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mlr3fselect (version 0.12.0)

FSelectInstanceMultiCrit: Class for Multi Criteria Feature Selection

Description

The FSelectInstanceMultiCrit specifies a feature selection problem for FSelectors. The function fsi() creates a FSelectInstanceMultiCrit and the function fselect() creates an instance internally.

Arguments

Resources

There are several sections about feature selection in the mlr3book.

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

Analysis

For analyzing the feature selection results, it is recommended to pass the archive to as.data.table(). The returned data table is joined with the benchmark result which adds the mlr3::ResampleResult for each feature set.

The archive provides various getters (e.g. $learners()) to ease the access. All getters extract by position (i) or unique hash (uhash). For a complete list of all getters see the methods section.

The benchmark result ($benchmark_result) allows to score the feature sets again on a different measure. Alternatively, measures can be supplied to as.data.table().

Super classes

bbotk::OptimInstance -> bbotk::OptimInstanceMultiCrit -> FSelectInstanceMultiCrit

Active bindings

result_feature_set

(list of character())
Feature sets for task subsetting.

Methods

Inherited methods

Method new()

Creates a new instance of this R6 class.

Usage

FSelectInstanceMultiCrit$new(
  task,
  learner,
  resampling,
  measures,
  terminator,
  store_benchmark_result = TRUE,
  store_models = FALSE,
  check_values = FALSE,
  callbacks = list()
)

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.

measures

(list of mlr3::Measure)
Measures to optimize. If NULL, mlr3's default measure is used.

terminator

(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 CallbackFSelect)
List of callbacks.

Method assign_result()

The FSelector object writes the best found feature subsets and estimated performance values here. For internal use.

Usage

FSelectInstanceMultiCrit$assign_result(xdt, ydt)

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 FSelectInstanceMultiCrit object. Can contain additional columns for extra information.

ydt

(data.table::data.table())
Optimal outcomes, e.g. the Pareto front.

Method print()

Printer.

Usage

FSelectInstanceMultiCrit$print(...)

Arguments

...

(ignored).

Method clone()

The objects of this class are cloneable with this method.

Usage

FSelectInstanceMultiCrit$clone(deep = FALSE)

Arguments

deep

Whether to make a deep clone.

Examples

Run this code
# Feature selection on Palmer Penguins data set
# \donttest{

task = tsk("penguins")

# Construct feature selection instance
instance = fsi(
  task = task,
  learner = lrn("classif.rpart"),
  resampling = rsmp("cv", folds = 3),
  measures = msrs(c("classif.ce", "time_train")),
  terminator = trm("evals", n_evals = 4)
)

# Choose optimization algorithm
fselector = fs("random_search", batch_size = 2)

# Run feature selection
fselector$optimize(instance)

# Optimal feature sets
instance$result_feature_set

# Inspect all evaluated sets
as.data.table(instance$archive)
# }

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