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.

Dictionary

This Tuner can be instantiated via the dictionary mlr_tuners or with the associated sugar function tnr():

TunerDesignPoints$new()
mlr_tuners$get("design_points")
tnr("design_points")

Parallelization

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).

Logging

All Tuners use a logger (as implemented in lgr) from package bbotk. Use lgr::get_logger("bbotk") to access and control the logger.

Parameters

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.

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").

See also

Super classes

mlr3tuning::Tuner -> mlr3tuning::TunerFromOptimizer -> TunerDesignPoints

Methods

Public methods

Inherited methods

Method new()

Creates a new instance of this R6 class.

Usage

TunerDesignPoints$new()


Method clone()

The objects of this class are cloneable with this method.

Usage

TunerDesignPoints$clone(deep = FALSE)

Arguments

deep

Whether to make a deep clone.

Examples

library(data.table) # retrieve task task = tsk("pima") # load learner and set search space learner = lrn("classif.rpart", cp = to_tune(1e-04, 1e-1, logscale = TRUE)) # hyperparameter tuning on the pima indians diabetes data set instance = tune( method = "design_points", task = task, learner = learner, resampling = rsmp("holdout"), measure = msr("classif.ce"), design = data.table(cp = c(log(1e-1), log(1e-2))) ) # best performing hyperparameter configuration instance$result
#> cp learner_param_vals x_domain classif.ce #> 1: -4.60517 <list[2]> <list[1]> 0.2578125
# all evaluated hyperparameter configuration as.data.table(instance$archive)
#> cp classif.ce x_domain_cp runtime_learners timestamp #> 1: -2.302585 0.3203125 0.10 0.012 2021-09-16 04:23:16 #> 2: -4.605170 0.2578125 0.01 0.014 2021-09-16 04:23:16 #> batch_nr resample_result #> 1: 1 <ResampleResult[20]> #> 2: 2 <ResampleResult[20]>
# fit final model on complete data set learner$param_set$values = instance$result_learner_param_vals learner$train(task)