Export custom training helpers

Author: DavisVaughan

NAMESPACE

@@ -57,13 +57,15 @@ export(.n_obs)
 export(.n_preds)
 export(.x)
 export(.y)
+export(C5.0_train)
 export(boost_tree)
 export(check_empty_ellipse)
 export(fit)
 export(fit.model_spec)
 export(fit_control)
 export(fit_xy)
 export(fit_xy.model_spec)
+export(keras_mlp)
 export(linear_reg)
 export(logistic_reg)
 export(make_classes)
@@ -97,6 +99,7 @@ export(varying_args)
 export(varying_args.model_spec)
 export(varying_args.recipe)
 export(varying_args.step)
+export(xgb_train)
 import(rlang)
 importFrom(dplyr,arrange)
 importFrom(dplyr,as_tibble)

R/boost_tree.R

@@ -257,6 +257,9 @@ check_args.boost_tree <- function(object) {
 
 # xgboost helpers --------------------------------------------------------------
 
+#' Training helper for xgboost
+#'
+#' @export
 xgb_train <- function(
   x, y,
   max_depth = 6, nrounds = 15, eta  = 0.3, colsample_bytree = 1,
@@ -399,6 +402,9 @@ xgb_by_tree <- function(tree, object, new_data, type, ...) {
 
 # C5.0 helpers -----------------------------------------------------------------
 
+#' Training helper for C5.0
+#'
+#' @export
 C5.0_train <-
   function(x, y, weights = NULL, trials = 15, minCases = 2, sample = 0, ...) {
     other_args <- list(...)

R/boost_tree_data.R

@@ -24,7 +24,7 @@ boost_tree_xgboost_data <-
     fit = list(
       interface = "matrix",
       protect = c("x", "y"),
-      func = c(pkg = NULL, fun = "xgb_train"),
+      func = c(pkg = "parsnip", fun = "xgb_train"),
       defaults =
         list(
           nthread = 1,
@@ -94,7 +94,7 @@ boost_tree_C5.0_data <-
     fit = list(
       interface = "data.frame",
       protect = c("x", "y", "weights"),
-      func = c(pkg = NULL, fun = "C5.0_train"),
+      func = c(pkg = "parsnip", fun = "C5.0_train"),
       defaults = list()
     ),
     classes = list(

R/mlp_data.R

@@ -22,7 +22,7 @@ mlp_keras_data <-
     fit = list(
       interface = "matrix",
       protect = c("x", "y"),
-      func = c(pkg = NULL, fun = "keras_mlp"),
+      func = c(pkg = "parsnip", fun = "keras_mlp"),
       defaults = list()
     ),
     pred = list(
@@ -131,6 +131,9 @@ class2ind <- function (x, drop2nd = FALSE) {
   y
 }
 
+#' MLP in Keras
+#'
+#' @export
 keras_mlp <-
   function(x, y,
            hidden_units = 5, decay = 0, dropout = 0, epochs = 20, act = "softmax",
@@ -155,7 +158,7 @@ keras_mlp <-
       else
         y <- matrix(y, ncol = 1)
     }
-    
+
     model <- keras::keras_model_sequential()
     if(decay > 0) {
       model %>%