Subclass for random search tuning.
The random points are sampled by paradox::generate_design_random().
Source
Bergstra J, Bengio Y (2012). “Random Search for Hyper-Parameter Optimization.” Journal of Machine Learning Research, 13(10), 281--305. https://jmlr.csail.mit.edu/papers/v13/bergstra12a.html.
Dictionary
This Tuner can be instantiated via the dictionary
mlr_tuners or with the associated sugar function tnr():
TunerRandomSearch$new()
mlr_tuners$get("random_search")
tnr("random_search")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.
Optimizer
This Tuner is based on bbotk::OptimizerRandomSearch which can be applied on any black box optimization problem. See also the documentation of bbotk.
Progress Bars
$optimize() supports progress bars via the package progressr
combined with a Terminator. Simply wrap the function in
progressr::with_progress() to enable them. We recommend to use package
progress as backend; enable with progressr::handlers("progress").
See also
Package mlr3hyperband for hyperband tuning.
Other Tuner:
mlr_tuners_cmaes,
mlr_tuners_design_points,
mlr_tuners_gensa,
mlr_tuners_grid_search,
mlr_tuners_irace,
mlr_tuners_nloptr,
mlr_tuners
Super classes
mlr3tuning::Tuner -> mlr3tuning::TunerFromOptimizer -> TunerRandomSearch
Examples
# retrieve task
task = tsk("pima")
# load learner and set search space
learner = lrn("classif.rpart", cp = to_tune(1e-04, 1e-1, logscale = TRUE))
# hyperparameter tuning on the pima indians diabetes data set
instance = tune(
method = "random_search",
task = task,
learner = learner,
resampling = rsmp("holdout"),
measure = msr("classif.ce"),
term_evals = 10
)
# best performing hyperparameter configuration
instance$result
#> cp learner_param_vals x_domain classif.ce
#> 1: -4.433908 <list[2]> <list[1]> 0.2460938
# all evaluated hyperparameter configuration
as.data.table(instance$archive)
#> cp classif.ce x_domain_cp runtime_learners timestamp
#> 1: -4.433908 0.2460938 0.0118680165 0.016 2022-08-13 04:25:26
#> 2: -8.714308 0.2656250 0.0001642193 0.016 2022-08-13 04:25:26
#> 3: -5.506266 0.2617188 0.0040612444 0.016 2022-08-13 04:25:26
#> 4: -2.642002 0.2773438 0.0712185711 0.020 2022-08-13 04:25:26
#> 5: -4.700957 0.2500000 0.0090865755 0.015 2022-08-13 04:25:26
#> 6: -6.457298 0.2656250 0.0015690289 0.017 2022-08-13 04:25:26
#> 7: -8.884045 0.2656250 0.0001385825 0.017 2022-08-13 04:25:26
#> 8: -7.126708 0.2656250 0.0008033594 0.017 2022-08-13 04:25:26
#> 9: -3.485727 0.2500000 0.0306314693 0.021 2022-08-13 04:25:26
#> 10: -7.211394 0.2656250 0.0007381277 0.015 2022-08-13 04:25:27
#> batch_nr warnings errors resample_result
#> 1: 1 0 0 <ResampleResult[21]>
#> 2: 2 0 0 <ResampleResult[21]>
#> 3: 3 0 0 <ResampleResult[21]>
#> 4: 4 0 0 <ResampleResult[21]>
#> 5: 5 0 0 <ResampleResult[21]>
#> 6: 6 0 0 <ResampleResult[21]>
#> 7: 7 0 0 <ResampleResult[21]>
#> 8: 8 0 0 <ResampleResult[21]>
#> 9: 9 0 0 <ResampleResult[21]>
#> 10: 10 0 0 <ResampleResult[21]>
# fit final model on complete data set
learner$param_set$values = instance$result_learner_param_vals
learner$train(task)