set_engine api changes

DESCRIPTION

@@ -1,6 +1,6 @@
 Package: parsnip
-Version: 0.0.0.9004
-Title: A Common API to Modeling and analysis Functions
+Version: 0.0.0.9005
+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(
     person("Max", "Kuhn", , "max@rstudio.com", c("aut", "cre")),

NAMESPACE

@@ -102,6 +102,7 @@ export(predict_raw)
 export(predict_raw.model_fit)
 export(rand_forest)
 export(set_args)
+export(set_engine)
 export(set_mode)
 export(show_call)
 export(surv_reg)

NEWS.md

@@ -1,3 +1,8 @@
+# parsnip 0.0.0.9005
+
+* The engine, and any associated arguments, are not specified using `set_engine`. There is no `engine` argument 
+
+
 # parsnip 0.0.0.9004
 
 * Arguments to modeling functions are now captured as quosures. 

R/arguments.R

@@ -50,7 +50,7 @@ prune_arg_list <- function(x, whitelist = NULL, modified = character(0)) {
   x
 }
 
-check_others <- function(args, obj, core_args) {
+check_eng_args <- function(args, obj, core_args) {
   # Make sure that we are not trying to modify an argument that
   # is explicitly protected in the method metadata or arg_key
   protected_args <- unique(c(obj$protect, core_args))
@@ -95,10 +95,17 @@ set_args <- function(object, ...) {
     if (any(main_args == i)) {
       object$args[[i]] <- the_dots[[i]]
     } else {
-      object$others[[i]] <- the_dots[[i]]
+      object$eng_args[[i]] <- the_dots[[i]]
     }
   }
-  object
+  new_model_spec(
+    cls = class(object)[1],
+    args = object$args,
+    eng_args = object$eng_args,
+    mode = object$mode,
+    method = NULL,
+    engine = object$engine
+  )
 }
 
 #' @rdname set_args
@@ -130,6 +137,6 @@ maybe_eval <- function(x) {
 
 eval_args <- function(spec, ...) {
   spec$args   <- purrr::map(spec$args,   maybe_eval)
-  spec$others <- purrr::map(spec$others, maybe_eval)
+  spec$eng_args <- purrr::map(spec$eng_args, maybe_eval)
   spec
 }

R/boost_tree.R

@@ -22,7 +22,7 @@
 #' }
 #' 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  `...` slot. If left to their defaults
+#'  set using the  `set_engine` function. 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.
@@ -46,11 +46,6 @@
 #' @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 ... 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
@@ -63,17 +58,12 @@
 #' \item \pkg{Spark}: `"spark"`
 #' }
 #'
-#' 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 `...`
-#'  argument to pass in the preferred values. For this type of
-#'  model, the template of the fit calls are:
+#'  model fit call.  For this type of model, the template of the
+#'  fit calls are:
 #'
 #' \pkg{xgboost} classification
 #'
@@ -109,7 +99,7 @@
 #'  reloaded and reattached to the `parsnip` object.
 #'
 #' @importFrom purrr map_lgl
-#' @seealso [varying()], [fit()]
+#' @seealso [varying()], [fit()], [set_engine()]
 #' @examples
 #' boost_tree(mode = "classification", trees = 20)
 #' # Parameters can be represented by a placeholder:
@@ -121,11 +111,7 @@ boost_tree <-
            mtry = NULL, trees = NULL, min_n = NULL,
            tree_depth = NULL, learn_rate = NULL,
            loss_reduction = NULL,
-           sample_size = NULL,
-           ...) {
-
-    others <- enquos(...)
-
+           sample_size = NULL) {
     args <- list(
       mtry = enquo(mtry),
       trees = enquo(trees),
@@ -136,18 +122,14 @@ boost_tree <-
       sample_size = enquo(sample_size)
     )
 
-    if (!(mode %in% boost_tree_modes))
-      stop("`mode` should be one of: ",
-           paste0("'", boost_tree_modes, "'", collapse = ", "),
-           call. = FALSE)
-
-    no_value <- !vapply(others, null_value, logical(1))
-    others <- others[no_value]
-
-    out <- list(args = args, others = others,
-                mode = mode, method = NULL, engine = NULL)
-    class(out) <- make_classes("boost_tree")
-    out
+    new_model_spec(
+      "boost_tree",
+      args,
+      eng_args = NULL,
+      mode,
+      method = NULL,
+      engine = NULL
+    )
   }
 
