exclude non-tunable engine arguments in tunable()

simonpcouch · simonpcouch · commit 21a56c85da2f · 2024-04-05T10:48:16.000-05:00
closes #1104
diff --git a/R/tunable.R b/R/tunable.R
@@ -248,7 +248,7 @@ tunable.linear_reg <- function(x, ...) {
   } else if (x$engine == "brulee") {
     res <- add_engine_parameters(res, brulee_linear_engine_args)
   }
-  res
+  res[!vapply(res$call_info, is.null, logical(1)), ]
 }
 
 #' @export
@@ -260,7 +260,7 @@ tunable.logistic_reg <- function(x, ...) {
   } else if (x$engine == "brulee") {
     res <- add_engine_parameters(res, brulee_logistic_engine_args)
   }
-  res
+  res[!vapply(res$call_info, is.null, logical(1)), ]
 }
 
 #' @export
@@ -272,7 +272,7 @@ tunable.multinomial_reg <- function(x, ...) {
   } else if (x$engine == "brulee") {
     res <- add_engine_parameters(res, brulee_multinomial_engine_args)
   }
-  res
+  res[!vapply(res$call_info, is.null, logical(1)), ]
 }
 
 #' @export
@@ -295,7 +295,7 @@ tunable.boost_tree <- function(x, ...) {
     res$call_info[res$name == "sample_size"] <-
       list(list(pkg = "dials", fun = "sample_prop"))
   }
-  res
+  res[!vapply(res$call_info, is.null, logical(1)), ]
 }
 
 #' @export
@@ -310,7 +310,7 @@ tunable.rand_forest <- function(x, ...) {
   } else if (x$engine == "aorsf") {
     res <- add_engine_parameters(res, aorsf_engine_args)
   }
-  res
+  res[!vapply(res$call_info, is.null, logical(1)), ]
 }
 
 #' @export
@@ -319,7 +319,7 @@ tunable.mars <- function(x, ...) {
   if (x$engine == "earth") {
     res <- add_engine_parameters(res, earth_engine_args)
   }
-  res
+  res[!vapply(res$call_info, is.null, logical(1)), ]
 }
 
 #' @export
@@ -333,7 +333,7 @@ tunable.decision_tree <- function(x, ...) {
                             partykit_engine_args %>%
                               dplyr::mutate(component = "decision_tree"))
   }
-  res
+  res[!vapply(res$call_info, is.null, logical(1)), ]
 }
 
 #' @export
@@ -343,7 +343,7 @@ tunable.svm_poly <- function(x, ...) {
     res$call_info[res$name == "degree"] <-
       list(list(pkg = "dials", fun = "prod_degree", range = c(1L, 3L)))
   }
-  res
+  res[!vapply(res$call_info, is.null, logical(1)), ]
 }
 
 
@@ -357,7 +357,7 @@ tunable.mlp <- function(x, ...) {
     res$call_info[res$name == "epochs"] <-
       list(list(pkg = "dials", fun = "epochs", range = c(5L, 500L)))
   }
-  res
+  res[!vapply(res$call_info, is.null, logical(1)), ]
 }
 
 #' @export
@@ -366,7 +366,7 @@ tunable.survival_reg <- function(x, ...) {
   if (x$engine == "flexsurvspline") {
     res <- add_engine_parameters(res, flexsurvspline_engine_args)
   }
-  res
+  res[!vapply(res$call_info, is.null, logical(1)), ]
 }
 
 # nocov end
diff --git a/tests/testthat/_snaps/tunable.md b/tests/testthat/_snaps/tunable.md
@@ -43,12 +43,11 @@
     Code
       tunable(spec %>% set_engine("glmnet", dfmax = tune()))
     Output
-      # A tibble: 3 x 5
+      # A tibble: 2 x 5
         name    call_info        source     component  component_id
         <chr>   <list>           <chr>      <chr>      <chr>       
       1 penalty <named list [2]> model_spec linear_reg main        
       2 mixture <named list [3]> model_spec linear_reg main        
-      3 dfmax   <NULL>           model_spec linear_reg engine      
 
 # tunable.logistic_reg()
 
@@ -95,12 +94,11 @@
     Code
       tunable(spec %>% set_engine("glmnet", dfmax = tune()))
     Output
-      # A tibble: 3 x 5
+      # A tibble: 2 x 5
         name    call_info        source     component    component_id
         <chr>   <list>           <chr>      <chr>        <chr>       
       1 penalty <named list [2]> model_spec logistic_reg main        
       2 mixture <named list [3]> model_spec logistic_reg main        
-      3 dfmax   <NULL>           model_spec logistic_reg engine      
 
