callback_batch_fselect: Create Feature Selection Callback

In mlr3fselect: Feature Selection for 'mlr3'

Create Feature Selection Callback

Description

Function to create a CallbackBatchFSelect. Predefined callbacks are stored in the dictionary mlr_callbacks and can be retrieved with clbk().

Feature selection callbacks can be called from different stages of feature selection. The stages are prefixed with ⁠on_*⁠.

Start Feature Selection
     - on_optimization_begin
    Start FSelect Batch
         - on_optimizer_before_eval
        Start Evaluation
             - on_eval_after_design
             - on_eval_after_benchmark
             - on_eval_before_archive
        End Evaluation
         - on_optimizer_after_eval
    End FSelect Batch
     - on_result
     - on_optimization_end
End Feature Selection

See also the section on parameters for more information on the stages. A feature selection callback works with bbotk::ContextBatch and ContextBatchFSelect.

Usage

callback_batch_fselect(
  id,
  label = NA_character_,
  man = NA_character_,
  on_optimization_begin = NULL,
  on_optimizer_before_eval = NULL,
  on_eval_after_design = NULL,
  on_eval_after_benchmark = NULL,
  on_eval_before_archive = NULL,
  on_optimizer_after_eval = NULL,
  on_result = NULL,
  on_optimization_end = NULL
)

Arguments

id	(character(1)) Identifier for the new instance.
label	(character(1)) Label for the new instance.
man	(character(1)) String in the format ⁠[pkg]::[topic]⁠ pointing to a manual page for this object. The referenced help package can be opened via method ⁠$help()⁠.
on_optimization_begin	(⁠function()⁠) Stage called at the beginning of the optimization. Called in Optimizer$optimize().
on_optimizer_before_eval	(⁠function()⁠) Stage called after the optimizer proposes points. Called in OptimInstance$eval_batch().
on_eval_after_design	(⁠function()⁠) Stage called after design is created. Called in ObjectiveFSelectBatch$eval_many().
on_eval_after_benchmark	(⁠function()⁠) Stage called after feature sets are evaluated. Called in ObjectiveFSelectBatch$eval_many().
on_eval_before_archive	(⁠function()⁠) Stage called before performance values are written to the archive. Called in ObjectiveFSelectBatch$eval_many().
on_optimizer_after_eval	(⁠function()⁠) Stage called after points are evaluated. Called in OptimInstance$eval_batch().
on_result	(⁠function()⁠) Stage called after result are written. Called in OptimInstance$assign_result().
on_optimization_end	(⁠function()⁠) Stage called at the end of the optimization. Called in Optimizer$optimize().

Details

When implementing a callback, each function must have two arguments named callback and context.

A callback can write data to the state (⁠$state⁠), e.g. settings that affect the callback itself. Avoid writing large data the state. This can slow down the feature selection when the evaluation of configurations is parallelized.

Feature selection callbacks access two different contexts depending on the stage. The stages on_eval_after_design, on_eval_after_benchmark, on_eval_before_archive access ContextBatchFSelect. This context can be used to customize the evaluation of a batch of feature sets. Changes to the state of callback are lost after the evaluation of a batch and changes to the fselect instance or the fselector are not possible. Persistent data should be written to the archive via ⁠$aggregated_performance⁠ (see ContextBatchFSelect). The other stages access bbotk::ContextBatch. This context can be used to modify the fselect instance, archive, fselector and final result. There are two different contexts because the evaluation can be parallelized i.e. multiple instances of ContextBatchFSelect exists on different workers at the same time.

Examples

# Write archive to disk
callback_batch_fselect("mlr3fselect.backup",
  on_optimization_end = function(callback, context) {
    saveRDS(context$instance$archive, "archive.rds")
  }
)

mlr3fselect documentation built on Sept. 11, 2024, 7:36 p.m.