Package: mlr3mbo 0.2.3

Lennart Schneider

mlr3mbo:Flexible Bayesian Optimization

A modern and flexible approach to Bayesian Optimization / Model Based Optimization building on the 'bbotk' package. 'mlr3mbo' is a toolbox providing both ready-to-use optimization algorithms as well as their fundamental building blocks allowing for straightforward implementation of custom algorithms. Single- and multi-objective optimization is supported as well as mixed continuous, categorical and conditional search spaces. Moreover, using 'mlr3mbo' for hyperparameter optimization of machine learning models within the 'mlr3' ecosystem is straightforward via 'mlr3tuning'. Examples of ready-to-use optimization algorithms include Efficient Global Optimization by Jones et al. (1998) <doi:10.1023/A:1008306431147>, ParEGO by Knowles (2006) <doi:10.1109/TEVC.2005.851274> and SMS-EGO by Ponweiser et al. (2008) <doi:10.1007/978-3-540-87700-4_78>.

Authors:Lennart Schneider [cre, aut], Jakob Richter [aut], Marc Becker [aut], Michel Lang [aut], Bernd Bischl [aut], Florian Pfisterer [aut], Martin Binder [aut], Sebastian Fischer [aut], Michael H. Buselli [cph], Wessel Dankers [cph], Carlos Fonseca [cph], Manuel Lopez-Ibanez [cph], Luis Paquete [cph]

mlr3mbo_0.2.3.tar.gz
mlr3mbo_0.2.3.tar.gz(r-4.5-noble)mlr3mbo_0.2.3.tar.gz(r-4.4-noble)
mlr3mbo_0.2.3.tgz(r-4.4-emscripten)mlr3mbo_0.2.3.tgz(r-4.3-emscripten)
mlr3mbo.pdf |mlr3mbo.html✨
mlr3mbo/json (API)
NEWS

# Installmlr3mbo in R:
install.packages('mlr3mbo',repos = c('https://cran.r-universe.dev', 'https://cloud.r-project.org'))

Peer review:

Bug tracker:https://github.com/mlr-org/mlr3mbo/issues

39 exports 1.16 score 26 dependencies 3 dependents 2.1k downloads

Last updated 4 days agofrom:a1c7d8e24c

Exports:acqfAcqFunctionAcqFunctionAEIAcqFunctionCBAcqFunctionEHVIAcqFunctionEHVIGHAcqFunctionEIAcqFunctionEIPSAcqFunctionMeanAcqFunctionPIAcqFunctionSDAcqFunctionSmsEgoacqoAcqOptimizerbayesopt_egobayesopt_emobayesopt_mpclbayesopt_paregobayesopt_smsegodefault_acqfunctiondefault_acqoptimizerdefault_gpdefault_loop_functiondefault_result_assignerdefault_rfdefault_surrogatemlr_acqfunctionsmlr_loop_functionsmlr_result_assignersOptimizerMborasResultAssignerResultAssignerArchiveResultAssignerSurrogatesrlrnSurrogateSurrogateLearnerSurrogateLearnerCollectionTunerMbo

Dependencies:backportsbbotkcheckmatecodetoolsdata.tabledigestevaluatefuturefuture.applyglobalslgrlistenvmlbenchmlr3mlr3measuresmlr3miscmlr3tuningpalmerpenguinsparadoxparallellyPRROCR6RcppRhpcBLASctlspacefillruuid

mlr3mbo

Rendered frommlr3mbo.Rmdusingknitr::rmarkdownon Jul 02 2024.

Last update: 2024-07-02
Started: 2022-11-18

Readme and manuals

Help Manual

Help pageTopics
mlr3mbo: Flexible Bayesian Optimizationmlr3mbo-package mlr3mbo
Syntactic Sugar Acquisition Function Constructionacqf
Acquisition Function Base ClassAcqFunction
Syntactic Sugar Acquisition Function Optimizer Constructionacqo
Acquisition Function OptimizerAcqOptimizer
Default Acquisition Functiondefault_acqfunction
Default Acquisition Function Optimizerdefault_acqoptimizer
Default Gaussian Processdefault_gp
Default Loop Functiondefault_loop_function
Default Result Assignerdefault_result_assigner
Default Random Forestdefault_rf
Default Surrogatedefault_surrogate
Loop Functions for Bayesian Optimizationloop_function
Defaults for OptimizerMbombo_defaults
Dictionary of Acquisition Functionsmlr_acqfunctions
Acquisition Function Augmented Expected ImprovementAcqFunctionAEI mlr_acqfunctions_aei
Acquisition Function Confidence BoundAcqFunctionCB mlr_acqfunctions_cb
Acquisition Function Expected Hypervolume ImprovementAcqFunctionEHVI mlr_acqfunctions_ehvi
Acquisition Function Expected Hypervolume Improvement via Gauss-Hermite QuadratureAcqFunctionEHVIGH mlr_acqfunctions_ehvigh
Acquisition Function Expected ImprovementAcqFunctionEI mlr_acqfunctions_ei
Acquisition Function Expected Improvement Per SecondAcqFunctionEIPS mlr_acqfunctions_eips
Acquisition Function MeanAcqFunctionMean mlr_acqfunctions_mean
Acquisition Function Probability of ImprovementAcqFunctionPI mlr_acqfunctions_pi
Acquisition Function Standard DeviationAcqFunctionSD mlr_acqfunctions_sd
Acquisition Function SMS-EGOAcqFunctionSmsEgo mlr_acqfunctions_smsego
Dictionary of Loop Functionsmlr_loop_functions
Sequential Single-Objective Bayesian Optimizationbayesopt_ego mlr_loop_functions_ego
Sequential Multi-Objective Bayesian Optimizationbayesopt_emo mlr_loop_functions_emo
Single-Objective Bayesian Optimization via Multipoint Constant Liarbayesopt_mpcl mlr_loop_functions_mpcl
Multi-Objective Bayesian Optimization via ParEGObayesopt_parego mlr_loop_functions_parego
Sequential Multi-Objective Bayesian Optimization via SMS-EGObayesopt_smsego mlr_loop_functions_smsego
Model Based Optimizationmlr_optimizers_mbo OptimizerMbo
Dictionary of Result Assignersmlr_result_assigners
Result Assigner Based on the Archivemlr_result_assigners_archive ResultAssignerArchive
Result Assigner Based on a Surrogate Mean Predictionmlr_result_assigners_surrogate ResultAssignerSurrogate
TunerBatch using Model Based Optimizationmlr_tuners_mbo TunerMbo
Syntactic Sugar Result Assigner Constructionras
Result Assigner Base ClassResultAssigner
Syntactic Sugar Surrogate Constructionsrlrn
Surrogate ModelSurrogate
Surrogate Model Containing a Single LearnerSurrogateLearner
Surrogate Model Containing Multiple LearnersSurrogateLearnerCollection