Surrogate Model Containing Multiple Learners
Source:R/SurrogateLearnerCollection.R
SurrogateLearnerCollection.Rd
Surrogate model containing multiple mlr3::LearnerRegr.
The mlr3::LearnerRegr are fit on the target variables as indicated via cols_y
.
Note that redundant mlr3::LearnerRegr must be deep clones.
Parameters
assert_insample_perf
logical(1)
Should the insample performance of the mlr3::LearnerRegr be asserted after updating the surrogate? If the assertion fails (i.e., the insample performance based on theperf_measure
does not meet theperf_threshold
), an error is thrown. Default isFALSE
.perf_measure
List of mlr3::MeasureRegr
Performance measures which should be use to assert the insample performance of the mlr3::LearnerRegr. Only relevant ifassert_insample_perf = TRUE
. Default is mlr3::mlr_measures_regr.rsq for each learner.perf_threshold
List of
numeric(1)
Thresholds the insample performance of the mlr3::LearnerRegr should be asserted against. Only relevant ifassert_insample_perf = TRUE
. Default is0
for each learner.catch_errors
logical(1)
Should errors during updating the surrogate be caught and propagated to theloop_function
which can then handle the failed acquisition function optimization (as a result of the failed surrogate) appropriately by, e.g., proposing a randomly sampled point for evaluation? Default isTRUE
.
Super class
mlr3mbo::Surrogate
-> SurrogateLearnerCollection
Active bindings
print_id
(
character
)
Id used when printing.n_learner
(
integer(1)
)
Returns the number of surrogate models.assert_insample_perf
(
numeric()
)
Asserts whether the current insample performance meets the performance threshold.packages
(
character()
)
Set of required packages. A warning is signaled if at least one of the packages is not installed, but loaded (not attached) later on-demand viarequireNamespace()
.feature_types
(
character()
)
Stores the feature types the surrogate can handle, e.g."logical"
,"numeric"
, or"factor"
. A complete list of candidate feature types, grouped by task type, is stored inmlr_reflections$task_feature_types
.properties
(
character()
)
Stores a set of properties/capabilities the surrogate has. A complete list of candidate properties, grouped by task type, is stored inmlr_reflections$learner_properties
.predict_type
(
character(1)
)
Retrieves the currently active predict type, e.g."response"
.
Methods
Method new()
Creates a new instance of this R6 class.
Usage
SurrogateLearnerCollection$new(
learners,
archive = NULL,
cols_x = NULL,
cols_y = NULL
)
Arguments
learners
(list of mlr3::LearnerRegr).
archive
(bbotk::Archive |
NULL
)
bbotk::Archive of the bbotk::OptimInstance.cols_x
(
character()
|NULL
)
Column id's of variables that should be used as features. By default, automatically inferred based on the archive.cols_y
(
character()
|NULL
)
Column id's of variables that should be used as targets. By default, automatically inferred based on the archive.
Method predict()
Predict mean response and standard error.
Returns a named list of data.tables.
Each contains the mean response and standard error for one col_y
.
Arguments
xdt
(
data.table::data.table()
)
New data. One row per observation.
Returns
list of data.table::data.table()
s with the columns mean
and se
.
Examples
if (requireNamespace("mlr3learners") &
requireNamespace("DiceKriging") &
requireNamespace("rgenoud") &
requireNamespace("ranger")) {
library(bbotk)
library(paradox)
library(mlr3learners)
fun = function(xs) {
list(y1 = xs$x^2, y2 = (xs$x - 2) ^ 2)
}
domain = ps(x = p_dbl(lower = -10, upper = 10))
codomain = ps(y1 = p_dbl(tags = "minimize"), y2 = p_dbl(tags = "minimize"))
objective = ObjectiveRFun$new(fun = fun, domain = domain, codomain = codomain)
instance = OptimInstanceBatchMultiCrit$new(
objective = objective,
terminator = trm("evals", n_evals = 5))
xdt = generate_design_random(instance$search_space, n = 4)$data
instance$eval_batch(xdt)
learner1 = default_gp()
learner2 = default_rf()
surrogate = srlrn(list(learner1, learner2), archive = instance$archive)
surrogate$update()
surrogate$learner
surrogate$learner[["y2"]]$model
}
#> Loading required namespace: ranger
#> Ranger result
#>
#> Call:
#> ranger::ranger(dependent.variable.name = task$target_names, data = task$data(), case.weights = task$weights$weight, keep.inbag = TRUE, num.threads = 1L, num.trees = 100L)
#>
#> Type: Regression
#> Number of trees: 100
#> Sample size: 4
#> Number of independent variables: 1
#> Mtry: 1
#> Target node size: 5
#> Variable importance mode: none
#> Splitrule: variance
#> OOB prediction error (MSE): 4791.712
#> R squared (OOB): -0.404484