changes for dev tune and column reordering

DESCRIPTION

@@ -31,11 +31,11 @@ Imports:
     rlang (>= 1.0.4),
     tidyr (>= 1.3.0),
     tidyselect (>= 1.1.2),
-    tune (>=  1.1.0),
+    tune (>= 1.1.2.9020),
     vctrs (>= 0.4.1),
     withr,
-    workflows,
-    yardstick (> 1.0.0)
+    workflows (>= 1.1.4),
+    yardstick (>= 1.3.0)
 Suggests:
     betacal,
     covr,
@@ -44,13 +44,15 @@ Suggests:
     mgcv,
     modeldata (>= 1.1.0),
     nnet,
-    parsnip,
+    parsnip (>= 1.2.0),
     quantregForest,
     randomForest,
     recipes,
     rmarkdown,
     rsample,
     testthat (>= 3.0.0)
+Remotes:
+    tidymodels/tune
 VignetteBuilder: 
     knitr
 ByteCompile: true

tests/testthat/test-cal-validate-multiclass.R

@@ -50,7 +50,7 @@ test_that("Isotonic validation with `fit_resamples` - Multiclass", {
   )
   expect_equal(
     names(val_with_pred$.predictions_cal[[1]]),
-    c(".row", "outcome", ".config", ".pred_one", ".pred_two", ".pred_three", ".pred_class")
+    c(".pred_one", ".pred_two", ".pred_three", ".row", "outcome", ".config", ".pred_class")
   )
   expect_equal(
     purrr::map_int(val_with_pred$splits, ~ holdout_length(.x)),

tests/testthat/test-cal-validate.R

@@ -48,7 +48,7 @@ test_that("Logistic validation with data frame input", {
   expect_equal(
     names(pred_rs),
     c(
-      "Class", ".row", ".config", ".pred_poor", ".pred_good", ".pred_class",
+      ".pred_class", ".pred_poor", ".pred_good", "Class", ".row", ".config",
       ".type"
     )
   )
@@ -272,7 +272,7 @@ test_that("Logistic validation with `fit_resamples`", {
   )
   expect_equal(
     names(val_with_pred$.predictions_cal[[1]]),
-    c(".row", "outcome", ".config", ".pred_class_1", ".pred_class_2", ".pred_class")
+    c(".pred_class_1", ".pred_class_2", ".row", "outcome", ".config", ".pred_class")
   )
   expect_equal(
     purrr::map_int(val_with_pred$splits, ~ holdout_length(.x)),
@@ -302,7 +302,7 @@ test_that("Isotonic classification validation with `fit_resamples`", {
   )
   expect_equal(
     names(val_with_pred$.predictions_cal[[1]]),
-    c(".row", "outcome", ".config", ".pred_class_1", ".pred_class_2", ".pred_class")
+    c(".pred_class_1", ".pred_class_2", ".row", "outcome", ".config", ".pred_class")
   )
   expect_equal(
     purrr::map_int(val_with_pred$splits, ~ holdout_length(.x)),
@@ -333,7 +333,7 @@ test_that("Bootstrapped isotonic classification validation with `fit_resamples`"
   )
   expect_equal(
     names(val_with_pred$.predictions_cal[[1]]),
-    c(".row", "outcome", ".config", ".pred_class_1", ".pred_class_2", ".pred_class")
+    c(".pred_class_1", ".pred_class_2", ".row", "outcome", ".config", ".pred_class")
   )
   expect_equal(
     purrr::map_int(val_with_pred$splits, ~ holdout_length(.x)),
@@ -363,7 +363,7 @@ test_that("Beta calibration validation with `fit_resamples`", {
   )
   expect_equal(
     names(val_with_pred$.predictions_cal[[1]]),
-    c(".row", "outcome", ".config", ".pred_class_1", ".pred_class_2", ".pred_class")
+    c(".pred_class_1", ".pred_class_2", ".row", "outcome", ".config", ".pred_class")
   )
   expect_equal(
     purrr::map_int(val_with_pred$splits, ~ holdout_length(.x)),
@@ -393,7 +393,7 @@ test_that("Multinomial calibration validation with `fit_resamples`", {
   )
   expect_equal(
     names(val_with_pred$.predictions_cal[[1]]),
-    c(".row", "outcome", ".config", ".pred_one", ".pred_two", ".pred_three", ".pred_class")
+    c(".pred_one", ".pred_two", ".pred_three", ".row", "outcome", ".config", ".pred_class")
   )
   expect_equal(
     purrr::map_int(val_with_pred$splits, ~ holdout_length(.x)),
@@ -427,7 +427,7 @@ test_that("Linear validation with `fit_resamples`", {
   )
   expect_equal(
     names(val_with_pred$.predictions_cal[[1]]),
-    c(".row", "outcome", ".config", ".pred")
+    c(".pred", ".row", "outcome", ".config")
   )
   expect_equal(
     purrr::map_int(val_with_pred$splits, ~ holdout_length(.x)),
@@ -457,15 +457,15 @@ test_that("Linear validation with `fit_resamples`", {
   expect_equal(sort(unique(pred$.type)), c("uncalibrated"))
   expect_equal(
     names(pred),
-    c(".row", "outcome", ".config", ".pred", ".type")
+    c(".pred", ".row", "outcome", ".config", ".type")
   )
   expect_equal(nrow(pred), nrow(val_obj$splits[[1]]$data))
 
   pred_rs <- collect_predictions(val_with_pred)
   expect_equal(sort(unique(pred_rs$.type)), c("calibrated", "uncalibrated"))
   expect_equal(
     names(pred_rs),
-    c(".row", "outcome", ".config", ".pred", ".type")
+    c(".pred", ".row", "outcome", ".config", ".type")
   )
   expect_equal(nrow(pred_rs), nrow(val_obj$splits[[1]]$data) * 2)
 })
@@ -494,7 +494,7 @@ test_that("Isotonic regression validation with `fit_resamples`", {
   )
   expect_equal(
     names(val_with_pred$.predictions_cal[[1]]),
-    c(".row", "outcome", ".config", ".pred")
+    c(".pred", ".row", "outcome", ".config")
   )
   expect_equal(
     purrr::map_int(val_with_pred$splits, ~ holdout_length(.x)),
@@ -528,7 +528,7 @@ test_that("Isotonic bootstrapped regression validation with `fit_resamples`", {
   )
   expect_equal(
     names(val_with_pred$.predictions_cal[[1]]),
-    c(".row", "outcome", ".config", ".pred")
+    c(".pred", ".row", "outcome", ".config")
   )
   expect_equal(
     purrr::map_int(val_with_pred$splits, ~ holdout_length(.x)),