@@ -25,9 +25,9 @@ vignette](https://mlr3mbo.mlr-org.com/dev/articles/mlr3mbo.html).
2525` mlr3mbo ` is built modular relying on the following
2626[ R6] ( https://cran.r-project.org/package=R6 ) classes:
2727
28- - ` Surrogate ` : Surrogate Model
29- - ` AcqFunction ` : Acquisition Function
30- - ` AcqOptimizer ` : Acquisition Function Optimizer
28+ - ` Surrogate ` : Surrogate Model
29+ - ` AcqFunction ` : Acquisition Function
30+ - ` AcqOptimizer ` : Acquisition Function Optimizer
3131
3232Based on these, Bayesian Optimization loops can be written, see, e.g.,
3333` bayesopt_ego ` for sequential single-objective BO.
@@ -58,7 +58,7 @@ obfun = ObjectiveRFun$new(
5858 domain = ps(x = p_dbl(lower = - 10 , upper = 10 )),
5959 codomain = ps(y1 = p_dbl(tags = " minimize" )))
6060
61- instance = OptimInstanceSingleCrit $ new (
61+ instance = oi (
6262 objective = obfun ,
6363 terminator = trm(" evals" , n_evals = 10 ))
6464
@@ -77,6 +77,7 @@ optimizer$optimize(instance)
7777```
7878
7979 ## x x_domain y1
80+ ## <num> <list> <num>
8081 ## 1: 0.03897209 <list[1]> 0.001518824
8182
8283Note that you can also use ` bb_optimize ` as a shorthand:
@@ -133,4 +134,5 @@ instance$result
133134```
134135
135136 ## cp learner_param_vals x_domain classif.ce
136- ## 1: -4.594102 <list[2]> <list[1]> 0.2109375
137+ ## <num> <list> <list> <num>
138+ ## 1: -4.381681 <list[2]> <list[1]> 0.2070312
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