Regression Abess Learner

Adaptive best-subset selection for regression. Calls abess::abess() from abess.

Dictionary

This Learner can be instantiated via lrn():

lrn("regr.abess")

Meta Information

• Task type: “regr”

• Predict Types: “response”

• Feature Types: “integer”, “numeric”

• Required Packages: mlr3, abess

Parameters

Id	Type	Default	Levels	Range
family	character	gaussian	gaussian, mgaussian, poisson, gamma	-
tune.path	character	sequence	sequence, gsection	-
tune.type	character	gic	gic, aic, bic, ebic, cv	-
normalize	integer	NULL		\((-\infty, \infty)\)
support.size	untyped	NULL		-
c.max	integer	2		\([1, \infty)\)
gs.range	untyped	NULL		-
lambda	numeric	0		\([0, \infty)\)
always.include	untyped	NULL		-
group.index	untyped	NULL		-
init.active.set	untyped	NULL		-
splicing.type	integer	2		\([1, 2]\)
max.splicing.iter	integer	20		\([1, \infty)\)
screening.num	integer	NULL		\([0, \infty)\)
important.search	integer	NULL		\([0, \infty)\)
warm.start	logical	TRUE	TRUE, FALSE	-
nfolds	integer	5		\((-\infty, \infty)\)
foldid	untyped	NULL		-
cov.update	logical	FALSE	TRUE, FALSE	-
newton	character	exact	exact, approx	-
newton.thresh	numeric	1e-06		\([0, \infty)\)
max.newton.iter	integer	NULL		\([1, \infty)\)
early.stop	logical	FALSE	TRUE, FALSE	-
ic.scale	numeric	1		\([0, \infty)\)
num.threads	integer	0		\([0, \infty)\)
seed	integer	1		\((-\infty, \infty)\)

Initial parameter values

• num.threads: This parameter is initialized to 1 (default is 0) to avoid conflicts with the mlr3 parallelization.

• family: Depends on the task type, if the parameter family is NULL, it is set to "binomial" for binary classification tasks and to "multinomial" for multiclass classification problems.

See also

• Dictionary of Learners: mlr3::mlr_learners.

• as.data.table(mlr_learners) for a table of available Learners in the running session (depending on the loaded packages).

• Chapter in the mlr3book: https://mlr3book.mlr-org.com/chapters/chapter2/data_and_basic_modeling.html#sec-learners

• mlr3learners for a selection of recommended learners.

• mlr3cluster for unsupervised clustering learners.

• mlr3pipelines to combine learners with pre- and postprocessing steps.

• mlr3tuning for tuning of hyperparameters, mlr3tuningspaces for established default tuning spaces.

Author

bbayukari

Super classes

mlr3::Learner -> mlr3::LearnerRegr -> LearnerRegrAbess

Methods

Public methods

• LearnerRegrAbess$new()

• LearnerRegrAbess$selected_features()

• LearnerRegrAbess$clone()

Inherited methods

• mlr3::Learner$base_learner()
• mlr3::Learner$configure()
• mlr3::Learner$encapsulate()
• mlr3::Learner$format()
• mlr3::Learner$help()
• mlr3::Learner$predict()
• mlr3::Learner$predict_newdata()
• mlr3::Learner$print()
• mlr3::Learner$reset()
• mlr3::Learner$train()
• mlr3::LearnerRegr$predict_newdata_fast()

---

LearnerRegrAbess$new()

Creates a new instance of this R6 class.

Usage

LearnerRegrAbess$new()

---

LearnerRegrAbess$selected_features()

Extract the name of selected features from the model by abess::extract().

Usage

LearnerRegrAbess$selected_features()

Returns

The names of selected features

---

LearnerRegrAbess$clone()

The objects of this class are cloneable with this method.

Usage

LearnerRegrAbess$clone(deep = FALSE)

Arguments

deep

Whether to make a deep clone.

Examples

# Define the Learner
learner = lrn("regr.abess")
print(learner)
#> 
#> ── <LearnerRegrAbess> (regr.abess): Fast Best Subset Selection for Regression ──
#> • Model: -
#> • Parameters: num.threads=1
#> • Packages: mlr3 and abess
#> • Predict Types: [response]
#> • Feature Types: integer and numeric
#> • Encapsulation: none (fallback: -)
#> • Properties: selected_features and weights
#> • Other settings: use_weights = 'use', predict_raw = 'FALSE'

# Define a Task
task = tsk("mtcars")

# Create train and test set
ids = partition(task)

# Train the learner on the training ids
learner$train(task, row_ids = ids$train)

print(learner$model)
#> Call:
#> abess.default(x = task$data(cols = task$feature_names), y = as.matrix(task$data(cols = task$target_names)), 
#>     num.threads = 1L)
#> 
#>   support.size       dev      GIC
#> 1            0 14.776190 56.55336
#> 2            1  3.237100 27.23181
#> 3            2  2.073073 20.43681
#> 4            3  1.944116 21.65167
#> 5            4  1.874165 23.44570
#> 6            5  1.860116 25.85126
#> 7            6  1.832897 28.10527
#> 8            7  1.818787 30.50656
#> 9            8  1.795680 32.80161


# Make predictions for the test rows
predictions = learner$predict(task, row_ids = ids$test)

# Score the predictions
predictions$score()
#> regr.mse 
#> 10.89597