Specialized bbotk::CallbackBatch for batch tuning.
Callbacks allow to customize the behavior of processes in mlr3tuning.
The callback_batch_tuning() function creates a CallbackBatchTuning.
Predefined callbacks are stored in the dictionary mlr_callbacks and can be retrieved with clbk().
For more information on tuning callbacks see callback_batch_tuning().
Super classes
mlr3misc::Callback -> bbotk::CallbackBatch -> CallbackBatchTuning
Public fields
on_eval_after_design(
function())
Stage called after design is created. Called inObjectiveTuningBatch$eval_many().on_resample_begin(
function())
Stage called at the beginning of an evaluation. Called inworkhorse()(internal).on_resample_before_train(
function())
Stage called before training the learner. Called inworkhorse()(internal).on_resample_before_predict(
function())
Stage called before predicting. Called inworkhorse()(internal).on_resample_end(
function())
Stage called at the end of an evaluation. Called inworkhorse()(internal).on_eval_after_benchmark(
function())
Stage called after hyperparameter configurations are evaluated. Called inObjectiveTuningBatch$eval_many().on_eval_before_archive(
function())
Stage called before performance values are written to the archive. Called inObjectiveTuningBatch$eval_many().on_tuning_result_begin(
function())
Stage called before the results are written. Called inTuningInstance*$assign_result().
Examples
# write archive to disk
callback_batch_tuning("mlr3tuning.backup",
on_optimization_end = function(callback, context) {
saveRDS(context$instance$archive, "archive.rds")
}
)
#> <CallbackBatchTuning:mlr3tuning.backup>
#> * Active Stages: on_optimization_end
