Merge pull request #169 from tidymodels/default-engine

Default engine changes for #166

Author: topepo

NEWS.md

@@ -1,3 +1,12 @@
+# parsnip 0.0.2.9000
+
+## New Features
+
+ * `add_rowindex()` can create a column called `.row` to a data frame. 
+ 
+ * If a computational engine is not explicitly set, a default will be used. Each default is documented on the corresponding model page. A warning is issued at fit time unless verbosity is zero.  
+
+
 # parsnip 0.0.2
 
 Small release driven by changes in `sample()` in the current r-devel. 

R/boost_tree.R

@@ -54,7 +54,7 @@
 #' The model can be created using the `fit()` function using the
 #'  following _engines_:
 #' \itemize{
-#' \item \pkg{R}:  `"xgboost"`, `"C5.0"`
+#' \item \pkg{R}:  `"xgboost"` (the default), `"C5.0"`
 #' \item \pkg{Spark}: `"spark"`
 #' }
 #'

R/decision_tree.R

@@ -34,7 +34,7 @@
 #' The model can be created using the `fit()` function using the
 #'  following _engines_:
 #' \itemize{
-#' \item \pkg{R}:  `"rpart"` or `"C5.0"` (classification only)
+#' \item \pkg{R}:  `"rpart"` (the default) or `"C5.0"` (classification only)
 #' \item \pkg{Spark}: `"spark"`
 #' }
 #'

R/fit.R

@@ -39,6 +39,10 @@
 #'  the data are converted to the required format. In this case, any
 #'  calls in the resulting model objects reference the temporary
 #'  objects used to fit the model.
+#'
+#' If the model engine has not been set, the model's default engine will be used
+#'  (as discussed on each model page). If the `verbosity` option of
+#'  [fit_control()] is greater than zero, a warning will be produced.
 #' @examples
 #' # Although `glm()` only has a formula interface, different
 #' # methods for specifying the model can be used
@@ -93,8 +97,13 @@ fit.model_spec <-
            ...
   ) {
     dots <- quos(...)
-    if (any(names(dots) == "engine"))
-      stop("Use `set_engine()` to supply the engine.", call. = FALSE)
+    if (is.null(object$engine)) {
+      eng_vals <- possible_engines(object)
+      object$engine <- eng_vals[1]
+      if (control$verbosity > 0) {
+        warning("Engine set to `", object$engine, "`", call. = FALSE)
+      }
+    }
 
     if (all(c("x", "y") %in% names(dots)))
       stop("`fit.model_spec()` is for the formula methods. Use `fit_xy()` instead.",
@@ -177,8 +186,13 @@ fit_xy.model_spec <-
            ...
   ) {
     dots <- quos(...)
-    if (any(names(dots) == "engine"))
-      stop("Use `set_engine()` to supply the engine.", call. = FALSE)
+    if (is.null(object$engine)) {
+      eng_vals <- possible_engines(object)
+      object$engine <- eng_vals[1]
+      if (control$verbosity > 0) {
+        warning("Engine set to `", object$engine, "`", call. = FALSE)
+      }
+    }
 
     if (object$engine != "spark" & NCOL(y) == 1 & !(is.vector(y) | is.factor(y))) {
       if (is.matrix(y)) {

R/linear_reg.R

@@ -36,7 +36,7 @@
 #' The model can be created using the `fit()` function using the
 #'  following _engines_:
 #' \itemize{
-#' \item \pkg{R}:  `"lm"` or `"glmnet"`
+#' \item \pkg{R}:  `"lm"`  (the default) or `"glmnet"`
 #' \item \pkg{Stan}:  `"stan"`
 #' \item \pkg{Spark}: `"spark"`
 #' \item \pkg{keras}: `"keras"`

R/logistic_reg.R

@@ -34,7 +34,7 @@
 #' The model can be created using the `fit()` function using the
 #'  following _engines_:
 #' \itemize{
-#' \item \pkg{R}:  `"glm"` or `"glmnet"`
+#' \item \pkg{R}:  `"glm"`  (the default) or `"glmnet"`
 #' \item \pkg{Stan}:  `"stan"`
 #' \item \pkg{Spark}: `"spark"`
 #' \item \pkg{keras}: `"keras"`

R/mars.R

@@ -33,7 +33,7 @@
 #' @details The model can be created using the `fit()` function using the
 #'  following _engines_:
 #' \itemize{
-#' \item \pkg{R}:  `"earth"`
+#' \item \pkg{R}:  `"earth"`  (the default)
 #' }
 #'
 #' @section Engine Details:

R/mlp.R

@@ -47,7 +47,7 @@
 #' The model can be created using the `fit()` function using the
 #'  following _engines_:
 #' \itemize{
-#' \item \pkg{R}:  `"nnet"`
+#' \item \pkg{R}:  `"nnet"` (the default)
 #' \item \pkg{keras}: `"keras"`
 #' }
 #'

R/multinom_reg.R

@@ -34,7 +34,7 @@
 #' The model can be created using the `fit()` function using the
 #'  following _engines_:
 #' \itemize{
-#' \item \pkg{R}:   `"glmnet"`
+#' \item \pkg{R}:   `"glmnet"`  (the default)
 #' \item \pkg{Stan}:  `"stan"`
 #' \item \pkg{keras}: `"keras"`
 #' }

R/nearest_neighbor.R

@@ -43,7 +43,7 @@
 #' The model can be created using the `fit()` function using the
 #'  following _engines_:
 #' \itemize{
-#' \item \pkg{R}:  `"kknn"`
+#' \item \pkg{R}:  `"kknn"`  (the default)
 #' }
 #'
 #' @section Engine Details: