#FIXME reread and fix!
#context("simple optims")
#test_that("simple optims", {
# maximum is x1 = 50, x2 = any, x3=a, y=70
# fit = function(x) {
# #print(x)
# x$x1 - is.null(x$x2) * 100 + 20 * (x$x3 == "a")
# }
#
# easy = makeParamSet(
# makeNumericParam("x1", lower = 0, upper = 100),
# makeNumericParam("x2", lower = 50, upper = 100, requires = quote(x1 <= 50)),
# makeDiscreteParam("x3", values = c("a", "blasdfkjaslkdjaslkdjflkjsdafj")))
#
# surrogate = makeLearner("regr.randomForest")
# ctrl = makeMBOControl(minimize = FALSE, iters=30)
# ctrl = setMBOControlInfill(crit = crit.mr, opt.focussearch.points = 100)
# opt = mbo(fit, easy, learner = surrogate, control= ctrl)
# expect_true(opt$x$x1 > 45 && opt$x$x1 <= 50 && is.na(opt$x$x2) && opt$x$x3 == "a" && opt$y > 68 && opt$y <= 70)
#})
# test_that("complex paramset" , {
# if (isExpensiveExampleOk()) {
# ps = makeParamSet(
# makeLogicalParam("use.clinical"),
# makeDiscreteParam("filter", values = c("none", "var", "uni", "kratz", "top21", "fmrmr", "pamr")),
# makeDiscreteParam("model", values = c("coxboost", "glmnet", "rfsrc", "penalized", "rpart")),
#
# makeLogicalParam("filter.use.clinical",
# requires = quote(filter == "uni" && isTRUE(use.clinical))),
# makeNumericParam("filter.perc", lower=0, upper=1/3,
# requires = quote(filter %in% c("var", "uni", "fmrmr"))),
# makeDiscreteParam("filter.fmrmr.combine", values = c("difference", "quotient"),
# requires = quote(filter == "fmrmr")),
# makeDiscreteParam("filter.fmrmr.relevance", values = c("cindex"),
# requires = quote(filter == "fmrmr")),
# makeDiscreteParam("filter.fmrmr.redundance", values = c("mi", "pearson"),
# requires = quote(filter == "fmrmr")),
# makeIntegerParam("filter.pamr.ngroup.survival", lower = 2L, upper = 3L,
# requires = quote(filter == "pamr")),
#
# makeNumericParam("coxboost.stepsize.factor", lower=0.1, upper=2,
# requires = quote(model == "coxboost")),
# makeNumericParam("coxboost.penalty", lower = 0.1, upper = 300,
# requires = quote(model == "coxboost")),
# makeDiscreteParam("penalized.penalty", values = c("L1", "L2"),
# requires = quote(model == "penalized")),
# makeNumericParam("glmnet.alpha", lower = 0, upper = 1,
# requires = quote(model == "glmnet")),
# makeDiscreteParam("rfsrc.splitrule", values = c("logrank", "logrankscore"),
# requires = quote(model == "rfsrc")),
# makeIntegerParam("rfsrc.nodesize", lower = 1L, upper = 10L,
# requires = quote(model == "rfsrc")),
# makeIntegerParam("rfsrc.ntree", lower = 50L, upper = 2000L,
# requires = quote(model == "rfsrc")),
# makeIntegerParam("rpart.minsplit", lower=1L, upper=30L,
# requires = quote(model == "rpart")),
# makeIntegerParam("rpart.minbucket", lower=1L, upper=15L,
# requires = quote(model == "rpart")),
# makeNumericParam("rpart.cp", lower=0.001, upper=0.1,
# requires = quote(model == "rpart"))
# )
#
# fit = function(x) {
# sum(viapply(x, nchar))
# }
#
# surrogate = makeLearner("regr.randomForest")
# ctrl = makeMBOControl()
# ctrl = setMBOControlInfill(ctrl, opt.focussearch.points = 10)
# opt = mbo(fit, ps, learner = surrogate, control = ctrl)
# }
# })
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