SurrogateLearner: Surrogate Model Containing a Single Learner

In mlr3mbo: Flexible Bayesian Optimization

Surrogate Model Containing a Single Learner

Description

Surrogate model containing a single mlr3::LearnerRegr.

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

mlr3::MeasureRegr
Performance measure 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.

perf_threshold

numeric(1)
Threshold the insample performance of the mlr3::LearnerRegr should be asserted against. Only relevant if assert_insample_perf = TRUE. Default is 0.

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.

Super class

mlr3mbo::Surrogate -> SurrogateLearner

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

• SurrogateLearner$new()

• SurrogateLearner$predict()

• SurrogateLearner$clone()

Inherited methods

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

---

Method new()

Creates a new instance of this R6 class.

Usage

SurrogateLearner$new(learner, archive = NULL, cols_x = NULL, col_y = NULL)

Arguments

learner

(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.

col_y

(character(1) | NULL)
Column id of variable that should be used as a target. By default, automatically inferred based on the archive.

---

Method predict()

Predict mean response and standard error.

Usage

SurrogateLearner$predict(xdt)

Arguments

xdt

(data.table::data.table())
New data. One row per observation.

Returns

data.table::data.table() with the columns mean and se.

---

Method clone()

The objects of this class are cloneable with this method.

Usage

SurrogateLearner$clone(deep = FALSE)

Arguments

deep

Whether to make a deep clone.

Examples

if (requireNamespace("mlr3learners") &
    requireNamespace("DiceKriging") &
    requireNamespace("rgenoud")) {
  library(bbotk)
  library(paradox)
  library(mlr3learners)

  fun = function(xs) {
    list(y = xs$x ^ 2)
  }
  domain = ps(x = p_dbl(lower = -10, upper = 10))
  codomain = ps(y = p_dbl(tags = "minimize"))
  objective = ObjectiveRFun$new(fun = fun, domain = domain, codomain = codomain)

  instance = OptimInstanceBatchSingleCrit$new(
    objective = objective,
    terminator = trm("evals", n_evals = 5))

  xdt = generate_design_random(instance$search_space, n = 4)$data

  instance$eval_batch(xdt)

  learner = default_gp()

  surrogate = srlrn(learner, archive = instance$archive)

  surrogate$update()

  surrogate$learner$model
}

mlr3mbo documentation built on Oct. 17, 2024, 1:06 a.m.