BoTorch MixedSingleTaskGP Regression Learner

Gaussian Process via botorch and gpytorch, using the MixedSingleTaskGP. See here for more details. Uses reticulate to interface with Python.

Dictionary

This Learner can be instantiated via lrn():

lrn("regr.botorch_mixedsingletaskgp")

Meta Information

• Task type: “regr”

• Predict Types: “response”, “se”

• Feature Types: “logical”, “integer”, “numeric”, “factor”

• Required Packages: mlr3, mlr3extralearners, reticulate

Parameters

Id	Type	Default	Levels
device	character	cpu	cpu, cuda
input_transform	character	-	normalize, standardize, log10, warp, none
outcome_transform	character	-	standardize, log, none

Author

Marc Becker

Super classes

mlr3::Learner -> mlr3::LearnerRegr -> LearnerRegrBotorchMixedSingleTaskGP

Active bindings

marshaled

(logical(1)) Whether the learner has been marshaled.

Methods

Public methods

• LearnerRegrBotorchMixedSingleTaskGP$new()

• LearnerRegrBotorchMixedSingleTaskGP$marshal()

• LearnerRegrBotorchMixedSingleTaskGP$unmarshal()

• LearnerRegrBotorchMixedSingleTaskGP$clone()

Inherited methods

• mlr3::Learner$base_learner()
• mlr3::Learner$configure()
• mlr3::Learner$encapsulate()
• mlr3::Learner$format()
• mlr3::Learner$help()
• mlr3::Learner$predict()
• mlr3::Learner$predict_newdata()
• mlr3::Learner$print()
• mlr3::Learner$reset()
• mlr3::Learner$selected_features()
• mlr3::Learner$train()
• mlr3::LearnerRegr$predict_newdata_fast()

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Method new()

Creates a new instance of this R6 class.

Usage

LearnerRegrBotorchMixedSingleTaskGP$new()

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Method marshal()

Marshal the learner's model.

Usage

LearnerRegrBotorchMixedSingleTaskGP$marshal(...)

Arguments

...

(any)
Additional arguments passed to mlr3::marshal_model().

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Method unmarshal()

Unmarshal the learner's model.

Usage

LearnerRegrBotorchMixedSingleTaskGP$unmarshal(...)

Arguments

...

(any)
Additional arguments passed to mlr3::unmarshal_model().

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Method clone()

The objects of this class are cloneable with this method.

Usage

LearnerRegrBotorchMixedSingleTaskGP$clone(deep = FALSE)

Arguments

deep

Whether to make a deep clone.