Merge pull request #91 from topepo/quosure-passthrough-max

quosure updates

Author: DavisVaughan

R/arguments.R

@@ -86,7 +86,7 @@ check_others <- function(args, obj, core_args) {
 #'
 #' @export
 set_args <- function(object, ...) {
-  the_dots <- list(...)
+  the_dots <- enquos(...)
   if (length(the_dots) == 0)
     stop("Please pass at least one named argument.", call. = FALSE)
   main_args <- names(object$args)

R/boost_tree.R

@@ -22,16 +22,14 @@
 #' }
 #' These arguments are converted to their specific names at the
 #'  time that the model is fit. Other options and argument can be
-#'  set using the `others` argument. If left to their defaults
+#'  set using the  `...` slot. If left to their defaults
 #'  here (`NULL`), the values are taken from the underlying model
 #'  functions.  If parameters need to be modified, `update` can be used
 #'  in lieu of recreating the object from scratch.
 #'
 #' @param mode A single character string for the type of model.
 #'  Possible values for this model are "unknown", "regression", or
 #'  "classification".
-#' @param others A named list of arguments to be used by the
-#'  underlying models (e.g., `xgboost::xgb.train`, etc.). .
 #' @param mtry An number for the number (or proportion) of predictors that will
 #'  be randomly sampled at each split when creating the tree models (`xgboost`
 #'  only).
@@ -48,8 +46,11 @@
 #' @param sample_size An number for the number (or proportion) of data that is
 #'  exposed to the fitting routine. For `xgboost`, the sampling is done at at
 #'  each iteration while `C5.0` samples once during traning.
-#' @param ... Used for method consistency. Any arguments passed to
-#'  the ellipses will result in an error. Use `others` instead.
+#' @param ... Other arguments to pass to the specific engine's
+#'  model fit function (see the Engine Details section below). This
+#'  should not include arguments defined by the main parameters to
+#'  this function. For the `update` function, the ellipses can
+#'  contain the primary arguments or any others.
 #' @details
 #' The data given to the function are not saved and are only used
 #'  to determine the _mode_ of the model. For `boost_tree`, the
@@ -62,12 +63,15 @@
 #' \item \pkg{Spark}: `"spark"`
 #' }
 #'
-#' Main parameter arguments (and those in `others`) can avoid
+#' Main parameter arguments (and those in `...`) can avoid
 #'  evaluation until the underlying function is executed by wrapping the
 #'  argument in [rlang::expr()] (e.g. `mtry = expr(floor(sqrt(p)))`).
 #'
+#'
+#' @section Engine Details:
+#'
 #' Engines may have pre-set default arguments when executing the
-#'  model fit call. These can be changed by using the `others`
+#'  model fit call. These can be changed by using the `...`
 #'  argument to pass in the preferred values. For this type of
 #'  model, the template of the fit calls are:
 #'
@@ -114,13 +118,18 @@
 
 boost_tree <-
   function(mode = "unknown",
-           ...,
            mtry = NULL, trees = NULL, min_n = NULL,
            tree_depth = NULL, learn_rate = NULL,
            loss_reduction = NULL,
            sample_size = NULL,
-           others = list()) {
-    check_empty_ellipse(...)
+           ...) {
+    others         <- enquos(...)
+    mtry           <- enquo(mtry)
+    trees          <- enquo(trees)
+    min_n          <- enquo(min_n)
+    learn_rate     <- enquo(learn_rate)
+    loss_reduction <- enquo(loss_reduction)
+    sample_size    <- enquo(sample_size)
 
     if (!(mode %in% boost_tree_modes))
       stop("`mode` should be one of: ",
@@ -184,10 +193,15 @@ update.boost_tree <-
            mtry = NULL, trees = NULL, min_n = NULL,
            tree_depth = NULL, learn_rate = NULL,
            loss_reduction = NULL, sample_size = NULL,
-           others = list(),
            fresh = FALSE,
            ...) {
-    check_empty_ellipse(...)
+    others         <- enquos(...)
+    mtry           <- enquo(mtry)
+    trees          <- enquo(trees)
+    min_n          <- enquo(min_n)
+    learn_rate     <- enquo(learn_rate)
+    loss_reduction <- enquo(loss_reduction)
+    sample_size    <- enquo(sample_size)
 
     args <- list(
       mtry = mtry, trees = trees, min_n = min_n, tree_depth = tree_depth,

