Add coverage notes

imbs-hl · Nov 7, 2024 · c4556a4 · c4556a4
1 parent f4b2c21
commit c4556a4
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Showing 3 changed files with 15 additions and 0 deletions.
diff --git a/R/Lrner.R b/R/Lrner.R
@@ -104,6 +104,8 @@ Lrner <- R6Class("Lrner",
                    #'
                    #' @export
                    #'
+                   # TODO: Covr me
+                   # nocov start
                    interface = function (x = "x",
                                          y = "y",
                                          object = "object",
@@ -124,6 +126,7 @@ Lrner <- R6Class("Lrner",
                                                original = c(x, y, object, data))
                      private$param_interface = param_interface
                    },
+                   # nocov end
                    #' @description
                    #' Tains the current learner (from class [Lrner]) on the current training data (from class [TrainData]).
                    #'
@@ -188,10 +191,13 @@ Lrner <- R6Class("Lrner",
                        lrn_param$x = train_data$getData()
                        lrn_param$y = train_data$getTargetValues()
                      } else {
+                       # TODO: covr me
+                       # nocov start
                        x_name = private$param_interface[private$param_interface$standard == "x", "original"]
                        y_name = private$param_interface[private$param_interface$standard == "y", "original"]
                        lrn_param[[x_name]] = train_data$getData()
                        lrn_param[[y_name]] = train_data$getTargetValues()
+                       # nocov end
                      }
                      base_model = do.call(eval(parse(text = lrn)), lrn_param)
                      model = Model$new(lrner = self,

diff --git a/R/Model.R b/R/Model.R
@@ -188,10 +188,13 @@ Model <- R6Class("Model",
                        pred_param[["object"]] = pred_param_object
                        pred_param[["data"]] = pred_param_data
                      } else {
+                       # TODO: covr me
+                       # nocov start
                        object_name = param_interface[param_interface$standard == "object", "original"]
                        data_name = param_interface[param_interface$standard == "data", "original"]
                        pred_param[[object_name]] = pred_param_object
                        pred_param[[data_name]] = pred_param_data
+                       # nocov end
                      }
                      lrn_package = private$lrner$getPackage()
                      if (is.null(lrn_package)) {

diff --git a/R/VarSel.R b/R/VarSel.R
@@ -86,6 +86,8 @@ VarSel <- R6Class("VarSel",
                     #'
                     #' @export
                     #'
+                    # TODO: Convr me
+                    # nocov start
                     interface = function (x = "x",
                                           y = "y",
                                           object = "object",
@@ -106,6 +108,7 @@ VarSel <- R6Class("VarSel",
                                                    original = c(x, y, object, data))
                       private$param_interface = param_interface
                     },
+                    # nocov end
                     #' @description
                     #' Tains the current learner (from class [Lrner]) on the current training data (from class [TrainData]).
                     #'
@@ -145,10 +148,13 @@ VarSel <- R6Class("VarSel",
                         varsel_param$x = train_data$getData()
                         varsel_param$y = train_data$getTargetValues()
                       } else {
+                        # TODO: covr me
+                        # nocov start
                         x_name = private$param_interface[private$param_interface$standard == "x", "original"]
                         y_name = private$param_interface[private$param_interface$standard == "y", "original"]
                         varsel_param[[x_name]] = train_data$getData()
                         varsel_param[[y_name]] = train_data$getTargetValues()
+                        # nocov end
                       }
                       varselected = do.call(eval(parse(text = varsel)),
                                             varsel_param)