Running ‘testthat.R’ [26s/54s]
Running the tests in ‘tests/testthat.R’ failed.
Complete output:
  > if (requireNamespace("testthat", quietly = TRUE)) {
  +   library("testthat")
  +   library("checkmate")
  +   library("mlr3")
  +   library("mlr3pipelines")
  +   library("mlr3fairness")
  +   test_check("mlr3fairness")
  + }
  INFO  [12:15:07.047] [mlr3] Running benchmark with 12 resampling iterations
  INFO  [12:15:08.137] [mlr3] Applying learner 'classif.rpart' on task 'adult_train' (iter 1/3)
  INFO  [12:15:08.508] [mlr3] Applying learner 'classif.rpart' on task 'adult_train' (iter 2/3)
  INFO  [12:15:08.738] [mlr3] Applying learner 'classif.rpart' on task 'adult_train' (iter 3/3)
  INFO  [12:15:08.956] [mlr3] Applying learner 'classif.featureless' on task 'adult_train' (iter 1/3)
  INFO  [12:15:09.111] [mlr3] Applying learner 'classif.featureless' on task 'adult_train' (iter 2/3)
  INFO  [12:15:09.340] [mlr3] Applying learner 'classif.featureless' on task 'adult_train' (iter 3/3)
  INFO  [12:15:09.495] [mlr3] Applying learner 'classif.rpart' on task 'compas' (iter 1/3)
  INFO  [12:15:09.728] [mlr3] Applying learner 'classif.rpart' on task 'compas' (iter 2/3)
  INFO  [12:15:09.901] [mlr3] Applying learner 'classif.rpart' on task 'compas' (iter 3/3)
  INFO  [12:15:10.086] [mlr3] Applying learner 'classif.featureless' on task 'compas' (iter 1/3)
  INFO  [12:15:10.224] [mlr3] Applying learner 'classif.featureless' on task 'compas' (iter 2/3)
  INFO  [12:15:10.330] [mlr3] Applying learner 'classif.featureless' on task 'compas' (iter 3/3)
  INFO  [12:15:10.462] [mlr3] Finished benchmark
  INFO  [12:15:11.784] [mlr3] Running benchmark with 12 resampling iterations
  INFO  [12:15:12.198] [mlr3] Applying learner 'classif.rpart' on task 'adult_train' (iter 1/3)
  INFO  [12:15:12.360] [mlr3] Applying learner 'classif.rpart' on task 'adult_train' (iter 2/3)
  INFO  [12:15:12.550] [mlr3] Applying learner 'classif.rpart' on task 'adult_train' (iter 3/3)
  INFO  [12:15:12.735] [mlr3] Applying learner 'classif.featureless' on task 'adult_train' (iter 1/3)
  INFO  [12:15:12.876] [mlr3] Applying learner 'classif.featureless' on task 'adult_train' (iter 2/3)
  INFO  [12:15:13.022] [mlr3] Applying learner 'classif.featureless' on task 'adult_train' (iter 3/3)
  INFO  [12:15:13.150] [mlr3] Applying learner 'classif.rpart' on task 'compas' (iter 1/3)
  INFO  [12:15:13.305] [mlr3] Applying learner 'classif.rpart' on task 'compas' (iter 2/3)
  INFO  [12:15:13.400] [mlr3] Applying learner 'classif.rpart' on task 'compas' (iter 3/3)
  INFO  [12:15:13.491] [mlr3] Applying learner 'classif.featureless' on task 'compas' (iter 1/3)
  INFO  [12:15:13.542] [mlr3] Applying learner 'classif.featureless' on task 'compas' (iter 2/3)
  INFO  [12:15:13.658] [mlr3] Applying learner 'classif.featureless' on task 'compas' (iter 3/3)
  INFO  [12:15:13.713] [mlr3] Finished benchmark
  INFO  [12:15:14.190] [mlr3] Running benchmark with 12 resampling iterations
  INFO  [12:15:14.220] [mlr3] Applying learner 'classif.rpart' on task 'adult_train' (iter 1/3)
  INFO  [12:15:14.382] [mlr3] Applying learner 'classif.rpart' on task 'adult_train' (iter 2/3)
  INFO  [12:15:14.449] [mlr3] Applying learner 'classif.rpart' on task 'adult_train' (iter 3/3)
  INFO  [12:15:14.516] [mlr3] Applying learner 'classif.featureless' on task 'adult_train' (iter 1/3)
  INFO  [12:15:14.574] [mlr3] Applying learner 'classif.featureless' on task 'adult_train' (iter 2/3)
  INFO  [12:15:14.623] [mlr3] Applying learner 'classif.featureless' on task 'adult_train' (iter 3/3)
  INFO  [12:15:14.711] [mlr3] Applying learner 'classif.rpart' on task 'compas' (iter 1/3)
  INFO  [12:15:15.040] [mlr3] Applying learner 'classif.rpart' on task 'compas' (iter 2/3)
  INFO  [12:15:15.198] [mlr3] Applying learner 'classif.rpart' on task 'compas' (iter 3/3)
