| callback_async_tuning | R Documentation |
Create Asynchronous Tuning Callback
Description
Function to create a CallbackAsyncTuning.
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 Worker
- on_worker_begin
Start Optimization on Worker
- on_optimizer_before_eval
Start Evaluation
- on_eval_after_xs
Start Resampling Iteration
- on_resample_begin
- on_resample_before_train
- on_resample_before_predict
- on_resample_end
End Resampling Iteration
- on_eval_after_resample
- on_eval_before_archive
End Evaluation
- on_optimizer_after_eval
End Optimization on Worker
- on_worker_end
End Worker
- 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 ContextAsyncTuning.
Usage
callback_async_tuning(
id,
label = NA_character_,
man = NA_character_,
on_optimization_begin = NULL,
on_worker_begin = NULL,
on_optimizer_before_eval = NULL,
on_eval_after_xs = NULL,
on_resample_begin = NULL,
on_resample_before_train = NULL,
on_resample_before_predict = NULL,
on_resample_end = NULL,
on_eval_after_resample = NULL,
on_eval_before_archive = NULL,
on_optimizer_after_eval = NULL,
on_worker_end = NULL,
on_tuning_result_begin = NULL,
on_result_begin = NULL,
on_result_end = NULL,
on_result = NULL,
on_optimization_end = NULL
)
Arguments
id |
(character(1))
Identifier for the new instance.
|
label |
(character(1))
Label for the new instance.
|
man |
(character(1))
String in the format [pkg]::[topic] pointing to a manual page for this object.
The referenced help package can be opened via method $help().
|
on_optimization_begin |
(function())
Stage called at the beginning of the optimization.
Called in Optimizer$optimize().
The functions must have two arguments named callback and context.
|
on_worker_begin |
(function())
Stage called at the beginning of the optimization on the worker.
Called in the worker loop.
The functions must have two arguments named callback and context.
|
on_optimizer_before_eval |
(function())
Stage called after the optimizer proposes points.
Called in OptimInstance$.eval_point().
The functions must have two arguments named callback and context.
The argument of instance$.eval_point(xs) and xs_trafoed and extra are available in the context.
Or xs and xs_trafoed of instance$.eval_queue() are available in the context.
|
on_eval_after_xs |
(function())
Stage called after xs is passed to the objective.
Called in ObjectiveTuningAsync$eval().
The functions must have two arguments named callback and context.
The argument of $.eval(xs) is available in the context.
|
on_resample_begin |
(function())
Stage called at the beginning of a resampling iteration.
Called in workhorse() (internal).
See also mlr3::callback_resample().
The functions must have two arguments named callback and context.
|
on_resample_before_train |
(function())
Stage called before training the learner.
Called in workhorse() (internal).
See also mlr3::callback_resample().
The functions must have two arguments named callback and context.
|
on_resample_before_predict |
(function())
Stage called before predicting.
Called in workhorse() (internal).
See also mlr3::callback_resample().
The functions must have two arguments named callback and context.
|
on_resample_end |
(function())
Stage called at the end of a resampling iteration.
Called in workhorse() (internal).
See also mlr3::callback_resample().
The functions must have two arguments named callback and context.
|
on_eval_after_resample |
(function())
Stage called after a hyperparameter configuration is evaluated.
Called in ObjectiveTuningAsync$eval().
The functions must have two arguments named callback and context.
The resample_result is available in the 'context
|
on_eval_before_archive |
(function())
Stage called before performance values are written to the archive.
Called in ObjectiveTuningAsync$eval().
The functions must have two arguments named callback and context.
The aggregated_performance is available in context.
|
on_optimizer_after_eval |
(function())
Stage called after points are evaluated.
Called in OptimInstance$.eval_point().
The functions must have two arguments named callback and context.
|
on_worker_end |
(function())
Stage called at the end of the optimization on the worker.
Called in the worker loop.
The functions must have two arguments named callback and context.
|
on_tuning_result_begin |
(function())
Stage called at the beginning of the result writing.
Called in TuningInstance*$assign_result().
The functions must have two arguments named callback and context.
The arguments of $assign_result(xdt, y, learner_param_vals, extra) are available in context.
|
on_result_begin |
(function())
Stage called at the beginning of the result writing.
Called in OptimInstance$assign_result().
The functions must have two arguments named callback and context.
The arguments of $.assign_result(xdt, y, extra) are available in the context.
|
on_result_end |
(function())
Stage called after the result is written.
Called in OptimInstance$assign_result().
The functions must have two arguments named callback and context.
The final result instance$result is available in the context.
|
on_result |
(function())
Deprecated.
Use on_result_end instead.
Stage called after the result is written.
Called in OptimInstance$assign_result().
|
on_optimization_end |
(function())
Stage called at the end of the optimization.
Called in Optimizer$optimize().
|
Details
When implementing a callback, each function must have two arguments named callback and context.
A callback can write data to the state ($state), e.g. settings that affect the callback itself.
Tuning callbacks access ContextAsyncTuning and mlr3::ContextResample.
