check CI runs clean

.github/workflows/R-CMD-check.yaml

@@ -49,7 +49,10 @@ jobs:
         with:
           r-version: ${{ matrix.config.r }}
           http-user-agent: ${{ matrix.config.http-user-agent }}
-          use-public-rspm: true
+          # P3M's macOS binaries are built with OpenMP enabled, but the runners
+          # have no libomp, so e.g. gower fails to load. CRAN's own macOS
+          # binaries are built without it, so use those instead.
+          use-public-rspm: ${{ matrix.config.os != 'macos-latest' }}
 
       - uses: r-lib/actions/setup-r-dependencies@v2
         with:

tests/testthat/_snaps/3.2.1.1/discretize_xgb.md

@@ -0,0 +1,208 @@
+# run_xgboost for classification
+
+    Code
+      xgboost
+    Output
+      ##### xgb.Booster
+      call:
+      xgboost::xgb.train(params = .params, data = .train, nrounds = 100,
+      evals = list(train = .train, test = .test), verbose = 0,
+      early_stopping_rounds = 10)
+      # of features: 13
+      # of rounds:  80
+      xgb.attributes:
+      best_iteration, best_score
+      callbacks:
+      early_stop, evaluation_log
+      evaluation_log:
+      iter train_logloss test_logloss
+      <num>         <num>        <num>
+      1     0.5739956    0.5763535
+      2     0.5546428    0.5563444
+      ---           ---          ---
+      79     0.4189363    0.4445886
+      80     0.4186880    0.4440128
+
+# run_xgboost for multi-classification
+
+    Code
+      xgboost
+    Output
+      ##### xgb.Booster
+      call:
+      xgboost::xgb.train(params = .params, data = .train, nrounds = 100,
+      evals = list(train = .train, test = .test), verbose = 0,
+      early_stopping_rounds = 10)
+      # of features: 30
+      # of rounds:  31
+      xgb.attributes:
+      best_iteration, best_score
+      callbacks:
+      early_stop, evaluation_log
+      evaluation_log:
+      iter train_mlogloss test_mlogloss
+      <num>          <num>         <num>
+      1       1.363338      1.377775
+      2       1.331378      1.352738
+      ---            ---           ---
+      30       1.160942      1.255252
+      31       1.159259      1.256760
+
+# run_xgboost for regression
+
+    Code
+      xgboost
+    Output
+      ##### xgb.Booster
+      call:
+      xgboost::xgb.train(params = .params, data = .train, nrounds = 100,
+      evals = list(train = .train, test = .test), verbose = 0,
+      early_stopping_rounds = 10)
+      # of features: 73
+      # of rounds:  25
+      xgb.attributes:
+      best_iteration, best_score
+      callbacks:
+      early_stop, evaluation_log
+      evaluation_log:
+      iter train_rmse test_rmse
+      <num>      <num>     <num>
+      1 0.15708398 0.1633305
+      2 0.14190657 0.1588983
+      ---        ---       ---
+      24 0.07039493 0.1211529
+      25 0.06975907 0.1206107
+
+# xgb_binning for classification
+
+    Code
+      xgb_binning
+    Output
+      [1]  1  2  3  5  6  9 12 15 20
+
+# xgb_binning for multi-classification
+
+    Code
+      xgb_binning
+    Output
+      [1] 26
+
+# xgb_binning for regression
+
+    Code
+      xgb_binning
+    Output
+      [1] 42.01972 42.02510 42.03122 42.03462 42.03840 42.04638 42.05236 42.05917
+
+# step_discretize_xgb for classification
+
+    Code
+      xgb_train_bins[1:10, ]
+    Output
+      # A tibble: 10 x 3
+         x               z             class
+         <fct>           <fct>         <fct>
+       1 [0.6808, Inf]   [0.3327, Inf] a    
