context("classif_randomForest")
test_that("classif_randomForest", {
requirePackagesOrSkip("randomForest", default.method = "load")
parset.list = list(
list(),
list(ntree = 50, mtry = 2),
list(ntree = 50, mtry = 4),
list(ntree = 200, mtry = 2),
list(ntree = 2000, mtry = 4, proximity = TRUE, oob.prox = TRUE)
)
old.predicts.list = list()
old.probs.list = list()
for (i in seq_along(parset.list)) {
parset = parset.list[[i]]
pars = list(formula = multiclass.formula, data = multiclass.train)
pars = c(pars, parset)
set.seed(getOption("mlr.debug.seed"))
m = do.call(randomForest::randomForest, pars)
set.seed(getOption("mlr.debug.seed"))
p = predict(m, newdata = multiclass.test, type = "response")
set.seed(getOption("mlr.debug.seed"))
p2 = predict(m, newdata = multiclass.test, type = "prob")
old.predicts.list[[i]] = p
old.probs.list[[i]] = p2
}
testSimpleParsets("classif.randomForest", multiclass.df, multiclass.target,
multiclass.train.inds, old.predicts.list, parset.list)
testProbParsets("classif.randomForest", multiclass.df, multiclass.target,
multiclass.train.inds, old.probs.list, parset.list)
tt = randomForest::randomForest
testCVParsets("classif.randomForest", multiclass.df, multiclass.target, tune.train = tt, parset.list = parset.list)
# FIXME test RF with one constant feature
#data = multiclass.df
#data = data[, c(1,5)]
#data[, 1] = 1
#task = makeClassifTask(data=data, target=multiclass.target)
#m = train(makeLearner("classif.randomForest"), task)
#p = predict(m, task=task)
})
test_that("fix factors work", {
data(iris)
n = nrow(iris)
data = iris
train = sample(1:n, floor(n * 0.9))
test = setdiff(1:n, train)
data$x = factor(sample(letters[1:3], n, replace = TRUE))
task = makeClassifTask(data = data[train, ], target = "Species")
learner = makeLearner("classif.randomForest", fix.factors.prediction = TRUE)
model = train(learner, task)
newdata = data[head(test, 1L), ]
newdata$Species = droplevels(newdata$Species)
expect_is(predict(model, newdata = newdata), "Prediction")
})
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