AutoTuner {mlr3tuning}

Class 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.

Details

The AutoTuner is a mlr3::Learner which wraps another mlr3::Learner and performs the following steps during ⁠$train()⁠:

1. 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.

2. 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.

3. 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

Both, the tuned mlr3::Learner and the AutoTuner itself can make use of validation data. the ⁠$validate⁠ field of the AutoTuner determines how validation is done during the final model fit. In most cases, this should be left as NULL. The ⁠$validate⁠ field of the tuned mlr3::Learner specifies how the validation data is constructed during the hyperparameter optimization.

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.

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

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.

validate

How to construct the internal validation data. This parameter can be either NULL, a ratio in $(0, 1)$, "test", or "predefined".

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

Public methods

• AutoTuner$new()

• AutoTuner$base_learner()

• AutoTuner$importance()

• AutoTuner$selected_features()

• AutoTuner$oob_error()

• AutoTuner$loglik()

• AutoTuner$print()

• AutoTuner$marshal()

• AutoTuner$unmarshal()

• AutoTuner$marshaled()

• AutoTuner$clone()

Inherited methods

• mlr3::Learner$format()
• mlr3::Learner$help()
• mlr3::Learner$predict()
• mlr3::Learner$predict_newdata()
• mlr3::Learner$reset()
• mlr3::Learner$train()

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,
  callbacks = NULL,
  rush = NULL,
  validate = 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).

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.

validate

(numeric(1), "test", "predefined" or NULL)
How to construct the internal validation data.

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)

# show tuning result
at$tuning_result

# model slot contains trained learner and tuning instance
at$model

# shortcut trained learner
at$learner

# shortcut tuning instance
at$tuning_instance


# 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)

# performance scores estimated on the outer resampling
rr$score()

# unbiased performance of the final model trained on the full data set
rr$aggregate()