Create Batch Tuning Callback — callback_batch

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.

Usage

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
)

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_optimizer_before_eval: (function())
Stage called after the optimizer proposes points. Called in OptimInstance$eval_batch(). The functions must have two arguments named callback and context. The argument of $eval_batch(xdt) is available in context.
on_eval_after_design: (function())
Stage called after the design is created. Called in ObjectiveTuningBatch$eval_many(). The functions must have two arguments named callback and context. The arguments of $eval_many(xss, resampling) are available in context. Additionally, the design is available in context.
on_resample_begin: (function())
Stage called at the beginning of a resampling iteration. Called in workhorse() (internal). See also mlr3::callback_resample(). The functions must have two arguments named callback and context.
on_resample_before_train: (function())
Stage called before training the learner. Called in workhorse() (internal). See also mlr3::callback_resample(). The functions must have two arguments named callback and context.
on_resample_before_predict: (function())
Stage called before predicting. Called in workhorse() (internal). See also mlr3::callback_resample(). The functions must have two arguments named callback and context.
on_resample_end: (function())
Stage called at the end of a resampling iteration. Called in workhorse() (internal). See also mlr3::callback_resample(). The functions must have two arguments named callback and context.
on_eval_after_benchmark: (function())
Stage called after hyperparameter configurations are evaluated. Called in ObjectiveTuningBatch$eval_many(). The functions must have two arguments named callback and context. The benchmark_result is available in context.
on_eval_before_archive: (function())
Stage called before performance values are written to the archive. Called in ObjectiveTuningBatch$eval_many(). The functions must have two arguments named callback and context. The aggregated_performance is available in context.
on_optimizer_after_eval: (function())
Stage called after points are evaluated. Called in OptimInstance$eval_batch(). The functions must have two arguments named callback and context. The new configurations and performances in instance$archive are available in context.
on_tuning_result_begin: (function())
Stage called at the beginning of the result writing. Called in TuningInstanceBatch$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 $assign_result(xdt, y, extra) are available in context.
on_result_end: (function())
Stage called after the result is written. Called in OptimInstance$assign_result(). The functions must have two arguments named callback and context. The final result instance$result is available in context.
on_result: (function())
Deprecated. Use on_result_end instead. Stage called after the result is written. Called in OptimInstance$assign_result(). The functions must have two arguments named callback and context.
on_optimization_end: (function())
Stage called at the end of the optimization. Called in Optimizer$optimize(). The functions must have two arguments named callback and context.

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 ContextBatchTuning.

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