library(mlr)
if (identical(Sys.getenv("TRAVIS"), "true") || identical(Sys.getenv("APPVEYOR"), "True")) {
p = normalizePath("~/.openml/cache", mustWork = FALSE)
dir.create(p, recursive = TRUE, showWarnings = FALSE)
setOMLConfig(apikey = Sys.getenv("OPENMLAPIKEY"), cachedir = p, arff.reader = "farff",
server = "http://test.openml.org/api/v1", confirm.upload = FALSE)
}
# add flows if they are missing
# flows = listOMLFlows(tag = "mlr_test_flows")
# lrn.list = list(
# makeLearner("classif.rpart", predict.type = "response"),
# makeLearner("classif.rpart", predict.type = "prob"),
# makeLearner("classif.logreg", predict.type = "response"),
# makeLearner("classif.logreg", predict.type = "prob"),
# makeLearner("classif.lda"),
# makeLearner("classif.randomForest"),
# makeFilterWrapper(makeLearner("classif.randomForest"), fw.perc = 0.5, fw.method = "variance"),
# makeOversampleWrapper(makeLearner("classif.randomForest"), osw.rate = 1),
# makeImputeWrapper(makeLearner("classif.randomForest"), class = imputeMedian()),
# makeOversampleWrapper(makeFilterWrapper(makeLearner("classif.randomForest"), fw.method = "variance"), osw.rate = 1),
# makeLearner("regr.rpart"),
# makeLearner("regr.lm")
# )
# lrn.ids = unique(unlist(lapply(lrn.list, function(x) paste0("mlr.", getLearnerId(x)))))
#
# if (nrow(flows) < length(lrn.ids)) {
# flow.ids = lapply(lrn.list, uploadOMLFlow, tag = "mlr_test_flows")
# }
Sys.setenv(NOT_CRAN = "true")
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