mlr3: mlr_assertions – R documentation

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mlr_assertions

Assertion for mlr3 Objects

Description

Functions intended to be used in packages extending mlr3. Most assertion functions ensure the right class attrbiture, 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.

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,
  properties = character(),
  .var.name = vname(learner)
)

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

assert_learnable(task, learner)

assert_measure(
  measure,
  task = NULL,
  learner = 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))

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))

Arguments

`b`	(DataBackend).
`task`	(Task).
`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).
`measures`	(list of Measure).
`resampling`	(Resampling).
`resamplings`	(list of Resampling).
`prediction`	(Prediction).
`bmr`	(BenchmarkResult).
`row_ids`	(`numeric()`).
`resample_result`	(ResampleResult).

mlr3

Machine Learning in R - Next Generation

v0.11.0

LGPL-3

Authors

Michel Lang [cre, aut] (<https://orcid.org/0000-0001-9754-0393>), Bernd Bischl [aut] (<https://orcid.org/0000-0001-6002-6980>), Jakob Richter [aut] (<https://orcid.org/0000-0003-4481-5554>), Patrick Schratz [aut] (<https://orcid.org/0000-0003-0748-6624>), Giuseppe Casalicchio [ctb] (<https://orcid.org/0000-0001-5324-5966>), Stefan Coors [ctb] (<https://orcid.org/0000-0002-7465-2146>), Quay Au [ctb] (<https://orcid.org/0000-0002-5252-8902>), Martin Binder [aut], Marc Becker [ctb] (<https://orcid.org/0000-0002-8115-0400>)

Initial release

mlr_assertions

Description

Usage

Arguments

mlr3

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