mlr_tuners_gensa | R Documentation |
Subclass for generalized simulated annealing tuning.
Calls GenSA::GenSA()
from package GenSA.
In contrast to the GenSA::GenSA()
defaults, we set smooth = FALSE
as a default.
This Tuner can be instantiated with the associated sugar function tnr()
:
tnr("gensa")
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. A batch contains of batch_size
times resampling$iters
jobs.
E.g., if you set a batch size of 10 points and do a 5-fold cross validation, you can
utilize up to 50 cores.
Parallelization is supported via package future (see mlr3::benchmark()
's
section on parallelization for more details).
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::OptimizerBatchGenSA which can be applied on any black box optimization problem. See also the documentation of bbotk.
smooth
logical(1)
temperature
numeric(1)
acceptance.param
numeric(1)
verbose
logical(1)
trace.mat
logical(1)
For the meaning of the control parameters, see GenSA::GenSA()
. 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.
In contrast to the GenSA::GenSA()
defaults, we set trace.mat = FALSE
.
Note that GenSA::GenSA()
uses smooth = TRUE
as a default.
In the case of using this optimizer for Hyperparameter Optimization you may
want to set smooth = FALSE
.
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.
$optimize()
supports progress bars via the package progressr
combined with a Terminator. Simply wrap the function in
progressr::with_progress()
to enable them. We recommend to use package
progress as backend; enable with progressr::handlers("progress")
.
mlr3tuning::Tuner
-> mlr3tuning::TunerBatch
-> mlr3tuning::TunerBatchFromOptimizerBatch
-> TunerBatchGenSA
new()
Creates a new instance of this R6 class.
TunerBatchGenSA$new()
clone()
The objects of this class are cloneable with this method.
TunerBatchGenSA$clone(deep = FALSE)
deep
Whether to make a deep clone.
Tsallis C, Stariolo DA (1996). “Generalized simulated annealing.” Physica A: Statistical Mechanics and its Applications, 233(1-2), 395–406. \Sexpr[results=rd]{tools:::Rd_expr_doi("10.1016/s0378-4371(96)00271-3")}.
Xiang Y, Gubian S, Suomela B, Hoeng J (2013). “Generalized Simulated Annealing for Global Optimization: The GenSA Package.” The R Journal, 5(1), 13. \Sexpr[results=rd]{tools:::Rd_expr_doi("10.32614/rj-2013-002")}.
Other Tuner:
Tuner
,
mlr_tuners
,
mlr_tuners_cmaes
,
mlr_tuners_design_points
,
mlr_tuners_grid_search
,
mlr_tuners_internal
,
mlr_tuners_irace
,
mlr_tuners_nloptr
,
mlr_tuners_random_search
# Hyperparameter Optimization
# load learner and set search space
learner = lrn("classif.rpart",
cp = to_tune(1e-04, 1e-1, logscale = TRUE)
)
# run hyperparameter tuning on the Palmer Penguins data set
instance = tune(
tuner = tnr("gensa"),
task = tsk("penguins"),
learner = learner,
resampling = rsmp("holdout"),
measure = msr("classif.ce"),
term_evals = 10
)
# best performing hyperparameter configuration
instance$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"))
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.