View source: R/CallbackBatchTuning.R
callback_batch_tuning | R Documentation |
Function to create a CallbackBatchTuning.
Predefined callbacks are stored in the dictionary mlr_callbacks and can be retrieved with clbk()
.
Tuning callbacks can be called from different stages of the 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 Start Resampling Iteration - on_resample_begin - on_resample_before_train - on_resample_before_predict - on_resample_end End Resampling Iteration - on_eval_after_benchmark - on_eval_before_archive End Evaluation - on_optimizer_after_eval End Tuner Batch - on_tuning_result_begin - on_result_begin - on_result_end - on_optimization_end End Tuning
See also the section on parameters for more information on the stages. A tuning callback works with ContextBatchTuning and mlr3::ContextResample.
callback_batch_tuning(
id,
label = NA_character_,
man = NA_character_,
on_optimization_begin = NULL,
on_optimizer_before_eval = NULL,
on_eval_after_design = NULL,
on_resample_begin = NULL,
on_resample_before_train = NULL,
on_resample_before_predict = NULL,
on_resample_end = NULL,
on_eval_after_benchmark = NULL,
on_eval_before_archive = NULL,
on_optimizer_after_eval = NULL,
on_tuning_result_begin = NULL,
on_result_begin = NULL,
on_result_end = NULL,
on_result = NULL,
on_optimization_end = NULL
)
id |
( |
label |
( |
man |
( |
on_optimization_begin |
( |
on_optimizer_before_eval |
( |
on_eval_after_design |
( |
on_resample_begin |
( |
on_resample_before_train |
( |
on_resample_before_predict |
( |
on_resample_end |
( |
on_eval_after_benchmark |
( |
on_eval_before_archive |
( |
on_optimizer_after_eval |
( |
on_tuning_result_begin |
( |
on_result_begin |
( |
on_result_end |
( |
on_result |
( |
on_optimization_end |
( |
When implementing a callback, each function must have two arguments named callback
and context
.
A callback can write data to the state ($state
), e.g. settings that affect the callback itself.
Tuning callbacks access ContextBatchTuning.
# write archive to disk
callback_batch_tuning("mlr3tuning.backup",
on_optimization_end = function(callback, context) {
saveRDS(context$instance$archive, "archive.rds")
}
)
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