如何用mlr3tuningspaces设置xgboost超带优化的“预算”标签？

Question

我正在尝试用超带优化xgboost，并且我想使用mlr3tuningspaces包中建议的默认调优空间。但是，在使用lts时，我找不到如何用“预算”标记超参数。

下面，我复制了mlr3hyperband包示例，以说明我的问题：

library(mlr3verse)
library(mlr3hyperband)
library(mlr3tuningspaces)

## this does not work, because I don't know how to tag a hyperparameter 
## with "budget" while using the suggested tuning space
search_space = lts("classif.xgboost.default")
search_space$values

## this works because it has a hyperparameter (nrounds) tagged with "bugdget"
search_space = ps(
  nrounds = p_int(lower = 1, upper = 16, tags = "budget"), 
  eta = p_dbl(lower = 0, upper = 1),
  booster = p_fct(levels = c("gbtree", "gblinear", "dart"))
)

# hyperparameter tuning on the pima indians diabetes data set
instance = tune(
  method = "hyperband",
  task = tsk("pima"),
  learner = lrn("classif.xgboost", eval_metric = "logloss"),
  resampling = rsmp("cv", folds = 3),
  measures = msr("classif.ce"),
  search_space = search_space,
  term_evals = 100
)

# best performing hyperparameter configuration
instance$result

Answer 1

谢谢你指出这个。我将把预算标签添加到默认的搜索空间中。在此之前，您可以使用此代码。

library(mlr3hyperband)
library(mlr3tuningspaces)
library(mlr3learners)

# get learner with search space in one go
learner = lts(lrn("classif.xgboost"))

# overwrite nrounds with budget tag
learner$param_set$values$nrounds = to_tune(p_int(1000, 5000, tags = "budget"))

instance = tune(
  method = "hyperband",
  task = tsk("pima"),
  learner = learner,
  resampling = rsmp("cv", folds = 3),
  measures = msr("classif.ce"),
  term_evals = 100
)

更新28.06.2022

版本0.3.0中的新API是

learner = lts(lrn("classif.xgboost"), nrounds = to_tune(p_int(1000, 5000, tags = "budget"))