R/linear_reg.R

@@ -12,25 +12,19 @@
 #' }
 #' These arguments are converted to their specific names at the
 #'  time that the model is fit. Other options and argument can be
-#'  set using the `others` argument. If left to their defaults
+#'  set using the  `...` slot. If left to their defaults
 #'  here (`NULL`), the values are taken from the underlying model
 #'  functions. If parameters need to be modified, `update` can be used
 #'  in lieu of recreating the object from scratch.
+#' @inheritParams boost_tree
 #' @param mode A single character string for the type of model.
 #'  The only possible value for this model is "regression".
-#' @param others A named list of arguments to be used by the
-#'  underlying models (e.g., `stats::lm`,
-#'  `rstanarm::stan_glm`, etc.). These are not evaluated
-#'  until the model is fit and will be substituted into the model
-#'  fit expression.
 #' @param penalty An non-negative number representing the
 #'  total amount of regularization (`glmnet` and `spark` only).
 #' @param mixture A number between zero and one (inclusive) that
 #'  represents the proportion of regularization that is used for the
 #'  L2 penalty (i.e. weight decay, or ridge regression) versus L1
 #'  (the lasso) (`glmnet` and `spark` only).
-#' @param ... Used for S3 method consistency. Any arguments passed to
-#'  the ellipses will result in an error. Use `others` instead.
 #'
 #' @details
 #' The data given to the function are not saved and are only used
@@ -45,8 +39,10 @@
 #' \item \pkg{Spark}: `"spark"`
 #' }
 #'
+#' @section Engine Details:
+#'
 #' Engines may have pre-set default arguments when executing the
-#'  model fit call. These can be changed by using the `others`
+#'  model fit call. These can be changed by using the `...`
 #'  argument to pass in the preferred values. For this type of
 #'  model, the template of the fit calls are:
 #'
@@ -105,11 +101,13 @@
 #' @importFrom purrr map_lgl
 linear_reg <-
   function(mode = "regression",
-           ...,
            penalty = NULL,
            mixture = NULL,
-           others = list()) {
-    check_empty_ellipse(...)
+           ...) {
+    others  <- enquos(...)
+    penalty <- enquo(penalty)
+    mixture <- enquo(mixture)
+
     if (!(mode %in% linear_reg_modes))
       stop(
         "`mode` should be one of: ",
@@ -121,7 +119,7 @@ linear_reg <-
       stop("The amount of regularization should be >= 0", call. = FALSE)
     if (is.numeric(mixture) && (mixture < 0 | mixture > 1))
       stop("The mixture proportion should be within [0,1]", call. = FALSE)
-    if (length(mixture) > 1)
+    if (is.numeric(mixture) && length(mixture) > 1)
       stop("Only one value of `mixture` is allowed.", call. = FALSE)
 
     args <- list(penalty = penalty, mixture = mixture)
@@ -156,11 +154,8 @@ print.linear_reg <- function(x, ...) {
 
 # ------------------------------------------------------------------------------
 
-#' @inheritParams linear_reg
+#' @inheritParams update.boost_tree
 #' @param object A linear regression model specification.
-#' @param fresh A logical for whether the arguments should be
-#'  modified in-place of or replaced wholesale.
-#' @return An updated model specification.
 #' @examples
 #' model <- linear_reg(penalty = 10, mixture = 0.1)
 #' model
@@ -172,10 +167,11 @@ print.linear_reg <- function(x, ...) {
 update.linear_reg <-
   function(object,
            penalty = NULL, mixture = NULL,
-           others = list(),
            fresh = FALSE,
            ...) {
-    check_empty_ellipse(...)
+    others  <- enquos(...)
+    penalty <- enquo(penalty)
+    mixture <- enquo(mixture)
 
     if (is.numeric(penalty) && penalty < 0)
       stop("The amount of regularization should be >= 0", call. = FALSE)

