OptimizerRandomSearch
class that implements a simple Random Search.
In order to support general termination criteria and parallelization, we
evaluate points in a batch-fashion of size batch_size
. Larger batches mean
we can parallelize more, smaller batches imply a more fine-grained checking
of termination criteria.
Source
Bergstra J, Bengio Y (2012). “Random Search for Hyper-Parameter Optimization.” Journal of Machine Learning Research, 13(10), 281--305. https://jmlr.csail.mit.edu/papers/v13/bergstra12a.html.
Dictionary
This Optimizer can be instantiated via the dictionary
mlr_optimizers or with the associated sugar function opt()
:
$get("random_search")
mlr_optimizersopt("random_search")
Progress Bars
$optimize()
supports progress bars via the package progressr
combined with a Terminator. Simply wrap the function in
progressr::with_progress()
to enable them. We recommend to use package
progress as backend; enable with progressr::handlers("progress")
.
Super class
bbotk::Optimizer
-> OptimizerRandomSearch
Examples
search_space = domain = ps(x = p_dbl(lower = -1, upper = 1))
codomain = ps(y = p_dbl(tags = "minimize"))
objective_function = function(xs) {
list(y = as.numeric(xs)^2)
}
objective = ObjectiveRFun$new(
fun = objective_function,
domain = domain,
codomain = codomain)
instance = OptimInstanceSingleCrit$new(
objective = objective,
search_space = search_space,
terminator = trm("evals", n_evals = 10))
optimizer = opt("random_search")
# modifies the instance by reference
optimizer$optimize(instance)
#> x x_domain y
#> 1: 0.05135715 <list[1]> 0.002637557
# returns best scoring evaluation
instance$result
#> x x_domain y
#> 1: 0.05135715 <list[1]> 0.002637557
# allows access of data.table of full path of all evaluations
as.data.table(instance$archive$data)
#> x y x_domain timestamp batch_nr
#> 1: 0.32311590 0.104403882 <list[1]> 2023-11-13 12:08:07 1
#> 2: -0.89204026 0.795735820 <list[1]> 2023-11-13 12:08:07 2
#> 3: -0.10669202 0.011383186 <list[1]> 2023-11-13 12:08:07 3
#> 4: -0.74033703 0.548098915 <list[1]> 2023-11-13 12:08:07 4
#> 5: -0.22526185 0.050742899 <list[1]> 2023-11-13 12:08:07 5
#> 6: -0.24371196 0.059395519 <list[1]> 2023-11-13 12:08:07 6
#> 7: -0.45547755 0.207459798 <list[1]> 2023-11-13 12:08:07 7
#> 8: -0.22226503 0.049401746 <list[1]> 2023-11-13 12:08:07 8
#> 9: 0.05135715 0.002637557 <list[1]> 2023-11-13 12:08:07 9
#> 10: -0.07573335 0.005735540 <list[1]> 2023-11-13 12:08:07 10