merge main

Merge commit '00f3cdb5500b55043866d22bd45f47bd01bc5ecf'

#Conflicts:
#	NEWS.md
#	tests/testthat/_snaps/misc.md
#	tests/testthat/test_misc.R

Author: EmilHvitfeldt

NAMESPACE

@@ -61,8 +61,6 @@ S3method(print,control_parsnip)
 S3method(print,model_fit)
 S3method(print,model_spec)
 S3method(print,nullmodel)
-S3method(req_pkgs,model_fit)
-S3method(req_pkgs,model_spec)
 S3method(required_pkgs,model_fit)
 S3method(required_pkgs,model_spec)
 S3method(set_args,default)

NEWS.md

@@ -2,14 +2,24 @@
 
 * glmnet models fitted with base-R family objects are now supported for `linear_reg()`, `logistic_reg()`, and `multinomial_reg()` (#890).
 
+* Moved forward with the deprecation of `req_pkgs()` in favor of `required_pkgs()`. The function will now error (#871).
+
 * Made `fit()` behave consistently with respect to missingness in the classification setting. Previously, `fit()` erroneously raised an error about the class of the outcome when there were no complete cases, and now always passes along complete cases to be handled by the modeling function (#888).
 
+* Fixed bug where model fits with factor predictors and `engine = "kknn"` would fail when the package's namespace hadn't been attached (#264).
+
 * `.organize_glmnet_pred()` now expects predictions for a single penalty value (#876).
 
+* Fixed bug where model fits with `engine = "earth"` would fail when the package's namespace hadn't been attached (#251).
+
 * Fixed bug with prediction from a boosted tree model fitted with `"xgboost"` using a custom objective function (#875).
 
 * Several internal functions (to help work with `Surv` objects) were added as a standalone file that can be used in other packages via `usethis::use_standalone("tidymodels/parsnip")`. 
 
+* Rather than being implemented in each method, the check for the `new_data` argument being mistakenly passed as `newdata` to `multi_predict()` now happens in the generic. Packages re-exporting the `multi_predict()` generic and implementing now-duplicate checks may see new failures and can remove their own analogous checks. This check already existed in all `predict()` methods (via `predict.model_fit()`) and all parsnip `multi_predict()` methods (#525).
+
+* `logistic_reg()` will now warn at `fit()` when the outcome has more than two levels (#545).
+
 * Functions now indicate what class the outcome was if the outcome is the wrong class (#887).
 
 # parsnip 1.0.4

R/aaa_multi_predict.R

@@ -22,6 +22,7 @@ multi_predict <- function(object, ...) {
     rlang::warn("Model fit failed; cannot make predictions.")
     return(NULL)
   }
+  check_for_newdata(...)
   UseMethod("multi_predict")
 }
 

R/auto_ml.R

@@ -17,6 +17,7 @@
 #' @param engine A single character string specifying what computational engine
 #'  to use for fitting.
 #'
+#' @templateVar modeltype auto_ml
 #' @template spec-details
 #'
 #' @template spec-references

R/bag_mars.R

@@ -14,6 +14,7 @@
 #'
 #' @inheritParams mars
 #'
+#' @templateVar modeltype bag_mars
 #' @template spec-details
 #'
 #' @template spec-references

R/bag_mlp.R

@@ -12,6 +12,7 @@
 #'
 #' @inheritParams mlp
 #'
+#' @templateVar modeltype bag_mlp
 #' @template spec-details
 #'
 #' @template spec-references

R/bag_tree.R

@@ -16,6 +16,7 @@
 #' factor is the minority class. If this is not the case, values between zero
 #' and one can be used to bias to the second level of the factor.
 #'
+#' @templateVar modeltype bag_tree
 #' @template spec-details
 #'
 #' @template spec-references

R/bart.R

@@ -35,6 +35,7 @@
 #' terminal node for different values of these parameters.
 #'
 #'
+#' @templateVar modeltype bart
 #' @template spec-details
 #'
 #' @template spec-references

R/boost_tree.R

@@ -39,6 +39,7 @@
 #' @param stop_iter The number of iterations without improvement before
 #'   stopping (specific engines only).
 #'
+#' @templateVar modeltype boost_tree
 #' @template spec-details
 #'
 #' @template spec-references
@@ -479,10 +480,6 @@ get_event_level <- function(model_spec){
 #' @param trees An integer vector for the number of trees in the ensemble.
 multi_predict._xgb.Booster <-
   function(object, new_data, type = NULL, trees = NULL, ...) {
-    if (any(names(enquos(...)) == "newdata")) {
-      rlang::abort("Did you mean to use `new_data` instead of `newdata`?")
-    }
-
     if (is.null(trees)) {
       trees <- object$fit$nIter
     }
@@ -607,9 +604,6 @@ C5.0_train <-
 #' @rdname multi_predict
 multi_predict._C5.0 <-
   function(object, new_data, type = NULL, trees = NULL, ...) {
-    if (any(names(enquos(...)) == "newdata"))
-      rlang::abort("Did you mean to use `new_data` instead of `newdata`?")
-
     if (is.null(trees))
       trees <- min(object$fit$trials)
     trees <- sort(trees)

R/c5_rules.R

@@ -30,6 +30,8 @@
 #'
 #' @references Quinlan R (1993). _C4.5: Programs for Machine Learning_. Morgan
 #' Kaufmann Publishers.
+#'
+#' @templateVar modeltype C5_rules
 #' @template spec-details
 #'
 #' @template spec-references