feat: improve docs for converters and better checks

NAMESPACE

@@ -42,6 +42,7 @@ S3method(as_resampling,Resampling)
 S3method(as_resamplings,default)
 S3method(as_resamplings,list)
 S3method(as_task,Task)
+S3method(as_task,default)
 S3method(as_task_classif,DataBackend)
 S3method(as_task_classif,Matrix)
 S3method(as_task_classif,TaskClassif)
@@ -200,6 +201,7 @@ export(as_tasks)
 export(as_tasks_unsupervised)
 export(assert_backend)
 export(assert_benchmark_result)
+export(assert_empty_ellipsis)
 export(assert_learnable)
 export(assert_learner)
 export(assert_learners)

R/BenchmarkResult.R

@@ -175,7 +175,11 @@ BenchmarkResult = R6Class("BenchmarkResult",
     #'
     #' @return [data.table::data.table()].
     score = function(measures = NULL, ids = TRUE, conditions = FALSE, predictions = TRUE) {
-      measures = as_measures(measures, task_type = self$task_type)
+      measures = if (is.null(measures)) {
+        default_measures(self$task_type)
+      } else {
+        assert_measures(as_measures(measures))
+      }
       assert_flag(ids)
       assert_flag(conditions)
       assert_flag(predictions)
@@ -230,7 +234,11 @@ BenchmarkResult = R6Class("BenchmarkResult",
     #' @param predict_sets (`character()`)\cr
     #'   The predict sets.
     obs_loss = function(measures = NULL, predict_sets = "test") {
-      measures = as_measures(measures, task_type = private$.data$task_type)
+      measures = if (is.null(measures)) {
+        default_measures(self$task_type)
+      } else {
+        assert_measures(as_measures(measures))
+      }
       map_dtr(self$resample_results$resample_result,
         function(rr) {
           rr$obs_loss(measures, predict_sets)
@@ -276,7 +284,11 @@ BenchmarkResult = R6Class("BenchmarkResult",
     #'
     #' @return [data.table::data.table()].
     aggregate = function(measures = NULL, ids = TRUE, uhashes = FALSE, params = FALSE, conditions = FALSE) {
-      measures = assert_measures(as_measures(measures, task_type = self$task_type))
+      measures = if (is.null(measures)) {
+        default_measures(self$task_type)
+      } else {
+        assert_measures(as_measures(measures))
+      }
       assert_flag(ids)
       assert_flag(uhashes)
       assert_flag(params)

R/Prediction.R

@@ -90,7 +90,11 @@ Prediction = R6Class("Prediction",
     #'
     #' @return [Prediction].
     score = function(measures = NULL, task = NULL, learner = NULL, train_set = NULL) {
-      measures = as_measures(measures, task_type = self$task_type)
+      measures = if (is.null(measures)) {
+        default_measures(self$task_type)
+      } else {
+        assert_measures(as_measures(measures))
+      }
       scores = map_dbl(measures, function(m) m$score(prediction = self, task = task, learner = learner, train_set = train_set))
       set_names(scores, ids(measures))
     },
@@ -105,7 +109,11 @@ Prediction = R6Class("Prediction",
     #' Note that some measures such as RMSE, do have an `$obs_loss`, but they require an
     #' additional transformation after aggregation, in this example taking the square-root.
     obs_loss = function(measures = NULL) {
-      measures = as_measures(measures, task_type = self$task_type)
+      measures = if (is.null(measures)) {
+        default_measures(self$task_type)
+      } else {
+        assert_measures(as_measures(measures))
+      }
       get_obs_loss(as.data.table(self), measures)
     },
 

