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:

R/multinom_reg.R

@@ -91,7 +91,7 @@
 #'  separately saved to disk. In a new session, the object can be
 #'  reloaded and reattached to the `parsnip` object.
 #'
-#' @seealso [[fit()]
+#' @seealso [fit()]
 #' @examples
 #' multinom_reg()
 #' # Parameters can be represented by a placeholder:

R/nearest_neighbor.R

@@ -70,7 +70,7 @@
 #' on new data. This also means that a single value of that function's
 #' `kernel` argument (a.k.a `weight_func` here) can be supplied
 #'
-#' @seealso [[fit()]
+#' @seealso [fit()]
 #'
 #' @examples
 #' nearest_neighbor(neighbors = 11)

R/predict_class.R

@@ -1,13 +1,13 @@
-# Other predict methods.
-#
-# These are internal functions not meant to be directly called by the user.
-#
-# @keywords internal
-# @rdname other_predict
-# @inheritParams predict.model_fit
-# @method predict_class model_fit
-# @export predict_class.model_fit
-# @export
+#' Other predict methods.
+#'
+#' These are internal functions not meant to be directly called by the user.
+#'
+#' @keywords internal
+#' @rdname other_predict
+#' @inheritParams predict.model_fit
+#' @method predict_class model_fit
+#' @export predict_class.model_fit
+#' @export
 predict_class.model_fit <- function(object, new_data, ...) {
   if (object$spec$mode != "classification")
     rlang::abort("`predict.model_fit()` is for predicting factor outcomes.")

R/predict_classprob.R

@@ -1,9 +1,9 @@
-# @keywords internal
-# @rdname other_predict
-# @inheritParams predict.model_fit
-# @method predict_classprob model_fit
-# @export predict_classprob.model_fit
-# @export
+#' @keywords internal
+#' @rdname other_predict
+#' @inheritParams predict.model_fit
+#' @method predict_classprob model_fit
+#' @export predict_classprob.model_fit
+#' @export
 #' @importFrom tibble as_tibble is_tibble tibble
 predict_classprob.model_fit <- function(object, new_data, ...) {
   if (object$spec$mode != "classification")

R/predict_interval.R

@@ -1,13 +1,13 @@
-# @keywords internal
-# @rdname other_predict
-# @param level A single numeric value between zero and one for the
-#  interval estimates.
-# @param std_error A single logical for wether the standard error should be
-#  returned (assuming that the model can compute it).
-# @inheritParams predict.model_fit
-# @method predict_confint model_fit
-# @export predict_confint.model_fit
-# @export
+#' @keywords internal
+#' @rdname other_predict
+#' @param level A single numeric value between zero and one for the
+#'  interval estimates.
+#' @param std_error A single logical for wether the standard error should be
+#'  returned (assuming that the model can compute it).
+#' @inheritParams predict.model_fit
+#' @method predict_confint model_fit
+#' @export predict_confint.model_fit
+#' @export
 predict_confint.model_fit <- function(object, new_data, level = 0.95, std_error = FALSE, ...) {
 
   if (is.null(object$spec$method$pred$conf_int))

R/predict_numeric.R

@@ -1,10 +1,9 @@
-# @keywords internal
-# @rdname other_predict
-# @inheritParams predict.model_fit
-# @method predict_numeric model_fit
-# @export predict_numeric.model_fit
-# @export
-
+#' @keywords internal
+#' @rdname other_predict
+#' @inheritParams predict.model_fit
+#' @method predict_numeric model_fit
+#' @export predict_numeric.model_fit
+#' @export
 predict_numeric.model_fit <- function(object, new_data, ...) {
   if (object$spec$mode != "regression")
     rlang::abort(glue::glue("`predict_numeric()` is for predicting numeric outcomes. ",
@@ -45,9 +44,9 @@ predict_numeric.model_fit <- function(object, new_data, ...) {
 }
 
 
-# @export
-# @keywords internal
-# @rdname other_predict
-# @inheritParams predict_numeric.model_fit
+#' @export
+#' @keywords internal
+#' @rdname other_predict
+#' @inheritParams predict_numeric.model_fit
 predict_numeric <- function(object, ...)
   UseMethod("predict_numeric")

R/predict_quantile.R

@@ -1,13 +1,13 @@
-# @keywords internal
-# @rdname other_predict
-# @param quant A vector of numbers between 0 and 1 for the quantile being
-#  predicted.
-# @inheritParams predict.model_fit
-# @method predict_quantile model_fit
-# @export predict_quantile.model_fit
-# @export
+#' @keywords internal
+#' @rdname other_predict
+#' @param quant A vector of numbers between 0 and 1 for the quantile being
+#'  predicted.
+#' @inheritParams predict.model_fit
+#' @method predict_quantile model_fit
+#' @export predict_quantile.model_fit
+#' @export
 predict_quantile.model_fit <-
-  function (object, new_data, quantile = (1:9)/10, ...) {
+  function(object, new_data, quantile = (1:9)/10, ...) {
 
     if (is.null(object$spec$method$pred$quantile))
       rlang::abort("No quantile prediction method defined for this engine.")

R/predict_raw.R

@@ -1,8 +1,7 @@
-# @rdname predict.model_fit
-# @inheritParams predict.model_fit
-# @method predict_raw model_fit
-# @export predict_raw.model_fit
-# @export
+#' @rdname predict.model_fit
+#' @method predict_raw model_fit
+#' @export predict_raw.model_fit
+#' @export
 predict_raw.model_fit <- function(object, new_data, opts = list(), ...) {
   protected_args <- names(object$spec$method$pred$raw$args)
   dup_args <- names(opts) %in% protected_args

R/rand_forest.R

@@ -94,7 +94,7 @@
 #'  reloaded and reattached to the `parsnip` object.
 #'
 #' @importFrom purrr map_lgl
-#' @seealso [[fit()]
+#' @seealso [fit()]
 #' @examples
 #' rand_forest(mode = "classification", trees = 2000)
 #' # Parameters can be represented by a placeholder:

R/surv_reg.R

@@ -64,7 +64,7 @@
 #'  predictions when there is a stratification variable and can be
 #'  overridden in other cases.
 #'
-#' @seealso [[fit()], [survival::Surv()]
+#' @seealso [fit()], [survival::Surv()]
 #' @references Jackson, C. (2016). `flexsurv`: A Platform for Parametric Survival
 #'  Modeling in R. _Journal of Statistical Software_, 70(8), 1 - 33.
 #' @examples

R/svm_poly.R

@@ -58,7 +58,7 @@
 #' \Sexpr[results=rd]{parsnip:::show_fit(parsnip:::svm_poly(mode = "regression"), "kernlab")}
 #'
 #' @importFrom purrr map_lgl
-#' @seealso [[fit()]
+#' @seealso [fit()]
 #' @examples
 #' svm_poly(mode = "classification", degree = 1.2)
 #' # Parameters can be represented by a placeholder:

R/translate.R

@@ -5,6 +5,7 @@
 #'  It translates generic parameters to their counterparts.
 #'
 #' @param x A model specification.
+#' @param engine The computational engine for the model (see `?set_engine`).
 #' @param ... Not currently used.
 #' @details
 #' `translate()` produces a _template_ call that lacks the specific
@@ -47,7 +48,9 @@ translate <- function(x, ...)
 #' @importFrom utils getFromNamespace
 #' @importFrom purrr list_modify
 #' @importFrom rlang lgl
+#' @rdname translate
 #' @export
+#' @export translate.default
 translate.default <- function(x, engine = x$engine, ...) {
   check_empty_ellipse(...)
   if (is.null(engine))