Surrogate Model Containing Multiple Learners

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 the perf_measure does not meet the perf_threshold), an error is thrown. Default is FALSE.

perf_measure

List of mlr3::MeasureRegr
Performance measures which should be use to assert the insample performance of the mlr3::LearnerRegr. Only relevant if assert_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 if assert_insample_perf = TRUE. Default is 0 for each learner.

catch_errors

logical(1)
Should errors during updating the surrogate be caught and propagated to the loop_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 is TRUE.

impute_method

character(1)
Method to impute missing values in the case of updating on an asynchronous bbotk::ArchiveAsync with pending evaluations. Can be "mean" to use mean imputation or "random" to sample values uniformly at random between the empirical minimum and maximum. Default is "random".

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 via requireNamespace().

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 in mlr_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 in mlr_reflections$learner_properties.

predict_type

(character(1))
Retrieves the currently active predict type, e.g. "response".

Methods

Public methods

• SurrogateLearnerCollection$new()

• SurrogateLearnerCollection$predict()

• SurrogateLearnerCollection$clone()

Inherited methods

• mlr3mbo::Surrogate$format()
• mlr3mbo::Surrogate$print()
• mlr3mbo::Surrogate$reset()
• mlr3mbo::Surrogate$update()

---

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.

Usage

SurrogateLearnerCollection$predict(xdt)

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.

---

Method clone()

The objects of this class are cloneable with this method.

Usage

SurrogateLearnerCollection$clone(deep = FALSE)

Arguments

deep

Whether to make a deep clone.

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$learner[["y1"]]$model

  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