Use new condition system for errors and warnings

R/BenchmarkResult.R

@@ -124,7 +124,7 @@ BenchmarkResult = R6Class("BenchmarkResult",
       if (!is.null(bmr)) {
         assert_benchmark_result(bmr)
         if (private$.data$iterations() && self$task_type != bmr$task_type) {
-          stopf("BenchmarkResult is of task type '%s', but must be '%s'", bmr$task_type, self$task_type)
+          error_input("BenchmarkResult is of task type '%s', but must be '%s'", bmr$task_type, self$task_type)
         }
 
         private$.data$combine(get_private(bmr)$.data)
@@ -425,8 +425,8 @@ BenchmarkResult = R6Class("BenchmarkResult",
     resample_result = function(i = NULL, uhash = NULL, task_id = NULL, learner_id = NULL,
       resampling_id = NULL) {
       uhash = private$.get_uhashes(i, uhash, learner_id, task_id, resampling_id)
-      if (length(uhash) != 1L) {
-        stopf("Method requires selecting exactly one ResampleResult, but got %s",
+      if (length(uhash) != 1) {
+        error_input("Method requires selecting exactly one ResampleResult, but got %s",
           length(uhash))
       }
       ResampleResult$new(private$.data, view = uhash)
@@ -598,7 +598,7 @@ BenchmarkResult = R6Class("BenchmarkResult",
         resampling_ids = resampling_ids), is.null)
 
       if (sum(!is.null(i), !is.null(uhashes), length(args) > 0L) > 1) {
-        stopf("At most one of `i`, `uhash`, or IDs can be provided.")
+        error_input("At most one of `i`, `uhash`, or IDs can be provided.")
       }
       if (!is.null(i)) {
         uhashes = self$uhashes
@@ -609,7 +609,7 @@ BenchmarkResult = R6Class("BenchmarkResult",
         uhashes = invoke(match.fun("uhashes"), bmr = self, .args = args)
       }
       if (length(uhashes) == 0L) {
-        stopf("No resample results found for the given arguments.")
+        error_input("No resample results found for the given arguments.")
       }
       uhashes
     },
@@ -714,7 +714,7 @@ uhash = function(bmr, learner_id = NULL, task_id = NULL, resampling_id = NULL) {
   assert_string(resampling_id, null.ok = TRUE)
   uhash = uhashes(bmr, learner_id, task_id, resampling_id)
   if (length(uhash) != 1) {
-    stopf("Expected exactly one uhash, got %s", length(uhash))
+    error_input("Expected exactly one uhash, got %s", length(uhash))
   }
   uhash
 }

R/DataBackendCbind.R

@@ -7,7 +7,7 @@ DataBackendCbind = R6Class("DataBackendCbind", inherit = DataBackend, cloneable
       pk = b1$primary_key
 
       if (pk != b2$primary_key) {
-        stopf("All backends to cbind must have the primary_key '%s'", pk)
+        error_input("All backends to cbind must have the primary_key '%s'", pk)
       }
 
       super$initialize(list(b1 = b1, b2 = b2), pk)

R/DataBackendDataTable.R

@@ -47,7 +47,7 @@ DataBackendDataTable = R6Class("DataBackendDataTable", inherit = DataBackend,
       super$initialize(setkeyv(data, primary_key), primary_key)
       ii = match(primary_key, names(data))
       if (is.na(ii)) {
-        stopf("Primary key '%s' not in 'data'", primary_key)
+        error_input("Primary key '%s' not in 'data'", primary_key)
       }
       private$.cache = set_names(replace(rep(NA, ncol(data)), ii, FALSE), names(data))
     },

R/DataBackendRbind.R

@@ -7,7 +7,7 @@ DataBackendRbind = R6Class("DataBackendRbind", inherit = DataBackend, cloneable
       pk = b1$primary_key
 
       if (pk != b2$primary_key) {
-        stopf("All backends to rbind must have the primary_key '%s'", pk)
+        error_input("All backends to rbind must have the primary_key '%s'", pk)
       }
 
       super$initialize(list(b1 = b1, b2 = b2), pk)

