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Single Criterion Feature Selection with Rush

Source: R/FSelectInstanceAsyncSingleCrit.R
FSelectInstanceAsyncSingleCrit.Rd

The FSelectInstanceAsyncSingleCrit specifies a feature selection problem for a FSelectorAsync. The function fsi_async() creates a FSelectInstanceAsyncSingleCrit and the function fselect() creates an instance internally.

Default Measures

If no measure is passed, the default measure is used. The default measure depends on the task type.

TaskDefault MeasurePackage
"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

Analysis

For analyzing the feature selection results, it is recommended to pass the ArchiveAsyncFSelect to as.data.table(). The returned data table contains the mlr3::ResampleResult for each feature subset evaluation.

Resources

There are several sections about feature selection in the mlr3book.

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

Super classes

bbotk::OptimInstance -> bbotk::OptimInstanceAsync -> bbotk::OptimInstanceAsyncSingleCrit -> FSelectInstanceAsyncSingleCrit

Methods

Public methods

Inherited methods

Method new()

Creates a new instance of this R6 class.

Usage

FSelectInstanceAsyncSingleCrit$new(
  task,
  learner,
  resampling,
  measure = NULL,
  terminator,
  store_benchmark_result = TRUE,
  store_models = FALSE,
  check_values = FALSE,
  callbacks = NULL,
  ties_method = "least_features",
  rush = NULL
)

Arguments

task

(mlr3::Task)
Task to operate on.

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

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

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.

rush

(Rush)
If a rush instance is supplied, the optimization runs without batches.

Method assign_result()

The FSelectorAsync object writes the best found point and estimated performance value here. For internal use.

Usage

FSelectInstanceAsyncSingleCrit$assign_result(xdt, y, extra = NULL, ...)

Arguments

xdt

(data.table::data.table())
x values as data.table. Each row is one point. Contains the value in the search space of the FSelectInstanceBatchMultiCrit object. Can contain additional columns for extra information.

y

(numeric(1))
Optimal outcome.

extra

(data.table::data.table())
Additional information.

...

(any)
ignored.

Method clone()

The objects of this class are cloneable with this method.

Usage

FSelectInstanceAsyncSingleCrit$clone(deep = FALSE)

Arguments

deep

Whether to make a deep clone.