
Hyperparameter Tuning with Non-linear Optimization
Source:R/TunerBatchNLoptr.R
mlr_tuners_nloptr.Rd
Subclass for non-linear optimization (NLopt). 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
.
Logging
All Tuners use a logger (as implemented in lgr) from package
bbotk.
Use lgr::get_logger("bbotk")
to access and control the logger.
Optimizer
This Tuner is based on bbotk::OptimizerBatchNLoptr which can be applied on any black box optimization problem. See also the documentation of bbotk.
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. If set to"custom"
, the start values can be passed via thestart
parameter.start
numeric()
Custom start values. Only applicable ifstart_values
parameter is set to"custom"
.approximate_eval_grad_f
logical(1)
Should gradients be numerically approximated via finite differences (nloptr::nl.grad). Only required for certain algorithms. Note that function evaluations required for the numerical gradient approximation will be logged as usual and are not treated differently than regular function evaluations by, e.g., Terminators.
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
.
Resources
There are several sections about hyperparameter optimization in the mlr3book.
Getting started with hyperparameter optimization.
An overview of all tuners can be found on our website.
Tune a support vector machine on the Sonar data set.
Learn about tuning spaces.
Estimate the model performance with nested resampling.
Learn about multi-objective optimization.
Simultaneously optimize hyperparameters and use early stopping with XGBoost.
Automate the tuning.
The gallery features a collection of case studies and demos about optimization.
Learn more advanced methods with the Practical Tuning Series.
Learn about hotstarting models.
Run the default hyperparameter configuration of learners as a baseline.
Use the Hyperband optimizer with different budget parameters.
The cheatsheet summarizes the most important functions of mlr3tuning.
Progress Bars
$optimize()
supports progress bars via the package progressr
combined with a Terminator. Simply wrap the function in
progressr::with_progress()
to enable them. We recommend to use package
progress as backend; enable with progressr::handlers("progress")
.
Super classes
mlr3tuning::Tuner
-> mlr3tuning::TunerBatch
-> mlr3tuning::TunerBatchFromOptimizerBatch
-> TunerBatchNLoptr
Examples
# Hyperparameter Optimization
# \donttest{
# load learner and set search space
learner = lrn("classif.rpart",
cp = to_tune(1e-04, 1e-1, logscale = TRUE)
)
# run hyperparameter tuning on the Palmer Penguins data set
instance = tune(
tuner = tnr("nloptr", algorithm = "NLOPT_LN_BOBYQA"),
task = tsk("penguins"),
learner = learner,
resampling = rsmp("holdout"),
measure = msr("classif.ce")
)
# best performing hyperparameter configuration
instance$result
#> cp learner_param_vals x_domain classif.ce
#> <num> <list> <list> <num>
#> 1: -7.51898 <list[2]> <list[1]> 0.06086957
# all evaluated hyperparameter configuration
as.data.table(instance$archive)
#> cp classif.ce runtime_learners timestamp warnings errors
#> <num> <num> <num> <POSc> <int> <int>
#> 1: -7.518980 0.06086957 0.006 2025-06-04 09:52:06 0 0
#> 2: -7.518980 0.06086957 0.006 2025-06-04 09:52:07 0 0
#> 3: -7.518980 0.06086957 0.005 2025-06-04 09:52:07 0 0
#> 4: -6.250460 0.06086957 0.006 2025-06-04 09:52:07 0 0
#> 5: -8.787500 0.06086957 0.006 2025-06-04 09:52:07 0 0
#> 6: -7.506295 0.06086957 0.005 2025-06-04 09:52:07 0 0
#> 7: -7.531666 0.06086957 0.024 2025-06-04 09:52:07 0 0
#> 8: -7.517712 0.06086957 0.006 2025-06-04 09:52:07 0 0
#> 9: -7.520249 0.06086957 0.006 2025-06-04 09:52:07 0 0
#> 10: -7.518980 0.06086957 0.006 2025-06-04 09:52:07 0 0
#> x_domain batch_nr resample_result
#> <list> <int> <list>
#> 1: <list[1]> 1 <ResampleResult>
#> 2: <list[1]> 2 <ResampleResult>
#> 3: <list[1]> 3 <ResampleResult>
#> 4: <list[1]> 4 <ResampleResult>
#> 5: <list[1]> 5 <ResampleResult>
#> 6: <list[1]> 6 <ResampleResult>
#> 7: <list[1]> 7 <ResampleResult>
#> 8: <list[1]> 8 <ResampleResult>
#> 9: <list[1]> 9 <ResampleResult>
#> 10: <list[1]> 10 <ResampleResult>
# fit final model on complete data set
learner$param_set$values = instance$result_learner_param_vals
learner$train(tsk("penguins"))
# }