testing update

Author: topepo

tests/testthat/_snaps/args_and_modes.md

@@ -12,7 +12,7 @@
       set_mode(rand_forest())
     Condition
       Error in `set_mode()`:
-      ! Available modes for model type rand_forest are: "unknown", "classification", "regression", and "censored regression".
+      ! Available modes for model type rand_forest are: "unknown", "classification", "regression", "censored regression", and "quantile regression".
 
 ---
 

tests/testthat/_snaps/rand_forest.md

@@ -21,7 +21,7 @@
       res <- translate(set_engine(rand_forest(mode = "classification"), NULL))
     Condition
       Error in `set_engine()`:
-      ! Missing engine. Possible mode/engine combinations are: classification {ranger, randomForest, spark} and regression {ranger, randomForest, spark}.
+      ! Missing engine. Possible mode/engine combinations are: classification {ranger, randomForest, spark, grf}, quantile regression {grf}, and regression {ranger, randomForest, spark, grf}.
 
 ---
 

tests/testthat/_snaps/registration.md

@@ -363,11 +363,12 @@
       show_model_info("rand_forest")
     Output
       Information for `rand_forest`
-       modes: unknown, classification, regression, censored regression 
+       modes: unknown, classification, regression, censored regression, quantile regression 
       
        engines: 
-         classification: randomForest, ranger1, spark
-         regression:     randomForest, ranger1, spark
+         classification:      grf1, randomForest, ranger1, spark
+         quantile regression: grf1
+         regression:          grf1, randomForest, ranger1, spark
       
       1The model can use case weights.
       
@@ -384,24 +385,34 @@
             mtry  --> feature_subset_strategy
             trees --> num_trees
             min_n --> min_instances_per_node
+         grf:          
+            mtry  --> mtry
+            trees --> num.trees
+            min_n --> min.node.size
       
        fit modules:
-               engine           mode
-               ranger classification
-               ranger     regression
-         randomForest classification
-         randomForest     regression
-                spark classification
-                spark     regression
+               engine                mode
+               ranger      classification
+               ranger          regression
+         randomForest      classification
+         randomForest          regression
+                spark      classification
+                spark          regression
+                  grf      classification
+                  grf          regression
+                  grf quantile regression
       
        prediction modules:
-                   mode       engine                    methods
-         classification randomForest           class, prob, raw
-         classification       ranger class, conf_int, prob, raw
-         classification        spark                class, prob
-             regression randomForest               numeric, raw
-             regression       ranger     conf_int, numeric, raw
-             regression        spark                    numeric
+                        mode       engine                    methods
+              classification          grf      class, conf_int, prob
+              classification randomForest           class, prob, raw
+              classification       ranger class, conf_int, prob, raw
+              classification        spark                class, prob
+         quantile regression          grf                   quantile
+                  regression          grf          conf_int, numeric
+                  regression randomForest               numeric, raw
+                  regression       ranger     conf_int, numeric, raw
+                  regression        spark                    numeric
       
 
 ---

tests/testthat/helper-objects.R

@@ -1,6 +1,6 @@
-ctrl          <- control_parsnip(verbosity = 1, catch = FALSE)
-caught_ctrl   <- control_parsnip(verbosity = 1, catch = TRUE)
-quiet_ctrl    <- control_parsnip(verbosity = 0, catch = TRUE)
+ctrl <- control_parsnip(verbosity = 1, catch = FALSE)
+caught_ctrl <- control_parsnip(verbosity = 1, catch = TRUE)
+quiet_ctrl <- control_parsnip(verbosity = 0, catch = TRUE)
 
 run_glmnet <- utils::compareVersion('3.6.0', as.character(getRversion())) > 0
 
@@ -29,15 +29,15 @@ if (rlang::is_installed("modeldata")) {
 
   # ------------------------------------------------------------------------------
 
-  hpc <- hpc_data[1:150, c(2:5, 8)]
+  hpc <- modeldata::hpc_data[1:150, c(2:5, 8)]
   num_hpc_pred <- names(hpc)[1:4]
   class_tab <- table(hpc$class, dnn = NULL)
   hpc_bad <-
     hpc |>
     dplyr::mutate(big_num = Inf)
 
   set.seed(352)
-  mlp_dat <- hpc[order(runif(150)),]
+  mlp_dat <- hpc[order(runif(150)), ]
 
   tr_mlp_dat <- mlp_dat[1:140, ]
   te_mlp_dat <- mlp_dat[141:150, ]
@@ -46,7 +46,7 @@ if (rlang::is_installed("modeldata")) {
   mlp_hpc_pred_list <- names(hpc)[1:4]
   nnet_hpc_pred_list <- names(hpc)[1:4]
 
