Merge pull request #242 from tidymodels/multinom-nnet

changes for #209

Author: topepo

NEWS.md

@@ -1,3 +1,18 @@
+# parsnip 0.0.5
+
+## Fixes
+
+* A bug ([#206](https://github.com/tidymodels/parsnip/issues/206) and [#234](https://github.com/tidymodels/parsnip/issues/234)) was fixed that caused an error when predicting with a multinomial `glmnet` model. 
+
+## Other Changes
+
+ * `glmnet` was removed as a dependency since the new version depends on 3.6.0 or greater. Keeping it would constrain `parsnip` to that same requirement. All `glmnet` tests are run locally. 
+ 
+## New Features
+
+ * `nnet` was added as an engine to `multinom_reg()` [#209](https://github.com/tidymodels/parsnip/issues/209)
+
+
 # parsnip 0.0.4
 
 ## New Features

R/misc.R

@@ -276,7 +276,8 @@ update_main_parameters <- function(args, param) {
   }
   param <- param[!has_extra_args]
 
-
-
   args <- utils::modifyList(args, param)
 }
+
+
+

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"`  (the default)
+#' \item \pkg{R}:   `"glmnet"`  (the default), `"nnet"`
 #' \item \pkg{Stan}:  `"stan"`
 #' \item \pkg{keras}: `"keras"`
 #' }
@@ -49,6 +49,10 @@
 #'
 #' \Sexpr[results=rd]{parsnip:::show_fit(parsnip:::multinom_reg(), "glmnet")}
 #'
+#' \pkg{nnet}
+#'
+#' \Sexpr[results=rd]{parsnip:::show_fit(parsnip:::multinom_reg(), "nnet")}
+#'
 #' \pkg{spark}
 #'
 #' \Sexpr[results=rd]{parsnip:::show_fit(parsnip:::multinom_reg(), "spark")}
@@ -197,6 +201,13 @@ organize_multnet_prob <- function(x, object) {
   as_tibble(x)
 }
 
+organize_nnet_prob <- function(x, object) {
+  format_classprobs(x)
+}
+
+
+
+
 # ------------------------------------------------------------------------------
 # glmnet call stack for multinomial regression using `predict` when object has
 # classes "_multnet" and "model_fit" (for class predictions):

R/multinom_reg_data.R

@@ -226,3 +226,85 @@ set_pred(
            x = quote(as.matrix(new_data)))
   )
 )
+
+
+# ------------------------------------------------------------------------------
+
+set_model_engine("multinom_reg", "classification", "nnet")
+set_dependency("multinom_reg", "nnet", "nnet")
+
+set_model_arg(
+  model = "multinom_reg",
+  eng = "nnet",
+  parsnip = "penalty",
+  original = "decay",
+  func = list(pkg = "dials", fun = "penalty"),
+  has_submodel = FALSE
+)
+
+set_fit(
+  model = "multinom_reg",
+  eng = "nnet",
+  mode = "classification",
+  value = list(
+    interface = "formula",
+    protect = c("formula", "data", "weights"),
+    func = c(pkg = "nnet", fun = "multinom"),
+    defaults = list(trace = FALSE)
+  )
+)
+
+
+set_pred(
+  model = "multinom_reg",
+  eng = "nnet",
+  mode = "classification",
+  type = "class",
+  value = list(
+    pre = NULL,
+    post = NULL,
+    func = c(fun = "predict"),
+    args =
+      list(
+        object = quote(object$fit),
+        newdata = quote(new_data),
+        type = "class"
+      )
+  )
+)
+
+set_pred(
+  model = "multinom_reg",
+  eng = "nnet",
+  mode = "classification",
+  type = "prob",
+  value = list(
+    pre = NULL,
+    post = organize_nnet_prob,
+    func = c(fun = "predict"),
+    args =
+      list(
+        object = quote(object$fit),
+        newdata = quote(new_data),
+        type = "prob"
+      )
+  )
+)
+
+set_pred(
+  model = "multinom_reg",
+  eng = "nnet",
+  mode = "classification",
+  type = "raw",
+  value = list(
+    pre = NULL,
+    post = NULL,
+    func = c(fun = "predict"),
+    args =
+      list(
+        object = quote(object$fit),
+        newdata = quote(new_data)
+      )
+  )
+)
+