Function to retrieve TuningSpace objects from mlr_tuning_spaces and further, allows a mlr3::Learner to be directly configured with a search space. This function belongs to mlr3::mlr_sugar family.
Usage
lts(x, ...)
# S3 method for class 'missing'
lts(x, ...)
# S3 method for class 'character'
lts(x, ...)
# S3 method for class 'Learner'
lts(x, ...)
ltss(x)Arguments
- x
(
character()| mlr3::Learner)
Ifcharacter, key passed the dictionary to retrieve the tuning space. If mlr3::Learner, default tuning space is added to the learner.- ...
(named list of paradox::TuneToken |
NULL)
Pass paradox::TuneToken to add or overwrite parameters in the tuning space. UseNULLto remove parameters (see examples).
Value
TuningSpace if x is character().
mlr3::Learner if x is mlr3::Learner.
Or a list of objects for the ltss() function.
missing, mlr_tuning_spaces dictionary
a character, TuningSpace
a mlr3::Learner, mlr3::Learner with paradox::TuneToken
a list(), list of TuningSpace or mlr3::Learner
Examples
# load tuning space
lts("classif.rpart.default")
#>
#> ── <TuningSpace> (classif.rpart.default): Classification Rpart with Default ────
#> id lower upper levels logscale
#> <char> <num> <num> <list> <lgcl>
#> 1: minsplit 2e+00 128.0 [NULL] TRUE
#> 2: minbucket 1e+00 64.0 [NULL] TRUE
#> 3: cp 1e-04 0.1 [NULL] TRUE
# load tuning space and add parameter
lts("classif.rpart.default", maxdepth = to_tune(1, 15))
#>
#> ── <TuningSpace> (classif.rpart.default): Classification Rpart with Default ────
#> id lower upper levels logscale
#> <char> <num> <num> <list> <lgcl>
#> 1: minsplit 2e+00 128.0 [NULL] TRUE
#> 2: minbucket 1e+00 64.0 [NULL] TRUE
#> 3: cp 1e-04 0.1 [NULL] TRUE
#> 4: maxdepth 1e+00 15.0 [NULL] FALSE
# load tuning space and remove parameter
lts("classif.rpart.default", minsplit = NULL)
#>
#> ── <TuningSpace> (classif.rpart.default): Classification Rpart with Default ────
#> id lower upper levels logscale
#> <char> <num> <num> <list> <lgcl>
#> 1: minbucket 1e+00 64.0 [NULL] TRUE
#> 2: cp 1e-04 0.1 [NULL] TRUE
# load tuning space and overwrite parameter
lts("classif.rpart.default", minsplit = to_tune(32, 128))
#>
#> ── <TuningSpace> (classif.rpart.default): Classification Rpart with Default ────
#> id lower upper levels logscale
#> <char> <num> <num> <list> <lgcl>
#> 1: minsplit 32.0000 128.0 [NULL] FALSE
#> 2: minbucket 1.0000 64.0 [NULL] TRUE
#> 3: cp 0.0001 0.1 [NULL] TRUE
# load learner and apply tuning space in one go
lts(lrn("classif.rpart"))
#> <LearnerClassifRpart:classif.rpart>: Classification Tree
#> * Model: -
#> * Parameters: cp=<RangeTuneToken>, minbucket=<RangeTuneToken>,
#> minsplit=<RangeTuneToken>, xval=0
#> * Packages: mlr3, rpart
#> * Predict Types: [response], prob
#> * Feature Types: logical, integer, numeric, factor, ordered
#> * Properties: importance, missings, multiclass, selected_features,
#> twoclass, weights
# load learner, overwrite parameter and apply tuning space
lts(lrn("classif.rpart"), minsplit = to_tune(32, 128))
#> <LearnerClassifRpart:classif.rpart>: Classification Tree
#> * Model: -
#> * Parameters: cp=<RangeTuneToken>, minbucket=<RangeTuneToken>,
#> minsplit=<RangeTuneToken>, xval=0
#> * Packages: mlr3, rpart
#> * Predict Types: [response], prob
#> * Feature Types: logical, integer, numeric, factor, ordered
#> * Properties: importance, missings, multiclass, selected_features,
#> twoclass, weights
# load multiple tuning spaces
ltss(c("classif.rpart.default", "classif.ranger.default"))
#> [[1]]
#>
#> ── <TuningSpace> (classif.rpart.default): Classification Rpart with Default ────
#> id lower upper levels logscale
#> <char> <num> <num> <list> <lgcl>
#> 1: minsplit 2e+00 128.0 [NULL] TRUE
#> 2: minbucket 1e+00 64.0 [NULL] TRUE
#> 3: cp 1e-04 0.1 [NULL] TRUE
#>
#> [[2]]
#>
#> ── <TuningSpace> (classif.ranger.default): Classification Ranger with Default ──
#> id lower upper levels logscale
#> <char> <num> <num> <list> <lgcl>
#> 1: mtry.ratio 0.0 1 [NULL] FALSE
#> 2: replace NA NA [NULL] FALSE
#> 3: sample.fraction 0.1 1 [NULL] FALSE
#> 4: num.trees 1.0 2000 [NULL] FALSE
#>