ContextEval | R Documentation |
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.
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.
mlr3misc::Context
-> ContextEval
objective_tuning
ObjectiveTuning.
xss
(list())
The hyperparameter configurations of the latest batch.
Contains the values on the learner scale i.e. transformations are applied.
See $xdt
in bbotk::ContextOptimization for the untransformed values.
design
(data.table::data.table)
The benchmark design of the latest batch.
benchmark_result
(mlr3::BenchmarkResult)
The benchmark result of the latest batch.
aggregated_performance
(data.table::data.table)
Aggregated performance scores and training time of the latest batch.
This data table is passed to the archive.
A callback can add additional columns which are also written to the archive.
new()
Creates a new instance of this R6 class.
ContextEval$new(objective_tuning)
objective_tuning
ObjectiveTuning.
id
(character(1)
)
Identifier for the new callback.
clone()
The objects of this class are cloneable with this method.
ContextEval$clone(deep = FALSE)
deep
Whether to make a deep clone.
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