mlr_tuners_design_points: Hyperparameter Tuning with via Design Points

Description Dictionary Parallelization Logging Parameters Progress Bars Super classes Methods See Also Examples

Description

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():

1
2
3
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").

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.

See Also

Package mlr3hyperband for hyperband tuning.

Other Tuner: mlr_tuners_cmaes, mlr_tuners_gensa, mlr_tuners_grid_search, mlr_tuners_irace, mlr_tuners_nloptr, mlr_tuners_random_search

Examples

 1
 2
 3
 4
 5
 6
 7
 8
 9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
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

# 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(task)

mlr3tuning documentation built on Sept. 14, 2021, 9:08 a.m.