TunerDesignPoints

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():

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.

Super classes

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

Methods

Public methods

• TunerDesignPoints$new()

• TunerDesignPoints$clone()

Inherited methods

• mlr3tuning::Tuner$format()

• mlr3tuning::Tuner$print()

• mlr3tuning::TunerFromOptimizer$optimize()

---

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(mlr3)
library(paradox)
library(data.table)
search_space = ParamSet$new(list(
  ParamDbl$new("cp", lower = 0.001, upper = 0.1)
))
terminator = trm("evals", n_evals = 3)
instance = TuningInstanceSingleCrit$new(
  task = tsk("iris"),
  learner = lrn("classif.rpart"),
  resampling = rsmp("holdout"),
  measure = msr("classif.ce"),
  search_space = search_space,
  terminator = terminator
)
design = data.table(cp = c(0.1, 0.01))
tt = tnr("design_points", design = design)
# modifies the instance by reference
tt$optimize(instance)
#>     cp learner_param_vals  x_domain classif.ce
#> 1: 0.1          <list[2]> <list[1]>       0.04
# returns best configuration and best performance
instance$result
#>     cp learner_param_vals  x_domain classif.ce
#> 1: 0.1          <list[2]> <list[1]>       0.04
# allows access of data.table of full path of all evaluations
instance$archive
#> <ArchiveTuning>
#>      cp classif.ce                                uhash  x_domain
#> 1: 0.10       0.04 962d6b58-e79e-407a-b2be-eec6684b23c5 <list[1]>
#> 2: 0.01       0.04 3c977b11-8fa4-4da3-9c3d-ca17deaca057 <list[1]>
#>              timestamp batch_nr
#> 1: 2020-09-28 04:30:42        1
#> 2: 2020-09-28 04:30:42        2