update snaps with new tibble printing (#532)

tests/testthat/_snaps/collect.md

@@ -56,9 +56,10 @@
       # Resampling results
       # Manual resampling 
       # A tibble: 1 x 6
-        splits         id               .metrics .notes   .predictions     .workflow 
-        <list>         <chr>            <list>   <list>   <list>           <list>    
-      1 <split [24/8]> train/test split <tibble> <tibble> <tibble [8 x 4]> <workflow>
+        splits         id               .metrics         .notes   .predic~1 .workflow 
+        <list>         <chr>            <list>           <list>   <list>    <list>    
+      1 <split [24/8]> train/test split <tibble [2 x 4]> <tibble> <tibble>  <workflow>
+      # ... with abbreviated variable name 1: .predictions
       
       There were issues with some computations:
       

tests/testthat/_snaps/filter-parameters.md

@@ -45,17 +45,18 @@
       # Tuning results
       # 10-fold cross-validation repeated 5 times 
       # A tibble: 50 x 6
-         splits           id      id2    .metrics          .notes   .predictions      
-         <list>           <chr>   <chr>  <list>            <list>   <list>            
-       1 <split [711/80]> Repeat1 Fold01 <tibble [30 x 7]> <tibble> <tibble [800 x 7]>
-       2 <split [712/79]> Repeat1 Fold02 <tibble [30 x 7]> <tibble> <tibble [790 x 7]>
-       3 <split [712/79]> Repeat1 Fold03 <tibble [30 x 7]> <tibble> <tibble [790 x 7]>
-       4 <split [712/79]> Repeat1 Fold04 <tibble [30 x 7]> <tibble> <tibble [790 x 7]>
-       5 <split [712/79]> Repeat1 Fold05 <tibble [30 x 7]> <tibble> <tibble [790 x 7]>
-       6 <split [712/79]> Repeat1 Fold06 <tibble [30 x 7]> <tibble> <tibble [790 x 7]>
-       7 <split [712/79]> Repeat1 Fold07 <tibble [30 x 7]> <tibble> <tibble [790 x 7]>
-       8 <split [712/79]> Repeat1 Fold08 <tibble [30 x 7]> <tibble> <tibble [790 x 7]>
-       9 <split [712/79]> Repeat1 Fold09 <tibble [30 x 7]> <tibble> <tibble [790 x 7]>
-      10 <split [712/79]> Repeat1 Fold10 <tibble [30 x 7]> <tibble> <tibble [790 x 7]>
-      # ... with 40 more rows
+         splits           id      id2    .metrics          .notes           .predict~1
+         <list>           <chr>   <chr>  <list>            <list>           <list>    
+       1 <split [711/80]> Repeat1 Fold01 <tibble [30 x 7]> <tibble [0 x 1]> <tibble>  
+       2 <split [712/79]> Repeat1 Fold02 <tibble [30 x 7]> <tibble [0 x 1]> <tibble>  
+       3 <split [712/79]> Repeat1 Fold03 <tibble [30 x 7]> <tibble [0 x 1]> <tibble>  
+       4 <split [712/79]> Repeat1 Fold04 <tibble [30 x 7]> <tibble [0 x 1]> <tibble>  
+       5 <split [712/79]> Repeat1 Fold05 <tibble [30 x 7]> <tibble [0 x 1]> <tibble>  
+       6 <split [712/79]> Repeat1 Fold06 <tibble [30 x 7]> <tibble [0 x 1]> <tibble>  
+       7 <split [712/79]> Repeat1 Fold07 <tibble [30 x 7]> <tibble [0 x 1]> <tibble>  
+       8 <split [712/79]> Repeat1 Fold08 <tibble [30 x 7]> <tibble [0 x 1]> <tibble>  
+       9 <split [712/79]> Repeat1 Fold09 <tibble [30 x 7]> <tibble [0 x 1]> <tibble>  
+      10 <split [712/79]> Repeat1 Fold10 <tibble [30 x 7]> <tibble [0 x 1]> <tibble>  
+      # ... with 40 more rows, and abbreviated variable name 1: .predictions
+      # i Use `print(n = ...)` to see more rows
 

tests/testthat/_snaps/last-fit.md

@@ -9,9 +9,10 @@
       # Resampling results
       # Manual resampling 
       # A tibble: 1 x 6
-        splits         id               .metrics .notes   .predictions     .workflow 
-        <list>         <chr>            <list>   <list>   <list>           <list>    
-      1 <split [24/8]> train/test split <tibble> <tibble> <tibble [8 x 4]> <workflow>
+        splits         id               .metrics         .notes   .predic~1 .workflow 
+        <list>         <chr>            <list>           <list>   <list>    <list>    
+      1 <split [24/8]> train/test split <tibble [2 x 4]> <tibble> <tibble>  <workflow>
+      # ... with abbreviated variable name 1: .predictions
 
 # argument order gives errors for recipe/formula
 

tests/testthat/_snaps/select_best.md

@@ -72,10 +72,11 @@
       The `maximize` argument is no longer needed. This value was ignored.
     Output
       # A tibble: 1 x 11
-            K weight_func exponent .metric  .estimator  mean     n std_err .config
-        <int> <chr>          <dbl> <chr>    <chr>      <dbl> <int>   <dbl> <chr>  
-      1    25 rank            1.99 accuracy binary     0.819    50 0.00517 Model06
-      # ... with 2 more variables: .best <dbl>, .bound <dbl>
+            K weigh~1 expon~2 .metric .esti~3  mean     n std_err .config .best .bound
+        <int> <chr>     <dbl> <chr>   <chr>   <dbl> <int>   <dbl> <chr>   <dbl>  <dbl>
+      1    25 rank       1.99 accura~ binary  0.819    50 0.00517 Model06 0.819  0.814
+      # ... with abbreviated variable names 1: weight_func, 2: exponent,
+      #   3: .estimator
 
 ---
 
@@ -128,10 +129,11 @@
       The `maximize` argument is no longer needed. This value was ignored.
     Output
       # A tibble: 1 x 11
-            K weight_func  exponent .metric  .estimator  mean     n std_err .config
-        <int> <chr>           <dbl> <chr>    <chr>      <dbl> <int>   <dbl> <chr>  
-      1    12 epanechnikov     1.96 accuracy binary     0.806    50 0.00569 Model02
-      # ... with 2 more variables: .best <dbl>, .loss <dbl>
+            K weight~1 expon~2 .metric .esti~3  mean     n std_err .config .best .loss
+        <int> <chr>      <dbl> <chr>   <chr>   <dbl> <int>   <dbl> <chr>   <dbl> <dbl>
+      1    12 epanech~    1.96 accura~ binary  0.806    50 0.00569 Model02 0.819  1.64
+      # ... with abbreviated variable names 1: weight_func, 2: exponent,
+      #   3: .estimator
 
 ---