mlr_tuners_design_points | R Documentation |
Subclass for tuning w.r.t. fixed design points.
We simply search over a set of points fully specified by the user. The points in the design are evaluated in order as given.
This Tuner can be instantiated with the associated sugar function tnr()
:
tnr("design_points")
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::OptimizerBatchDesignPoints which can be applied on any black box optimization problem. See also the documentation of bbotk.
batch_size
integer(1)
Maximum number of configurations to try in a batch.
design
data.table::data.table
Design points to try in search, one per row.
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.
$optimize()
supports progress bars via the package progressr
combined with a Terminator. Simply wrap the function in
progressr::with_progress()
to enable them. We recommend to use package
progress as backend; enable with progressr::handlers("progress")
.
mlr3tuning::Tuner
-> mlr3tuning::TunerBatch
-> mlr3tuning::TunerBatchFromOptimizerBatch
-> TunerBatchDesignPoints
new()
Creates a new instance of this R6 class.
TunerBatchDesignPoints$new()
clone()
The objects of this class are cloneable with this method.
TunerBatchDesignPoints$clone(deep = FALSE)
deep
Whether to make a deep clone.
Package mlr3hyperband for hyperband tuning.
Other Tuner:
Tuner
,
mlr_tuners
,
mlr_tuners_cmaes
,
mlr_tuners_gensa
,
mlr_tuners_grid_search
,
mlr_tuners_internal
,
mlr_tuners_irace
,
mlr_tuners_nloptr
,
mlr_tuners_random_search
# Hyperparameter Optimization
# load learner and set search space
learner = lrn("classif.rpart",
cp = to_tune(1e-04, 1e-1),
minsplit = to_tune(2, 128),
minbucket = to_tune(1, 64)
)
# create design
design = mlr3misc::rowwise_table(
~cp, ~minsplit, ~minbucket,
0.1, 2, 64,
0.01, 64, 32,
0.001, 128, 1
)
# run hyperparameter tuning on the Palmer Penguins data set
instance = tune(
tuner = tnr("design_points", design = design),
task = tsk("penguins"),
learner = learner,
resampling = rsmp("holdout"),
measure = msr("classif.ce")
)
# best performing hyperparameter configuration
instance$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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