View source: R/CallbackTuning.R
callback_tuning | R Documentation |
Function to create a CallbackTuning.
Predefined callbacks are stored in the dictionary mlr_callbacks and can be retrieved with clbk()
.
Tuning callbacks can be called from different stages of tuning process.
The stages are prefixed with on_*
.
Start Tuning - on_optimization_begin Start Tuner 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 Tuner Batch - on_result - on_optimization_end End Tuning
See also the section on parameters for more information on the stages. A tuning callback works with bbotk::ContextOptimization and ContextEval.
callback_tuning(
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
)
id |
( |
label |
( |
man |
( |
on_optimization_begin |
( |
on_optimizer_before_eval |
( |
on_eval_after_design |
( |
on_eval_after_benchmark |
( |
on_eval_before_archive |
( |
on_optimizer_after_eval |
( |
on_result |
( |
on_optimization_end |
( |
When implementing a callback, each functions 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 tuning process when the evaluation of configurations is parallelized.
Tuning callbacks access two different contexts depending on the stage.
The stages on_eval_after_design
, on_eval_after_benchmark
, on_eval_before_archive
access ContextEval.
This context can be used to customize the evaluation of a batch of hyperparameter configurations.
Changes to the state of callback are lost after the evaluation of a batch and changes to the tuning instance or the tuner are not possible.
Persistent data should be written to the archive via $aggregated_performance
(see ContextEval).
The other stages access ContextOptimization.
This context can be used to modify the tuning instance, archive, tuner and final result.
There are two different contexts because the evaluation can be parallelized i.e. multiple instances of ContextEval exists on different workers at the same time.
# write archive to disk
callback_tuning("mlr3tuning.backup",
on_optimization_end = function(callback, context) {
saveRDS(context$instance$archive, "archive.rds")
}
)
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