R/ResampleResult.R

@@ -143,7 +143,11 @@ ResampleResult = R6Class("ResampleResult",
     #'
     #' @return [data.table::data.table()].
     score = function(measures = NULL, ids = TRUE, conditions = FALSE, predictions = TRUE) {
-      measures = as_measures(measures, task_type = private$.data$task_type)
+      measures = if (is.null(measures)) {
+        default_measures(self$task_type)
+      } else {
+        assert_measures(as_measures(measures))
+      }
       assert_flag(ids)
       assert_flag(conditions)
       assert_flag(predictions)
@@ -196,7 +200,11 @@ ResampleResult = R6Class("ResampleResult",
     #' @param predict_sets (`character()`)\cr
     #'   The predict sets.
     obs_loss = function(measures = NULL, predict_sets = "test") {
-      measures = as_measures(measures, task_type = self$task_type)
+      measures = if (is.null(measures)) {
+        default_measures(self$task_type)
+      } else {
+        assert_measures(as_measures(measures))
+      }
       tab = map_dtr(self$predictions(predict_sets), as.data.table, .idcol = "iteration")
       get_obs_loss(tab, measures)
     },
@@ -208,7 +216,11 @@ ResampleResult = R6Class("ResampleResult",
     #'
     #' @return Named `numeric()`.
     aggregate = function(measures = NULL) {
-      measures = as_measures(measures, task_type = private$.data$task_type)
+      measures = if (is.null(measures)) {
+        default_measures(self$task_type)
+      } else {
+        assert_measures(as_measures(measures))
+      }
       resample_result_aggregate(self, measures)
     },
 

R/as_learner.R

@@ -16,6 +16,7 @@ as_learner = function(x, ...) { # nolint
 #'   Whether to discard the state.
 #' @rdname as_learner
 as_learner.Learner = function(x, clone = FALSE, discard_state = FALSE, ...) { # nolint
+  assert_empty_ellipsis(...)
   if (isTRUE(clone) && isTRUE(discard_state)) {
     clone_without(x, "state")
   } else if (isTRUE(clone)) {

R/as_measure.R

@@ -17,12 +17,14 @@ as_measure = function(x, ...) { # nolint
 #' @export
 #' @rdname as_measure
 as_measure.NULL = function(x, task_type = NULL, ...) { # nolint
+  assert_empty_ellipsis(...)
   default_measures(task_type)[[1L]]
 }
 
 #' @export
 #' @rdname as_measure
 as_measure.Measure = function(x, clone = FALSE, ...) { # nolint
+  assert_empty_ellipsis(...)
   if (isTRUE(clone)) x$clone() else x
 }
 

R/as_resampling.R

@@ -2,7 +2,7 @@
 #'
 #' @description
 #' Convert object to a [Resampling] or a list of [Resampling].
-#'
+#' This method e.g. allows to convert an [`mlr3oml::OMLTask`] to a [`Resampling`].
 #' @inheritParams as_task
 #' @export
 as_resampling = function(x, ...) { # nolint
@@ -12,6 +12,7 @@ as_resampling = function(x, ...) { # nolint
 #' @export
 #' @rdname as_resampling
 as_resampling.Resampling = function(x, clone = FALSE, ...) { # nolint
+  assert_empty_ellipsis(...)
   if (isTRUE(clone)) x$clone() else x
 }
 

R/as_task.R

@@ -2,6 +2,8 @@
 #'
 #' @description
 #' Convert object to a [Task] or a list of [Task].
+#' This method e.g. allows to convert an [`mlr3oml::OMLTask`] to a [`Task`] and additionally supports cloning.
+#' In order to construct a [Task] from a `data.frame`, use task-specific converters such as [`as_task_classif()`] or [`as_task_regr()`].
 #'
 #' @param x (any)\cr
 #'   Object to convert.
@@ -12,11 +14,17 @@ as_task = function(x, ...) {
   UseMethod("as_task")
 }
 
+#' @export
+as_task.default = function(x, ...) {
+  stopf("No method for class '%s'. To create a task from a `data.frame`, use dedicated converters such as `as_task_classif()` or `as_task_regr()`.", class(x)[1L])
+}
+
 #' @rdname as_task
 #' @param clone (`logical(1)`)\cr
 #'   If `TRUE`, ensures that the returned object is not the same as the input `x`.
 #' @export
 as_task.Task = function(x, clone = FALSE, ...) { # nolint
+  assert_empty_ellipsis(...)
   if (isTRUE(clone)) x$clone(deep = TRUE) else x
 }
 

R/as_task_classif.R

@@ -4,7 +4,7 @@
 #' Convert object to a [TaskClassif].
 #' This is a S3 generic. mlr3 ships with methods for the following objects:
 #'
-#' 1. [TaskClassif]: ensure the identity
+#' 1. [TaskClassif]: returns the object as-is, possibly cloned.
 #' 2. [`formula`], [data.frame()], [matrix()], [Matrix::Matrix()] and [DataBackend]: provides an alternative to the constructor of [TaskClassif].
 #' 3. [TaskRegr]: Calls [convert_task()].
 #'

