template for prediction objects

Author: topepo

NAMESPACE

@@ -104,6 +104,7 @@ export(nearest_neighbor)
 export(null_model)
 export(nullmodel)
 export(pred_types)
+export(pred_value_template)
 export(predict.model_fit)
 export(rand_forest)
 export(rpart_train)

R/aaa_models.R

@@ -78,6 +78,10 @@ pred_types <-
 #' @param value A list that conforms to the `fit_obj` or `pred_obj` description
 #'  above, depending on context.
 #' @param items A character string of objects in the model environment.
+#' @param pre,post Optional functions for pre- and post-processing of prediction
+#'  results.
+#' @param ... Optional arguments that should be passed into the `args` slot for
+#'  prediction objects
 #' @keywords internal
 #' @details These functions are available for users to add their
 #'  own models or engines (in package or otherwise) so that they can
@@ -692,3 +696,15 @@ get_from_env <- function(items) {
   rlang::env_get(mod_env, items)
 }
 
+# ------------------------------------------------------------------------------
+
+#' @rdname get_model_env
+#' @keywords internal
+#' @export
+pred_value_template <-  function(pre = NULL, post = NULL, func, ...) {
+  if (rlang::is_missing(func)) {
+    stop("Please supply a value to `func`. See `?set_pred`.", call. = FALSE)
+  }
+  list(pre = pre, post = post, func = func, args = list(...))
+}
+

man/get_model_env.Rd

@@ -210,22 +210,19 @@ A similar call can be used to define  the class probability module (if they can
 
 As an example of the `post` function, the data frame created by `mda:::predict.mda` will be converted to a tibble. The arguments are `x` (the raw results coming from the predict method) and `object` (the `parsnip` model fit object). The latter has a sub-object called `lvl` which is a character string of the outcome's factor levels (if any). 
 
-We register the probability module:
+We register the probability module. There is a template function that makes this slightly easier to format the objects:
 
 ```{r mda-prob}
 prob_info <-
-  list(
-    pre = NULL,
+  pred_value_template(
     post = function(x, object) {
-        tibble::as_tibble(x)
-      },
+      tibble::as_tibble(x)
+    },
     func = c(fun = "predict"),
-    args =
-      list(
-        object = quote(object$fit),
-        newdata = quote(new_data),
-        type = "posterior"
-      )
+    # Now everything else is put into the `args` slot
+    object = quote(object$fit),
+    newdata = quote(new_data),
+    type = "posterior"
   )
 
 set_pred(