mlr3tuning (version 0.20.0)

extract_inner_tuning_results: Extract Inner Tuning Results

Description

Extract inner tuning results of nested resampling. Implemented for mlr3::ResampleResult and mlr3::BenchmarkResult.

Usage

extract_inner_tuning_results(x, tuning_instance, ...)
# S3 method for ResampleResult
extract_inner_tuning_results(x, tuning_instance = FALSE, ...)
# S3 method for BenchmarkResult
extract_inner_tuning_results(x, tuning_instance = FALSE, ...)

Value

data.table::data.table().

Arguments

x

(mlr3::ResampleResult | mlr3::BenchmarkResult).

tuning_instance

(logical(1))
If TRUE, tuning instances are added to the table.

...

(any)
Additional arguments.

Data structure

The returned data table has the following columns:

• experiment (integer(1))
  Index, giving the according row number in the original benchmark grid.

• iteration (integer(1))
  Iteration of the outer resampling.

• One column for each hyperparameter of the search spaces.

• One column for each performance measure.

• learner_param_vals (list())
  Hyperparameter values used by the learner. Includes fixed and proposed hyperparameter values.

• x_domain (list())
  List of transformed hyperparameter values.

• tuning_instance (TuningInstanceSingleCrit | TuningInstanceMultiCrit)
  Optionally, tuning instances.

• task_id (character(1)).

• learner_id (character(1)).

• resampling_id (character(1)).

Details

The function iterates over the AutoTuner objects and binds the tuning results to a data.table::data.table(). The AutoTuner must be initialized with store_tuning_instance = TRUE and mlr3::resample() or mlr3::benchmark() must be called with store_models = TRUE. Optionally, the tuning instance can be added for each iteration.

Examples

# Nested Resampling on Palmer Penguins Data Set

learner = lrn("classif.rpart",
  cp = to_tune(1e-04, 1e-1, logscale = TRUE))

# create auto tuner
at = auto_tuner(
  tuner = tnr("random_search"),
  learner = learner,
  resampling = rsmp ("holdout"),
  measure = msr("classif.ce"),
  term_evals = 4)

resampling_outer = rsmp("cv", folds = 2)
rr = resample(tsk("iris"), at, resampling_outer, store_models = TRUE)

# extract inner results
extract_inner_tuning_results(rr)