mlr_tuners_cmaes | R Documentation |
Subclass for Covariance Matrix Adaptation Evolution Strategy (CMA-ES).
Calls adagio::pureCMAES()
from package adagio.
This Tuner can be instantiated with the associated sugar function tnr()
:
tnr("cmaes")
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 adagio::pureCMAES()
.
Note that we have removed all control parameters which refer to the termination of the algorithm and where our terminators allow to obtain the same behavior.
$optimize()
supports progress bars via the package progressr
combined with a bbotk::Terminator. Simply wrap the function in
progressr::with_progress()
to enable them. We recommend to use package
progress as backend; enable with progressr::handlers("progress")
.
All Tuners use a logger (as implemented in lgr) from package
bbotk.
Use lgr::get_logger("bbotk")
to access and control the logger.
This Tuner is based on bbotk::OptimizerBatchCmaes which can be applied on any black box optimization problem. See also the documentation of bbotk.
There are several sections about hyperparameter optimization in the mlr3book.
The gallery features a collection of case studies and demos about optimization.
Use the Hyperband optimizer with different budget parameters.
mlr3tuning::Tuner
-> mlr3tuning::TunerBatch
-> mlr3tuning::TunerBatchFromOptimizerBatch
-> TunerBatchCmaes
new()
Creates a new instance of this R6 class.
TunerBatchCmaes$new()
clone()
The objects of this class are cloneable with this method.
TunerBatchCmaes$clone(deep = FALSE)
deep
Whether to make a deep clone.
Hansen N (2016). “The CMA Evolution Strategy: A Tutorial.” 1604.00772.
Other Tuner:
Tuner
,
mlr_tuners
,
mlr_tuners_design_points
,
mlr_tuners_gensa
,
mlr_tuners_grid_search
,
mlr_tuners_internal
,
mlr_tuners_irace
,
mlr_tuners_nloptr
,
mlr_tuners_random_search
# Hyperparameter Optimization
# load learner and set search space
learner = lrn("classif.rpart",
cp = to_tune(1e-04, 1e-1, logscale = TRUE),
minsplit = to_tune(p_dbl(2, 128, trafo = as.integer)),
minbucket = to_tune(p_dbl(1, 64, trafo = as.integer))
)
# run hyperparameter tuning on the Palmer Penguins data set
instance = tune(
tuner = tnr("cmaes"),
task = tsk("penguins"),
learner = learner,
resampling = rsmp("holdout"),
measure = msr("classif.ce"),
term_evals = 10)
# best performing hyperparameter configuration
instance$result
# all evaluated hyperparameter configuration
as.data.table(instance$archive)
# fit final model on complete data set
learner$param_set$values = instance$result_learner_param_vals
learner$train(tsk("penguins"))
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