R/DataBackendRename.R

@@ -16,14 +16,14 @@ DataBackendRename = R6Class("DataBackendRename", inherit = DataBackend, cloneabl
       new = new[ii]
 
       if (self$primary_key %chin% old) {
-        stopf("Renaming the primary key is not supported")
+        error_input("Renaming the primary key is not supported")
       }
 
 
       resulting_names = map_values(b$colnames, old, new)
       dup = anyDuplicated(resulting_names)
       if (dup > 0L) {
-        stopf("Duplicated column name after rename: %s", resulting_names[dup])
+        error_input("Duplicated column name after rename: %s", resulting_names[dup])
       }
 
       self$old = old

R/HotstartStack.R

@@ -87,11 +87,11 @@ HotstartStack = R6Class("HotstartStack",
 
       walk(learners, function(learner) {
         if (!is.null(get0("validate", learner))) {
-          stopf("Hotstart learners that did validation is currently not supported.")
+          error_input("Hotstart learners that did validation is currently not supported.")
         } else if (is.null(learner$model)) {
-          stopf("Learners must be trained before adding them to the hotstart stack.")
+          error_input("Learners must be trained before adding them to the hotstart stack.")
         } else if (is_marshaled_model(learner$model)) {
-          stopf("Learners must be unmarshaled before adding them to the hotstart stack.")
+          error_input("Learners must be unmarshaled before adding them to the hotstart stack.")
         }
       })
 

R/Learner.R

@@ -375,14 +375,14 @@ Learner = R6Class("Learner",
     predict = function(task, row_ids = NULL) {
       # improve error message for the common mistake of passing a data.frame here
       if (is.data.frame(task)) {
-        stopf("To predict on data.frames, use the method `$predict_newdata()` instead of `$predict()`")
+        error_input("To predict on data.frames, use the method `$predict_newdata()` instead of `$predict()`")
       }
       task = assert_task(as_task(task))
       assert_predictable(task, self)
       row_ids = assert_row_ids(row_ids, task = task, null.ok = TRUE)
 
       if (is.null(self$state$model) && is.null(self$state$fallback_state$model)) {
-        stopf("Cannot predict, Learner '%s' has not been trained yet", self$id)
+        error_input("Cannot predict, Learner '%s' has not been trained yet", self$id)
       }
 
       # we need to marshal for call-r prediction and parallel prediction, but afterwards we reset the model
@@ -452,7 +452,7 @@ Learner = R6Class("Learner",
     predict_newdata = function(newdata, task = NULL) {
       if (is.null(task)) {
         if (is.null(self$state$train_task)) {
-          stopf("No task stored, and no task provided")
+          error_input("No task stored, and no task provided")
         }
         task = self$state$train_task$clone()
       } else {
@@ -618,7 +618,7 @@ Learner = R6Class("Learner",
             fallback$id, self$id, str_collapse(missing_properties), class = "Mlr3WarningConfigFallbackProperties")
         }
       } else if (method == "none" && !is.null(fallback)) {
-        stopf("Fallback learner must be `NULL` if encapsulation is set to `none`.")
+        error_input("Fallback learner must be `NULL` if encapsulation is set to `none`.")
       }
 
       private$.encapsulation = c(train = method, predict = method)
@@ -665,7 +665,7 @@ Learner = R6Class("Learner",
         for (i in seq_along(new_values)) {
           nn = ndots[[i]]
           if (!exists(nn, envir = self, inherits = FALSE)) {
-            stopf("Cannot set argument '%s' for '%s' (not a parameter, not a field).%s",
+            error_config("Cannot set argument '%s' for '%s' (not a parameter, not a field).%s",
               nn, class(self)[1L], did_you_mean(nn, c(param_ids, setdiff(names(self), ".__enclos_env__")))) # nolint
           }
           self[[nn]] = new_values[[i]]
@@ -681,10 +681,10 @@ Learner = R6Class("Learner",
     #' If set to `"error"`, an error is thrown, otherwise all features are returned.
     selected_features = function() {
       if (is.null(self$model)) {
-        stopf("No model stored")
+        error_input("No model stored")
       }
       if (private$.selected_features_impute == "error") {
-        stopf("Learner does not support feature selection")
+        error_input("Learner does not support feature selection")
       } else {
         self$state$feature_names
       }
@@ -790,15 +790,15 @@ Learner = R6Class("Learner",
 
       assert_string(rhs, .var.name = "predict_type")
       if (rhs %nin% self$predict_types) {
-        stopf("Learner '%s' does not support predict type '%s'", self$id, rhs)
+        error_input("Learner '%s' does not support predict type '%s'", self$id, rhs) # TODO error_learner?
       }
       private$.predict_type = rhs
     },
 
