Hyperparameter Tuning with Non-linear Optimization

TunerNLoptr class that implements non-linear optimization. Calls nloptr::nloptr from package nloptr.

Source

Johnson, G S (2020). “The NLopt nonlinear-optimization package.” https://github.com/stevengj/nlopt.

Details

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.

Dictionary

This Tuner can be instantiated via the dictionary mlr_tuners or with the associated sugar function tnr():

mlr_tuners$get("nloptr")
tnr("nloptr")

Logging

All Tuners use a logger (as implemented in lgr) from package bbotk. Use lgr::get_logger("bbotk") to access and control the logger.

Parameters

algorithm

character(1)

eval_g_ineq

function()

xtol_rel

numeric(1)

xtol_abs

numeric(1)

ftol_rel

numeric(1)

ftol_abs

numeric(1)

start_values

character(1)
Create random start values or based on center of search space? In the latter case, it is the center of the parameters before a trafo is applied.

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.

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").

See also

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

Super classes

mlr3tuning::Tuner -> mlr3tuning::TunerFromOptimizer -> TunerNLoptr

Methods

Public methods

• TunerNLoptr$new()

• TunerNLoptr$clone()

Inherited methods

• mlr3tuning::Tuner$format()

• mlr3tuning::Tuner$print()

• mlr3tuning::TunerFromOptimizer$optimize()

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Method new()

Creates a new instance of this R6 class.

Usage

TunerNLoptr$new()

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Method clone()

The objects of this class are cloneable with this method.

Usage

TunerNLoptr$clone(deep = FALSE)

Arguments

deep

Whether to make a deep clone.

Examples

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$new(
  task = tsk("iris"),
  learner = lrn("classif.rpart"),
  resampling = rsmp("holdout"),
  measure = msr("classif.ce"),
  search_space = search_space,
  terminator = terminator
)
tt = tnr("nloptr", algorithm = "NLOPT_LN_BOBYQA")
# modifies the instance by reference
tt$optimize(instance)
# returns best configuration and best performance
instance$result
# allows access of data.table of full path of all evaluations
instance$archive
}