mlr3fselect (version 1.3.0)

auto_fselector: Function 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.

Usage

auto_fselector(
  fselector,
  learner,
  resampling,
  measure = NULL,
  term_evals = NULL,
  term_time = NULL,
  terminator = NULL,
  store_fselect_instance = TRUE,
  store_benchmark_result = TRUE,
  store_models = FALSE,
  check_values = FALSE,
  callbacks = NULL,
  ties_method = "least_features",
  id = NULL
)

Value

AutoFSelector.

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.

term_evals

(integer(1))
Number of allowed evaluations. Ignored if terminator is passed.

term_time

(integer(1))
Maximum allowed time in seconds. Ignored if terminator is passed.

terminator

(bbotk::Terminator)
Stop criterion of the feature selection.

store_fselect_instance

(logical(1))
If TRUE (default), stores the internally created FSelectInstanceBatchSingleCrit 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 CallbackBatchFSelect)
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.

id

(character(1))
Identifier for the new instance.

Resources

There are several sections about feature selection in the mlr3book.

• Estimate Model Performance with nested resampling.

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.

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

# 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()
# }