+       2 [0.5749,0.6808) [0.3327, Inf] b    
+       3 [0.3687,0.5749) [-Inf,0.3327) b    
+       4 [0.5749,0.6808) [0.3327, Inf] b    
+       5 [0.6808, Inf]   [0.3327, Inf] a    
+       6 [0.5749,0.6808) [0.3327, Inf] a    
+       7 [0.5749,0.6808) [-Inf,0.3327) b    
+       8 [0.5749,0.6808) [-Inf,0.3327) a    
+       9 [0.2779,0.3687) [0.3327, Inf] a    
+      10 [0.3687,0.5749) [0.3327, Inf] b    
+
+---
+
+    Code
+      xgb_test_bins[1:10, ]
+    Output
+      # A tibble: 10 x 3
+         x               z             class
+         <fct>           <fct>         <fct>
+       1 [0.5749,0.6808) [-Inf,0.3327) b    
+       2 [0.5749,0.6808) [0.3327, Inf] b    
+       3 [0.3687,0.5749) [-Inf,0.3327) b    
+       4 [0.2779,0.3687) [0.3327, Inf] b    
+       5 [0.6808, Inf]   [0.3327, Inf] a    
+       6 [0.3687,0.5749) [-Inf,0.3327) b    
+       7 [0.2779,0.3687) [0.3327, Inf] a    
+       8 [0.6808, Inf]   [0.3327, Inf] a    
+       9 [0.3687,0.5749) [0.3327, Inf] b    
+      10 [0.3687,0.5749) [0.3327, Inf] b    
+
+---
+
+    Code
+      prep(step_discretize_xgb(recipe(class ~ ., data = sim_tr_cls[1:9, ]),
+      all_predictors(), outcome = "class"))
+    Condition
+      Error in `step_discretize_xgb()`:
+      Caused by error in `prep()`:
+      ! Too few observations in the early stopping validation set.
+      i Consider increasing the `sample_val` parameter.
+
+---
+
+    Code
+      set.seed(1)
+      prep(step_discretize_xgb(recipe(Status ~ ., data = credit_data_train), Time,
+      outcome = "Status"), retain = TRUE)
+    Condition
+      Warning:
+      More than 20 unique training set values are required.
+      i Predictors 'Time' were not processed; their original values will be used.
+    Message
+
+      -- Recipe ----------------------------------------------------------------------
+
+      -- Inputs 
+      Number of variables by role
+      outcome:    1
+      predictor: 13
+
+      -- Training information 
+      Training data contained 3340 data points and 301 incomplete rows.
+
+      -- Operations 
+      * Discretizing variables using xgboost: <none> | Trained
+
+# step_discretize_xgb for multi-classification
+
+    Code
+      xgb_train_bins[1:10, ]
+    Output
+      # A tibble: 10 x 3
+         x               z              class
+         <fct>           <fct>          <fct>
+       1 [0.6879, Inf]   [0.09924, Inf] c    
+       2 [0.5863,0.6879) [0.09924, Inf] b    
+       3 [0.3821,0.5863) [-Inf,0.09924) b    
+       4 [0.5863,0.6879) [0.09924, Inf] b    
+       5 [0.6879, Inf]   [0.09924, Inf] c    
+       6 [0.5863,0.6879) [0.09924, Inf] c    
+       7 [0.5863,0.6879) [-Inf,0.09924) b    
+       8 [0.5863,0.6879) [0.09924, Inf] c    
+       9 [-Inf,0.2887)   [0.09924, Inf] a    
+      10 [0.3821,0.5863) [0.09924, Inf] b    
+
+---
+
+    Code
+      xgb_test_bins[1:10, ]
+    Output
+      # A tibble: 10 x 3
+         x               z              class
+         <fct>           <fct>          <fct>
+       1 [0.5863,0.6879) [0.09924, Inf] b    
+       2 [0.5863,0.6879) [0.09924, Inf] b    
+       3 [0.3821,0.5863) [0.09924, Inf] b    
+       4 [0.2887,0.3821) [0.09924, Inf] b    
+       5 [0.6879, Inf]   [0.09924, Inf] c    
+       6 [0.3821,0.5863) [-Inf,0.09924) b    
+       7 [0.2887,0.3821) [0.09924, Inf] a    
+       8 [0.6879, Inf]   [0.09924, Inf] c    
+       9 [0.3821,0.5863) [0.09924, Inf] b    
+      10 [0.3821,0.5863) [0.09924, Inf] b    
+