ArchiveBatchFSelect: Class for Logging Evaluated Feature Sets
In mlr3fselect: Feature Selection for 'mlr3'
ArchiveBatchFSelect R Documentation
Class for Logging Evaluated Feature Sets
Description
The ArchiveBatchFSelect stores all evaluated feature sets and performance scores.
Details
The ArchiveBatchFSelect 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 ($benchmark_result) are linked by the uhash column.
If the archive is passed to as.data.table(), both are joined automatically.
Data structure
The table ($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.
Analysis
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().
S3 Methods
-
as.data.table.ArchiveBatchFSelect(x, exclude_columns = "uhash", measures = NULL)
Returns a tabular view of all evaluated feature sets.
ArchiveBatchFSelect -> data.table::data.table()
-
x (ArchiveBatchFSelect)
-
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.
Super classes
bbotk::Archive -> bbotk::ArchiveBatch -> ArchiveBatchFSelect
Public fields
benchmark_result(mlr3::BenchmarkResult)
Benchmark result.
Active bindings
ties_method(character(1))
Method to handle ties.
Methods
Public methods
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Inherited methods
Method new()
Creates a new instance of this R6 class.
Usage
ArchiveBatchFSelect$new(
search_space,
codomain,
check_values = TRUE,
ties_method = "least_features"
)
Arguments
search_space(paradox::ParamSet)
Search space.
Internally created from provided mlr3::Task by instance.
codomain(bbotk::Codomain)
Specifies codomain of objective function i.e. a set of performance measures.
Internally created from provided mlr3::Measures by instance.
check_values(logical(1))
If TRUE (default), hyperparameter configurations are check for validity.
ties_method(character(1))
The method to break ties when selecting sets while optimizing and when selecting the best set.
Can be "least_features" or "random".
The option "least_features" (default) selects the feature set with the least features.
If there are multiple best feature sets with the same number of features, one is selected randomly.
The random method returns a random feature set from the best feature sets.
Ignored if multiple measures are used.
Method add_evals()
Adds function evaluations to the archive table.
Usage
ArchiveBatchFSelect$add_evals(xdt, xss_trafoed = NULL, ydt)
Arguments
xdt(data.table::data.table())
x values as data.table. Each row is one point. Contains the value in
the search space of the FSelectInstanceBatchMultiCrit object. Can contain
additional columns for extra information.
xss_trafoed(list())
Ignored in feature selection.
ydt(data.table::data.table())
Optimal outcome.
Method 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 $learners() to get learners with models.
Usage
ArchiveBatchFSelect$learner(i = NULL, uhash = NULL)
Arguments
i(integer(1))
The iteration value to filter for.
uhash(logical(1))
The uhash value to filter for.
Method 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.
Usage
ArchiveBatchFSelect$learners(i = NULL, uhash = NULL)
Arguments
i(integer(1))
The iteration value to filter for.
uhash(logical(1))
The uhash value to filter for.
Method predictions()
Retrieve list of mlr3::Prediction objects of the i-th evaluation, by
position or by unique hash uhash. i and uhash are mutually
exclusive.
Usage
ArchiveBatchFSelect$predictions(i = NULL, uhash = NULL)
Arguments
i(integer(1))
The iteration value to filter for.
uhash(logical(1))
The uhash value to filter for.
Method resample_result()
Retrieve mlr3::ResampleResult of the i-th evaluation, by position
or by unique hash uhash. i and uhash are mutually exclusive.
Usage
ArchiveBatchFSelect$resample_result(i = NULL, uhash = NULL)
Arguments
i(integer(1))
The iteration value to filter for.
uhash(logical(1))
The uhash value to filter for.
Method print()
Printer.
Usage
ArchiveBatchFSelect$print()
Arguments
...(ignored).
Method best()
Returns the best scoring feature sets.
Usage
ArchiveBatchFSelect$best(batch = NULL, ties_method = NULL)
Arguments
batch(integer())
The batch number(s) to limit the best results to.
Default is all batches.
ties_method(character(1))
Method to handle ties.
If NULL (default), the global ties method set during initialization is used.
The default global ties method is least_features which selects the feature set with the least features.
If there are multiple best feature sets with the same number of features, one is selected randomly.
The random method returns a random feature set from the best feature sets.
Returns
data.table::data.table()
Method clone()
The objects of this class are cloneable with this method.
Usage
ArchiveBatchFSelect$clone(deep = FALSE)
Arguments
deepWhether to make a deep clone.
mlr3fselect documentation built on Oct. 30, 2024, 9:19 a.m.
