ArchiveFSelect | R Documentation |
The ArchiveFSelect stores all evaluated feature sets and performance scores.
The ArchiveFSelect is a container around a data.table::data.table()
.
Each row corresponds to a single evaluation of a feature set.
See the section on Data Structure for more information.
The archive stores additionally a mlr3::BenchmarkResult ($benchmark_result
) that records the resampling experiments.
Each experiment corresponds to a single evaluation of a feature set.
The table ($data
) and the benchmark result ($benchmark_result
) are linked by the uhash
column.
If the archive is passed to as.data.table()
, both are joined automatically.
The table ($data
) has the following columns:
One column for each feature of the task ($search_space
).
One column for each performance measure ($codomain
).
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.
uhash
(character(1)
)
Connects each feature set to the resampling experiment stored in the mlr3::BenchmarkResult.
For analyzing the feature selection results, it is recommended to pass the archive to as.data.table()
.
The returned data table is joined with the benchmark result which adds the mlr3::ResampleResult for each feature set.
The archive provides various getters (e.g. $learners()
) to ease the access.
All getters extract by position (i
) or unique hash (uhash
).
For a complete list of all getters see the methods section.
The benchmark result ($benchmark_result
) allows to score the feature sets again on a different measure.
Alternatively, measures can be supplied to as.data.table()
.
as.data.table.ArchiveFSelect(x, exclude_columns = "uhash", measures = NULL)
Returns a tabular view of all evaluated feature sets.
ArchiveFSelect -> data.table::data.table()
x
(ArchiveFSelect)
exclude_columns
(character()
)
Exclude columns from table. Set to NULL
if no column should be excluded.
measures
(list of mlr3::Measure)
Score feature sets on additional measures.
bbotk::Archive
-> ArchiveFSelect
benchmark_result
(mlr3::BenchmarkResult)
Benchmark result.
new()
Creates a new instance of this R6 class.
ArchiveFSelect$new(search_space, codomain, check_values = TRUE)
search_space
(paradox::ParamSet)
Search space.
Internally created from provided mlr3::Task by instance.
codomain
(bbotk::Codomain)
Specifies codomain of objective function i.e. a set of performance measures.
Internally created from provided mlr3::Measures by instance.
check_values
(logical(1)
)
If TRUE
(default), hyperparameter configurations are check for validity.
learner()
Retrieve mlr3::Learner of the i-th evaluation, by position or by unique hash uhash
.
i
and uhash
are mutually exclusive.
Learner does not contain a model. Use $learners()
to get learners with models.
ArchiveFSelect$learner(i = NULL, uhash = NULL)
i
(integer(1)
)
The iteration value to filter for.
uhash
(logical(1)
)
The uhash
value to filter for.
learners()
Retrieve list of trained mlr3::Learner objects of the i-th evaluation,
by position or by unique hash uhash
. i
and uhash
are mutually
exclusive.
ArchiveFSelect$learners(i = NULL, uhash = NULL)
i
(integer(1)
)
The iteration value to filter for.
uhash
(logical(1)
)
The uhash
value to filter for.
predictions()
Retrieve list of mlr3::Prediction objects of the i-th evaluation, by
position or by unique hash uhash
. i
and uhash
are mutually
exclusive.
ArchiveFSelect$predictions(i = NULL, uhash = NULL)
i
(integer(1)
)
The iteration value to filter for.
uhash
(logical(1)
)
The uhash
value to filter for.
resample_result()
Retrieve mlr3::ResampleResult of the i-th evaluation, by position
or by unique hash uhash
. i
and uhash
are mutually exclusive.
ArchiveFSelect$resample_result(i = NULL, uhash = NULL)
i
(integer(1)
)
The iteration value to filter for.
uhash
(logical(1)
)
The uhash
value to filter for.
print()
Printer.
ArchiveFSelect$print()
...
(ignored).
clone()
The objects of this class are cloneable with this method.
ArchiveFSelect$clone(deep = FALSE)
deep
Whether to make a deep clone.
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