context("classif_gbm")
test_that("classif_gbm", {
requirePackagesOrSkip("gbm", default.method = "load")
parset.list = list(
list(),
list(n.trees = 600),
list(interaction.depth = 2)
)
old.predicts.list = list()
old.probs.list = list()
mydata = binaryclass.train
mydata[, binaryclass.target] = as.numeric(mydata[, binaryclass.target] == getTaskDesc(binaryclass.task)$positive)
for (i in seq_along(parset.list)) {
parset = parset.list[[i]]
pars = list(binaryclass.formula, data = mydata, distribution = "bernoulli")
pars = c(pars, parset)
set.seed(getOption("mlr.debug.seed"))
capture.output({
m = do.call(gbm::gbm, pars)
})
set.seed(getOption("mlr.debug.seed"))
p = gbm::predict.gbm(m, newdata = binaryclass.test, n.trees = length(m$trees), type = "response")
old.probs.list[[i]] = p
p = as.factor(ifelse(p > 0.5, "M", "R"))
old.predicts.list[[i]] = p
}
testSimpleParsets("classif.gbm", binaryclass.df, binaryclass.target, binaryclass.train.inds, old.predicts.list, parset.list)
testProbParsets("classif.gbm", binaryclass.df, binaryclass.target, binaryclass.train.inds, old.probs.list, parset.list)
set.seed(getOption("mlr.debug.seed"))
m = gbm::gbm(multiclass.formula, data = multiclass.train, n.trees = 300, interaction.depth = 2, distribution = "multinomial")
p = gbm::predict.gbm(m, newdata = multiclass.test, n.trees = 300)
y = factor(apply(p[, , 1], 1, function(r) colnames(p)[which.max(r)]))
testSimple("classif.gbm", multiclass.df, multiclass.target, multiclass.train.inds, y,
parset = list(n.trees = 300, interaction.depth = 2, distribution = "multinomial"))
})
test_that("classif_gbm keep.data is passed correctly", {
train(makeLearner("classif.gbm", keep.data = FALSE), binaryclass.task)
train(makeLearner("classif.gbm", keep.data = TRUE), binaryclass.task)
})
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