TunerNLoptr
class that implements non-linear optimization. Calls
nloptr::nloptr from package nloptr.
Johnson, G S (2020). “The NLopt nonlinear-optimization package.” https://github.com/stevengj/nlopt.
The termination conditions stopval
, maxtime
and maxeval
of
nloptr::nloptr()
are deactivated and replaced by the bbotk::Terminator
subclasses. The x and function value tolerance termination conditions
(xtol_rel = 10^-4
, xtol_abs = rep(0.0, length(x0))
,
ftol_rel = 0.0
and ftol_abs = 0.0
) are still available and implemented with
their package defaults. To deactivate these conditions, set them to -1
.
This Tuner can be instantiated via the dictionary
mlr_tuners or with the associated sugar function tnr()
:
mlr_tuners$get("nloptr") tnr("nloptr")
All Tuners use a logger (as implemented in lgr) from package
bbotk.
Use lgr::get_logger("bbotk")
to access and control the logger.
algorithm
x0
eval_g_ineq
function()
xtol_rel
xtol_abs
ftol_rel
ftol_abs
For the meaning of the control parameters, see nloptr::nloptr()
and
nloptr::nloptr.print.options()
.
The termination conditions stopval
, maxtime
and maxeval
of
nloptr::nloptr()
are deactivated and replaced by the Terminator
subclasses. The x and function value tolerance termination conditions
(xtol_rel = 10^-4
, xtol_abs = rep(0.0, length(x0))
, ftol_rel = 0.0
and
ftol_abs = 0.0
) are still available and implemented with their package
defaults. To deactivate these conditions, set them to -1
.
Package mlr3hyperband for hyperband tuning.
Other Tuner:
mlr_tuners_cmaes
,
mlr_tuners_design_points
,
mlr_tuners_gensa
,
mlr_tuners_grid_search
,
mlr_tuners_random_search
mlr3tuning::Tuner
-> mlr3tuning::TunerFromOptimizer
-> TunerNLoptr
new()
Creates a new instance of this R6 class.
TunerNLoptr$new()
clone()
The objects of this class are cloneable with this method.
TunerNLoptr$clone(deep = FALSE)
deep
Whether to make a deep clone.
if (FALSE) { library(mlr3) library(paradox) library(data.table) search_space = ParamSet$new(list( ParamDbl$new("cp", lower = 0.001, upper = 0.1) )) # We use the internal termination criterion xtol_rel terminator = trm("none") instance = TuningInstanceSingleCrit$new( task = tsk("iris"), learner = lrn("classif.rpart"), resampling = rsmp("holdout"), measure = msr("classif.ce"), search_space = search_space, terminator = terminator ) tt = tnr("nloptr", x0 = 0.1, algorithm = "NLOPT_LN_BOBYQA") # modifies the instance by reference tt$optimize(instance) # returns best configuration and best performance instance$result # allows access of data.table of full path of all evaluations instance$archive }