mlr：带调优的过滤器方法

ml教程的这一部分：解释如何使用带有FilterRapper的TuneWrapper来调整过滤器的阈值。但如果我的过滤器也有需要调整的超参数，比如随机林变量重要性过滤器，该怎么办？除了阈值，我似乎无法调整任何参数

例如：

library(survival)
library(mlr)

data(veteran)
set.seed(24601)
task_id = "MAS"
mas.task <- makeSurvTask(id = task_id, data = veteran, target = c("time", "status"))
mas.task <- createDummyFeatures(mas.task)
tuning = makeResampleDesc("CV", iters=5, stratify=TRUE)                             # Tuning: 5-fold CV, no repeats

cox.filt.rsfrc.lrn = makeTuneWrapper(
      makeFilterWrapper(
        makeLearner(cl="surv.coxph", id = "cox.filt.rfsrc", predict.type="response"), 
        fw.method="randomForestSRC_importance",
        cache=TRUE,
        ntree=2000
      ), 
      resampling = tuning, 
      par.set = makeParamSet(
          makeIntegerParam("fw.abs", lower=2, upper=10),
          makeIntegerParam("mtry", lower = 5, upper = 15),
          makeIntegerParam("nodesize", lower=3, upper=25)
      ), 
      control = makeTuneControlRandom(maxit=20),
      show.info = TRUE)

两级调整-失败

cox.lrn =  makeLearner(cl="surv.coxph", id = "cox.filt.rfsrc", predict.type="response")
cox.filt = makeFilterWrapper(cox.lrn,
                     fw.method="randomForestSRC_importance",
                     cache=TRUE,
                     ntree=2000)
cox.tune = makeTuneWrapper(cox.filt, 
                   resampling = tuning, 
                   measures=list(cindex),
                   par.set = makeParamSet(
                     makeIntegerParam("fw.abs", lower=2, upper=10)
                   ),
                   control = makeTuneControlRandom(maxit=20),
                   show.info = TRUE)

cox.tune2 = makeTuneWrapper(cox.tune, 
                   resampling = tuning, 
                   measures=list(cindex),
                   par.set = makeParamSet(
                     makeIntegerParam("mtry", lower = 5, upper = 15),
                     makeIntegerParam("nodesize", lower=3, upper=25)
                   ),
                   control = makeTuneControlRandom(maxit=20),
                   show.info = TRUE)

Error in makeBaseWrapper(id, learner$type, learner, learner.subclass = c(learner.subclass,  : 
  Cannot wrap a tuning wrapper around another optimization wrapper!

---

看起来您当前无法调整过滤器的超参数。您可以通过将某些参数传入

makeFilterRapper（）

来手动更改它们，但不能对其进行调优。 在过滤方面，您只能调整

fw.abs

、

fw.perc

或

fw.tresh

中的一个

我不知道使用不同的HyperPAR作为随机森林过滤器时，对排名的影响有多大。检查稳健性的一种方法是在

getFeatureImportance（）

的帮助下，使用

mtry

和friends的不同设置来比较单个RF模型的匹配排名。如果这些之间存在非常高的秩相关性，您可以安全地忽略RF滤波器的调谐。（可能您想使用一个与此问题完全不同的过滤器？）

如果您坚持使用此功能，则可能需要提高软件包的PR:）

---

你试过用一个进入过滤器包装器的曲调包装器包装学习者吗？@pat-s我不能让它工作，但这确实是必要的。我编辑了这个问题，以展示我尝试过的其他东西。

cox.lrn =  makeLearner(cl="surv.coxph", id = "cox.filt.rfsrc", predict.type="response")
cox.filt = makeFilterWrapper(cox.lrn,
                     fw.method="randomForestSRC_importance",
                     cache=TRUE,
                     ntree=2000)
cox.tune = makeTuneWrapper(cox.filt, 
                   resampling = tuning, 
                   measures=list(cindex),
                   par.set = makeParamSet(
                     makeIntegerParam("fw.abs", lower=2, upper=10)
                   ),
                   control = makeTuneControlRandom(maxit=20),
                   show.info = TRUE)

cox.tune2 = makeTuneWrapper(cox.tune, 
                   resampling = tuning, 
                   measures=list(cindex),
                   par.set = makeParamSet(
                     makeIntegerParam("mtry", lower = 5, upper = 15),
                     makeIntegerParam("nodesize", lower=3, upper=25)
                   ),
                   control = makeTuneControlRandom(maxit=20),
                   show.info = TRUE)

Error in makeBaseWrapper(id, learner$type, learner, learner.subclass = c(learner.subclass,  : 
  Cannot wrap a tuning wrapper around another optimization wrapper!

lrn = makeLearner(cl = "surv.coxph", id = "cox.filt.rfsrc", predict.type = "response")

filter_wrapper = makeFilterWrapper(
  lrn,
  fw.method = "randomForestSRC_importance",
  cache = TRUE,
  ntrees = 2000
)

cox.filt.rsfrc.lrn = makeTuneWrapper(
  filter_wrapper,
  resampling = tuning,
  par.set = makeParamSet(
    makeIntegerParam("fw.abs", lower = 2, upper = 10)
  ),
  control = makeTuneControlRandom(maxit = 20),
  show.info = TRUE)