mlrMBO贝叶斯优化中SVM超参数调整的r-误差

Question

我正在尝试为分类任务优化SVM，这对我尝试过的许多其他模型都有效。然而，当我在基于模型的优化函数中使用SVM时，它会返回一个错误："；checkStuff中的错误(有趣、设计、学习者、控制(：提供的学习者不支持因子参数">

附件为相关代码。在我的训练任务中，所有自变量都是数字，唯一的因素是我感兴趣的结果。

library(mlr)
library(mlrMBO)
library(dplyr)
library(PRROC)
library(ggplot2)
library(DiceKriging)
traindf <- read.csv("/Users/njr/Google Drive/HMS IR Research/NSQIP Research/Endovascular/randomtraining.csv")
testdf <- read.csv("/Users/njr/Google Drive/HMS IR Research/NSQIP Research/Endovascular/randomtesting.csv")
traindf$Amputation<-as.factor(traindf$Amputation)
testdf$Amputation <- as.factor(testdf$Amputation)
trn.task = makeClassifTask(data = traindf, target = "Amputation", positive = "2")
test.task = makeClassifTask(data = testdf, target = "Amputation", positive = "2")
set.seed(9)
svmlrn =  makeLearner("classif.svm", predict.type = "prob")
svm_model <- mlr::train(svmlrn, task = trn.task)
res = makeResampleDesc("CV", iters = 10, stratify = TRUE)
par5 = makeParamSet(
makeDiscreteParam("kernel", values = c("radial", "polynomial", "linear")),
makeNumericParam("cost", -15, 15, trafo = function(x) 2^x),
makeNumericParam("gamma", -15, 15, trafo = function(x) 2^x, requires = quote(kernel == "radial")),
makeIntegerParam("degree", lower = 1, upper = 4, requires = quote(kernel == "polynomial"))
)
mbo.ctrl = makeMBOControl()
mbo.ctrl = setMBOControlInfill(mbo.ctrl, crit = crit.ei)
mbo.ctrl = setMBOControlTermination(mbo.ctrl, iters = 35, max.evals = 25)
design.mat = generateRandomDesign(n = 50, par.set = par5)
surrogate.lrn = makeLearner("regr.km", predict.type = "se")
ctrl = mlr::makeTuneControlMBO(learner = surrogate.lrn, mbo.control = mbo.ctrl, mbo.design = design.mat)
parallelStartMulticore(cpus = 8L)
res.mbo = tuneParams(makeLearner("classif.svm"), trn.task, resampling = res, par.set = par5, control = ctrl, 
show.info = TRUE, measures = auc)
parallelStop()

这是回溯错误代码：

6.
stop("Provided learner does not support factor parameters.")
5.
checkStuff(fun, design, learner, control)
4.
initOptProblem(fun = fun, design = design, learner = learner, control = control, show.info = show.info, more.args = more.args)
3.
mlrMBO::mbo(tff, design = control$mbo.design, learner = control$learner, control = mbo.control, show.info = FALSE)
2.
sel.func(learner, task, resampling, measures, par.set, control, opt.path, show.info, resample.fun)
1.
tuneParams(makeLearner("classif.svm"), trn.task, resampling = res, par.set = par5, control = ctrl, show.info = TRUE, measures = auc)

Answer 1

问题是您的参数集有一个分类参数(内核(，而您使用的代理模型(regr.km(不支持这一点。例如，您可以尝试使用随机林作为代理模型。