-  hpc_nnet_dat <- hpc_data[1:150, c(2:5, 8)]
+  hpc_nnet_dat <- modeldata::hpc_data[1:150, c(2:5, 8)]
 
   # ------------------------------------------------------------------------------
 
@@ -56,7 +56,7 @@ if (rlang::is_installed("modeldata")) {
     fit(compounds ~ ., data = hpc)
 
   lending_club <-
-    lending_club |>
+    modeldata::lending_club |>
     dplyr::slice(1:200) |>
     dplyr::mutate(big_num = Inf)
 
@@ -73,7 +73,7 @@ if (rlang::is_installed("modeldata")) {
     dplyr::select(price, beds, baths, sqft, latitude, longitude)
 
   sac_train <- Sacramento_small[-(1:5), ]
-  sac_test  <- Sacramento_small[  1:5 , ]
+  sac_test <- Sacramento_small[1:5, ]
 
   # ------------------------------------------------------------------------------
   # For sparse tibble testing

tests/testthat/test-registration.R

@@ -3,45 +3,60 @@ test_that('adding a new model', {
 
   mod_items <- get_model_env() |> rlang::env_names()
   sponges <- grep("sponge", mod_items, value = TRUE)
-  exp_obj <- c('sponge_modes', 'sponge_fit', 'sponge_args',
-               'sponge_predict', 'sponge_pkgs', 'sponge')
+  exp_obj <- c(
+    'sponge_modes',
+    'sponge_fit',
+    'sponge_args',
+    'sponge_predict',
+    'sponge_pkgs',
+    'sponge'
+  )
   expect_equal(sort(sponges), sort(exp_obj))
 
   expect_equal(
     get_from_env("sponge"),
     tibble(engine = character(0), mode = character(0))
   )
 
-expect_equal(
-  get_from_env("sponge_pkgs"),
-  tibble(engine = character(0), pkg = list(), mode = character(0))
-)
-
-expect_equal(
-  get_from_env("sponge_modes"), "unknown"
-)
-
-expect_equal(
-  get_from_env("sponge_args"),
-  dplyr::tibble(engine = character(0), parsnip = character(0),
-                original = character(0), func = vector("list"),
-                has_submodel = logical(0))
-)
-
-expect_equal(
-  get_from_env("sponge_fit"),
-  tibble(engine = character(0), mode = character(0), value = vector("list"))
-)
-
-expect_equal(
-  get_from_env("sponge_predict"),
-  tibble(engine = character(0), mode = character(0),
-         type = character(0), value = vector("list"))
-)
-
-expect_snapshot(error = TRUE, set_new_model())
-expect_snapshot(error = TRUE, set_new_model(2))
-expect_snapshot(error = TRUE, set_new_model(letters[1:2]))
+  expect_equal(
+    get_from_env("sponge_pkgs"),
+    tibble(engine = character(0), pkg = list(), mode = character(0))
+  )
+
+  expect_equal(
+    get_from_env("sponge_modes"),
+    "unknown"
+  )
+
+  expect_equal(
+    get_from_env("sponge_args"),
+    dplyr::tibble(
+      engine = character(0),
+      parsnip = character(0),
+      original = character(0),
+      func = vector("list"),
+      has_submodel = logical(0)
+    )
+  )
+
+  expect_equal(
+    get_from_env("sponge_fit"),
+    tibble(engine = character(0), mode = character(0), value = vector("list"))
+  )
+
+  expect_equal(
+    get_from_env("sponge_predict"),
+    tibble(
+      engine = character(0),
+      mode = character(0),
+      type = character(0),
+      value = vector("list")
+    )
+  )
+
+  expect_snapshot(error = TRUE, set_new_model())
+  expect_snapshot(error = TRUE, set_new_model(2))
+  expect_snapshot(error = TRUE, set_new_model(letters[1:2]))
 })
 