  INFO  [12:15:15.329] [mlr3] Applying learner 'classif.featureless' on task 'compas' (iter 1/3)
  INFO  [12:15:15.500] [mlr3] Applying learner 'classif.featureless' on task 'compas' (iter 2/3)
  INFO  [12:15:15.630] [mlr3] Applying learner 'classif.featureless' on task 'compas' (iter 3/3)
  INFO  [12:15:15.764] [mlr3] Finished benchmark
  [ FAIL 21 | WARN 14 | SKIP 30 | PASS 99 ]
  
  ══ Skipped tests (30) ══════════════════════════════════════════════════════════
  • On CRAN (30): 'test_datasets.R:16:3', 'test_datasets.R:32:3',
    'test_datasets.R:47:3', 'test_learners_fairml.R:2:5',
    'test_learners_fairml.R:12:5', 'test_learners_fairml.R:25:5',
    'test_learners_fairml.R:39:5', 'test_learners_fairml.R:51:5',
    'test_learners_fairml_ptas.R:2:5', 'test_learners_fairml_ptas.R:17:5',
    'test_measure_subgroup.R:47:3', 'test_measure_subgroup.R:66:3',
    'test_pipeop_eod.R:2:3', 'test_pipeop_eod.R:17:3', 'test_pipeop_eod.R:57:3',
    'test_pipeop_explicit_pta.R:3:5', 'test_pipeop_explicit_pta.R:17:5',
    'test_pipeop_reweighing.R:2:3', 'test_pipeop_reweighing.R:15:3',
    'test_pipeop_reweighing.R:25:3', 'test_pipeop_reweighing.R:34:3',
    'test_pipeop_reweighing.R:51:3', 'test_pipeop_reweighing.R:61:3',
    'test_report_modelcard_datasheet.R:2:3',
    'test_report_modelcard_datasheet.R:18:3',
    'test_report_modelcard_datasheet.R:34:3',
    'test_use_modelcard_datasheet.R:2:3', 'test_use_modelcard_datasheet.R:17:3',
    'test_use_modelcard_datasheet.R:31:3', 'test_write_files.R:2:3'
  
  ══ Failed tests ════════════════════════════════════════════════════════════════
  ── Error ('test_measure_subgroup.R:20:3'): measure ─────────────────────────────
  Error in `prediction$clone()$filter(rws)$score(self$base_measure, task = task, 
      ...)`: unused argument (weights = NULL)
  Backtrace:
       ▆
    1. └─l$train(t)$predict(t)$score(m, t) at test_measure_subgroup.R:20:3
    2.   └─mlr3:::.__Prediction__score(...)
    3.     └─mlr3misc::map_dbl(...)
    4.       └─mlr3misc:::map_mold(.x, .f, NA_real_, ...)
    5.         └─base::vapply(.x, .f, FUN.VALUE = .value, USE.NAMES = FALSE, ...)
    6.           └─mlr3 (local) FUN(X[[i]], ...)
    7.             └─m$score(prediction = self, task = task, learner = learner, train_set = train_set)
    8.               └─mlr3:::.__Measure__score(...)
    9.                 └─mlr3:::score_single_measure(self, task, learner, train_set, prediction)
   10.                   └─get_private(measure)$.score(...)
   11.                     └─mlr3fairness:::.__MeasureSubgroup__.score(...)
  ── Error ('test_measure_subgroup.R:32:3'): measure ─────────────────────────────
  Error in `prediction$clone()$filter(rws)$score(self$base_measure, task = task, 
      ...)`: unused argument (weights = NULL)
  Backtrace:
       ▆
    1. └─l$train(t)$predict(t)$score(m, t) at test_measure_subgroup.R:32:3
    2.   └─mlr3:::.__Prediction__score(...)
    3.     └─mlr3misc::map_dbl(...)
    4.       └─mlr3misc:::map_mold(.x, .f, NA_real_, ...)
    5.         └─base::vapply(.x, .f, FUN.VALUE = .value, USE.NAMES = FALSE, ...)
    6.           └─mlr3 (local) FUN(X[[i]], ...)
    7.             └─m$score(prediction = self, task = task, learner = learner, train_set = train_set)
    8.               └─mlr3:::.__Measure__score(...)
    9.                 └─mlr3:::score_single_measure(self, task, learner, train_set, prediction)
   10.                   └─get_private(measure)$.score(...)
   11.                     └─mlr3fairness:::.__MeasureSubgroup__.score(...)
  ── Error ('test_measures.R:51:9'): fairness measures work as expcted ───────────
  Error in `prediction$clone()$filter(rws)$score(base_measure, task = task, 
      ...)`: unused argument (weights = NULL)
  Backtrace:
       ▆
    1. └─prd$score(measures = ms, task = tsk) at test_measures.R:51:9
    2.   └─mlr3:::.__Prediction__score(...)
    3.     └─mlr3misc::map_dbl(...)