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| ArchiveBatchFSelect | R Documentation |
Class for Logging Evaluated Feature Sets
Description
The ArchiveBatchFSelect stores all evaluated feature sets and performance scores.
Details
The ArchiveBatchFSelect 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 ($benchmark_result) are linked by the uhash column.
If the archive is passed to as.data.table(), both are joined automatically.
Data structure
The table ($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.
Analysis
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().
S3 Methods
-
as.data.table.ArchiveBatchFSelect(x, exclude_columns = "uhash", measures = NULL)
Returns a tabular view of all evaluated feature sets.
ArchiveBatchFSelect ->data.table::data.table()
-
x(ArchiveBatchFSelect) -
exclude_columns(character())
Exclude columns from table. Set toNULLif no column should be excluded. -
measures(list of mlr3::Measure)
Score feature sets on additional measures.
-
Super classes
bbotk::Archive -> bbotk::ArchiveBatch -> ArchiveBatchFSelect
Public fields
benchmark_result(mlr3::BenchmarkResult)
Benchmark result.
Active bindings
ties_method(
character(1))
Method to handle ties.
Methods
Public methods
Inherited methods
Method new()
Creates a new instance of this R6 class.
Usage
ArchiveBatchFSelect$new( search_space, codomain, check_values = TRUE, ties_method = "least_features" )
Arguments
search_space(paradox::ParamSet)
Search space. Internally created from provided mlr3::Task by instance.codomain(bbotk::Codomain)
Specifies codomain of objective function i.e. a set of performance measures. Internally created from provided mlr3::Measures by instance.check_values(
logical(1))
IfTRUE(default), hyperparameter configurations are check for validity.ties_method(
character(1))
The method to break ties when selecting sets while optimizing and when selecting the best set. Can be"least_features"or"random". The option"least_features"(default) selects the feature set with the least features. If there are multiple best feature sets with the same number of features, one is selected randomly. Therandommethod returns a random feature set from the best feature sets. Ignored if multiple measures are used.
Method add_evals()
Adds function evaluations to the archive table.
Usage
ArchiveBatchFSelect$add_evals(xdt, xss_trafoed = NULL, ydt)
Arguments
xdt(
data.table::data.table())
x values asdata.table. Each row is one point. Contains the value in the search space of the FSelectInstanceBatchMultiCrit object. Can contain additional columns for extra information.xss_trafoed(
list())
Ignored in feature selection.ydt(
data.table::data.table())
Optimal outcome.
Method 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 $learners() to get learners with models.
Usage
ArchiveBatchFSelect$learner(i = NULL, uhash = NULL)
Arguments
i(
integer(1))
The iteration value to filter for.uhash(
logical(1))
Theuhashvalue to filter for.
Method 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.
Usage
ArchiveBatchFSelect$learners(i = NULL, uhash = NULL)
Arguments
i(
integer(1))
The iteration value to filter for.uhash(
logical(1))
Theuhashvalue to filter for.
Method predictions()
Retrieve list of mlr3::Prediction objects of the i-th evaluation, by
position or by unique hash uhash. i and uhash are mutually
exclusive.
Usage
ArchiveBatchFSelect$predictions(i = NULL, uhash = NULL)
Arguments
i(
integer(1))
The iteration value to filter for.uhash(
logical(1))
Theuhashvalue to filter for.
Method resample_result()
Retrieve mlr3::ResampleResult of the i-th evaluation, by position
or by unique hash uhash. i and uhash are mutually exclusive.
Usage
ArchiveBatchFSelect$resample_result(i = NULL, uhash = NULL)
Arguments
i(
integer(1))
The iteration value to filter for.uhash(
logical(1))
Theuhashvalue to filter for.
Method print()
Printer.
Usage
ArchiveBatchFSelect$print()
Arguments
...(ignored).
Method best()
Returns the best scoring feature sets.
Usage
ArchiveBatchFSelect$best(batch = NULL, ties_method = NULL)
Arguments
batch(
integer())
The batch number(s) to limit the best results to. Default is all batches.ties_method(
character(1))
Method to handle ties. IfNULL(default), the global ties method set during initialization is used. The default global ties method isleast_featureswhich selects the feature set with the least features. If there are multiple best feature sets with the same number of features, one is selected randomly. Therandommethod returns a random feature set from the best feature sets.
Returns
data.table::data.table()
Method clone()
The objects of this class are cloneable with this method.
Usage
ArchiveBatchFSelect$clone(deep = FALSE)
Arguments
deepWhether to make a deep clone.
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