mlr_sugar function

Syntactic Sugar for Object Construction

Functions to retrieve objects, set hyperparameters and assign to fields in one go. Relies on mlr3misc::dictionary_sugar_get() to extract objects from the respective mlr3misc::Dictionary :

• tsk() for a Task from mlr_tasks .
• tsks() for a list of Task s from mlr_tasks .
• tgen() for a TaskGenerator from mlr_task_generators .
• tgens() for a list of TaskGenerator s from mlr_task_generators .
• lrn() for a Learner from mlr_learners .
• lrns() for a list of Learner s from mlr_learners .
• rsmp() for a Resampling from mlr_resamplings .
• rsmps() for a list of Resampling s from mlr_resamplings .
• msr() for a Measure from mlr_measures .
• msrs() for a list of Measure s from mlr_measures .

Helper function to configure the $validate field(s) of a Learner.

This is especially useful for learners such as AutoTuner of list("mlr3tuning") or GraphLearner of list("mlr3pipelines") which have multiple levels of $validate fields., where the $validate fields need to be configured on multiple levels.

tsk(.key, ...)

tsks(.keys, ...)

tgen(.key, ...)

tgens(.keys, ...)

lrn(.key, ...)

lrns(.keys, ...)

rsmp(.key, ...)

rsmps(.keys, ...)

msr(.key, ...)

msrs(.keys, ...)

set_validate(learner, validate, ...)

Arguments

• .key: (character(1))

  Key passed to the respective dictionary to retrieve the object.

• ...: (any)

  Additional arguments.

• .keys: (character())

  Keys passed to the respective dictionary to retrieve multiple objects.

• learner: (any)

  The learner.

• validate: (numeric(1), "predefined", "test", or NULL)

  Which validation set to use.

Returns

R6::R6Class object of the respective type, or a list of R6::R6Class objects for the plural versions.

Modified Learner

Examples

# penguins task with new id
tsk("penguins", id = "penguins2")

# classification tree with different hyperparameters
# and predict type set to predict probabilities
lrn("classif.rpart", cp = 0.1, predict_type = "prob")

# multiple learners with predict type 'prob'
lrns(c("classif.featureless", "classif.rpart"), predict_type = "prob")
learner = lrn("classif.debug")
set_validate(learner, 0.2)
learner$validate