 
@@ -58,7 +73,6 @@ test_that('adding a new mode', {
   expect_equal(get_from_env("sponge_modes"), c("unknown", "classification"))
 
   expect_snapshot(error = TRUE, set_model_mode("sponge"))
-
 })
 
 
@@ -75,7 +89,10 @@ test_that('adding a new engine', {
   expect_equal(get_from_env("sponge_modes"), c("unknown", "classification"))
 
   expect_snapshot(error = TRUE, set_model_engine("sponge", eng = "gum"))
-  expect_snapshot(error = TRUE, set_model_engine("sponge", mode = "classification"))
+  expect_snapshot(
+    error = TRUE,
+    set_model_engine("sponge", mode = "classification")
+  )
   expect_snapshot(
     error = TRUE,
     set_model_engine("sponge", mode = "regression", eng = "gum")
@@ -90,7 +107,10 @@ test_that('adding a new package', {
 
   expect_snapshot(error = TRUE, set_dependency("sponge", "gum", letters[1:2]))
   expect_snapshot(error = TRUE, set_dependency("sponge", "gummies", "trident"))
-  expect_snapshot(error = TRUE, set_dependency("sponge",  "gum", "trident", mode = "regression"))
+  expect_snapshot(
+    error = TRUE,
+    set_dependency("sponge", "gum", "trident", mode = "regression")
+  )
 
   expect_equal(
     get_from_env("sponge_pkgs"),
@@ -100,16 +120,20 @@ test_that('adding a new package', {
   set_dependency("sponge", "gum", "juicy-fruit", mode = "classification")
   expect_equal(
     get_from_env("sponge_pkgs"),
-    tibble(engine = "gum",
-           pkg = list(c("trident", "juicy-fruit")),
-           mode = "classification")
+    tibble(
+      engine = "gum",
+      pkg = list(c("trident", "juicy-fruit")),
+      mode = "classification"
+    )
   )
 
   expect_equal(
     get_dependency("sponge"),
-    tibble(engine = "gum",
-           pkg = list(c("trident", "juicy-fruit")),
-           mode = "classification")
+    tibble(
+      engine = "gum",
+      pkg = list(c("trident", "juicy-fruit")),
+      mode = "classification"
+    )
   )
 })
 
@@ -140,9 +164,13 @@ test_that('adding a new argument', {
 
   expect_equal(
     get_from_env("sponge_args"),
-    tibble(engine = "gum", parsnip = "modeling", original = "modelling",
-           func = list(list(pkg = "foo", fun = "bar")),
-           has_submodel = FALSE)
+    tibble(
+      engine = "gum",
+      parsnip = "modeling",
+      original = "modelling",
+      func = list(list(pkg = "foo", fun = "bar")),
+      has_submodel = FALSE
+    )
   )
 
   expect_snapshot(
@@ -252,7 +280,6 @@ test_that('adding a new argument', {
 })
 
 
-
 # ------------------------------------------------------------------------------
 
 test_that('adding a new fit', {
@@ -273,7 +300,7 @@ test_that('adding a new fit', {
 
   fit_env_data <- get_from_env("sponge_fit")
   expect_equal(
-    fit_env_data[ 1:2],
+    fit_env_data[1:2],
     tibble(engine = "gum", mode = "classification")
   )
 
@@ -405,7 +432,7 @@ test_that('adding a new predict method', {
 
   pred_env_data <- get_from_env("sponge_predict")
   expect_equal(
-    pred_env_data[ 1:3],
+    pred_env_data[1:3],
     tibble(engine = "gum", mode = "classification", type = "class")
   )
 
@@ -415,7 +442,7 @@ test_that('adding a new predict method', {
   )
 
   expect_equal(
-    get_pred_type("sponge", "class")[ 1:3],
+    get_pred_type("sponge", "class")[1:3],
     tibble(engine = "gum", mode = "classification", type = "class")
   )
 
@@ -446,7 +473,6 @@ test_that('adding a new predict method', {
     )
   )
 
-
   expect_snapshot(
     error = TRUE,
     set_pred(
@@ -520,16 +546,13 @@ test_that('adding a new predict method', {
       value = class_vals_2
     )
   )
-
 })
 
 
-
 test_that('showing model info', {
   expect_snapshot(show_model_info("rand_forest"))
 
   # ensure that we don't mention case weight support when the
   # notation would be ambiguous (#1000)
   expect_snapshot(show_model_info("mlp"))
 })
-