R/logistic_reg.R

@@ -12,25 +12,19 @@
 #' }
 #' These arguments are converted to their specific names at the
 #'  time that the model is fit. Other options and argument can be
-#'  set using the `others` argument. If left to their defaults
+#'  set using the  `...` slot. If left to their defaults
 #'  here (`NULL`), the values are taken from the underlying model
 #'  functions. If parameters need to be modified, `update` can be used
 #'  in lieu of recreating the object from scratch.
+#' @inheritParams boost_tree
 #' @param mode A single character string for the type of model.
 #'  The only possible value for this model is "classification".
-#' @param others A named list of arguments to be used by the
-#'  underlying models (e.g., `stats::glm`,
-#'  `rstanarm::stan_glm`, etc.). These are not evaluated
-#'  until the model is fit and will be substituted into the model
-#'  fit expression.
 #' @param penalty An non-negative number representing the
 #'  total amount of regularization (`glmnet` and `spark` only).
 #' @param mixture A number between zero and one (inclusive) that
 #'  represents the proportion of regularization that is used for the
 #'  L2 penalty (i.e. weight decay, or ridge regression) versus L1
 #'  (the lasso) (`glmnet` and `spark` only).
-#' @param ... Used for S3 method consistency. Any arguments passed to
-#'  the ellipses will result in an error. Use `others` instead.
 #' @details
 #' For `logistic_reg`, the mode will always be "classification".
 #'
@@ -42,8 +36,10 @@
 #' \item \pkg{Spark}: `"spark"`
 #' }
 #'
+#' @section Engine Details:
+#'
 #' Engines may have pre-set default arguments when executing the
-#'  model fit call. These can be changed by using the `others`
+#'  model fit call. These can be changed by using the `...`
 #'  argument to pass in the preferred values. For this type of
 #'  model, the template of the fit calls are:
 #'
@@ -103,11 +99,13 @@
 #' @importFrom purrr map_lgl
 logistic_reg <-
   function(mode = "classification",
-           ...,
            penalty = NULL,
            mixture = NULL,
-           others = list()) {
-    check_empty_ellipse(...)
+           ...) {
+    others  <- enquos(...)
+    penalty <- enquo(penalty)
+    mixture <- enquo(mixture)
+
     if (!(mode %in% logistic_reg_modes))
       stop(
         "`mode` should be one of: ",
@@ -152,11 +150,8 @@ print.logistic_reg <- function(x, ...) {
 
 # ------------------------------------------------------------------------------
 
-#' @inheritParams logistic_reg
+#' @inheritParams update.boost_tree
 #' @param object A logistic regression model specification.
-#' @param fresh A logical for whether the arguments should be
-#'  modified in-place of or replaced wholesale.
-#' @return An updated model specification.
 #' @examples
 #' model <- logistic_reg(penalty = 10, mixture = 0.1)
 #' model
@@ -168,10 +163,11 @@ print.logistic_reg <- function(x, ...) {
 update.logistic_reg <-
   function(object,
            penalty = NULL, mixture = NULL,
-           others = list(),
            fresh = FALSE,
            ...) {
-    check_empty_ellipse(...)
+    others  <- enquos(...)
+    penalty <- enquo(penalty)
+    mixture <- enquo(mixture)
 
     if (is.numeric(penalty) && penalty < 0)
       stop("The amount of regularization should be >= 0", call. = FALSE)

R/logistic_reg_data.R

@@ -30,7 +30,7 @@ logistic_reg_glm_data <-
       func = c(pkg = "stats", fun = "glm"),
       defaults =
         list(
-          family = expr(binomial)
+          family = expr(stats::binomial)
         )
     ),
     classes = list(
@@ -151,7 +151,7 @@ logistic_reg_stan_data <-
       func = c(pkg = "rstanarm", fun = "stan_glm"),
       defaults =
         list(
-          family = expr(binomial)
+          family = expr(stats::binomial)
         )
     ),
     classes = list(

