fix family/closure error

R/Lrnr_pkg_SuperLearner.R

@@ -20,7 +20,7 @@
 #' @field ... Additional arguments. Currently unused.
 #'
 #' @importFrom R6 R6Class
-#' @importFrom stats gaussian
+#' @importFrom stats gaussian predict
 #'
 #' @export
 #
@@ -48,7 +48,7 @@ Lrnr_pkg_SuperLearner <- R6Class(classname = "Lrnr_pkg_SuperLearner",
           family <- family()
         }
       }
-      fit_object <- wrapper(task$Y, task$X, newX, family = stats::family,
+      fit_object <- wrapper(task$Y, task$X, newX, family = family,
                             obsWeights = task$weights, id = task$id)$fit
       return(fit_object)
     },
@@ -61,8 +61,8 @@ Lrnr_pkg_SuperLearner <- R6Class(classname = "Lrnr_pkg_SuperLearner",
           family <- family()
         }
       }
-      predictions = predict(private$.fit_object, newdata = task$X,
-                            family = family)
+      predictions = stats::predict(private$.fit_object, newdata = task$X,
+                                   family = family)
       return(predictions)
     },
     .required_packages = c("SuperLearner")

R/Lrnr_pkg_SuperLearner_method.R

@@ -1,36 +1,38 @@
-#support for SuperLearner wrappers, obviously not the most efficient approach, we should reimplement as many as possible
+#' Methods for SuperLearner Objects
+#'
+#' Support for \code{SuperLearner} wrappers, obviously not the most efficient
+#' approach, we should reimplement as many as possible.
+#'
 #' @importFrom assertthat assert_that is.count is.flag
+#'
 #' @export
-#' @rdname undocumented_learner
+#
 Lrnr_pkg_SuperLearner_method <- R6Class(classname = "Lrnr_pkg_SuperLearner_method",
-                     inherit= Lrnr_base,
-                     portable = TRUE,
-                     class = TRUE,
-                     public = list(
-                       initialize = function(method, ...) {
-                         params=list(method=method, ...)
-                         super$initialize(params=params, ...)
-                       }),
-                     private = list(
-                       .train = function(task) {
-                         method <- self$params$method
-                         X <- as.matrix(task$X)
-                         Y <- task$Y
-
-                         fit_object <- method$computeCoef(X, Y, names(X), verbose=FALSE, obsWeights=task$weights)
-                         return(fit_object)
-
-                       },
-                       .predict = function(task){
-                         coef <- private$.fit_object$coef
-                         X <- as.matrix(task$X)
-
-                         predictions = method$computePred(X, coef)
-                         return(predictions)
-                       },
-                       .required_packages = c("SuperLearner")
-
-                       )
+                                        inherit = Lrnr_base, portable = TRUE,
+                                        class = TRUE,
+  public = list(
+    initialize = function(method, ...) {
+      params = list(method = method, ...)
+      super$initialize(params=params, ...)
+    }
+  ),
+  private = list(
+    .train = function(task) {
+      method <- self$params$method
+      X <- as.matrix(task$X)
+      Y <- task$Y
 
+      fit_object <- method$computeCoef(X, Y, names(X), verbose = FALSE,
+                                       obsWeights = task$weights)
+      return(fit_object)
+    },
+    .predict = function(task) {
+      coef <- private$.fit_object$coef
+      X <- as.matrix(task$X)
+      predictions = method$computePred(X, coef)
+      return(predictions)
+    },
+    .required_packages = c("SuperLearner")
+  )
 )