extract_inner_fselect_archives | R Documentation |
Extract inner feature selection archives of nested resampling.
Implemented for mlr3::ResampleResult and mlr3::BenchmarkResult.
The function iterates over the AutoFSelector objects and binds the archives to a data.table::data.table()
.
AutoFSelector must be initialized with store_fselect_instance = TRUE
and resample()
or benchmark()
must be called with store_models = TRUE
.
extract_inner_fselect_archives(x, exclude_columns = "uhash")
x |
(mlr3::ResampleResult | mlr3::BenchmarkResult). |
exclude_columns |
( |
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 feature of the task.
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)
)
Feature sets are evaluated in batches. Each batch has a unique batch
number.
resample_result
(mlr3::ResampleResult)
Resample result of the inner resampling.
task_id
(character(1)
).
learner_id
(character(1)
).
resampling_id
(character(1)
).
# Nested Resampling on Palmer Penguins Data Set
# create auto fselector
at = auto_fselector(
fselector = fs("random_search"),
learner = lrn("classif.rpart"),
resampling = rsmp ("holdout"),
measure = msr("classif.ce"),
term_evals = 4)
resampling_outer = rsmp("cv", folds = 2)
rr = resample(tsk("penguins"), at, resampling_outer, store_models = TRUE)
# extract inner archives
extract_inner_fselect_archives(rr)
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