new sugar functions

Author: mllg

R/Filter.R

@@ -111,7 +111,7 @@ Filter = R6Class("Filter",
 
     calculate = function(task, nfeat = NULL) {
 
-      task = assert_task(task, feature_types = self$feature_types, task_properties = self$task_properties)
+      task = assert_task(as_task(task), feature_types = self$feature_types, task_properties = self$task_properties)
       fn = task$feature_names
 
       if (task$nrow == 0L) {

R/FilterImportance.R

@@ -21,7 +21,7 @@ FilterImportance = R6Class("FilterImportance", inherit = Filter,
   public = list(
     learner = NULL,
     initialize = function(id = "importance", learner = "classif.rpart") {
-      self$learner = learner = assert_learner(learner, properties = "importance", clone = TRUE)
+      self$learner = learner = assert_learner(as_learner(learner, clone = TRUE), properties = "importance")
 
       super$initialize(
         id = id,

R/FilterPerformance.R

@@ -25,11 +25,9 @@ FilterPerformance = R6Class("FilterPerformance", inherit = Filter,
     measure = NULL,
 
     initialize = function(id = "performance", learner = "classif.rpart", resampling = "holdout", measure = NULL) {
-      self$learner = learner = assert_learner(learner, properties = "importance", clone = TRUE)
-      self$resampling = assert_resampling(resampling)
-      if (is.null(measure))
-        measure = head(default_measures(learner$task_type), 1L)
-      self$measure = assert_measure(measure, learner = learner)
+      self$learner = learner = assert_learner(as_learner(learner, clone = TRUE), properties = "importance")
+      self$resampling = assert_resampling(as_resampling(resampling))
+      self$measure = assert_measure(as_measure(measure, task_type = learner$task_type, clone = TRUE), learner = learner)
 
       super$initialize(
         id = id,