TuningInstanceSingleCrit: Single Criterion Tuning Instance for Batch Tuning

In mlr3tuning: Hyperparameter Optimization for 'mlr3'

Single Criterion Tuning Instance for Batch Tuning

Description

TuningInstanceSingleCrit is a deprecated class that is now a wrapper around TuningInstanceBatchSingleCrit.

Super classes

bbotk::OptimInstance -> bbotk::OptimInstanceBatch -> bbotk::OptimInstanceBatchSingleCrit -> mlr3tuning::TuningInstanceBatchSingleCrit -> TuningInstanceSingleCrit

Methods

Public methods

• TuningInstanceSingleCrit$new()

• TuningInstanceSingleCrit$clone()

Inherited methods

• bbotk::OptimInstance$clear()
• bbotk::OptimInstance$format()
• bbotk::OptimInstance$print()
• bbotk::OptimInstanceBatch$eval_batch()
• bbotk::OptimInstanceBatch$objective_function()
• mlr3tuning::TuningInstanceBatchSingleCrit$assign_result()

---

Method new()

Creates a new instance of this R6 class.

Usage

TuningInstanceSingleCrit$new(
  task,
  learner,
  resampling,
  measure = NULL,
  terminator,
  search_space = NULL,
  store_benchmark_result = TRUE,
  store_models = FALSE,
  check_values = FALSE,
  callbacks = NULL
)

Arguments

task

(mlr3::Task)
Task to operate on.

learner

(mlr3::Learner)
Learner to tune.

resampling

(mlr3::Resampling)
Resampling that is used to evaluate the performance of the hyperparameter configurations. Uninstantiated resamplings are instantiated during construction so that all configurations are evaluated on the same data splits. Already instantiated resamplings are kept unchanged. Specialized Tuner change the resampling e.g. to evaluate a hyperparameter configuration on different data splits. This field, however, always returns the resampling passed in construction.

measure

(mlr3::Measure)
Measure to optimize. If NULL, default measure is used.

terminator

(bbotk::Terminator)
Stop criterion of the tuning process.

search_space

(paradox::ParamSet)
Hyperparameter search space. If NULL (default), the search space is constructed from the paradox::TuneToken of the learner's parameter set (learner$param_set).

store_benchmark_result

(logical(1))
If TRUE (default), store resample result of evaluated hyperparameter configurations in archive as mlr3::BenchmarkResult.

store_models

(logical(1))
If TRUE, fitted models are stored in the benchmark result (archive$benchmark_result). If store_benchmark_result = FALSE, models are only stored temporarily and not accessible after the tuning. This combination is needed for measures that require a model.

check_values

(logical(1))
If TRUE, hyperparameter values are checked before evaluation and performance scores after. If FALSE (default), values are unchecked but computational overhead is reduced.

callbacks

(list of mlr3misc::Callback)
List of callbacks.

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Method clone()

The objects of this class are cloneable with this method.

Usage

TuningInstanceSingleCrit$clone(deep = FALSE)

Arguments

deep

Whether to make a deep clone.

mlr3tuning documentation built on Sept. 11, 2024, 7:23 p.m.