View source: R/extract_inner_tuning_results.R
extract_inner_tuning_results | R Documentation |
Extract inner tuning results of nested resampling. Implemented for mlr3::ResampleResult and mlr3::BenchmarkResult.
extract_inner_tuning_results(x, tuning_instance, ...)
## S3 method for class 'ResampleResult'
extract_inner_tuning_results(x, tuning_instance = FALSE, ...)
## S3 method for class 'BenchmarkResult'
extract_inner_tuning_results(x, tuning_instance = FALSE, ...)
x |
(mlr3::ResampleResult | mlr3::BenchmarkResult). |
tuning_instance |
( |
... |
(any) |
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.
data.table::data.table()
.
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
(TuningInstanceBatchSingleCrit | TuningInstanceBatchMultiCrit)
Optionally, tuning instances.
task_id
(character(1)
).
learner_id
(character(1)
).
resampling_id
(character(1)
).
# 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)
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