revert nnet case weights

Author: topepo

R/mlp_data.R

@@ -233,7 +233,7 @@ set_fit(
   mode = "regression",
   value = list(
     interface = "formula",
-    protect = c("formula", "data", "weights"),
+    protect = c("formula", "data"),
     func = c(pkg = "nnet", fun = "nnet"),
     defaults = list(trace = FALSE)
   )
@@ -257,7 +257,7 @@ set_fit(
   mode = "classification",
   value = list(
     interface = "formula",
-    protect = c("formula", "data", "weights"),
+    protect = c("formula", "data"),
     func = c(pkg = "nnet", fun = "nnet"),
     defaults = list(trace = FALSE)
   )

R/multinom_reg_data.R

@@ -282,7 +282,7 @@ set_fit(
   mode = "classification",
   value = list(
     interface = "formula",
-    protect = c("formula", "data", "weights"),
+    protect = c("formula", "data"),
     func = c(pkg = "nnet", fun = "multinom"),
     defaults = list(trace = FALSE)
   )

man/details_mlp_nnet.Rd

@@ -64,7 +64,7 @@ mlp(
 
 ## Case weights
 
-```{r child = "template-uses-case-weights.Rmd"}
+```{r child = "template-no-case-weights.Rmd"}
 ```
 
 ## Examples 

man/details_multinom_reg_nnet.Rd

@@ -43,9 +43,8 @@ mlp(
 ## Computational engine: nnet 
 ## 
 ## Model fit template:
-## nnet::nnet(formula = missing_arg(), data = missing_arg(), weights = missing_arg(), 
-##     size = integer(1), decay = double(1), maxit = integer(1), 
-##     trace = FALSE, linout = TRUE)
+## nnet::nnet(formula = missing_arg(), data = missing_arg(), size = integer(1), 
+##     decay = double(1), maxit = integer(1), trace = FALSE, linout = TRUE)
 ```
 
 Note that parsnip automatically sets linear activation in the last layer. 
@@ -75,9 +74,8 @@ mlp(
 ## Computational engine: nnet 
 ## 
 ## Model fit template:
-## nnet::nnet(formula = missing_arg(), data = missing_arg(), weights = missing_arg(), 
-##     size = integer(1), decay = double(1), maxit = integer(1), 
-##     trace = FALSE, linout = FALSE)
+## nnet::nnet(formula = missing_arg(), data = missing_arg(), size = integer(1), 
+##     decay = double(1), maxit = integer(1), trace = FALSE, linout = FALSE)
 ```
 
 
@@ -93,9 +91,7 @@ scale each so that each predictor has mean zero and a variance of one.
 ## Case weights
 
 
-This model can utilize case weights during model fitting. To use them, see the documentation in [case_weights] and the examples on `tidymodels.org`. 
-
-The `fit()` and `fit_xy()` arguments have arguments called `case_weights` that expect vectors of case weights. 
+The underlying model implementation does not allow for case weights. 
 
 ## Examples 
 

man/rmd/mlp_nnet.Rmd

@@ -46,9 +46,10 @@ The "Fitting and Predicting with parsnip" article contains [examples](https://pa
 
 ## Case weights
 
-```{r child = "template-uses-case-weights.Rmd"}
+```{r child = "template-no-case-weights.Rmd"}
 ```
 
+
 ## References
 
  - Luraschi, J, K Kuo, and E Ruiz. 2019. _Mastering nnet with R_. O'Reilly Media

man/rmd/mlp_nnet.md

@@ -32,7 +32,7 @@ multinom_reg(penalty = double(1)) %>%
 ## 
 ## Model fit template:
 ## nnet::multinom(formula = missing_arg(), data = missing_arg(), 
-##     weights = missing_arg(), decay = double(1), trace = FALSE)
+##     decay = double(1), trace = FALSE)
 ```
 
 ## Preprocessing requirements
@@ -51,9 +51,8 @@ The "Fitting and Predicting with parsnip" article contains [examples](https://pa
 ## Case weights
 
 
-This model can utilize case weights during model fitting. To use them, see the documentation in [case_weights] and the examples on `tidymodels.org`. 
+The underlying model implementation does not allow for case weights. 
 
-The `fit()` and `fit_xy()` arguments have arguments called `case_weights` that expect vectors of case weights. 
 
 ## References