AutoFSelector

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

1. The wrapped (inner) learner is trained on the feature subsets via resampling. The feature selection can be specified by providing a FSelector, a bbotk::Terminator, a mlr3::Resampling and a mlr3::Measure.

2. A final model is fit on the complete training data with the best found feature subset.

During $predict() the AutoFSelector just calls the predict method of the wrapped (inner) learner.

Note that this approach allows to perform nested resampling by passing an AutoFSelector object to mlr3::resample() or mlr3::benchmark(). To access the inner resampling results, set store_fselect_instance = TRUE and execute mlr3::resample() or mlr3::benchmark() with store_models = TRUE.

Super class

mlr3::Learner -> AutoFSelector

Public fields

instance_args

(list())
All arguments from construction to create the FSelectInstanceSingleCrit.

fselector

(FSelector)
Stores the feature selection algorithm.

Active bindings

archive

([ArchiveFSelect)
Returns FSelectInstanceSingleCrit archive.

learner

(mlr3::Learner)
Trained learner.

fselect_instance

(FSelectInstanceSingleCrit)
Internally created feature selection instance with all intermediate results.

fselect_result

(named list())
Short-cut to $result from FSelectInstanceSingleCrit.

Methods

Public methods

• AutoFSelector$new()

• AutoFSelector$clone()

Inherited methods

• mlr3::Learner$continue()

• mlr3::Learner$format()

• mlr3::Learner$help()

• mlr3::Learner$predict()

• mlr3::Learner$predict_newdata()

• mlr3::Learner$print()

• mlr3::Learner$reset()

• mlr3::Learner$train()

• mlr3::Learner$update()

---

Method new()

Creates a new instance of this R6 class.

Usage

AutoFSelector$new(
  learner,
  resampling,
  measure,
  terminator,
  fselector,
  store_fselect_instance = TRUE,
  store_benchmark_result = TRUE,
  store_models = FALSE,
  check_values = FALSE
)

Arguments

learner

(mlr3::Learner)
Learner to optimize the feature subset for, see FSelectInstanceSingleCrit.

resampling

(mlr3::Resampling)
Resampling strategy during feature selection, see FSelectInstanceSingleCrit. This mlr3::Resampling is meant to be the inner resampling, operating on the training set of an arbitrary outer resampling. For this reason it is not feasible to pass an instantiated mlr3::Resampling here.

measure

(mlr3::Measure)
Performance measure to optimize.

terminator

(bbotk::Terminator)
When to stop feature selection, see FSelectInstanceSingleCrit.

fselector

(FSelector)
Feature selection algorithm to run.

store_fselect_instance

(logical(1))
If TRUE (default), stores the internally created FSelectInstanceSingleCrit with all intermediate results in slot $fselect_instance.

store_benchmark_result

(logical(1))
Store benchmark result in archive?

store_models

(logical(1)). Store models in benchmark result?

check_values

(logical(1))
Check the parameters before the evaluation and the results for validity?

---

Method clone()

The objects of this class are cloneable with this method.

Usage

AutoFSelector$clone(deep = FALSE)

Arguments

deep

Whether to make a deep clone.

Examples

library(mlr3)

task = tsk("iris")
learner = lrn("classif.rpart")
resampling = rsmp("holdout")
measure = msr("classif.ce")

terminator = trm("evals", n_evals = 3)
fselector = fs("exhaustive_search")
afs = AutoFSelector$new(learner, resampling, measure, terminator, fselector,
  store_fselect_instance = TRUE)

afs$train(task)
afs$model
#> $learner
#> <LearnerClassifRpart:classif.rpart>
#> * Model: rpart
#> * Parameters: xval=0
#> * Packages: rpart
#> * Predict Type: response
#> * Feature types: logical, integer, numeric, factor, ordered
#> * Properties: importance, missings, multiclass, selected_features,
#>   twoclass, weights
#> 
#> $features
#> [1] "Petal.Length"
#> 
#> $fselect_instance
#> <FSelectInstanceSingleCrit>
#> * State:  Optimized
#> * Objective: <ObjectiveFSelect:classif.rpart_on_iris>
#> * Search Space:
#> <ParamSet>
#>              id    class lower upper      levels        default value
#> 1: Petal.Length ParamLgl    NA    NA  TRUE,FALSE <NoDefault[3]>      
#> 2:  Petal.Width ParamLgl    NA    NA  TRUE,FALSE <NoDefault[3]>      
#> 3: Sepal.Length ParamLgl    NA    NA  TRUE,FALSE <NoDefault[3]>      
#> 4:  Sepal.Width ParamLgl    NA    NA  TRUE,FALSE <NoDefault[3]>      
#> * Terminator: <TerminatorEvals>
#> * Terminated: TRUE
#> * Result:
#>    Petal.Length Petal.Width Sepal.Length Sepal.Width     features  x_domain
#> 1:         TRUE       FALSE        FALSE       FALSE Petal.Length <list[4]>
#>    classif.ce
#> 1:       0.06
#> * Archive:
#> <ArchiveFSelect>
#>    Petal.Length Petal.Width Sepal.Length Sepal.Width classif.ce
#> 1:         TRUE       FALSE        FALSE       FALSE       0.06
#> 2:        FALSE        TRUE        FALSE       FALSE       0.06
#> 3:        FALSE       FALSE         TRUE       FALSE       0.50
#> 4:        FALSE       FALSE        FALSE        TRUE       0.44
#>                                   uhash  x_domain           timestamp batch_nr
#> 1: 5c677a30-6d99-4368-afce-d03b2c75f2c9 <list[4]> 2020-10-31 04:26:08        1
#> 2: ed181a41-73c1-4d6c-878d-e0645079128c <list[4]> 2020-10-31 04:26:08        1
#> 3: 5c8a7458-f585-417d-b0fe-86c08a59d7cf <list[4]> 2020-10-31 04:26:08        1
#> 4: da527107-1e4a-495f-8210-8cae8b8de267 <list[4]> 2020-10-31 04:26:08        1
#> 
afs$learner
#> <LearnerClassifRpart:classif.rpart>
#> * Model: rpart
#> * Parameters: xval=0
#> * Packages: rpart
#> * Predict Type: response
#> * Feature types: logical, integer, numeric, factor, ordered
#> * Properties: importance, missings, multiclass, selected_features,
#>   twoclass, weights