Feature Selection via Random Search

FSelectorRandomSearch class that implements a simple Random Search.

In order to support general termination criteria and parallelization, we evaluate feature sets in a batch-fashion of size batch_size. Larger batches mean we can parallelize more, smaller batches imply a more fine-grained checking of termination criteria.

Source

Bergstra J, Bengio Y (2012). “Random Search for Hyper-Parameter Optimization.” Journal of Machine Learning Research, 13(10), 281--305. https://jmlr.csail.mit.edu/papers/v13/bergstra12a.html.

Dictionary

This FSelector can be instantiated via the dictionary mlr_fselectors or with the associated sugar function fs():

mlr_fselectors$get("random_search")
fs("random_search")

Parameters

max_features

integer(1)
Maximum number of features. By default, number of features in mlr3::Task.

batch_size

integer(1)
Maximum number of feature sets to try in a batch.

Super class

mlr3fselect::FSelector -> FSelectorRandomSearch

Methods

Public methods

• FSelectorRandomSearch$new()

• FSelectorRandomSearch$clone()

Inherited methods

• mlr3fselect::FSelector$format()

• mlr3fselect::FSelector$optimize()

• mlr3fselect::FSelector$print()

---

Method new()

Creates a new instance of this R6 class.

Usage

FSelectorRandomSearch$new()

---

Method clone()

The objects of this class are cloneable with this method.

Usage

FSelectorRandomSearch$clone(deep = FALSE)

Arguments

deep

Whether to make a deep clone.

Examples

library(mlr3)

terminator = trm("evals", n_evals = 5)

instance = FSelectInstanceSingleCrit$new(
  task = tsk("iris"),
  learner = lrn("classif.rpart"),
  resampling = rsmp("holdout"),
  measure = msr("classif.ce"),
  terminator = terminator
)

fselector = fs("random_search")
# \donttest{
# Modifies the instance by reference
fselector$optimize(instance)
#>    Petal.Length Petal.Width Sepal.Length Sepal.Width                  features
#> 1:         TRUE       FALSE         TRUE       FALSE Petal.Length,Sepal.Length
#>    classif.ce
#> 1:       0.04

# Returns best scoring evaluation
instance$result
#>    Petal.Length Petal.Width Sepal.Length Sepal.Width                  features
#> 1:         TRUE       FALSE         TRUE       FALSE Petal.Length,Sepal.Length
#>    classif.ce
#> 1:       0.04

# Allows access of data.table of full path of all evaluations
as.data.table(instance$archive)# }
#>    Petal.Length Petal.Width Sepal.Length Sepal.Width classif.ce
#> 1:        FALSE        TRUE         TRUE        TRUE       0.06
#> 2:        FALSE       FALSE        FALSE        TRUE       0.40
#> 3:         TRUE       FALSE         TRUE       FALSE       0.04
#> 4:         TRUE       FALSE         TRUE        TRUE       0.04
#> 5:        FALSE       FALSE        FALSE        TRUE       0.40
#>    runtime_learners           timestamp batch_nr      resample_result
#> 1:            0.062 2021-09-17 04:15:56        1 <ResampleResult[20]>
#> 2:            0.066 2021-09-17 04:15:56        2 <ResampleResult[20]>
#> 3:            0.083 2021-09-17 04:15:57        3 <ResampleResult[20]>
#> 4:            0.062 2021-09-17 04:15:57        4 <ResampleResult[20]>
#> 5:            0.068 2021-09-17 04:15:57        5 <ResampleResult[20]>