ContextEval: Evaluation Context

ContextEvalR Documentation

Evaluation Context

Description

The ContextEval allows CallbackTunings to access and modify data while a batch of hyperparameter configurations is evaluated. See section on active bindings for a list of modifiable objects. See callback_tuning() for a list of stages which access ContextEval.

Details

This context is re-created each time a new batch of hyperparameter configurations is evaluated. Changes to ⁠$objective_tuning⁠, ⁠$design⁠ ⁠$benchmark_result⁠ are discarded after the function is finished. Modification on the data table in ⁠$aggregated_performance⁠ are written to the archive. Any number of columns can be added.

Super class

mlr3misc::Context -> ContextEval

Public fields

objective_tuning

ObjectiveTuning.

Active bindings

xss

(list())
The hyperparameter configurations of the latest batch. Contains the values on the learner scale i.e. transformations are applied. See ⁠$xdt⁠ in bbotk::ContextOptimization for the untransformed values.

design

(data.table::data.table)
The benchmark design of the latest batch.

benchmark_result

(mlr3::BenchmarkResult)
The benchmark result of the latest batch.

aggregated_performance

(data.table::data.table)
Aggregated performance scores and training time of the latest batch. This data table is passed to the archive. A callback can add additional columns which are also written to the archive.

Methods

Public methods

Inherited methods

Method new()

Creates a new instance of this R6 class.

Usage
ContextEval$new(objective_tuning)
Arguments
objective_tuning

ObjectiveTuning.

id

(character(1))
Identifier for the new callback.


Method clone()

The objects of this class are cloneable with this method.

Usage
ContextEval$clone(deep = FALSE)
Arguments
deep

Whether to make a deep clone.


mlr3tuning documentation built on Nov. 21, 2023, 1:06 a.m.