mlr3 (version 0.1.4)

mlr_sugar: Syntactic Sugar for Object Construction

Description

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 Tasks from mlr_tasks.

• tgen() for a TaskGenerator from mlr_task_generators.

• tgens() for a list of TaskGenerators from mlr_task_generators.

• lrn() for a Learner from mlr_learners.

• lrns() for a list of Learners from mlr_learners.

• rsmp() for a Resampling from mlr_resamplings.

• rsmps() for a list of Resamplings from mlr_resamplings.

• msr() for a Measure from mlr_measures.

• msrs() for a list of Measures from mlr_measures.

Usage

tsk(.key, ...)
tsks(.keys, ...)
tgen(.key, ...)
tgens(.keys, ...)
lrn(.key, ...)
lrns(.keys, ...)
rsmp(.key, ...)
rsmps(.keys, ...)
msr(.key, ...)
msrs(.keys, ...)

Arguments

.key

:: character(1) Key passed to the respective mlr3misc::Dictionary to retrieve the object.

...

:: named list() Named arguments passed to the constructor, to be set as parameters in the paradox::ParamSet, or to be set as public field. See mlr3misc::dictionary_sugar_get() for more details.

.keys

:: character() Keys passed to the respective mlr3misc::Dictionary to retrieve multiple objects.

Value

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

Examples

# NOT RUN {
# iris task with new id
tsk("iris", id = "iris2")

# 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'
lapply(c("classif.featureless", "classif.rpart"), lrn, predict_type = "prob")
# }