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Assertion for mlr3 Objects

Source: R/assertions.R, R/helper.R
mlr_assertions.Rd

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.