Single Criterion Tuning InstanceSource:
Specifies a general single-criteria tuning scenario, including objective
function and archive for Tuners to act upon. This class stores an
ObjectiveTuning object that encodes the black box objective function which
a Tuner has to optimize. It allows the basic operations of querying the
objective at design points (
$eval_batch()), storing the evaluations in the
internal ArchiveTuning and accessing the final result (
Evaluations of hyperparameter configurations are performed in batches by
mlr3::benchmark() internally. Before a batch is evaluated, the
bbotk::Terminator is queried for the remaining budget. If the available
budget is exhausted, an exception is raised, and no further evaluations can
be performed from this point on.
The tuner is also supposed to store its final result, consisting of a
selected hyperparameter configuration and associated estimated performance
values, by calling the method
Param values for the optimal learner call.
Creates a new instance of this R6 class.
This defines the resampled performance of a learner on a task, a feasibility region for the parameters the tuner is supposed to optimize, and a termination criterion.
TuningInstanceSingleCrit$new( task, learner, resampling, measure = NULL, terminator, search_space = NULL, store_benchmark_result = TRUE, store_models = FALSE, check_values = FALSE, allow_hotstart = FALSE, keep_hotstart_stack = FALSE )
Task to operate on.
Learner to tune.
Resampling that is used to evaluated the performance of the hyperparameter configurations. Uninstantiated resamplings are instantiated during construction so that all configurations are evaluated on the same data splits. Already instantiated resamplings are kept unchanged. Specialized Tuner change the resampling e.g. to evaluate a hyperparameter configuration on different data splits. This field, however, always returns the resampling passed in construction.
Measure to optimize. If
NULL, default measure is used.
Stop criterion of the tuning process.
TRUE(default), store resample result of evaluated hyperparameter configurations in archive as mlr3::BenchmarkResult.
TRUE, fitted models are stored in the benchmark result (
store_benchmark_result = FALSE, models are only stored temporarily and not accessible after the tuning. This combination is needed for measures that require a model.
TRUE, hyperparameter values are checked before evaluation and performance scores after. If
FALSE(default), values are unchecked but computational overhead is reduced.
Allow to hotstart learners with previously fitted models. See also mlr3::HotstartStack. The learner must support hotstarting. Sets
store_models = TRUE.
TRUE, mlr3::HotstartStack is kept in
The Tuner object writes the best found point and estimated performance value here. For internal use.
(List of named
Fixed parameter values of the learner that are neither part of the
library(data.table) # define search space search_space = ps( cp = p_dbl(lower = 0.001, upper = 0.1), minsplit = p_int(lower = 1, upper = 10) ) # initialize instance instance = TuningInstanceSingleCrit$new( task = tsk("iris"), learner = lrn("classif.rpart"), resampling = rsmp("holdout"), measure = msr("classif.ce"), search_space = search_space, terminator = trm("evals", n_evals = 5) ) # generate design design = data.table(cp = c(0.05, 0.01), minsplit = c(5, 3)) # eval design instance$eval_batch(design) # show archive instance$archive #> <ArchiveTuning> #> cp minsplit classif.ce runtime_learners timestamp batch_nr #> 1: 0.05 5 0.06 0.007 2022-06-24 04:27:32.42 1 #> 2: 0.01 3 0.04 0.007 2022-06-24 04:27:32.42 1 #> warnings errors resample_result #> 1: 0 0 <ResampleResult> #> 2: 0 0 <ResampleResult> ### error handling # get a learner which breaks with 50% probability # set encapsulation + fallback learner = lrn("classif.debug", error_train = 0.5) learner$encapsulate = c(train = "evaluate", predict = "evaluate") learner$fallback = lrn("classif.featureless") # define search space search_space = ps( x = p_dbl(lower = 0, upper = 1) ) instance = TuningInstanceSingleCrit$new( task = tsk("wine"), learner = learner, resampling = rsmp("cv", folds = 3), measure = msr("classif.ce"), search_space = search_space, terminator = trm("evals", n_evals = 5) ) instance$eval_batch(data.table(x = 1:5 / 5))