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multilabel-classification - 如何在 mlr 中使用调整参数进行多标签分类？

问题描述

问题

我正在尝试使用 mlr 包在 r 中运行多标签分类。我使用https://www.rdocumentation.org/packages/mlr/versions/2.19.0/topics/makeMultilabelClassifierChainsWrapper来实现多标签分类。但我需要添加超参数调整。这似乎会产生各种问题。我按照https://mlr.mlr-org.com/articles/tutorial/tune.html上的示例调整参数。tuneParams 需要参数 resample 并且我被卡住了。

示例数据

age <- c(round(rnorm(120,mean = 50,sd = 10)))
sex <- c(round(rnorm(120,mean = 0.5,sd = 0.2)))
l1 <- as.logical(c(round(rnorm(120,mean = 0.5,sd = 0.2))))
l2 <- as.logical(c(round(rnorm(120,mean = 0.5,sd = 0.2))))
l3 <- as.logical(c(round(rnorm(120,mean = 0.5,sd = 0.2))))
l4 <- as.logical(c(round(rnorm(120,mean = 0.5,sd = 0.2))))
data <- as.data.frame(cbind(age,sex,l1,l2,l3,l4))

实际上，我有 12 个标签，但为了便于查看，我省略了其他标签。这个想法是 l1 直到 l4 是逻辑向量。不知何故，这不起作用，所以我希望你能解决这个问题。但请注意，这不是我的主要问题。

代码

task <- makeMultilabelTask(data = data, target = label_bact) 

ps <- makeParamSet(
    makeDiscreteParam("ntree",values = c(50,100,150,200,300,500,550)),
    makeDiscreteParam("mtry",values = c(1,2,3,4,5))
)
ctrl <- makeTuneControlGrid()
rdesc <- makeResampleDesc(method = "CV",iters = 5, predict = "test",
                          stratify.cols = c(l1,l2,l3,l4)
measure  <- acc
learner <- "classif.randomForest"
lrn <- makeLearner(learner)
lrn <- makeMultilabelClassifierChainsWrapper(lrn, order = NULL) 
lrn <- setPredictType(lrn,"prob")
res <- tuneParams(lrn,task = task,resample = rdesc, par.set = ps,control = ctrl)

错误

我得到的错误：

Error in tuneParams(lrn, task = task, resample = rdesc, par.set = ps,  : 
  Assertion on 'resample.fun' failed: Must be a function, not 'CVDesc/ResampleDesc'.

所以我添加了代码行：

r <- resample(learner = lrn,task = task,rdesc)

这告诉我

Error in makeResampleInstance(resampling, task = task) : 
  Stratification for tasks of type 'multilabel' not supported

查看

这通过以下方式得到证实：

>rdesc
Resample description: cross-validation with 5 iterations.
Predict: test
Stratification: FALSE

问题

提前致谢！

标签： multilabel-classificationhyperparametersmlr

解决方案

尝试这个：

rf <- makeLearner("classif.randomForest", predict.type = "response", par.vals = list(ntree = 200, mtry = 3))
     
rf$par.vals <- list(importance = TRUE)
     
rf=makeDownsampleWrapper(rf, dw.stratify = TRUE)

rf=makeMultilabelClassifierChainsWrapper( rf ) 

rf_param <- makeParamSet(
makeIntegerParam("ntree",lower = 50, upper = 500),
makeIntegerParam("mtry", lower = 3, upper = 10),
makeIntegerParam("nodesize", lower = 10, upper = 50)
)

rancontrol <- makeTuneControlRandom(maxit = 5L)

#set 3 fold cross validation
 set_cv <- makeResampleDesc("CV",iters = 3L)

#hypertuning
 rf_tune <- tuneParams(learner = rf, resampling = set_cv,  task =base_pred.task, par.set = rf_param, control = rancontrol, measures = multilabel.hamloss)

这个对我有用！我希望它对你有用

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