prototype of changes for #431

Author: topepo

R/linear_reg.R

@@ -107,13 +107,22 @@ translate.linear_reg <- function(x, engine = x$engine, ...) {
   x <- translate.default(x, engine, ...)
 
   if (engine == "glmnet") {
-    # See discussion in https://github.com/tidymodels/parsnip/issues/195
-    x$method$fit$args$lambda <- NULL
+    if (any(names(x$eng_args) == "path_values")) {
+      # Since we decouple the parsnip `penalty` argument from being the same
+      # as the glmnet `lambda` value, this allows users to set the path
+      # differently from the default that glmnet uses. See
+      # https://github.com/tidymodels/parsnip/issues/431
+      x$method$fit$args$lambda <- x$eng_args$path_values
+      x$eng_args$path_values <- NULL
+      x$method$fit$args$path_values <- NULL
+    } else {
+      # See discussion in https://github.com/tidymodels/parsnip/issues/195
+      x$method$fit$args$lambda <- NULL
+    }
     # Since the `fit` information is gone for the penalty, we need to have an
     # evaluated value for the parameter.
     x$args$penalty <- rlang::eval_tidy(x$args$penalty)
   }
-
   x
 }
 

man/linear_reg.Rd

@@ -23,14 +23,26 @@ linear_reg() %>%
   translate()
 ```
 
-For `glmnet` models, the full regularization path is always fit regardless of the
-value given to `penalty`. Also, there is the option to pass  multiple values (or
-no values) to the `penalty` argument. When using the  `predict()` method in these
-cases, the return value depends on  the value of `penalty`. When using
-`predict()`, only a single  value of the penalty can be used. When predicting on
-multiple  penalties, the `multi_predict()` function can be used. It  returns a
-tibble with a list column called `.pred` that contains  a tibble with all of the
-penalty results.
+`linear_reg()` requires a single value for the `penalty` argument (a number
+or `tune()`). Despite this, the full regularization path is always fit
+regardless of the value given to `penalty`. To pass in a custom sequence of
+values for `lambda`, use the argument `path_values` in `set_engine()`. This
+will assign the value of the glmnet `lambda` parameter without disturbing
+the value given in `linear_reg(penalty)`. For example: 
+
+```{r glmnet-path}
+linear_reg(penalty = .1) %>% 
+  set_engine("glmnet", path_values = c(0, 10^seq(-10, 1, length.out = 20))) %>% 
+  set_mode("regression") %>% 
+  translate()
+```
+
+When using `predict()`, the single penalty value used for prediction is the one 
+given to `linear_reg()`. 
+
+To predict on multiple  penalties, the `multi_predict()` function can be used. 
+It  returns a tibble with a list column called `.pred` that contains  a tibble 
+with all of the penalty results.
 
 ## stan