 #' @export
@@ -167,6 +149,7 @@ print.boost_tree <- function(x, ...) {
 #' @export
 #' @inheritParams boost_tree
 #' @param object A boosted tree model specification.
+#' @param ... Not used for `update`.
 #' @param fresh A logical for whether the arguments should be
 #'  modified in-place of or replaced wholesale.
 #' @return An updated model specification.
@@ -183,10 +166,8 @@ update.boost_tree <-
            mtry = NULL, trees = NULL, min_n = NULL,
            tree_depth = NULL, learn_rate = NULL,
            loss_reduction = NULL, sample_size = NULL,
-           fresh = FALSE,
-           ...) {
-
-    others <- enquos(...)
+           fresh = FALSE, ...) {
+    update_dot_check(...)
 
     args <- list(
       mtry = enquo(mtry),
@@ -209,23 +190,27 @@ update.boost_tree <-
         object$args[names(args)] <- args
     }
 
-    if (length(others) > 0) {
-      if (fresh)
-        object$others <- others
-      else
-        object$others[names(others)] <- others
-    }
-
-    object
+    new_model_spec(
+      "boost_tree",
+      args = object$args,
+      eng_args = object$eng_args,
+      mode = object$mode,
+      method = NULL,
+      engine = object$engine
+    )
   }
 
 # ------------------------------------------------------------------------------
 
 #' @export
-translate.boost_tree <- function(x, engine, ...) {
+translate.boost_tree <- function(x, engine = x$engine, ...) {
+  if (is.null(engine)) {
+    message("Used `engine = 'xgboost'` for translation.")
+    engine <- "xgboost"
+  }
   x <- translate.default(x, engine, ...)
 
-  if (x$engine == "spark") {
+  if (engine == "spark") {
     if (x$mode == "unknown")
       stop(
         "For spark boosted trees models, the mode cannot be 'unknown' ",

R/convert_data.R

@@ -76,7 +76,7 @@ convert_form_to_xy_fit <-function(
   if (indicators) {
     x <- model.matrix(mod_terms, mod_frame, contrasts)
   } else {
-    # this still ignores -vars in formula ¯\_(ツ)_/¯
+    # this still ignores -vars in formula
     x <- model.frame(mod_terms, data)
     y_cols <- attr(mod_terms, "response")
     if (length(y_cols) > 0)

R/descriptors.R

@@ -318,11 +318,11 @@ make_descr <- function(object) {
     expr_main <- map_lgl(object$args, has_exprs)
   else
     expr_main <- FALSE
-  if (length(object$others) > 0)
-    expr_others <- map_lgl(object$others, has_exprs)
+  if (length(object$eng_args) > 0)
+    expr_eng_args <- map_lgl(object$eng_args, has_exprs)
   else
-    expr_others <- FALSE
-  any(expr_main) | any(expr_others)
+    expr_eng_args <- FALSE
+  any(expr_main) | any(expr_eng_args)
 }
 
 # Locate descriptors -----------------------------------------------------------
@@ -331,7 +331,7 @@ make_descr <- function(object) {
 requires_descrs <- function(object) {
   any(c(
     map_lgl(object$args, has_any_descrs),
-    map_lgl(object$others, has_any_descrs)
+    map_lgl(object$eng_args, has_any_descrs)
   ))
 }
 

R/engines.R

@@ -52,3 +52,43 @@ check_installs <- function(x) {
     }
   }
 }
+
+#' Declare a computational engine and specific arguments
+#'
+#' `set_engine` is used to specify which package or system will be used
+#'  to fit the model, along with any arguments specific to that software.
+#'
+#' @param object A model specification.
+#' @param engine A character string for the software that should
+#'  be used to fit the model. This is highly dependent on the type
+#'  of model (e.g. linear regression, random forest, etc.).
+#' @param ... Any optional arguments associated with the chosen computational
+#'  engine. These are captured as quosures and can be `varying()`.
+#' @return An updated model specification.
+#' @examples
+#' # First, set general arguments using the standardized names
+#' mod <-
+#'   logistic_reg(mixture = 1/3) %>%
+#'   # now say how you want to fit the model and another other options
+#'   set_engine("glmnet", nlambda = 10)
+#' translate(mod, engine = "glmnet")
+#' @export
+set_engine <- function(object, engine, ...) {
+  if (!inherits(object, "model_spec")) {
+    stop("`object` should have class 'model_spec'.", call. = FALSE)
+  }
+  if (!is.character(engine) | length(engine) != 1)
+    stop("`engine` should be a single character value.", call. = FALSE)
+
+  object$engine <- engine
+  object <- check_engine(object)
+
+  new_model_spec(
+    cls = class(object)[1],
+    args = object$args,
+    eng_args = enquos(...),
+    mode = object$mode,
+    method = NULL,
+    engine = object$engine
+  )
+}