Class for Automatic Tuning

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.

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$learner. Access the tuned hyperparameters via at$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.

Validation

The AutoTuner itself does not have the "validation" property. To enable validation during the tuning, set the $validate field of the tuned learner. This is also possible via set_validate().

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.

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.

Getting started with hyperparameter optimization.
An overview of all tuners can be found on our website.
Tune a support vector machine on the Sonar data set.
Learn about tuning spaces.
Estimate the model performance with nested resampling.
Learn about multi-objective optimization.
Simultaneously optimize hyperparameters and use early stopping with XGBoost.
Automate the tuning.

The gallery features a collection of case studies and demos about optimization.

Learn more advanced methods with the Practical Tuning Series.
Learn about hotstarting models.
Run the default hyperparameter configuration of learners as a baseline.
Use the Hyperband optimizer with different budget parameters.

The cheatsheet summarizes the most important functions of mlr3tuning.

Super class

mlr3::Learner -> AutoTuner

Public fields

instance_args: (list())
All arguments from construction to create the TuningInstanceBatchSingleCrit.
tuner: (Tuner)
Optimization algorithm.

Active bindings

internal_valid_scores: Retrieves the inner validation scores as a named list(). Returns NULL if learner is not trained yet.
archive: ArchiveBatchTuning
Archive of the TuningInstanceBatchSingleCrit.
learner: (mlr3::Learner)
Trained learner
tuning_instance: (TuningInstanceAsyncSingleCrit | TuningInstanceBatchSingleCrit)
Internally created tuning instance with all intermediate results.
tuning_result: (data.table::data.table)
Short-cut to result from tuning instance.
predict_type: (character(1))
Stores the currently active predict type, e.g. "response". Must be an element of $predict_types.
hash: (character(1))
Hash (unique identifier) for this object.
phash: (character(1))
Hash (unique identifier) for this partial object, excluding some components which are varied systematically during tuning (parameter values) or feature selection (feature names).

Methods

Inherited methods

Method `new()`

Creates a new instance of this R6 class.

Usage

AutoTuner$new(
  tuner,
  learner,
  resampling,
  measure = NULL,
  terminator,
  search_space = NULL,
  internal_search_space = NULL,
  store_tuning_instance = TRUE,
  store_benchmark_result = TRUE,
  store_models = FALSE,
  check_values = FALSE,
  callbacks = NULL,
  rush = NULL,
  id = NULL
)

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: (bbotk::Terminator)
Stop criterion of the tuning process.
search_space: (paradox::ParamSet)
Hyperparameter search space. If NULL (default), the search space is constructed from the paradox::TuneToken of the learner's parameter set (learner$param_set).
internal_search_space: (paradox::ParamSet or NULL)
The internal search space.
store_tuning_instance: (logical(1))
If TRUE (default), stores the internally created TuningInstanceBatchSingleCrit 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.
callbacks: (list of mlr3misc::Callback)
List of callbacks.
rush: (Rush)
If a rush instance is supplied, the tuning runs without batches.
id: (character(1))
Identifier for the new instance.

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.

Returns

mlr3::Learner.

Method `importance()`

The importance scores of the final model.

Usage

AutoTuner$importance()

Returns

Named numeric().

Method `selected_features()`

The selected features of the final model.

Usage

AutoTuner$selected_features()

Returns

character().

Method `oob_error()`

The out-of-bag error of the final model.

Usage

AutoTuner$oob_error()

Returns

numeric(1).

Method `loglik()`

The log-likelihood of the final model.

Usage

AutoTuner$loglik()

Returns

logLik. Printer.

Method `print()`

Usage

AutoTuner$print()

Arguments

...: (ignored).

Method `marshal()`

Marshal the learner.

Usage

AutoTuner$marshal(...)

Arguments

...: (any)
Additional parameters.

Returns

self

Method `unmarshal()`

Unmarshal the learner.

Usage

AutoTuner$unmarshal(...)

Arguments

...: (any)
Additional parameters.

Returns

self

Method `marshaled()`

Whether the learner is marshaled.

Usage

AutoTuner$marshaled()

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.

Examples

# Automatic Tuning

# split to train and external set
task = tsk("penguins")
split = partition(task, ratio = 0.8)

# load learner and set search space
learner = lrn("classif.rpart",
  cp = to_tune(1e-04, 1e-1, logscale = TRUE)
)

# create auto tuner
at = auto_tuner(
  tuner = tnr("random_search"),
  learner = learner,
  resampling = rsmp ("holdout"),
  measure = msr("classif.ce"),
  term_evals = 4)

# tune hyperparameters and fit final model
at$train(task, row_ids = split$train)

# predict with final model
at$predict(task, row_ids = split$test)
#> <PredictionClassif> for 69 observations:
#>  row_ids     truth  response
#>        1    Adelie    Adelie
#>        2    Adelie    Adelie
#>        9    Adelie    Adelie
#>      ---       ---       ---
#>      318 Chinstrap Chinstrap
#>      334 Chinstrap Chinstrap
#>      338 Chinstrap Chinstrap

