Switch from GPfit to GauPro for Gaussian Processes

DESCRIPTION

@@ -24,7 +24,7 @@ Imports:
     generics (>= 0.1.2),
     ggplot2,
     glue (>= 1.6.2),
-    GPfit,
+    GauPro (>= 0.2.15),
     hardhat (>= 1.4.2),
     parallel,
     parsnip (>= 1.2.1.9003),
@@ -66,4 +66,4 @@ Encoding: UTF-8
 Language: en-US
 LazyData: true
 Roxygen: list(markdown = TRUE)
-RoxygenNote: 7.3.2
+RoxygenNote: 7.3.3

NAMESPACE

@@ -255,7 +255,6 @@ export(val_class_or_null)
 import(rlang)
 import(vctrs)
 import(workflows)
-importFrom(GPfit,GP_fit)
 importFrom(cli,cli_abort)
 importFrom(cli,cli_alert)
 importFrom(cli,cli_alert_danger)

NEWS.md

@@ -1,5 +1,13 @@
 # tune (development version)
 
+## Breaking Changes
+
+* The Gaussian process model package was changed from \pkg{GPfit} to \pkg{GauPro} because the former is no longer actively maintained. There are some differences: 
+
+   - Fit diagnostics are computed and reported. If the fit quality is poor, an "uncertainty sample" that is furthest away from the existing data is used as the new candidate. 
+   - The GP no longer uses binary indicators for qualitative predictors. Instead, a "categorical kernel" is used for those parameter columns. Fewer starting values are required with this change. 
+   - For numeric predictors, the Matern 3/2 kernel is always used. 
+
 # tune 2.0.1
 
 * Fixed a bug where `int_pctl()` wouldn't work on `last_fit()` outcomes when future parallelism was enabled. (#1099) 

R/0_imports.R

@@ -12,7 +12,6 @@
 #' @importFrom stats model.matrix model.response model.frame update median
 #' @importFrom yardstick rsq rmse accuracy roc_auc brier_survival brier_class
 #' @importFrom tidyr unnest nest
-#' @importFrom GPfit GP_fit
 #' @importFrom parsnip get_from_env required_pkgs
 #' @importFrom recipes all_predictors all_outcomes
 #' @importFrom ggplot2 ggplot aes xlab geom_point geom_errorbar facet_wrap ylab
@@ -124,8 +123,9 @@ utils::globalVariables(
     ".num_models",
     "model_stage",
     "predict_stage",
-    "user",
-    "num"
+    "se",
+    "num",
+    "user"
   )
 )
 

R/checks.R

@@ -244,10 +244,7 @@ check_bayes_initial_size <- function(num_param, num_grid, race = FALSE) {
     c(
       `!` = "{msg}",
       `*` = cli::pluralize(
-        "There are {cli::qty(diff)}{?as many/more} tuning parameters
-          {cli::qty(diff)}{?as/than} there are initial points.
-          This is likely to cause numerical issues in the first few
-          search iterations."
+        "This is likely to cause numerical issues in the first few search iterations."
       )
     )