如何使用 mlr3tuningspaces 为 xgboost 超频段优化设置"budget"标签？

Question

我正在尝试用超带来调优xgboost，我想使用mlr3tuningspaces中建议的默认调优空间。包中。然而，我不知道如何用"budget"标记超参数。而使用lts.

下面，我复制了mlr3hyperband说明我的问题的包示例:

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

谢谢你指出这一点。我会将预算标签添加到默认搜索空间。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

0.3.0版本的新API是

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