mlr3 (version 0.1.1)

mlr_assertions: Assertion for mlr3 Objects

Description

Functions intended to be used in packages extending mlr3.

Usage

assert_backend(b, .var.name = vname(b))
assert_task(task, task_type = NULL, feature_types = NULL,
  task_properties = NULL, clone = FALSE, min_rows = 1,
  min_cols = 1)
assert_tasks(tasks, feature_types = NULL, task_properties = NULL,
  clone = FALSE)
assert_learner(learner, task = NULL, properties = character(0L),
  clone = FALSE)
assert_learners(learners, task = NULL, properties = character(0L),
  clone = FALSE)
assert_measure(measure, task_type = NULL, task = NULL,
  learner = NULL, clone = FALSE)
assert_measures(measures, task_type = NULL, task = NULL,
  learner = NULL, clone = FALSE)
assert_resampling(resampling, instantiated = NULL, clone = FALSE)
assert_resamplings(resamplings, instantiated = NULL, clone = FALSE)
assert_prediction(prediction)
assert_resample_result(resample_result,
  .var.name = vname(resample_result))
assert_benchmark_result(bmr, .var.name = vname(bmr))
assert_row_ids(row_ids, type = NULL, .var.name = vname(row_ids))

Arguments

b

:: DataBackend.

task

:: Task.

task_type

:: character(1) Task type, e.g. "classif" or "regr".

feature_types

:: character() Set of allowed feature types.

task_properties

:: character() Set of required task properties.

min_rows

:: integer() Minimum amount of required observations.

min_cols

:: integer() Minimum amount of required features.

tasks

:: list of Task.

learner

:: Learner.

learners

:: list of Learner.

measure

:: Measure.

measures

:: list of Measure.

resampling

:: Resampling.

resamplings

:: list of Resampling.

prediction

:: Prediction.

resample_result

:: ResampleResult.

bmr

:: BenchmarkResult.

row_ids

:: vector().