Rewriting ParamSet ids from mlr3::paradox() - mlr3

Question

Let's say I have the following ParamSet object:
my_ps = paradox::ps(
minsplit = p_int(1, 64, logscale = TRUE),
cp = p_dbl(1e-04, 1, logscale = TRUE))
Is it possible to rename minsplit to survTree.minsplit without changing anything else?
The reason for this is that I use some learners as part of a GraphLearner and so their parameters names changed and I would like to have some code that adds the learner$id in front the parameters to use later for tuning (rather than rewriting them from scratch with the new names)

Answer 1

I think I have a partial solution here. It is only partial, because it does not support the transformation.
Where it works:
library(paradox)
my_ps = paradox::ps(
minsplit = p_int(1, 64),
cp = p_dbl(1e-04, 1)
)
my_ps$set_id = "john"
my_psc = ParamSetCollection$new(list(my_ps))
print(my_psc)
#> <ParamSetCollection>
#> id class lower upper nlevels default value
#> 1: john.minsplit ParamInt 1e+00 64 64 <NoDefault[3]>
#> 2: john.cp ParamDbl 1e-04 1 Inf <NoDefault[3]>
Created on 2022-12-07 by the reprex package (v2.0.1)
Where it does not:
library(paradox)
my_ps = paradox::ps(
minsplit = p_int(1, 64, logscale = TRUE),
cp = p_dbl(1e-04, 1)
)
my_ps$set_id = "john"
my_psc = ParamSetCollection$new(list(my_ps))
#> Error in .__ParamSetCollection__initialize(self = self, private = private, : Building a collection out sets, where a ParamSet has a trafo is currently unsupported!
Created on 2022-12-07 by the reprex package (v2.0.1)
The underlying problem is that we did not solve the problem of how to reconcile the parameter transformations of individual ParamSets and a possible parameter transformation of the ParamSetCollection
I fear that there is currently no neat solution for your problem.

Answer 2

Sorry I can not comment yet, this is not exactly the solution you are looking for but I hope this will fix the problem you are having.
You can set the param_space in the learner, before putting it in the graph, i.e. sticking with your search space. After you create the GraphLearner regularly it will have the desired search space.
A concrete example:
library(mlr3verse)
learner = lrn("regr.rpart", cp = to_tune(0.1, 0.2))
glrn = as_learner(po("pca") %>>% po("learner", learner))
at = auto_tuner(
"random_search",
glrn,
rsmp("holdout"),
term_evals = 10
)
task = tsk("mtcars")
at$train(task)