Merge pull request #230 from tidymodels/control-parsnip

new control function name

DESCRIPTION

@@ -1,5 +1,5 @@
 Package: parsnip
-Version: 0.0.3.9002
+Version: 0.0.4
 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

@@ -32,8 +32,8 @@ S3method(predict_raw,"_elnet")
 S3method(predict_raw,"_lognet")
 S3method(predict_raw,"_multnet")
 S3method(print,boost_tree)
+S3method(print,control_parsnip)
 S3method(print,decision_tree)
-S3method(print,fit_control)
 S3method(print,linear_reg)
 S3method(print,logistic_reg)
 S3method(print,mars)
@@ -90,6 +90,7 @@ export(C5.0_train)
 export(add_rowindex)
 export(boost_tree)
 export(check_empty_ellipse)
+export(control_parsnip)
 export(decision_tree)
 export(fit)
 export(fit.model_spec)

NEWS.md

@@ -1,22 +1,23 @@
-# parsnip 0.0.3.9001
+# parsnip 0.0.4
 
 ## New Features
 
-* The time elapsed during model fitting is stored in the `$elapsed` slot of the
-parsnip model object, and is printed when the model object is printed.
+* The time elapsed during model fitting is stored in the `$elapsed` slot of the parsnip model object, and is printed when the model object is printed.
 
 * Some default parameter ranges were updated for SVM, KNN, and MARS models. 
 
+* The model `udpate()` methods gained a `parameters` argument for cases when the parameters are contained in a tibble or list. 
+
+* `fit_control()` is soft-deprecated in favor of `control_parsnip()`. 
+
 ## Fixes
-* [A bug](https://github.com/tidymodels/parsnip/issues/222) was fixed standardizing
-the output column types of `multi_predict` and `predict` for `multinom_reg`.
+
+* [A bug](https://github.com/tidymodels/parsnip/issues/222) was fixed standardizing the output column types of `multi_predict` and `predict` for `multinom_reg`.
 
 * [A bug](https://github.com/tidymodels/parsnip/issues/208) was fixed related to using data descriptors and `fit_xy()`. 
 
 * A bug was fixed related to the column names generated by `multi_predict()`. The top-level tibble will always have a column named `.pred` and this list column contains tibbles across sub-models. The column names for these sub-model tibbles will have names consistent with `predict()` (which was previously incorrect). See [43c15db](https://github.com/tidymodels/parsnip/commit/43c15db377ea9ef27483ff209f6bd0e98cb830d2).
 
-* The model `udpate()` methods gained a `parameters` argument for cases when the parameters are contained in a tibble or list. 
-
 * [A bug](https://github.com/tidymodels/parsnip/issues/174) was fixed standardizing the column names of `nnet` class probability predictions.
 
 

R/boost_tree.R

@@ -99,7 +99,7 @@
 #'  reloaded and reattached to the `parsnip` object.
 #'
 #' @importFrom purrr map_lgl
-#' @seealso [varying()], [fit()], [set_engine()]
+#' @seealso [[fit()], [set_engine()]
 #' @examples
 #' boost_tree(mode = "classification", trees = 20)
 #' # Parameters can be represented by a placeholder:

R/fit_control.R → R/control_parsnip.R

@@ -16,18 +16,25 @@
 #'  class "try-error".
 #' @return An S3 object with class "fit_control" that is a named list with the
 #' results of the function call
+#' @details
+#' `fit_control()` is deprecated in favor of `control_parsnip()`.
 #' @export
 #'
-
-fit_control <- function(verbosity = 1L, catch = FALSE) {
+control_parsnip <- function(verbosity = 1L, catch = FALSE) {
   res <- list(verbosity = verbosity, catch = catch)
   res <- check_control(res)
-  class(res) <- "fit_control"
+  class(res) <- "control_parsnip"
   res
 }
 
 #' @export
-print.fit_control <- function(x, ...) {
+#' @rdname control_parsnip
+fit_control <- function(verbosity = 1L, catch = FALSE) {
+  control_parsnip(verbosity = verbosity, catch = catch)
+}
+
+#' @export
+print.control_parsnip <- function(x, ...) {
   cat("parsnip control object\n")
   if (x$verbosity > 1)
     cat(" - verbose level", x$verbosity, "\n")

R/decision_tree.R

@@ -84,7 +84,7 @@
 #'  reloaded and reattached to the `parsnip` object.
 #'
 #' @importFrom purrr map_lgl
-#' @seealso [varying()], [fit()]
+#' @seealso [[fit()]
 #' @examples
 #' decision_tree(mode = "classification", tree_depth = 5)
 #' # Parameters can be represented by a placeholder:

