如何在 mlr3 中重复 glmnet 的超参数调整(alpha 和/或 lambda)

本文介绍了如何在 mlr3 中重复 glmnet 的超参数调整(alpha 和/或 lambda)的处理方法，对大家解决问题具有一定的参考价值，需要的朋友们下面随着小编来一起学习吧！

问题描述

我想在 mlr3 中重复 glmnet 的超参数调整(alpha 和/或 lambda)避免可变性在较小的数据集中

在caret中，我可以用"repeatedcv"

因为我真的很喜欢 mlr3 系列包，所以我想用它们进行分析.但是，我不确定如何在 mlr3

中执行此步骤的正确方法

示例数据

#library图书馆(插入符号)图书馆(mlr3verse)图书馆(mlbench)# 获取示例数据数据(PimaIndiansDiabetes，包=mlbench")数据 <- PimaIndiansDiabetes# 获取小的训练数据train.data <- 数据[1:60,]

caret approach (tuning alpha and lambda) using "cv" and "repeatedcv"


trControlCv <- trainControl("cv",
             number = 5,
             classProbs = TRUE,
             savePredictions = TRUE,
             summaryFunction = twoClassSummary)

# use "repeatedcv" to avoid variability in smaller data sets
trControlRCv <- trainControl("repeatedcv",
             number = 5,
             repeats= 20,
             classProbs = TRUE,
             savePredictions = TRUE,
             summaryFunction = twoClassSummary)

# train and extract coefficients with "cv" and different set.seed
set.seed(2323)
model <- train(
  diabetes ~., data = train.data, method = "glmnet",
  trControl = trControlCv,
  tuneLength = 10,
  metric="ROC"
)

coef(model$finalModel, model$finalModel$lambdaOpt) -> coef1

set.seed(23)
model <- train(
  diabetes ~., data = train.data, method = "glmnet",
  trControl = trControlCv,
  tuneLength = 10,
  metric="ROC"
)

coef(model$finalModel, model$finalModel$lambdaOpt) -> coef2


# train and extract coefficients with "repeatedcv" and different set.seed
set.seed(13)

model <- train(
  diabetes ~., data = train.data, method = "glmnet",
  trControl = trControlRCv,
  tuneLength = 10,
  metric="ROC"
)

coef(model$finalModel, model$finalModel$lambdaOpt) -> coef3


set.seed(55)
model <- train(
  diabetes ~., data = train.data, method = "glmnet",
  trControl = trControlRCv,
  tuneLength = 10,
  metric="ROC"
)

coef(model$finalModel, model$finalModel$lambdaOpt) -> coef4

Demonstrate different coefficients with cross-validation and same coefficients with repeated cross-validation

# with "cv" I get different coefficients
identical(coef1, coef2)
#> [1] FALSE

# with "repeatedcv" I get the same coefficients
identical(coef3,coef4)
#> [1] TRUE

FIRST mlr3 approach using cv.glmnet (does internally tune lambda)

# create elastic net regression
glmnet_lrn = lrn("classif.cv_glmnet", predict_type = "prob")

# define train task
train.task <- TaskClassif$new("train.data", train.data, target = "diabetes")

# create learner
learner = as_learner(glmnet_lrn)

# train the learner with different set.seed
set.seed(2323)
learner$train(train.task)
coef(learner$model, s = "lambda.min") -> coef1

set.seed(23)
learner$train(train.task)
coef(learner$model, s = "lambda.min") -> coef2

Demonstrate different coefficients with cross-validation

# compare coefficients
coef1
#> 9 x 1 sparse Matrix of class "dgCMatrix"
#>                        1
#> (Intercept) -3.323460895
#> age          0.005065928
#> glucose      0.019727881
#> insulin      .
#> mass         .
#> pedigree     .
#> pregnant     0.001290570
#> pressure     .
#> triceps      0.020529162
coef2
#> 9 x 1 sparse Matrix of class "dgCMatrix"
#>                        1
#> (Intercept) -3.146190752
#> age          0.003840963
#> glucose      0.019015433
#> insulin      .
#> mass         .
#> pedigree     .
#> pregnant     .
#> pressure     .
#> triceps      0.018841557

Update 1: the progress I made

According to the comment below and this comment I could use rsmp andAutoTuner

This answer suggests not to tune cv.glmnet but glmnet (which was not available in ml3 at that time)

SECOND mlr3 approach using glmnet (repeats the tuning of alpha and lambda)

# define train task
train.task <- TaskClassif$new("train.data", train.data, target = "diabetes")

# create elastic net regression
glmnet_lrn = lrn("classif.glmnet", predict_type = "prob")

# turn to learner
learner = as_learner(glmnet_lrn)

# make search space
search_space = ps(
  alpha = p_dbl(lower = 0, upper = 1),
  s = p_dbl(lower = 1, upper = 1)
)

# set terminator
terminator = trm("evals", n_evals = 20)

#set tuner
tuner = tnr("grid_search", resolution = 3)

# tune the learner
at = AutoTuner$new(
  learner = learner,
  rsmp("repeated_cv"),
  measure = msr("classif.ce"),
  search_space = search_space,
  terminator = terminator,
  tuner=tuner)

at
#> <AutoTuner:classif.glmnet.tuned>
#> * Model: -
#> * Parameters: list()
#> * Packages: glmnet
#> * Predict Type: prob
#> * Feature types: logical, integer, numeric
#> * Properties: multiclass, twoclass, weights

Open Question

How can I demonstrate that my second approach is valid and that I get same or similar coefficients with different seeds? ie. how can I extract the coefficients for the final model of the AutoTuner

set.seed(23)
at$train(train.task) -> tune1

set.seed(2323)
at$train(train.task) -> tune2

解决方案

Repeated hyperparameter tuning (alpha and lambda) of glmnet can be done using the SECOND mlr3 approach as stated above.The coefficients can be extracted with stats::coef and the stored values in the AutoTuner

coef(tune1$model$learner$model, alpha=tune1$tuning_result$alpha,s=tune1$tuning_result$s)
# 9 x 1 sparse Matrix of class "dgCMatrix"
# 1
# (Intercept) -1.6359082102
# age          0.0075541841
# glucose      0.0044351365
# insulin      0.0005821515
# mass         0.0077104934
# pedigree     0.0911233031
# pregnant     0.0164721202
# pressure     0.0007055435
# triceps      0.0056942014
coef(tune2$model$learner$model, alpha=tune2$tuning_result$alpha,s=tune2$tuning_result$s)
# 9 x 1 sparse Matrix of class "dgCMatrix"
# 1
# (Intercept) -1.6359082102
# age          0.0075541841
# glucose      0.0044351365
# insulin      0.0005821515
# mass         0.0077104934
# pedigree     0.0911233031
# pregnant     0.0164721202
# pressure     0.0007055435
# triceps      0.0056942014

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