# show tuning result
at$tuning_result
#>           cp learner_param_vals  x_domain classif.ce
#>        <num>             <list>    <list>      <num>
#> 1: -4.797088          <list[2]> <list[1]> 0.05434783

# model slot contains trained learner and tuning instance
at$model
#> $learner
#> <LearnerClassifRpart:classif.rpart>: Classification Tree
#> * Model: rpart
#> * Parameters: cp=0.008254, xval=0
#> * Packages: mlr3, rpart
#> * Predict Types:  [response], prob
#> * Feature Types: logical, integer, numeric, factor, ordered
#> * Properties: importance, missings, multiclass, selected_features,
#>   twoclass, weights
#> 
#> $tuning_instance
#> <TuningInstanceBatchSingleCrit>
#> * State:  Optimized
#> * Objective: <ObjectiveTuningBatch:classif.rpart_on_penguins>
#> * Search Space:
#>        id    class    lower     upper nlevels
#>    <char>   <char>    <num>     <num>   <num>
#> 1:     cp ParamDbl -9.21034 -2.302585     Inf
#> * Terminator: <TerminatorEvals>
#> * Result:
#>           cp classif.ce
#>        <num>      <num>
#> 1: -4.797088 0.05434783
#> * Archive:
#>           cp classif.ce
#>        <num>      <num>
#> 1: -2.529580 0.08695652
#> 2: -4.797088 0.05434783
#> 3: -2.447415 0.08695652
#> 4: -4.854704 0.05434783
#> 
#> attr(,"class")
#> [1] "auto_tuner_model" "list"            

# shortcut trained learner
at$learner
#> <LearnerClassifRpart:classif.rpart>: Classification Tree
#> * Model: rpart
#> * Parameters: cp=0.008254, xval=0
#> * Packages: mlr3, rpart
#> * Predict Types:  [response], prob
#> * Feature Types: logical, integer, numeric, factor, ordered
#> * Properties: importance, missings, multiclass, selected_features,
#>   twoclass, weights

# shortcut tuning instance
at$tuning_instance
#> <TuningInstanceBatchSingleCrit>
#> * State:  Optimized
#> * Objective: <ObjectiveTuningBatch:classif.rpart_on_penguins>
#> * Search Space:
#>        id    class    lower     upper nlevels
#>    <char>   <char>    <num>     <num>   <num>
#> 1:     cp ParamDbl -9.21034 -2.302585     Inf
#> * Terminator: <TerminatorEvals>
#> * Result:
#>           cp classif.ce
#>        <num>      <num>
#> 1: -4.797088 0.05434783
#> * Archive:
#>           cp classif.ce
#>        <num>      <num>
#> 1: -2.529580 0.08695652
#> 2: -4.797088 0.05434783
#> 3: -2.447415 0.08695652
#> 4: -4.854704 0.05434783


# Nested Resampling

at = auto_tuner(
  tuner = tnr("random_search"),
  learner = learner,
  resampling = rsmp ("holdout"),
  measure = msr("classif.ce"),
  term_evals = 4)

resampling_outer = rsmp("cv", folds = 3)
rr = resample(task, at, resampling_outer, store_models = TRUE)

# retrieve inner tuning results.
extract_inner_tuning_results(rr)
#>    iteration        cp classif.ce learner_param_vals  x_domain  task_id
#>        <int>     <num>      <num>             <list>    <list>   <char>
#> 1:         1 -2.421343 0.06578947          <list[2]> <list[1]> penguins
#> 2:         2 -7.917442 0.06493506          <list[2]> <list[1]> penguins
#> 3:         3 -8.698664 0.03947368          <list[2]> <list[1]> penguins
#>             learner_id resampling_id
#>                 <char>        <char>
#> 1: classif.rpart.tuned            cv
#> 2: classif.rpart.tuned            cv
#> 3: classif.rpart.tuned            cv

# performance scores estimated on the outer resampling
rr$score()
#>     task_id          learner_id resampling_id iteration classif.ce
#>      <char>              <char>        <char>     <int>      <num>
#> 1: penguins classif.rpart.tuned            cv         1 0.06956522
#> 2: penguins classif.rpart.tuned            cv         2 0.06956522
#> 3: penguins classif.rpart.tuned            cv         3 0.01754386
#> Hidden columns: task, learner, resampling, prediction_test

# unbiased performance of the final model trained on the full data set
rr$aggregate()
#> classif.ce 
#> 0.05222476

Details

Validation

Nested Resampling

Default Measures

Resources

Super class

Public fields

Active bindings

Methods

Public methods

Method new()

Usage

Arguments

Method base_learner()

Usage

Arguments

Returns

Method importance()

Usage

Returns

Method selected_features()

Usage

Returns

Method oob_error()

Usage

Returns

Method loglik()

Usage

Returns

Method print()

Usage

Arguments

Method marshal()

Usage

Arguments

Returns

Method unmarshal()

Usage

Arguments

Returns

Method marshaled()

Usage

Method clone()

Usage

Arguments

Examples

Method `new()`

Method `base_learner()`

Method `importance()`

Method `selected_features()`

Method `oob_error()`

Method `loglik()`

Method `print()`

Method `marshal()`

Method `unmarshal()`

Method `marshaled()`

Method `clone()`