extract_inner_tuning_archives {mlr3tuning}

Extract Inner Tuning Archives

Description

Extract inner tuning archives of nested resampling. Implemented for mlr3::ResampleResult and mlr3::BenchmarkResult. The function iterates over the AutoTuner objects and binds the tuning archives to a data.table::data.table(). AutoTuner must be initialized with store_tuning_instance = TRUE and mlr3::resample() or mlr3::benchmark() must be called with store_models = TRUE.

Usage

extract_inner_tuning_archives(
  x,
  unnest = "x_domain",
  exclude_columns = "uhash"
)

Arguments

x

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

unnest

(character())
Transforms list columns to separate columns. By default, x_domain is unnested. Set to NULL if no column should be unnested.

exclude_columns

(character())
Exclude columns from result table. Set to NULL if no column should be excluded.

Value

data.table::data.table().

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.

• runtime_learners (numeric(1))
  Sum of training and predict times logged in learners per mlr3::ResampleResult / evaluation. This does not include potential overhead time.

• timestamp (POSIXct)
  Time stamp when the evaluation was logged into the archive.

• batch_nr (integer(1))
  Hyperparameters are evaluated in batches. Each batch has a unique batch number.

• x_domain (list())
  List of transformed hyperparameter values. By default this column is unnested.

• ⁠x_domain_*⁠ (any)
  Separate column for each transformed hyperparameter.

• resample_result (mlr3::ResampleResult)
  Resample result of the inner resampling.

• task_id (character(1)).

• learner_id (character(1)).

• resampling_id (character(1)).

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 archives
extract_inner_tuning_archives(rr)