mlr_assertions: Assertion for mlr3 Objects

In mlr3: Machine Learning in R - Next Generation

Assertion for mlr3 Objects

Description

Functions intended to be used in packages extending mlr3. Most assertion functions ensure the right class attribute, and optionally additional properties. Additionally, the following compound assertions are implemented:

• assert_learnable(task, learner)
  (Task, Learner) -> NULL
  Checks if the learner is applicable to the task. This includes type checks on the type, the feature types, and properties.

If an assertion fails, an exception is raised. Otherwise, the input object is returned invisibly.

Asserts whether the input is a valid value for the ⁠$validate⁠ field of a Learner.

Usage

assert_backend(b, .var.name = vname(b))

assert_task(
  task,
  task_type = NULL,
  feature_types = NULL,
  task_properties = NULL,
  .var.name = vname(task)
)

assert_tasks(
  tasks,
  task_type = NULL,
  feature_types = NULL,
  task_properties = NULL,
  .var.name = vname(tasks)
)

assert_learner(
  learner,
  task = NULL,
  task_type = NULL,
  properties = character(),
  .var.name = vname(learner)
)

assert_learners(
  learners,
  task = NULL,
  task_type = NULL,
  properties = character(),
  .var.name = vname(learners)
)

assert_learnable(task, learner)

assert_predictable(task, learner)

assert_measure(
  measure,
  task = NULL,
  learner = NULL,
  prediction = NULL,
  .var.name = vname(measure)
)

assert_measures(
  measures,
  task = NULL,
  learner = NULL,
  .var.name = vname(measures)
)

assert_resampling(
  resampling,
  instantiated = NULL,
  .var.name = vname(resampling)
)

assert_resamplings(
  resamplings,
  instantiated = NULL,
  .var.name = vname(resamplings)
)

assert_prediction(prediction, .var.name = vname(prediction), null.ok = FALSE)

assert_resample_result(rr, .var.name = vname(rr))

assert_benchmark_result(bmr, .var.name = vname(bmr))

assert_row_ids(row_ids, null.ok = FALSE, .var.name = vname(row_ids))

assert_validate(x)

Arguments

b	(DataBackend).
task	(Task).
task_type	(character(1)).
feature_types	(character()) Feature types the learner operates on. Must be a subset of mlr_reflections$task_feature_types.
task_properties	(character()) Set of required task properties.
tasks	(list of Task).
learner	(Learner).
learners	(list of Learner).
measure	(Measure).
prediction	(Prediction).
measures	(list of Measure).
resampling	(Resampling).
resamplings	(list of Resampling).
rr	(ResampleResult).
bmr	(BenchmarkResult).
row_ids	integer() Row indices.
x	(any) The input to check.

mlr3 documentation built on Oct. 18, 2024, 5:11 p.m.