 # tunable.multinom_reg()
 
@@ -244,7 +242,7 @@
     Code
       tunable(spec %>% set_engine("xgboost", feval = tune()))
     Output
-      # A tibble: 9 x 5
+      # A tibble: 8 x 5
         name           call_info        source     component  component_id
         <chr>          <list>           <chr>      <chr>      <chr>       
       1 tree_depth     <named list [2]> model_spec boost_tree main        
@@ -255,7 +253,6 @@
       6 loss_reduction <named list [2]> model_spec boost_tree main        
       7 sample_size    <named list [2]> model_spec boost_tree main        
       8 stop_iter      <named list [2]> model_spec boost_tree main        
-      9 feval          <NULL>           model_spec boost_tree engine      
 
 # tunable.rand_forest()
 
@@ -310,13 +307,12 @@
     Code
       tunable(spec %>% set_engine("ranger", min.bucket = tune()))
     Output
-      # A tibble: 4 x 5
-        name       call_info        source     component   component_id
-        <chr>      <list>           <chr>      <chr>       <chr>       
-      1 mtry       <named list [2]> model_spec rand_forest main        
-      2 trees      <named list [2]> model_spec rand_forest main        
-      3 min_n      <named list [2]> model_spec rand_forest main        
-      4 min.bucket <NULL>           model_spec rand_forest engine      
+      # A tibble: 3 x 5
+        name  call_info        source     component   component_id
+        <chr> <list>           <chr>      <chr>       <chr>       
+      1 mtry  <named list [2]> model_spec rand_forest main        
+      2 trees <named list [2]> model_spec rand_forest main        
+      3 min_n <named list [2]> model_spec rand_forest main        
 
 # tunable.mars()
 
@@ -347,13 +343,12 @@
     Code
       tunable(spec %>% set_engine("earth", minspan = tune()))
     Output
-      # A tibble: 4 x 5
+      # A tibble: 3 x 5
         name         call_info        source     component component_id
         <chr>        <list>           <chr>      <chr>     <chr>       
       1 num_terms    <named list [3]> model_spec mars      main        
       2 prod_degree  <named list [2]> model_spec mars      main        
       3 prune_method <named list [2]> model_spec mars      main        
-      4 minspan      <NULL>           model_spec mars      engine      
 
 # tunable.decision_tree()
 
@@ -405,13 +400,12 @@
     Code
       tunable(spec %>% set_engine("rpart", parms = tune()))
     Output
-      # A tibble: 4 x 5
+      # A tibble: 3 x 5
         name            call_info        source     component     component_id
         <chr>           <list>           <chr>      <chr>         <chr>       
       1 tree_depth      <named list [2]> model_spec decision_tree main        
       2 min_n           <named list [2]> model_spec decision_tree main        
       3 cost_complexity <named list [2]> model_spec decision_tree main        
-      4 parms           <NULL>           model_spec decision_tree engine      
 
 # tunable.svm_poly()
 
@@ -444,14 +438,13 @@
     Code
       tunable(spec %>% set_engine("kernlab", tol = tune()))
     Output
-      # A tibble: 5 x 5
+      # A tibble: 4 x 5
         name         call_info        source     component component_id
         <chr>        <list>           <chr>      <chr>     <chr>       
       1 cost         <named list [3]> model_spec svm_poly  main        
       2 degree       <named list [3]> model_spec svm_poly  main        
       3 scale_factor <named list [2]> model_spec svm_poly  main        
       4 margin       <named list [2]> model_spec svm_poly  main        
-      5 tol          <NULL>           model_spec svm_poly  engine      
 
 # tunable.mlp()
 
@@ -511,15 +504,14 @@
     Code
       tunable(spec %>% set_engine("keras", ragged = tune()))
     Output
-      # A tibble: 6 x 5
+      # A tibble: 5 x 5
         name         call_info        source     component component_id
         <chr>        <list>           <chr>      <chr>     <chr>       
       1 hidden_units <named list [2]> model_spec mlp       main        
       2 penalty      <named list [2]> model_spec mlp       main        
       3 dropout      <named list [2]> model_spec mlp       main        
       4 epochs       <named list [2]> model_spec mlp       main        
       5 activation   <named list [2]> model_spec mlp       main        
-      6 ragged       <NULL>           model_spec mlp       engine      
 
 # tunable.survival_reg()
 
@@ -544,8 +536,7 @@
     Code
       tunable(spec %>% set_engine("survival", parms = tune()))
     Output
-      # A tibble: 1 x 5
-        name  call_info source     component    component_id
-        <chr> <list>    <chr>      <chr>        <chr>       
-      1 parms <NULL>    model_spec survival_reg engine      
+      # A tibble: 0 x 5
+      # i 5 variables: name <chr>, call_info <list>, source <chr>, component <chr>,
+      #   component_id <chr>
 

