# NOT RUN {
library("mlr3")
library("mlr3learners")
set.seed(1234)
task = tsk("iris")
# use a proxy for preprocessing and a proxy for learning, i.e.,
# no preprocessing and classif.kknn
g = po("proxy", id = "preproc", param_vals = list(content = po("nop"))) %>>%
po("proxy", id = "learner", param_vals = list(content = lrn("classif.kknn")))
rr_kknn = resample(task, learner = GraphLearner$new(g), resampling = rsmp("cv", folds = 3))
rr_kknn$aggregate(msr("classif.ce"))
# use pca for preprocessing and classif.rpart as the learner
g$param_set$values$preproc.content = po("pca")
g$param_set$values$learner.content = lrn("classif.rpart")
rr_pca_rpart = resample(task, learner = GraphLearner$new(g), resampling = rsmp("cv", folds = 3))
rr_pca_rpart$aggregate(msr("classif.ce"))
# }
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