update tunables and tests

Author: topepo

R/tunable.R

@@ -211,7 +211,7 @@ flexsurvspline_engine_args <-
 tune_activations <- c("relu", "tanh", "elu", "log_sigmoid", "tanhshrink")
 tune_sched <- c("none", "decay_time", "decay_expo", "cyclic", "step")
 
-brulee_args <-
+brulee_mlp_args <-
   tibble::tibble(
     name = c('epochs', 'hidden_units', 'hidden_units_2', 'activation', 'activation_2',
              'penalty', 'mixture', 'dropout', 'learn_rate', 'momentum', 'batch_size',
@@ -232,12 +232,17 @@ brulee_args <-
       list(pkg = "dials", fun = "class_weights"),
       list(pkg = "dials", fun = "rate_schedule", values = tune_sched)
     )
+  ) %>%
+  dplyr::mutate(source = "model_spec")
+
+brulee_mlp_only_args <-
+  tibble::tibble(
+    name =
+      c('hidden_units', 'hidden_units_2', 'activation', 'activation_2', 'dropout')
   )
 
 # ------------------------------------------------------------------------------
 
-
-
 #' @export
 tunable.linear_reg <- function(x, ...) {
   res <- NextMethod()
@@ -246,18 +251,57 @@ tunable.linear_reg <- function(x, ...) {
       list(list(pkg = "dials", fun = "mixture", range = c(0.05, 1.00)))
   } else if (x$engine == "brulee") {
     res <-
-      brulee_args %>%
-      dplyr::filter(name %in% tune_args(x)$name) %>%
-      dplyr::full_join(res %>% dplyr::select(-call_info), by = "name")
+      brulee_mlp_args %>%
+      dplyr::anti_join(brulee_mlp_only_args, by = "name") %>%
+      dplyr::filter(name != "class_weights") %>%
+      dplyr::mutate(
+        component = "linear_reg",
+        component_id = ifelse(name %in% names(formals("linear_reg")), "main", "engine")
+      ) %>%
+      dplyr::select(name, call_info, source, component, component_id)
   }
   res
 }
 
 #' @export
-tunable.logistic_reg <- tunable.linear_reg
 
 #' @export
-tunable.multinom_reg <- tunable.linear_reg
+tunable.logistic_reg <- function(x, ...) {
+  res <- NextMethod()
+  if (x$engine == "glmnet") {
+    res$call_info[res$name == "mixture"] <-
+      list(list(pkg = "dials", fun = "mixture", range = c(0.05, 1.00)))
+  } else if (x$engine == "brulee") {
+    res <-
+      brulee_mlp_args %>%
+      dplyr::anti_join(brulee_mlp_only_args, by = "name") %>%
+      dplyr::mutate(
+        component = "logistic_reg",
+        component_id = ifelse(name %in% names(formals("logistic_reg")), "main", "engine")
+      ) %>%
+      dplyr::select(name, call_info, source, component, component_id)
+  }
+  res
+}
+
+#' @export
+tunable.multinom_reg <- function(x, ...) {
+  res <- NextMethod()
+  if (x$engine == "glmnet") {
+    res$call_info[res$name == "mixture"] <-
+      list(list(pkg = "dials", fun = "mixture", range = c(0.05, 1.00)))
+  } else if (x$engine == "brulee") {
+    res <-
+      brulee_mlp_args %>%
+      dplyr::anti_join(brulee_mlp_only_args, by = "name") %>%
+      dplyr::mutate(
+        component = "multinom_reg",
+        component_id = ifelse(name %in% names(formals("multinom_reg")), "main", "engine")
+      ) %>%
+      dplyr::select(name, call_info, source, component, component_id)
+  }
+  res
+}
 
 #' @export
 tunable.boost_tree <- function(x, ...) {
@@ -335,9 +379,15 @@ tunable.mlp <- function(x, ...) {
   res <- NextMethod()
   if (grepl("brulee", x$engine)) {
     res <-
-      brulee_args %>%
-      dplyr::filter(name %in% tune_args(x)$name) %>%
-      dplyr::full_join(res %>% dplyr::select(-call_info), by = "name")
+      brulee_mlp_args %>%
+      dplyr::mutate(
+        component = "mlp",
+        component_id = ifelse(name %in% names(formals("mlp")), "main", "engine")
+      ) %>%
+      dplyr::select(name, call_info, source, component, component_id)
+    if (x$engine == "brulee") {
+      res <- res[!grepl("_2", res$name),]
+    }
   }
   res
 }

man/details_mlp_brulee.Rd

@@ -7,7 +7,7 @@ For this engine, there are multiple modes: classification and regression
 
 
 
-This model has 6 tuning parameters:
+This model has 7 tuning parameters:
 
 - `epochs`: # Epochs (type: integer, default: 100L)
 
@@ -17,6 +17,8 @@ This model has 6 tuning parameters:
 
 - `penalty`: Amount of Regularization (type: double, default: 0.001)
 
+- `mixture`: Proportion of Lasso Penalty (type: double, default: 0.0)
+
 - `dropout`: Dropout Rate (type: double, default: 0.0)
 
 - `learn_rate`: Learning Rate (type: double, default: 0.01)
@@ -27,16 +29,16 @@ Both `penalty` and `dropout` should be not be used in the same model.
 
 Other engine arguments of interest: 
 
- - `momentum()`: A number used to use historical gradient infomration during optimization.
- - `batch_size()`: An integer for the number of training set points in each batch.
- - `class_weights()`: Numeric class weights. See [brulee::brulee_mlp()].
- - `stop_iter()`: A non-negative integer for how many iterations with no improvement before stopping. (default: 5L).
- - `rate_schedule()`: A function to change the learning rate over epochs. See [brulee::schedule_decay_time()] for details. 
+ - `momentum`: A number used to use historical gradient infomration during optimization.
+ - `batch_size`: An integer for the number of training set points in each batch.
+ - `class_weights`: Numeric class weights. See [brulee::brulee_mlp()].
+ - `stop_iter`: A non-negative integer for how many iterations with no improvement before stopping. (default: 5L).
+ - `rate_schedule`: A function to change the learning rate over epochs. See [brulee::schedule_decay_time()] for details. 
 
 ## Translation from parsnip to the original package (regression)
 
 
-```r
+``` r
 mlp(
   hidden_units = integer(1),
   penalty = double(1),
@@ -74,7 +76,7 @@ Note that parsnip automatically sets linear activation in the last layer.
 ## Translation from parsnip to the original package (classification)
 
 
-```r
+``` r
 mlp(
   hidden_units = integer(1),
   penalty = double(1),

man/parsnip-package.Rd

@@ -7,7 +7,7 @@ For this engine, there are multiple modes: classification and regression
 
 
 
-This model has 6 tuning parameters:
+This model has 7 tuning parameters:
 
 - `epochs`: # Epochs (type: integer, default: 100L)
 
@@ -17,6 +17,8 @@ This model has 6 tuning parameters:
 
 - `penalty`: Amount of Regularization (type: double, default: 0.001)
 
+- `mixture`: Proportion of Lasso Penalty (type: double, default: 0.0)
+
 - `dropout`: Dropout Rate (type: double, default: 0.0)
 
 - `learn_rate`: Learning Rate (type: double, default: 0.01)
@@ -28,17 +30,17 @@ Both `penalty` and `dropout` should be not be used in the same model.
 Other engine arguments of interest: 
 
  - `hidden_layer_2` and `activation_2` control the format of the second layer. 
- - `momentum()`: A number used to use historical gradient information during optimization.
- - `batch_size()`: An integer for the number of training set points in each batch.
- - `class_weights()`: Numeric class weights. See [brulee::brulee_mlp()].
- - `stop_iter()`: A non-negative integer for how many iterations with no improvement before stopping. (default: 5L).
- - `rate_schedule()`: A function to change the learning rate over epochs. See [brulee::schedule_decay_time()] for details. 
+ - `momentum`: A number used to use historical gradient information during optimization.
+ - `batch_size`: An integer for the number of training set points in each batch.
+ - `class_weights`: Numeric class weights. See [brulee::brulee_mlp()].
+ - `stop_iter`: A non-negative integer for how many iterations with no improvement before stopping. (default: 5L).
+ - `rate_schedule`: A function to change the learning rate over epochs. See [brulee::schedule_decay_time()] for details. 
 
 
 ## Translation from parsnip to the original package (regression)
 
 
-```r
+``` r
 mlp(
   hidden_units = integer(1),
   penalty = double(1),
@@ -78,12 +80,12 @@ mlp(
 ##     hidden_units_2 = integer(1), activation_2 = character(1))
 ```
 
-Note that parsnip automatically sets linear activation in the last layer. 
+Note that parsnip automatically sets the linear activation in the last layer. 
 
 ## Translation from parsnip to the original package (classification)
 
 
-```r
+``` r
 mlp(
   hidden_units = integer(1),
   penalty = double(1),

man/rmd/mlp_brulee.md

@@ -175,3 +175,59 @@
       Error in `fit()`:
       ! Please install the glmnet package to use this engine.
 
+# tunables
+
+    Code
+      linear_reg() %>% tunable()
+    Output
+      # A tibble: 0 x 5
+      # i 5 variables: name <chr>, call_info <list>, source <chr>, component <chr>,
+      #   component_id <chr>
+
+---
+
+    Code
+      linear_reg() %>% set_engine("brulee") %>% tunable()
+    Output
+      # A tibble: 8 x 5
+        name          call_info        source     component  component_id
+        <chr>         <list>           <chr>      <chr>      <chr>       
+      1 epochs        <named list [3]> model_spec linear_reg engine      
+      2 penalty       <named list [2]> model_spec linear_reg main        
+      3 mixture       <named list [2]> model_spec linear_reg main        
+      4 learn_rate    <named list [3]> model_spec linear_reg engine      
+      5 momentum      <named list [3]> model_spec linear_reg engine      
+      6 batch_size    <named list [2]> model_spec linear_reg engine      
+      7 stop_iter     <named list [2]> model_spec linear_reg engine      
+      8 rate_schedule <named list [3]> model_spec linear_reg engine      
+
+---
+
+    Code
+      linear_reg() %>% set_engine("glmnet") %>% tunable()
+    Output
+      # 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        
+
+---
+
+    Code
+      linear_reg() %>% set_engine("quantreg") %>% tunable()
+    Output
+      # A tibble: 0 x 5
+      # i 5 variables: name <chr>, call_info <list>, source <chr>, component <chr>,
+      #   component_id <chr>
+
+---
+
+    Code
+      linear_reg() %>% set_engine("keras") %>% tunable()
+    Output
+      # A tibble: 1 x 5
+        name    call_info        source     component  component_id
+        <chr>   <list>           <chr>      <chr>      <chr>       
+      1 penalty <named list [2]> model_spec linear_reg main        
+

man/rmd/mlp_brulee_two_layer.md

@@ -139,3 +139,51 @@
       Error in `fit()`:
       ! For the LiblineaR engine, `penalty` must be `> 0`, not 0.
 
+# tunables
+
+    Code
+      logistic_reg() %>% tunable()
+    Output
+      # A tibble: 0 x 5
+      # i 5 variables: name <chr>, call_info <list>, source <chr>, component <chr>,
+      #   component_id <chr>
+
+---
+
+    Code
+      logistic_reg() %>% set_engine("brulee") %>% tunable()
+    Output
+      # A tibble: 9 x 5
+        name          call_info        source     component    component_id
+        <chr>         <list>           <chr>      <chr>        <chr>       
+      1 epochs        <named list [3]> model_spec logistic_reg engine      
+      2 penalty       <named list [2]> model_spec logistic_reg main        
+      3 mixture       <named list [2]> model_spec logistic_reg main        
+      4 learn_rate    <named list [3]> model_spec logistic_reg engine      
+      5 momentum      <named list [3]> model_spec logistic_reg engine      
+      6 batch_size    <named list [2]> model_spec logistic_reg engine      
+      7 class_weights <named list [2]> model_spec logistic_reg engine      
+      8 stop_iter     <named list [2]> model_spec logistic_reg engine      
+      9 rate_schedule <named list [3]> model_spec logistic_reg engine      
+
+---
+
+    Code
+      logistic_reg() %>% set_engine("glmnet") %>% tunable()
+    Output
+      # 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        
+
+---
+
+    Code
+      logistic_reg() %>% set_engine("keras") %>% tunable()
+    Output
+      # A tibble: 1 x 5
+        name    call_info        source     component    component_id
+        <chr>   <list>           <chr>      <chr>        <chr>       
+      1 penalty <named list [2]> model_spec logistic_reg main        
+