R/as_task_regr.R

@@ -4,7 +4,7 @@
 #' Convert object to a [TaskRegr].
 #' This is a S3 generic. mlr3 ships with methods for the following objects:
 #'
-#' 1. [TaskRegr]: ensure the identity
+#' 1. [TaskRegr]: returns the object as-is, possibly cloned.
 #' 2. [`formula`], [data.frame()], [matrix()], [Matrix::Matrix()] and [DataBackend]: provides an alternative to the constructor of [TaskRegr].
 #' 3. [TaskClassif]: Calls [convert_task()].
 #'

R/assertions.R

@@ -405,3 +405,29 @@ assert_param_values = function(x, n_learners = NULL, .var.name = vname(x)) {
   }
   invisible(x)
 }
+
+#' @title Assert Empty Ellipsis
+#' @description
+#' Assert that `...` arguments are empty.
+#' Use this function in S3-methods to ensure that misspelling of arguments does not go unnoticed.
+#' @param ... (any)\cr
+#'    Ellipsis arguments to check.
+#' @keywords internal
+#' @return `NULL`
+#' @export
+assert_empty_ellipsis = function(...) {
+  if (...length()) {
+    names = ...names()
+    if (is.null(names)) {
+      stopf("Received %i unnamed argument that was not used.", ...length())
+    } else {
+      names2 = names[names != ""]
+      if (length(names2) == length(names)) {
+        stopf("Received the following named arguments that were unused: %s.", paste0(names2, collapse = ", "))
+      } else {
+        stopf("Received unused arguments: %i unnamed, as well as named arguments %s.", length(names) - length(names2), paste0(names2, collapse = ", "))
+      }
+    }
+  }
+  NULL
+}

tests/testthat/test_Learner.R

@@ -549,8 +549,7 @@ test_that("learner state contains internal valid task information", {
 test_that("validation task with 0 observations", {
   learner = lrn("classif.debug", validate = "predefined")
   task = tsk("iris")
-  task$internal_valid_task = integer(0)
-  expect_error({learner$train(task)}, "has 0 observations")
+  expect_warning({task$internal_valid_task = integer(0)})
 })
 
 test_that("column info is compared during predict", {

tests/testthat/test_as_learner.R

@@ -21,3 +21,7 @@ test_that("discard_state", {
   as_learner(learner3, clone = FALSE, discard_state = TRUE)
   expect_null(learner3$state)
 })
+
+test_that("error when arguments are misspelled", {
+  expect_error(as_learner(lrn("classif.rpart"), clone2 = TRUE), "Received the following")
+})

tests/testthat/test_as_measure.R

@@ -14,3 +14,7 @@ test_that("as_measure conversion", {
   default = as_measures(NULL, task_type = "classif")
   expect_list(default, types = "Measure")
 })
+
+test_that("error when arguments are misspelled", {
+  expect_error(as_measure(msr("classif.acc"), clone2 = TRUE), "Received the following")
+})

tests/testthat/test_as_resampling.R

@@ -10,3 +10,7 @@ test_that("as_resampling conversion", {
   expect_list(as_resamplings(resampling), types = "Resampling")
   expect_list(as_resamplings(list(resampling)), types = "Resampling")
 })
+
+test_that("error when arguments are misspelled", {
+  expect_error(as_resampling(rsmp("holdout"), clone2 = TRUE), "Received the following")
+})

tests/testthat/test_as_task.R

@@ -22,3 +22,7 @@ test_that("as_task_xx error messages (#944)", {
     "subset of"
   )
 })
+
+test_that("error when arguments are misspelled", {
+  expect_error(as_task(tsk("iris"), clone2 = TRUE), "Received the following")
+})

tests/testthat/test_assertions.R

@@ -0,0 +1,8 @@
+test_that("assert_empty_ellipsis works", {
+  expect_error(assert_empty_ellipsis(1), "Received 1 unnamed argument")
+  expect_error(assert_empty_ellipsis(1, 2), "Received 2 unnamed argument")
+  expect_error(assert_empty_ellipsis(a = 1), "that were unused: a")
+  expect_error(assert_empty_ellipsis(a = 1, b = 2), "that were unused: a, b")
+  expect_error(assert_empty_ellipsis(a = 1, b = 1, 2), "1 unnamed, as well as named arguments a, b")
+  expect_null(assert_empty_ellipsis())
+})