mlr_assertions function

Assertion for mlr3 Objects

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.

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(),
  unique_ids = FALSE,
  .var.name = vname(learners)
)

assert_learnable(task, learner, param_values = NULL)

assert_predictable(task, learner)

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

assert_scorable(
  measure,
  task,
  learner,
  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 ).

• param_values: (list())

  TuneToken are not allowed in the parameter set of the learner. If the param_values overwrite the TuneToken, the assertion will pass.

• 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.