Merge pull request #276 from tidymodels/even-more-exports

Even more exports

Author: topepo

DESCRIPTION

@@ -1,5 +1,5 @@
 Package: parsnip
-Version: 0.0.5.9000
+Version: 0.0.5.9001
 Title: A Common API to Modeling and Analysis Functions
 Description: A common interface is provided to allow users to specify a model without having to remember the different argument names across different functions or computational engines (e.g. 'R', 'Spark', 'Stan', etc). 
 Authors@R: c(

NAMESPACE

@@ -25,12 +25,18 @@ S3method(predict,model_spec)
 S3method(predict,nullmodel)
 S3method(predict_class,"_lognet")
 S3method(predict_class,"_multnet")
+S3method(predict_class,model_fit)
 S3method(predict_classprob,"_lognet")
 S3method(predict_classprob,"_multnet")
+S3method(predict_classprob,model_fit)
+S3method(predict_confint,model_fit)
 S3method(predict_numeric,"_elnet")
+S3method(predict_numeric,model_fit)
+S3method(predict_quantile,model_fit)
 S3method(predict_raw,"_elnet")
 S3method(predict_raw,"_lognet")
 S3method(predict_raw,"_multnet")
+S3method(predict_raw,model_fit)
 S3method(print,boost_tree)
 S3method(print,control_parsnip)
 S3method(print,decision_tree)
@@ -92,7 +98,10 @@ export(boost_tree)
 export(check_empty_ellipse)
 export(check_final_param)
 export(control_parsnip)
+export(convert_args)
+export(convert_stan_interval)
 export(decision_tree)
+export(eval_args)
 export(fit)
 export(fit.model_spec)
 export(fit_control)
@@ -122,6 +131,13 @@ export(null_value)
 export(nullmodel)
 export(pred_value_template)
 export(predict.model_fit)
+export(predict_class.model_fit)
+export(predict_classprob.model_fit)
+export(predict_confint.model_fit)
+export(predict_numeric)
+export(predict_numeric.model_fit)
+export(predict_quantile.model_fit)
+export(predict_raw.model_fit)
 export(rand_forest)
 export(rpart_train)
 export(set_args)
@@ -144,6 +160,7 @@ export(svm_poly)
 export(svm_rbf)
 export(tidy.model_fit)
 export(translate)
+export(translate.default)
 export(update_dot_check)
 export(update_main_parameters)
 export(varying)

R/aaa.R

@@ -13,7 +13,13 @@ maybe_multivariate <- function(results, object) {
   results
 }
 
+#' Convenience function for intervals
 #' @importFrom stats quantile
+#' @export
+#' @keywords internal
+#' @param x A fitted model object
+#' @param level Level of uncertainty for intervals
+#' @param lower Is `level` the lower level?
 convert_stan_interval <- function(x, level = 0.95, lower = TRUE) {
   alpha <- (1 - level) / 2
   if (!lower) {
@@ -24,6 +30,10 @@ convert_stan_interval <- function(x, level = 0.95, lower = TRUE) {
   res
 }
 
+#' Make a table of arguments
+#' @param model_name A character string for the model
+#' @keywords internal
+#' @export
 convert_args <- function(model_name) {
   envir <- get_model_env()
 

R/aaa_multi_predict.R

@@ -117,18 +117,18 @@ multi_predict_args.default <- function(object, ...) {
 #' @export
 #' @rdname has_multi_predict
 multi_predict_args.model_fit <- function(object, ...) {
-  existing_mthds <- methods("multi_predict")
-  cls <- class(object)
-  tst <- paste0("multi_predict.", cls)
-  .fn <- tst[tst %in% existing_mthds]
-  if (length(.fn) == 0) {
-    return(NA_character_)
+  model_type <- class(object$spec)[1]
+  arg_info <- get_from_env(paste0(model_type, "_args"))
+  arg_info <- arg_info[arg_info$engine == object$spec$engine,]
+  arg_info <- arg_info[arg_info$has_submodel,]
+
+  if (nrow(arg_info) == 0) {
+    res <- NA_character_
+  } else {
+    res <- arg_info[["parsnip"]]
   }
 
-  .fn <- getFromNamespace(.fn, ns = "parsnip")
-  omit <- c('object', 'new_data', 'type', '...')
-  args <- names(formals(.fn))
-  args[!(args %in% omit)]
+  res
 }
 
 #' @export

R/arguments.R

@@ -103,6 +103,11 @@ maybe_eval <- function(x) {
   y
 }
 
+#' Evaluate parsnip model arguments
+#' @export
+#' @keywords internal
+#' @param spec A model specification
+#' @param ... Not used.
 eval_args <- function(spec, ...) {
   spec$args   <- purrr::map(spec$args,   maybe_eval)
   spec$eng_args <- purrr::map(spec$eng_args, maybe_eval)

R/linear_reg.R

@@ -105,7 +105,7 @@
 #'  separately saved to disk. In a new session, the object can be
 #'  reloaded and reattached to the `parsnip` object.
 #'
-#' @seealso [[fit()], [set_engine()]
+#' @seealso [fit()], [set_engine()]
 #' @examples
 #' linear_reg()
 #' # Parameters can be represented by a placeholder:

R/linear_reg_data.R

@@ -293,7 +293,7 @@ set_model_arg(
   parsnip = "penalty",
   original = "reg_param",
   func = list(pkg = "dials", fun = "penalty"),
-  has_submodel = TRUE
+  has_submodel = FALSE
 )
 
 set_model_arg(

R/logistic_reg.R

@@ -104,7 +104,7 @@
 #'  separately saved to disk. In a new session, the object can be
 #'  reloaded and reattached to the `parsnip` object.
 #'
-#' @seealso [[fit()]
+#' @seealso [fit()]
 #' @examples
 #' logistic_reg()
 #' # Parameters can be represented by a placeholder:

R/mars.R

@@ -62,7 +62,7 @@
 #'  attached.
 #'
 #' @importFrom purrr map_lgl
-#' @seealso [[fit()]
+#' @seealso [fit()]
 #' @examples
 #' mars(mode = "regression", num_terms = 5)
 #' @export

R/mlp.R

@@ -84,7 +84,7 @@
 #' \Sexpr[results=rd]{parsnip:::show_fit(parsnip:::mlp(mode = "regression"), "nnet")}
 #'
 #' @importFrom purrr map_lgl
-#' @seealso [[fit()]
+#' @seealso [fit()]
 #' @examples
 #' mlp(mode = "classification", penalty = 0.01)
 #' # Parameters can be represented by a placeholder: