tests/testthat/_snaps/aaa-metric_set.md

In yardstick: Tidy Characterizations of Model Performance

print metric_set works

Code
  metric_set(accuracy)
Output
  A metric set, consisting of:
  - `accuracy()`, a class metric | direction: maximize

Code
  metric_set(roc_auc)
Output
  A metric set, consisting of:
  - `roc_auc()`, a probability metric | direction: maximize

Code
  metric_set(ranked_prob_score)
Output
  A metric set, consisting of:
  - `ranked_prob_score()`, a ordered probability metric | direction: minimize

Code
  metric_set(rmse)
Output
  A metric set, consisting of:
  - `rmse()`, a numeric metric | direction: minimize

Code
  metric_set(concordance_survival)
Output
  A metric set, consisting of:
  - `concordance_survival()`, a static survival metric | direction: maximize

Code
  metric_set(brier_survival)
Output
  A metric set, consisting of:
  - `brier_survival()`, a dynamic survival metric | direction: minimize

Code
  metric_set(brier_survival_integrated)
Output
  A metric set, consisting of:
  - `brier_survival_integrated()`, a integrated survival metric | direction:
  minimize

Code
  metric_set(royston_survival)
Output
  A metric set, consisting of:
  - `royston_survival()`, a linear predictor survival metric | direction:
  maximize

Code
  metric_set(weighted_interval_score)
Output
  A metric set, consisting of:
  - `weighted_interval_score()`, a quantile metric | direction: minimize

Code
  metric_set(accuracy, roc_auc, ranked_prob_score)
Output
  A metric set, consisting of:
  - `accuracy()`, a class metric                        | direction: maximize
  - `roc_auc()`, a probability metric                   | direction: maximize
  - `ranked_prob_score()`, a ordered probability metric | direction: minimize

Code
  metric_set(concordance_survival, brier_survival, brier_survival_integrated,
    royston_survival)
Output
  A metric set, consisting of:
  - `concordance_survival()`, a static survival metric          | direction:
  maximize
  - `brier_survival()`, a dynamic survival metric               | direction:
  minimize
  - `brier_survival_integrated()`, a integrated survival metric | direction:
  minimize
  - `royston_survival()`, a linear predictor survival metric    | direction:
  maximize

metric_set() errors on bad input

Code
  metric_set("x")
Condition
  Error in `metric_set()`:
  ! All inputs to `metric_set()` must be functions. These inputs are not: 1.

Code
  metric_set(rmse, "x")
Condition
  Error in `metric_set()`:
  ! All inputs to `metric_set()` must be functions. These inputs are not: 2.

metric_set() errors on empty input

Code
  metric_set()
Condition
  Error in `metric_set()`:
  ! At least 1 function must be supplied to `...`.

metric_set() errors on mixing incombatible metrics

Code
  metric_set(rmse, accuracy)
Condition
  Error in `metric_set()`:
  x The combination of metric functions must be:
  * only numeric metrics.
  * a mix of class metrics and class probability metrics.
  * a mix of dynamic and static survival metrics.
  i The following metric function types are being mixed:
  - numeric (rmse)
  - class (accuracy)

Code
  metric_set(rmse, accuracy, brier_survival)
Condition
  Error in `metric_set()`:
  x The combination of metric functions must be:
  * only numeric metrics.
  * a mix of class metrics and class probability metrics.
  * a mix of dynamic and static survival metrics.
  i The following metric function types are being mixed:
  - numeric (rmse)
  - class (accuracy)
  - dynamic_survival (brier_survival)

Code
  metric_set(rmse, accuracy, brier_survival, weighted_interval_score)
Condition
  Error in `metric_set()`:
  x The combination of metric functions must be:
  * only numeric metrics.
  * a mix of class metrics and class probability metrics.
  * a mix of dynamic and static survival metrics.
  i The following metric function types are being mixed:
  - numeric (rmse)
  - class (accuracy)
  - dynamic_survival (brier_survival)
  - quantile (weighted_interval_score)

metric_set() errors contain env name for unknown functions (#128)

Code
  metric_set(accuracy, foobar, sens, rlang::abort)
Condition
  Error in `metric_set()`:
  x The combination of metric functions must be:
  * only numeric metrics.
  * a mix of class metrics and class probability metrics.
  * a mix of dynamic and static survival metrics.
  i The following metric function types are being mixed:
  - class (accuracy, sens)
  - other (foobar <test>, abort <namespace:rlang>)

Code
  metric_set(accuracy, foobar, sens, rlang::abort)
Condition
  Error in `metric_set()`:
  x The combination of metric functions must be:
  * only numeric metrics.
  * a mix of class metrics and class probability metrics.
  * a mix of dynamic and static survival metrics.
  i The following metric function types are being mixed:
  - class (accuracy, sens)
  - other (foobar <test>, abort <namespace:rlang>)

metric_set() gives an informative error for a single non-metric function (#181)

Code
  metric_set(foobar)
Condition
  Error in `metric_set()`:
  x The combination of metric functions must be:
  * only numeric metrics.
  * a mix of class metrics and class probability metrics.
  * a mix of dynamic and static survival metrics.
  i The following metric function types are being mixed:
  - other (foobar <test>)

errors informatively for unevaluated metric factories

Code
  metric_set(demographic_parity)
Condition
  Error in `metric_set()`:
  ! The input `demographic_parity` is a groupwise metric (`?yardstick::new_groupwise_metric()`) factory and must be passed a data-column before addition to a metric set.
  i Did you mean to type e.g. `demographic_parity(col_name)`?

Code
  metric_set(demographic_parity, roc_auc)
Condition
  Error in `metric_set()`:
  ! The input `demographic_parity` is a groupwise metric (`?yardstick::new_groupwise_metric()`) factory and must be passed a data-column before addition to a metric set.
  i Did you mean to type e.g. `demographic_parity(col_name)`?

Code
  metric_set(demographic_parity, equal_opportunity)
Condition
  Error in `metric_set()`:
  ! The inputs `demographic_parity` and `equal_opportunity` are groupwise metric (`?yardstick::new_groupwise_metric()`) factories and must be passed a data-column before addition to a metric set.
  i Did you mean to type e.g. `demographic_parity(col_name)`?

Code
  metric_set(demographic_parity, equal_opportunity, roc_auc)
Condition
  Error in `metric_set()`:
  ! The inputs `demographic_parity` and `equal_opportunity` are groupwise metric (`?yardstick::new_groupwise_metric()`) factories and must be passed a data-column before addition to a metric set.
  i Did you mean to type e.g. `demographic_parity(col_name)`?

propagates 'caused by' error message when specifying the wrong column name

Code
  set(two_class_example, truth, Class1, estimate = predicted, case_weights = weight)
Condition
  Error in `metric_set()`:
  ! Failed to compute `accuracy()`.
  Caused by error:
  ! Can't select columns that don't exist.
  x Column `weight` doesn't exist.

errors informatively when estimate is not named for class metrics

Code
  set(two_class_example, truth, predicted)
Condition
  Error in `set()`:
  ! `estimate` is required for class metrics but was not provided.
  i In a metric set, the `estimate` argument must be named.
  i Example: `my_metrics(data, truth, estimate = my_column)`

errors informatively when estimate is not named for survival metrics

Code
  set(lung_surv, surv_obj, .pred_time)
Condition
  Error in `set()`:
  ! `estimate` is required for static or linear predictor survival metrics but was not provided.
  i In a metric set, the `estimate` argument must be named.
  i Example: `my_metrics(data, truth, estimate = my_column)`

yardstick documentation built on April 8, 2026, 1:06 a.m.