BoTorch Fully Bayesian SingleTaskGP Regression Learner

Fully Bayesian Gaussian Process via botorch, using the SaasFullyBayesianSingleTaskGP model. Unlike standard GP models that use MAP estimation for hyperparameters, this model uses MCMC (NUTS) to learn full posterior distributions over kernel hyperparameters. See here for more details. Uses reticulate to interface with Python.

Dictionary

This Learner can be instantiated via lrn():

lrn("regr.botorch_fullybayesian")

Meta Information

• Task type: “regr”

• Predict Types: “response”, “se”

• Feature Types: “integer”, “numeric”

• Required Packages: mlr3, mlr3extralearners, reticulate

Parameters

Id	Type	Default	Levels	Range
device	character	cpu	cpu, cuda	-
input_transform	character	-	normalize, standardize, log10, warp, none	-
outcome_transform	character	-	standardize, none	-
warmup_steps	integer	-		\([1, \infty)\)
num_samples	integer	-		\([1, \infty)\)
thinning	integer	-		\([1, \infty)\)
max_tree_depth	integer	-		\([1, \infty)\)
disable_progbar	logical	-	TRUE, FALSE	-
jit_compile	logical	-	TRUE, FALSE	-

Author

Marc Becker

Super classes

mlr3::Learner -> mlr3::LearnerRegr -> LearnerRegrBotorchFullyBayesian

Active bindings

marshaled

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

Methods

Public methods

• LearnerRegrBotorchFullyBayesian$new()

• LearnerRegrBotorchFullyBayesian$marshal()

• LearnerRegrBotorchFullyBayesian$unmarshal()

• LearnerRegrBotorchFullyBayesian$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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LearnerRegrBotorchFullyBayesian$new()

Creates a new instance of this R6 class.

Usage

LearnerRegrBotorchFullyBayesian$new()

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LearnerRegrBotorchFullyBayesian$marshal()

Marshal the learner's model.

Usage

LearnerRegrBotorchFullyBayesian$marshal(...)

Arguments

...

(any)
Additional arguments passed to marshal_model().

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LearnerRegrBotorchFullyBayesian$unmarshal()

Unmarshal the learner's model.

Usage

LearnerRegrBotorchFullyBayesian$unmarshal(...)

Arguments

...

(any)
Additional arguments passed to unmarshal_model().

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LearnerRegrBotorchFullyBayesian$clone()

The objects of this class are cloneable with this method.

Usage

LearnerRegrBotorchFullyBayesian$clone(deep = FALSE)

Arguments

deep

Whether to make a deep clone.