     #' @template field_param_set
     param_set = function(rhs) {
       if (!missing(rhs) && !identical(rhs, private$.param_set)) {
-        stopf("param_set is read-only.")
+        error_input("param_set is read-only.")
       }
       private$.param_set
     },
@@ -866,7 +866,7 @@ Learner = R6Class("Learner",
     # return: Numeric vector of weights or `no_weights_val` (default NULL)
     .get_weights = function(task, no_weights_val = NULL) {
       if ("weights" %nin% self$properties) {
-        stop("private$.get_weights should not be used in Learners that do not have the 'weights' property.")
+        error_mlr3("private$.get_weights should not be used in Learners that do not have the 'weights' property.")
       }
       if (self$use_weights == "use" && "weights_learner" %in% task$properties) {
         task$weights_learner$weight
@@ -916,7 +916,7 @@ default_values.Learner = function(x, search_space, task, ...) { # nolint
   values = default_values(x$param_set)
 
   if (any(search_space$ids() %nin% names(values))) {
-    stopf("Could not find default values for the following parameters: %s",
+    error_input("Could not find default values for the following parameters: %s",
       str_collapse(setdiff(search_space$ids(), names(values))))
   }
 

R/LearnerClassif.R

@@ -77,7 +77,7 @@ LearnerClassif = R6Class("LearnerClassif", inherit = Learner,
     #'
     #' @return `list()` with elements `"response"` or `"prob"` depending on the predict type.
     predict_newdata_fast = function(newdata, task = NULL) {
-      if (is.null(task) && is.null(self$state$train_task)) stopf("No task stored, and no task provided")
+      if (is.null(task) && is.null(self$state$train_task)) error_input("No task stored, and no task provided")
       feature_names = self$state$train_task$feature_names %??% task$feature_names
       class_names = self$state$train_task$class_names %??% task$class_names
 

R/LearnerClassifDebug.R

@@ -113,7 +113,7 @@ LearnerClassifDebug = R6Class("LearnerClassifDebug", inherit = LearnerClassif,
     #' @return Named `numeric()`.
     importance = function() {
       if (is.null(self$model)) {
-        stopf("No model stored")
+        error_input("No model stored")
       }
       fns = self$state$feature_names
       set_names(rep(0, length(fns)), fns)
@@ -124,7 +124,7 @@ LearnerClassifDebug = R6Class("LearnerClassifDebug", inherit = LearnerClassif,
     #' @return `character()`.
     selected_features = function() {
       if (is.null(self$model)) {
-        stopf("No model stored")
+        error_input("No model stored")
       }
       character(0)
     }
@@ -180,10 +180,10 @@ LearnerClassifDebug = R6Class("LearnerClassifDebug", inherit = LearnerClassif,
         message("Message from classif.debug->train()")
       }
       if (roll("warning_train")) {
-        warningf("Warning from classif.debug->train()")
+        warning_mlr3("Warning from classif.debug->train()")
       }
       if (roll("error_train")) {
-        stopf("Error from classif.debug->train()")
+        error_learner_train("Error from classif.debug->train()")
       }
       if (roll("segfault_train")) {
         get("attach")(structure(list(), class = "UserDefinedDatabase"))
@@ -192,7 +192,7 @@ LearnerClassifDebug = R6Class("LearnerClassifDebug", inherit = LearnerClassif,
       valid_truth = if (!is.null(task$internal_valid_task)) task$internal_valid_task$truth()
 
       if (isTRUE(pv$early_stopping) && is.null(valid_truth)) {
-        stopf("Early stopping is only possible when a validation task is present.")
+        error_config("Early stopping is only possible when a validation task is present.")
       }
 
       model = list(
@@ -248,7 +248,7 @@ LearnerClassifDebug = R6Class("LearnerClassifDebug", inherit = LearnerClassif,
     },
     .predict = function(task) {
       if (!is.null(self$model$marshal_pid) && self$model$marshal_pid != Sys.getpid()) {
-        stopf("Model was not unmarshaled correctly")
+        error_mlr3("Model was not unmarshaled correctly")
       }
       n = task$nrow
       pv = self$param_set$get_values(tags = "predict")
@@ -265,10 +265,10 @@ LearnerClassifDebug = R6Class("LearnerClassifDebug", inherit = LearnerClassif,
         message("Message from classif.debug->predict()")
       }
       if (roll("warning_predict")) {
-        warningf("Warning from classif.debug->predict()")
+        warning_mlr3("Warning from classif.debug->predict()")
       }
       if (roll("error_predict")) {
-        stopf("Error from classif.debug->predict()")
+        error_learner_predict("Error from classif.debug->predict()")
       }
       if (roll("segfault_predict")) {
         get("attach")(structure(list(), class = "UserDefinedDatabase"))

R/LearnerClassifFeatureless.R

@@ -54,7 +54,7 @@ LearnerClassifFeatureless = R6Class("LearnerClassifFeatureless", inherit = Learn
     #' @return Named `numeric()`.
     importance = function() {
       if (is.null(self$model)) {
-        stopf("No model stored")
+        error_learner("No model stored")
       }
       fn = self$model$features
       named_vector(fn, 0)
@@ -65,7 +65,7 @@ LearnerClassifFeatureless = R6Class("LearnerClassifFeatureless", inherit = Learn
     #' @return `character(0)`.
     selected_features = function() {
       if (is.null(self$model)) {
-        stopf("No model stored")
+        error_learner("No model stored")
       }
       character()
     }

