mlr3fselect (version 0.3.0)

FSelectInstanceMultiCrit: Multi Criterion Feature Selection Instance

Description

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

• FSelectInstanceMultiCrit$new()

• FSelectInstanceMultiCrit$assign_result()

• FSelectInstanceMultiCrit$clone()

• bbotk::OptimInstance$eval_batch()

• bbotk::OptimInstance$format()

• bbotk::OptimInstance$objective_function()

• bbotk::OptimInstance$print()

Method new()

Creates a new instance of this R6 class.

Usage

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

Arguments

task

(mlr3::Task) Task to operate on.

learner

(mlr3::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

(bbotk::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.

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 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

# NOT RUN {
library(mlr3)
library(data.table)

# Objects required to define the performance evaluator
task = tsk("iris")
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()
# }