Hyperparameter Tuning with Generalized Simulated Annealing

Subclass for generalized simulated annealing tuning. Calls GenSA::GenSA() from package GenSA.

Source

Tsallis C, Stariolo DA (1996). “Generalized simulated annealing.” Physica A: Statistical Mechanics and its Applications, 233(1-2), 395–406. 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. doi:10.32614/rj-2013-002 .

Details

In contrast to the GenSA::GenSA() defaults, we set smooth = FALSE as a default.

Dictionary

This Tuner can be instantiated with the associated sugar function tnr():

tnr("gensa")

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::OptimizerBatchGenSA which can be applied on any black box optimization problem. See also the documentation of bbotk.

Parameters

par: numeric()
Initial parameter values. Default is NULL, in which case, default values will be generated automatically.
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. By default, nloptr will generate start values automatically. Custom start values can be passed via the par parameter.

For the meaning of the control parameters, see GenSA::GenSA(). 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.

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

Tuner -> TunerBatch -> TunerBatchFromOptimizerBatch -> TunerBatchGenSA

Methods

Inherited methods

`TunerBatchGenSA$new()`

Creates a new instance of this R6 class.

Usage

TunerBatchGenSA$new()

`TunerBatchGenSA$clone()`

The objects of this class are cloneable with this method.

Usage

TunerBatchGenSA$clone(deep = FALSE)

Arguments

deep: Whether to make a deep clone.

Examples

# example only runs if GenSA is available
if (mlr3misc::require_namespaces("GenSA", quietly = TRUE)) {
# 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("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"))
}