initSMBO: Initialize a manual sequential MBO run.

In pday2/mlrMBO_mP: Bayesian Optimization and Model-Based Optimization of Expensive Black-Box Functions

Description

When you want to run a human-in-the-loop MBO run you need to initialize it first.

Usage

initSMBO(
  par.set,
  design,
  learner = NULL,
  control,
  minimize = rep(TRUE, control$n.objectives),
  noisy = FALSE,
  show.info = getOption("mlrMBO.show.info", TRUE)
)

Arguments

par.set	[ParamSet] See mbo.
design	[data.frame] Initial design as data frame. If the y-values are not already present in design, mbo will evaluate the points. If the parameters have corresponding trafo functions, the design must not be transformed before it is passed! Functions to generate designs are available in ParamHelpers: generateDesign, generateGridDesign, generateRandomDesign. Default is NULL, which means generateDesign is called and a design of size 4 times number of all parameters is created The points are drawn via maximinLHS to maximize the minimal distance between design points.
learner	[Learner] Regression learner from mlr, which is used as a surrogate to model our fitness function. If NULL (default), the default learner is determined as described here: mbo_default_learner.
control	[MBOControl] Control object for mbo.
minimize	[logical] Should objective values of the target functions be minimized? One value par objective. Default is TRUE for every objective.
noisy	[logical(1)] Is the target function noisy? Default is FALSE.
show.info	[logical(1)] Verbose output on console? Default is TRUE.

Value

[OptState]

pday2/mlrMBO_mP documentation built on July 21, 2020, 12:16 a.m.