R/fit.R

@@ -19,7 +19,7 @@
 #'  outcome(s), predictors, case weights, etc). Note: when needed, a
 #'  \emph{named argument} should be used.
 #' @param control A named list with elements `verbosity` and
-#'  `catch`. See [fit_control()].
+#'  `catch`. See [control_parsnip()].
 #' @param ... Not currently used; values passed here will be
 #'  ignored. Other options required to fit the model should be
 #'  passed using `set_engine()`.
@@ -42,7 +42,7 @@
 #'
 #' 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.
+#'  [control_parsnip()] 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
@@ -81,7 +81,7 @@
 #'  The return value will also have a class related to the fitted model (e.g.
 #'  `"_glm"`) before the base class of `"model_fit"`.
 #'
-#' @seealso [set_engine()], [fit_control()], `model_spec`, `model_fit`
+#' @seealso [set_engine()], [control_parsnip()], `model_spec`, `model_fit`
 #' @param x A matrix or data frame of predictors.
 #' @param y A vector, matrix or data frame of outcome data.
 #' @rdname fit
@@ -91,7 +91,7 @@ fit.model_spec <-
   function(object,
            formula = NULL,
            data = NULL,
-           control = fit_control(),
+           control = control_parsnip(),
            ...
   ) {
     if (object$mode == "unknown") {
@@ -182,7 +182,7 @@ fit_xy.model_spec <-
   function(object,
            x = NULL,
            y = NULL,
-           control = fit_control(),
+           control = control_parsnip(),
            ...
   ) {
     object <- check_mode(object, levels(y))

R/linear_reg.R

@@ -98,7 +98,7 @@
 #'  separately saved to disk. In a new session, the object can be
 #'  reloaded and reattached to the `parsnip` object.
 #'
-#' @seealso [varying()], [fit()], [set_engine()]
+#' @seealso [[fit()], [set_engine()]
 #' @examples
 #' linear_reg()
 #' # Parameters can be represented by a placeholder:

R/logistic_reg.R

@@ -97,7 +97,7 @@
 #'  separately saved to disk. In a new session, the object can be
 #'  reloaded and reattached to the `parsnip` object.
 #'
-#' @seealso [varying()], [fit()]
+#' @seealso [[fit()]
 #' @examples
 #' logistic_reg()
 #' # Parameters can be represented by a placeholder:

R/mars.R

@@ -55,7 +55,7 @@
 #'  attached.
 #'
 #' @importFrom purrr map_lgl
-#' @seealso [varying()], [fit()]
+#' @seealso [[fit()]
 #' @examples
 #' mars(mode = "regression", num_terms = 5)
 #' @export

R/mlp.R

@@ -77,7 +77,7 @@
 #' \Sexpr[results=rd]{parsnip:::show_fit(parsnip:::mlp(mode = "regression"), "nnet")}
 #'
 #' @importFrom purrr map_lgl
-#' @seealso [varying()], [fit()]
+#' @seealso [[fit()]
 #' @examples
 #' mlp(mode = "classification", penalty = 0.01)
 #' # Parameters can be represented by a placeholder:

R/multinom_reg.R

@@ -80,7 +80,7 @@
 #'  separately saved to disk. In a new session, the object can be
 #'  reloaded and reattached to the `parsnip` object.
 #'
-#' @seealso [varying()], [fit()]
+#' @seealso [[fit()]
 #' @examples
 #' multinom_reg()
 #' # Parameters can be represented by a placeholder:

R/nearest_neighbor.R

@@ -63,7 +63,7 @@
 #' 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 [varying()], [fit()]
+#' @seealso [[fit()]
 #'
 #' @examples
 #' nearest_neighbor(neighbors = 11)

R/nullmodel.R

@@ -155,7 +155,7 @@ predict.nullmodel <- function (object, new_data = NULL, type  = NULL, ...) {
 #' \Sexpr[results=rd]{parsnip:::show_fit(parsnip:::null_model(mode = "regression"), "parsnip")}
 #'
 #' @importFrom purrr map_lgl
-#' @seealso [varying()], [fit()]
+#' @seealso [fit()]
 #' @examples
 #' null_model(mode = "regression")
 #' @export

R/rand_forest.R

@@ -87,7 +87,7 @@
 #'  reloaded and reattached to the `parsnip` object.
 #'
 #' @importFrom purrr map_lgl
-#' @seealso [varying()], [fit()]
+#' @seealso [[fit()]
 #' @examples
 #' rand_forest(mode = "classification", trees = 2000)
 #' # Parameters can be represented by a placeholder:

R/surv_reg.R

@@ -57,7 +57,7 @@
 #'  predictions when there is a stratification variable and can be
 #'  overridden in other cases.
 #'
-#' @seealso [varying()], [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

@@ -51,7 +51,7 @@
 #' \Sexpr[results=rd]{parsnip:::show_fit(parsnip:::svm_poly(mode = "regression"), "kernlab")}
 #'
 #' @importFrom purrr map_lgl
-#' @seealso [varying()], [fit()]
+#' @seealso [[fit()]
 #' @examples
 #' svm_poly(mode = "classification", degree = 1.2)
 #' # Parameters can be represented by a placeholder:

R/svm_rbf.R

@@ -50,7 +50,7 @@
 #' \Sexpr[results=rd]{parsnip:::show_fit(parsnip:::svm_rbf(mode = "regression"), "kernlab")}
 #'
 #' @importFrom purrr map_lgl
-#' @seealso [varying()], [fit()]
+#' @seealso [fit()]
 #' @examples
 #' svm_rbf(mode = "classification", rbf_sigma = 0.2)
 #' # Parameters can be represented by a placeholder: