R-MLR:为已包装的学习者调整超参数 [英] R-MLR : get tuned hyperparameters for a wrapped learner

问题描述

我正在使用mlr包在R中构建xgboost分类任务:

I'm building an xgboost classification task in R using the mlr package :

# define task
Task <- mlr::makeClassifTask(id = "classif.xgboost",
                             data = df, 
                             target = "response", 
                             weights = NULL, 
                             positive = "yes", 
                             check.data = TRUE,
                             blocking = folds)

# make a base learner
lrnBase <- makeLearner(cl = "classif.xgboost", 
                       predict.type = "prob", 
                       # "response" (= labels) or "prob" (= labels and probabilities)
                       predict.threshold = NULL
                       )

我必须对我的一门课进行欠采样:

I have to undersample one of my classes :

lrnUnder <- makeUndersampleWrapper(learner = lrnBase, usw.rate = 0.2, usw.cl = "no")

我还必须调整学习者的一些超参数:

I also have to tune some of the learner's hyperparameters:

paramSet <- makeParamSet(makeNumericParam(id = "eta", lower = 0.005, upper = 0.4),
                         makeIntegerParam(id = "nrounds", lower = 1, upper = 100))

tuneControl <- makeTuneControlRandom(maxit = 100)
resampin <- makeResampleDesc(method = "CV",
                             iters = 4L,
                             predict = "test")

lrnTune <- makeTuneWrapper(learner = lrnUnder,
                           resampling = resampin, 
                           measures = fp,
                           par.set = paramSet,
                           control = tuneControl)

我的第一个问题是，如何获得最终调整的超参数(而不是与CV的每次迭代对应的未调整的超参数，所以不能按参数)?在mlr教程中，我发现我必须train我的模型如下:

My first question is that how can I get the FINAL tuned hyper-parameters (and not tuned hyper-parametrs corresponding to each iteration of CV so not by extract argument) ? In the mlr tutorial I found out that I have to train my model as follows :

mdl <- mlr::train(learner = lrnTune, task = Task)
getTuneResult(mdl)

，但是如果没有nested resampling，这将不起作用.因此，当我将此代码块添加到我的代码中时，它可以正常工作:

but this does not work without a nested resampling. So when I add this block to my code it works :

resampout.desc <- makeResampleDesc(method = "CV",
                                   iters = length(levels(folds)),
                                   predict = "both",
                                   fixed = TRUE)
resampout <- makeResampleInstance(desc = resampout.desc, task = Task)

resamp <- mlr::resample(learner = lrnTune,
                        task = Task,
                        resampling = resampout, # outer
                        measures = f1, 
                        models = FALSE,
                        extract = getTuneResult,
                        keep.pred = TRUE)

我的第二个问题是，原则上，如果我不想进行嵌套的重采样(即评估我的模型)?还是我可以简单地组成一个无包装的学习者并使用tuneParams进行调整?

My second question is that, in principal, do I have to wrap my learner if I don't want to do a nested resampling (i.e evaluate the performance of my model) ? Or can I simply make a non-wrapped learner and perform my tuning using tuneParams ?

由于我对包装式学习器的功能和嵌套的重采样感到有些困惑，所以在此先感谢您的帮助.

Thank you in advance for your help since I got a bit confused about the functionality of wrapped learners and the nested resampling.

推荐答案

您可以使用tuneParams()调整学习者的知识，然后按照本教程中的说明提取最佳的超参数(

You can use tuneParams() to tune a learner and then extract the best hyperparameters as described in the tutorial (https://mlr.mlr-org.com/articles/tutorial/tune.html). You certainly don't have to wrap your learner; the point of doing this is so you can simply train a model without having to worry about what the hyperparameters are. You should do a nested resampling though as otherwise your performance estimated may be biased.

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