mlr3tuning (version 0.20.0)

ContextEval: 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.

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

• ContextEval$new()

• ContextEval$clone()

Inherited methods

• mlr3misc::Context$format()
• mlr3misc::Context$print()

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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.

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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.

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.