Question

ml教程的这一部分：https://mlr.mlr-org.com/articles/tutorial/nested_resampling.html#filter-methods-with-tuning解释了如何将TuneWrapper与FilterWrapper一起使用以调整过滤器的阈值。但是，如果我的过滤器也需要调整的超参数（例如随机森林变量重要性过滤器）怎么办？除了阈值外，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)

产生错误消息：

checkTunerParset（学习器，参数集，度量，控件）中的错误：只能调整存在学习者参数的参数：mtry，nodesize

有什么方法可以调整随机森林的超参数吗？

编辑：在评论中提出建议之后的其他尝试：

将调谐器环绕基础学习器，然后再送入过滤器（未显示过滤器）-失败

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

Error in checkTunerParset(learner, par.set, measures, control) : 
Can only tune parameters for which learner parameters exist: mtry,nodesize,fw.abs

两个级别的调整-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!

Answer 1

您目前似乎无法调整过滤器的超参数。您可以通过在makeFilterWrapper()中传递某些参数来手动更改它们，但不能对其进行调整。在过滤时，您只能调整fw.abs，fw.perc或fw.tresh之一。

我不知道当对RandomForest过滤器使用不同的hyperpars时，对排名的影响有多大。一种检查鲁棒性的方法是在mtry的帮助下，使用getFeatureImportance()和朋友的不同设置来比较单个RF模型拟合的等级。如果它们之间的相关性很高，则可以放心地忽略RF滤波器的调谐。（也许您要使用完全不附带此问题的其他过滤器？）

如果您坚持要使用此功能，则可能需要提高软件包的PR：）

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)

mlr：具有调整功能的过滤器方法

1 个答案: