Learn R Programming
mlr3fselect (version 0.12.0)

AutoFSelector: Class for Automatic Feature Selection

Description

The AutoFSelector wraps a mlr3::Learner and augments it with an automatic feature selection. The auto_fselector() function creates an AutoFSelector object.

Arguments

Resources

There are several sections about feature selection in the mlr3book.

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

Nested Resampling

Nested resampling can be performed 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 (see examples). The mlr3::Resampling passed to the AutoFSelector 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.

Super class

mlr3::Learner -> AutoFSelector

Public fields

instance_args

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

fselector

(FSelector)
Optimization 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

(data.table::data.table)
Short-cut to $result from FSelectInstanceSingleCrit.

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

AutoFSelector$new(
  fselector,
  learner,
  resampling,
  measure = NULL,
  terminator,
  store_fselect_instance = TRUE,
  store_benchmark_result = TRUE,
  store_models = FALSE,
  check_values = FALSE,
  callbacks = list(),
  ties_method = "least_features"
)

Arguments

fselector

(FSelector)
Optimization algorithm.

learner

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

resampling

(mlr3::Resampling)
Resampling that is used to evaluated the performance of the feature subsets. Uninstantiated resamplings are instantiated during construction so that all feature subsets are evaluated on the same data splits. Already instantiated resamplings are kept unchanged.

measure

(mlr3::Measure)
Measure to optimize. If NULL, default measure is used.

terminator

(Terminator)
Stop criterion of the feature selection.

store_fselect_instance

(logical(1))
If TRUE (default), stores the internally created FSelectInstanceSingleCrit with all intermediate results in slot $fselect_instance. Is set to TRUE, if store_models = TRUE

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?

callbacks

(list of CallbackFSelect)
List of callbacks.

ties_method

(character(1))
The method to break ties when selecting sets while optimizing and when selecting the best set. Can be "least_features" or "random". The option "least_features" (default) selects the feature set with the least features. If there are multiple best feature sets with the same number of features, one is selected randomly. The random method returns a random feature set from the best feature sets. Ignored if multiple measures are used.

Method base_learner()

Extracts the base learner from nested learner objects like GraphLearner in mlr3pipelines. If recursive = 0, the (tuned) learner is returned.

Usage

AutoFSelector$base_learner(recursive = Inf)

Arguments

recursive

(integer(1))
Depth of recursion for multiple nested objects.

Returns

Learner.

Method importance()

The importance scores of the final model.

Usage

AutoFSelector$importance()

Returns

Named numeric().

Method selected_features()

The selected features of the final model. These features are selected internally by the learner.

Usage

AutoFSelector$selected_features()

Returns

character().

Method oob_error()

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

Usage

AutoFSelector$oob_error()

Returns

numeric(1).

Method loglik()

The log-likelihood of the final model.

Usage

AutoFSelector$loglik()

Returns

logLik. Printer.

Method print()

Usage

AutoFSelector$print()

Arguments

...

(ignored).

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.

Details

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.

Examples

Run this code
# Automatic Feature Selection
# \donttest{

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

# create auto fselector
afs = auto_fselector(
  fselector = fs("random_search"),
  learner = lrn("classif.rpart"),
  resampling = rsmp ("holdout"),
  measure = msr("classif.ce"),
  term_evals = 4)

# optimize feature subset and fit final model
afs$train(task, row_ids = split$train)

# predict with final model
afs$predict(task, row_ids = split$test)

# show result
afs$fselect_result

# model slot contains trained learner and fselect instance
afs$model

# shortcut trained learner
afs$learner

# shortcut fselect instance
afs$fselect_instance


# Nested Resampling

afs = auto_fselector(
  fselector = fs("random_search"),
  learner = lrn("classif.rpart"),
  resampling = rsmp ("holdout"),
  measure = msr("classif.ce"),
  term_evals = 4)

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

# retrieve inner feature selection results.
extract_inner_fselect_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()
# }

Run the code above in your browser using DataLab