Specifies a general feature selection scenario, including objective function and archive for feature selection algorithms to act upon. This class stores an ObjectiveFSelect object that encodes the black box objective function which an FSelector has to optimize. It allows the basic operations of querying the objective at feature subsets ($eval_batch()), storing the evaluations in the internal bbotk::Archive and accessing the final result ($result).

Evaluations of feature subsets are performed in batches by calling mlr3::benchmark() internally. Before a batch is evaluated, the bbotk::Terminator is queried for the remaining budget. If the available budget is exhausted, an exception is raised, and no further evaluations can be performed from this point on.

The FSelector is also supposed to store its final result, consisting of the selected feature subsets and associated estimated performance values, by calling the method instance$assign_result(). ## Super classes bbotk::OptimInstance -> bbotk::OptimInstanceMultiCrit -> FSelectInstanceMultiCrit ## Active bindings result_feature_set (list() of character()) Feature sets for task subsetting. ## Methods ### Public methods Inherited methods ### Method new() Creates a new instance of this R6 class. #### Usage FSelectInstanceMultiCrit$new(
learner,
resampling,
measures,
terminator,
store_models = FALSE,
check_values = TRUE,
store_benchmark_result = TRUE
)

#### Arguments

task

learner
resampling

(mlr3::Resampling)
Uninstantiated resamplings are instantiated during construction so that all configurations are evaluated on the same data splits.

measures

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

terminator
store_models

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

check_values

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

store_benchmark_result

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

### Method assign_result()

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

#### Arguments

deep

Whether to make a deep clone.

## Examples

library(mlr3)
library(data.table)

# Objects required to define the performance evaluator
measures = msrs(c("classif.ce", "classif.acc"))
learner = lrn("classif.rpart")
resampling = rsmp("cv")
terminator = trm("evals", n_evals = 8)

inst = FSelectInstanceMultiCrit$new( task = task, learner = learner, resampling = resampling, measures = measures, terminator = terminator ) # Try some feature subsets xdt = data.table( Petal.Length = c(TRUE, FALSE), Petal.Width = c(FALSE, TRUE), Sepal.Length = c(TRUE, FALSE), Sepal.Width = c(FALSE, TRUE) ) inst$eval_batch(xdt)

# Get archive data
inst$archive$data()
#>    Petal.Length Petal.Width Sepal.Length Sepal.Width classif.ce classif.acc
#> 1:         TRUE       FALSE         TRUE       FALSE 0.06000000   0.9400000
#> 2:        FALSE        TRUE        FALSE        TRUE 0.04666667   0.9533333
#>                                   uhash  x_domain           timestamp batch_nr
#> 1: bf5347f1-a256-4a9e-b2a2-ba653972a211 <list[4]> 2020-10-31 04:26:11        1
#> 2: 07e78f07-82e2-4f3c-9578-503868bdb1df <list[4]> 2020-10-31 04:26:11        1