auto_tuner {mlr3tuning} | R Documentation |
Function for Automatic Tuning
Description
The AutoTuner wraps a mlr3::Learner and augments it with an automatic tuning process for a given set of hyperparameters.
The auto_tuner()
function creates an AutoTuner object.
Usage
auto_tuner(
tuner,
learner,
resampling,
measure = NULL,
term_evals = NULL,
term_time = NULL,
terminator = NULL,
search_space = NULL,
store_tuning_instance = TRUE,
store_benchmark_result = TRUE,
store_models = FALSE,
check_values = FALSE,
callbacks = NULL,
validate = NULL,
rush = NULL
)
Arguments
tuner |
(Tuner) |
learner |
(mlr3::Learner) |
resampling |
(mlr3::Resampling) |
measure |
(mlr3::Measure) |
term_evals |
( |
term_time |
( |
terminator |
(bbotk::Terminator) |
search_space |
(paradox::ParamSet) |
store_tuning_instance |
( |
store_benchmark_result |
( |
store_models |
( |
check_values |
( |
callbacks |
(list of mlr3misc::Callback) |
validate |
( |
rush |
( |
Details
The AutoTuner is a mlr3::Learner which wraps another mlr3::Learner and performs the following steps during $train()
:
The hyperparameters of the wrapped (inner) learner are trained on the training data via resampling. The tuning can be specified by providing a Tuner, a bbotk::Terminator, a search space as paradox::ParamSet, a mlr3::Resampling and a mlr3::Measure.
The best found hyperparameter configuration is set as hyperparameters for the wrapped (inner) learner stored in
at$learner
. Access the tuned hyperparameters viaat$tuning_result
.A final model is fit on the complete training data using the now parametrized wrapped learner. The respective model is available via field
at$learner$model
.
During $predict()
the AutoTuner
just calls the predict method of the wrapped (inner) learner.
A set timeout is disabled while fitting the final model.
Value
Default Measures
If no measure is passed, the default measure is used. The default measure depends on the task type.
Task | Default Measure | Package |
"classif" | "classif.ce" | mlr3 |
"regr" | "regr.mse" | mlr3 |
"surv" | "surv.cindex" | mlr3proba |
"dens" | "dens.logloss" | mlr3proba |
"classif_st" | "classif.ce" | mlr3spatial |
"regr_st" | "regr.mse" | mlr3spatial |
"clust" | "clust.dunn" | mlr3cluster |
Resources
There are several sections about hyperparameter optimization in the mlr3book.
-
Automate the tuning.
Estimate the model performance with nested resampling.
The gallery features a collection of case studies and demos about optimization.
Nested Resampling
Nested resampling is performed by passing an AutoTuner to mlr3::resample()
or mlr3::benchmark()
.
To access the inner resampling results, set store_tuning_instance = TRUE
and execute mlr3::resample()
or mlr3::benchmark()
with store_models = TRUE
(see examples).
The mlr3::Resampling passed to the AutoTuner is meant to be the inner resampling, operating on the training set of an arbitrary outer resampling.
For this reason, the inner resampling should be not instantiated.
If an instantiated resampling is passed, the AutoTuner fails when a row id of the inner resampling is not present in the training set of the outer resampling.
Examples
at = auto_tuner(
tuner = tnr("random_search"),
learner = lrn("classif.rpart", cp = to_tune(1e-04, 1e-1, logscale = TRUE)),
resampling = rsmp ("holdout"),
measure = msr("classif.ce"),
term_evals = 4)
at$train(tsk("pima"))