How to set "budget" tag for xgboost hyperband optimization with mlr3tuningspaces? - xgboost

Question

I am trying to tune xgboost with hyperband and I would like to use the suggested default tuning space from the mlr3tuningspaces package. However, I don't find how to tag a hyperparameter with "budget" while using lts .
Below, I reproduced the mlr3hyperband package example to illustrate my issue:
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

Thanks for pointing this out. I will add the budget tag to the default search space. Until then you can use this code.
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
)
Update 28.06.2022
The new API in version 0.3.0 is
learner = lts(lrn("classif.xgboost"), nrounds = to_tune(p_int(1000, 5000, tags = "budget"))