Original file line number	Diff line number	Diff line change
`@@ -248,7 +248,7 @@ tunable.linear_reg <- function(x, ...) {`
`248`	`248`	`} else if (x$engine == "brulee") {`
`249`	`249`	`res <- add_engine_parameters(res, brulee_linear_engine_args)`
`250`	`250`	`}`
`251`		`- res`
	`251`	`+ res[!vapply(res$call_info, is.null, logical(1)), ]`
`252`	`252`	`}`
`253`	`253`
`254`	`254`	`#' @export`
`@@ -260,7 +260,7 @@ tunable.logistic_reg <- function(x, ...) {`
`260`	`260`	`} else if (x$engine == "brulee") {`
`261`	`261`	`res <- add_engine_parameters(res, brulee_logistic_engine_args)`
`262`	`262`	`}`
`263`		`- res`
	`263`	`+ res[!vapply(res$call_info, is.null, logical(1)), ]`
`264`	`264`	`}`
`265`	`265`
`266`	`266`	`#' @export`
`@@ -272,7 +272,7 @@ tunable.multinomial_reg <- function(x, ...) {`
`272`	`272`	`} else if (x$engine == "brulee") {`
`273`	`273`	`res <- add_engine_parameters(res, brulee_multinomial_engine_args)`
`274`	`274`	`}`
`275`		`- res`
	`275`	`+ res[!vapply(res$call_info, is.null, logical(1)), ]`
`276`	`276`	`}`
`277`	`277`
`278`	`278`	`#' @export`
`@@ -295,7 +295,7 @@ tunable.boost_tree <- function(x, ...) {`
`295`	`295`	`res$call_info[res$name == "sample_size"] <-`
`296`	`296`	`list(list(pkg = "dials", fun = "sample_prop"))`
`297`	`297`	`}`
`298`		`- res`
	`298`	`+ res[!vapply(res$call_info, is.null, logical(1)), ]`
`299`	`299`	`}`
`300`	`300`
`301`	`301`	`#' @export`
`@@ -310,7 +310,7 @@ tunable.rand_forest <- function(x, ...) {`
`310`	`310`	`} else if (x$engine == "aorsf") {`
`311`	`311`	`res <- add_engine_parameters(res, aorsf_engine_args)`
`312`	`312`	`}`
`313`		`- res`
	`313`	`+ res[!vapply(res$call_info, is.null, logical(1)), ]`
`314`	`314`	`}`
`315`	`315`
`316`	`316`	`#' @export`
`@@ -319,7 +319,7 @@ tunable.mars <- function(x, ...) {`
`319`	`319`	`if (x$engine == "earth") {`
`320`	`320`	`res <- add_engine_parameters(res, earth_engine_args)`
`321`	`321`	`}`
`322`		`- res`
	`322`	`+ res[!vapply(res$call_info, is.null, logical(1)), ]`
`323`	`323`	`}`
`324`	`324`
`325`	`325`	`#' @export`
`@@ -333,7 +333,7 @@ tunable.decision_tree <- function(x, ...) {`
`333`	`333`	`partykit_engine_args %>%`
`334`	`334`	`dplyr::mutate(component = "decision_tree"))`
`335`	`335`	`}`
`336`		`- res`
	`336`	`+ res[!vapply(res$call_info, is.null, logical(1)), ]`
`337`	`337`	`}`
`338`	`338`
`339`	`339`	`#' @export`
`@@ -343,7 +343,7 @@ tunable.svm_poly <- function(x, ...) {`
`343`	`343`	`res$call_info[res$name == "degree"] <-`
`344`	`344`	`list(list(pkg = "dials", fun = "prod_degree", range = c(1L, 3L)))`
`345`	`345`	`}`
`346`		`- res`
	`346`	`+ res[!vapply(res$call_info, is.null, logical(1)), ]`
`347`	`347`	`}`
`348`	`348`
`349`	`349`
`@@ -357,7 +357,7 @@ tunable.mlp <- function(x, ...) {`
`357`	`357`	`res$call_info[res$name == "epochs"] <-`
`358`	`358`	`list(list(pkg = "dials", fun = "epochs", range = c(5L, 500L)))`
`359`	`359`	`}`
`360`		`- res`
	`360`	`+ res[!vapply(res$call_info, is.null, logical(1)), ]`
`361`	`361`	`}`
`362`	`362`
`363`	`363`	`#' @export`
`@@ -366,7 +366,7 @@ tunable.survival_reg <- function(x, ...) {`
`366`	`366`	`if (x$engine == "flexsurvspline") {`
`367`	`367`	`res <- add_engine_parameters(res, flexsurvspline_engine_args)`
`368`	`368`	`}`
`369`		`- res`
	`369`	`+ res[!vapply(res$call_info, is.null, logical(1)), ]`
`370`	`370`	`}`
`371`	`371`
`372`	`372`	`# nocov end`