R/LearnerClassifRpart.R

@@ -55,7 +55,7 @@ LearnerClassifRpart = R6Class("LearnerClassifRpart", inherit = LearnerClassif,
     #' @return Named `numeric()`.
     importance = function() {
       if (is.null(self$model)) {
-        stopf("No model stored")
+        error_learner("No model stored")
       }
       # importance is only present if there is at least on split
       sort(self$model$variable.importance %??% set_names(numeric()), decreasing = TRUE)
@@ -66,7 +66,7 @@ LearnerClassifRpart = R6Class("LearnerClassifRpart", inherit = LearnerClassif,
     #' @return `character()`.
     selected_features = function() {
       if (is.null(self$model)) {
-        stopf("No model stored")
+        error_learner("No model stored")
       }
       setdiff(self$model$frame$var, "<leaf>")
     }

R/LearnerRegr.R

@@ -69,7 +69,7 @@ LearnerRegr = R6Class("LearnerRegr", inherit = Learner,
     #'
     #' @return `list()` with elements `"response"`, `"se"` or `"quantiles"` depending on the predict type.
     predict_newdata_fast = function(newdata, task = NULL) {
-      if (is.null(task) && is.null(self$state$train_task)) stopf("No task stored, and no task provided")
+      if (is.null(task) && is.null(self$state$train_task)) error_input("No task stored, and no task provided")
       feature_names = self$state$train_task$feature_names %??% task$feature_names
 
       # add data and most common used meta data
@@ -134,7 +134,7 @@ LearnerRegr = R6Class("LearnerRegr", inherit = Learner,
       }
 
       if ("quantiles" %nin% self$predict_types) {
-        stopf("Learner does not support predicting quantiles")
+        error_config("Learner does not support predicting quantiles")
       }
       private$.quantiles = assert_numeric(rhs, lower = 0, upper = 1, any.missing = FALSE, min.len = 1L, sorted = TRUE, .var.name = "quantiles")
 
@@ -151,7 +151,7 @@ LearnerRegr = R6Class("LearnerRegr", inherit = Learner,
       }
 
       if ("quantiles" %nin% self$predict_types) {
-        stopf("Learner does not support predicting quantiles")
+        error_config("Learner does not support predicting quantiles")
       }
 
       private$.quantile_response = assert_number(rhs, lower = 0, upper = 1, .var.name = "response")

R/LearnerRegrDebug.R

@@ -64,7 +64,7 @@ LearnerRegrDebug = R6Class("LearnerRegrDebug", inherit = LearnerRegr,
     #' @return Named `numeric()`.
     importance = function() {
       if (is.null(self$model)) {
-        stopf("No model stored")
+       error_input("No model stored")
       }
       fns = self$state$feature_names
       set_names(rep(0, length(fns)), fns)
@@ -75,7 +75,7 @@ LearnerRegrDebug = R6Class("LearnerRegrDebug", inherit = LearnerRegr,
     #' @return `character()`.
     selected_features = function() {
       if (is.null(self$model)) {
-        stopf("No model stored")
+        error_input("No model stored")
       }
       character(0)
     }
@@ -88,13 +88,13 @@ LearnerRegrDebug = R6Class("LearnerRegrDebug", inherit = LearnerRegr,
       }
 
       if (roll("message_train")) {
-        message("Message from classif.debug->train()")
+        message("Message from regr.debug->train()")
       }
       if (roll("warning_train")) {
-        warningf("Warning from classif.debug->train()")
+        warning_mlr3("Warning from regr.debug->train()")
       }
       if (roll("error_train")) {
-        stopf("Error from classif.debug->train()")
+        error_learner_train("Error from regr.debug->train()")
       }
       if (roll("segfault_train")) {
         get("attach")(structure(list(), class = "UserDefinedDatabase"))
@@ -131,13 +131,13 @@ LearnerRegrDebug = R6Class("LearnerRegrDebug", inherit = LearnerRegr,
       }
 
       if (roll("message_predict")) {
-        message("Message from classif.debug->predict()")
+        message("Message from regr.debug->predict()")
       }
       if (roll("warning_predict")) {
-        warningf("Warning from classif.debug->predict()")
+        warning_mlr3("Warning from regr.debug->predict()")
       }
       if (roll("error_predict")) {
-        stopf("Error from classif.debug->predict()")
+        error_learner_predict("Error from regr.debug->predict()")
       }
       if (roll("segfault_predict")) {
         get("attach")(structure(list(), class = "UserDefinedDatabase"))