R/mars.R

@@ -17,26 +17,20 @@
 #' }
 #' These arguments are converted to their specific names at the
 #'  time that the model is fit. Other options and argument can be
-#'  set using the `others` argument. If left to their defaults
+#'  set using the  `...` slot. If left to their defaults
 #'  here (`NULL`), the values are taken from the underlying model
 #'  functions. If parameters need to be modified, `update` can be used
 #'  in lieu of recreating the object from scratch.
 #'
+#' @inheritParams boost_tree
 #' @param mode A single character string for the type of model.
 #'  Possible values for this model are "unknown", "regression", or
 #'  "classification".
-#' @param others A named list of arguments to be used by the
-#'  underlying models (e.g., `earth::earth`, etc.). If the outcome is a factor
-#'  and `mode = "classification"`, `others` can include the `glm` argument to
-#'  `earth::earth`. If this argument is not passed, it will be added prior to
-#'  the fitting occurs.
 #' @param num_terms The number of features that will be retained in the
 #'    final model, including the intercept.
 #' @param prod_degree The highest possible interaction degree.
 #' @param prune_method The pruning method.
-#' @param ... Used for method consistency. Any arguments passed to
-#'  the ellipses will result in an error. Use `others` instead.
-#' @details Main parameter arguments (and those in `others`) can avoid
+#' @details Main parameter arguments (and those in `...`) can avoid
 #'  evaluation until the underlying function is executed by wrapping the
 #'  argument in [rlang::expr()].
 #'
@@ -46,8 +40,10 @@
 #' \item \pkg{R}:  `"earth"`
 #' }
 #'
+#' @section Engine Details:
+#'
 #' Engines may have pre-set default arguments when executing the
-#'  model fit call. These can be changed by using the `others`
+#'  model fit call. These can be changed by using the `...`
 #'  argument to pass in the preferred values. For this type of
 #'  model, the template of the fit calls are:
 #'
@@ -71,10 +67,12 @@
 
 mars <-
   function(mode = "unknown",
-           ...,
            num_terms = NULL, prod_degree = NULL, prune_method = NULL,
-           others = list()) {
-    check_empty_ellipse(...)
+           ...) {
+    others  <- enquos(...)
+    num_terms    <- enquo(num_terms)
+    prod_degree  <- enquo(prod_degree)
+    prune_method <- enquo(prune_method)
 
     if (!(mode %in% mars_modes))
       stop("`mode` should be one of: ",
@@ -87,7 +85,7 @@ mars <-
       stop("`num_terms` should be >= 1", call. = FALSE)
     if (!is_varying(prune_method) &&
         !is.null(prune_method) &&
-        !is.character(prune_method))
+         is.character(prune_method))
       stop("`prune_method` should be a single string value", call. = FALSE)
 
     args <- list(num_terms = num_terms,
@@ -118,11 +116,8 @@ print.mars <- function(x, ...) {
 # ------------------------------------------------------------------------------
 
 #' @export
-#' @inheritParams mars
+#' @inheritParams update.boost_tree
 #' @param object A MARS model specification.
-#' @param fresh A logical for whether the arguments should be
-#'  modified in-place of or replaced wholesale.
-#' @return An updated model specification.
 #' @examples
 #' model <- mars(num_terms = 10, prune_method = "none")
 #' model
@@ -134,10 +129,12 @@ print.mars <- function(x, ...) {
 update.mars <-
   function(object,
            num_terms = NULL, prod_degree = NULL, prune_method = NULL,
-           others = list(),
            fresh = FALSE,
            ...) {
-    check_empty_ellipse(...)
+    others  <- enquos(...)
+    num_terms    <- enquo(num_terms)
+    prod_degree  <- enquo(prod_degree)
+    prune_method <- enquo(prune_method)
 
     args <- list(num_terms = num_terms,
                  prod_degree = prod_degree,