Inherited methods
Method new()
Creates a new instance of this R6 class.
Usage
AutoTuner$new(
tuner,
learner,
resampling,
measure = NULL,
terminator,
search_space = NULL,
store_tuning_instance = TRUE,
store_benchmark_result = TRUE,
store_models = FALSE,
check_values = FALSE,
allow_hotstart = FALSE,
keep_hotstart_stack = FALSE,
evaluate_default = FALSE,
callbacks = list()
)
Arguments
tuner
(Tuner)
Optimization algorithm.
learner
(mlr3::Learner)
Learner to tune.
resampling
(mlr3::Resampling)
Resampling that is used to evaluate the performance of the hyperparameter configurations.
Uninstantiated resamplings are instantiated during construction so that all configurations are evaluated on the same data splits.
Already instantiated resamplings are kept unchanged.
Specialized Tuner change the resampling e.g. to evaluate a hyperparameter configuration on different data splits.
This field, however, always returns the resampling passed in construction.
measure
(mlr3::Measure)
Measure to optimize. If NULL
, default measure is used.
terminator
(Terminator)
Stop criterion of the tuning process.
search_space
(paradox::ParamSet)
Hyperparameter search space. If NULL
(default), the search space is
constructed from the TuneToken of the learner's parameter set
(learner$param_set).
store_tuning_instance
(logical(1)
)
If TRUE
(default), stores the internally created TuningInstanceSingleCrit with all intermediate results in slot $tuning_instance
.
store_benchmark_result
(logical(1)
)
If TRUE
(default), store resample result of evaluated hyperparameter
configurations in archive as mlr3::BenchmarkResult.
store_models
(logical(1)
)
If TRUE
, fitted models are stored in the benchmark result
(archive$benchmark_result
). If 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.
check_values
(logical(1)
)
If TRUE
, hyperparameter values are checked before evaluation and
performance scores after. If FALSE
(default), values are unchecked but
computational overhead is reduced.
allow_hotstart
(logical(1)
)
Allow to hotstart learners with previously fitted models. See also
mlr3::HotstartStack. The learner must support hotstarting. Sets
store_models = TRUE
.
keep_hotstart_stack
(logical(1)
)
If TRUE
, mlr3::HotstartStack is kept in $objective$hotstart_stack
after tuning.
evaluate_default
(logical(1)
)
If TRUE
, learner is evaluated with hyperparameters set to their default
values at the start of the optimization.
callbacks
(list of CallbackTuning)
List of callbacks.
Method base_learner()
Extracts the base learner from nested learner objects like GraphLearner
in mlr3pipelines.
If recursive = 0
, the (tuned) learner is returned.
Usage
AutoTuner$base_learner(recursive = Inf)
Arguments
recursive
(integer(1)
)
Depth of recursion for multiple nested objects.
Method importance()
The importance scores of the final model.
Usage
AutoTuner$importance()
Method selected_features()
The selected features of the final model.
Usage
AutoTuner$selected_features()
Method oob_error()
The out-of-bag error of the final model.
Usage
AutoTuner$oob_error()
Method loglik()
The log-likelihood of the final model.
Method clone()
The objects of this class are cloneable with this method.
Usage
AutoTuner$clone(deep = FALSE)
Arguments
deep
Whether to make a deep clone.