    4.       └─mlr3misc:::map_mold(.x, .f, NA_real_, ...)
    5.         └─base::vapply(.x, .f, FUN.VALUE = .value, USE.NAMES = FALSE, ...)
    6.           └─mlr3 (local) FUN(X[[i]], ...)
    7.             └─m$score(prediction = self, task = task, learner = learner, train_set = train_set)
    8.               └─mlr3:::.__Measure__score(...)
    9.                 └─mlr3:::score_single_measure(self, task, learner, train_set, prediction)
   10.                   └─get_private(measure)$.score(...)
   11.                     └─mlr3fairness:::.__MeasureFairness__.score(...)
   12.                       └─mlr3fairness:::score_groupwise(...)
   13.                         └─mlr3misc::map_dbl(...)
   14.                           └─mlr3misc:::map_mold(.x, .f, NA_real_, ...)
   15.                             └─base::vapply(.x, .f, FUN.VALUE = .value, USE.NAMES = FALSE, ...)
   16.                               └─mlr3fairness (local) FUN(X[[i]], ...)
  ── Error ('test_measures.R:71:9'): fairness measures work as expected - simulated data ──
  Error in `prediction$clone()$filter(rws)$score(base_measure, task = task, 
      ...)`: unused argument (weights = NULL)
  Backtrace:
       ▆
    1. └─prd$score(measures = ms, task = tsk) at test_measures.R:71:9
    2.   └─mlr3:::.__Prediction__score(...)
    3.     └─mlr3misc::map_dbl(...)
    4.       └─mlr3misc:::map_mold(.x, .f, NA_real_, ...)
    5.         └─base::vapply(.x, .f, FUN.VALUE = .value, USE.NAMES = FALSE, ...)
    6.           └─mlr3 (local) FUN(X[[i]], ...)
    7.             └─m$score(prediction = self, task = task, learner = learner, train_set = train_set)
    8.               └─mlr3:::.__Measure__score(...)
    9.                 └─mlr3:::score_single_measure(self, task, learner, train_set, prediction)
   10.                   └─get_private(measure)$.score(...)
   11.                     └─mlr3fairness:::.__MeasureFairness__.score(...)
   12.                       └─mlr3fairness:::score_groupwise(...)
   13.                         └─mlr3misc::map_dbl(...)
   14.                           └─mlr3misc:::map_mold(.x, .f, NA_real_, ...)
   15.                             └─base::vapply(.x, .f, FUN.VALUE = .value, USE.NAMES = FALSE, ...)
   16.                               └─mlr3fairness (local) FUN(X[[i]], ...)
  ── Error ('test_measures.R:92:3'): fairness errors on missing pta, works with ──
  Error in `prediction$clone()$filter(rws)$score(base_measure, task = task, 
      ...)`: unused argument (weights = NULL)
  Backtrace:
       ▆
    1. ├─testthat::expect_equal(...) at test_measures.R:92:3
    2. │ └─testthat::quasi_label(enquo(object), label, arg = "object")
    3. │   └─rlang::eval_bare(expr, quo_get_env(quo))
    4. ├─base::unname(prd$score(msr("fairness.acc"), task = task))
    5. └─prd$score(msr("fairness.acc"), task = task)
    6.   └─mlr3:::.__Prediction__score(...)
    7.     └─mlr3misc::map_dbl(...)
    8.       └─mlr3misc:::map_mold(.x, .f, NA_real_, ...)
    9.         └─base::vapply(.x, .f, FUN.VALUE = .value, USE.NAMES = FALSE, ...)
   10.           └─mlr3 (local) FUN(X[[i]], ...)
   11.             └─m$score(prediction = self, task = task, learner = learner, train_set = train_set)
   12.               └─mlr3:::.__Measure__score(...)
   13.                 └─mlr3:::score_single_measure(self, task, learner, train_set, prediction)
   14.                   └─get_private(measure)$.score(...)
   15.                     └─mlr3fairness:::.__MeasureFairness__.score(...)
   16.                       └─mlr3fairness:::score_groupwise(...)
   17.                         └─mlr3misc::map_dbl(...)
   18.                           └─mlr3misc:::map_mold(.x, .f, NA_real_, ...)
   19.                             └─base::vapply(.x, .f, FUN.VALUE = .value, USE.NAMES = FALSE, ...)
   20.                               └─mlr3fairness (local) FUN(X[[i]], ...)
  ── Error ('test_measures.R:111:3'): fairness works with non-binary pta ─────────
  Error in `prediction$clone()$filter(rws)$score(base_measure, task = task, 
      ...)`: unused argument (weights = NULL)
  Backtrace:
       ▆
    1. ├─checkmate::expect_number(...) at test_measures.R:111:3
    2. └─prd$score(msr("fairness.acc"), task = task)
    3.   └─mlr3:::.__Prediction__score(...)
    4.     └─mlr3misc::map_dbl(...)
    5.       └─mlr3misc:::map_mold(.x, .f, NA_real_, ...)
    6.         └─base::vapply(.x, .f, FUN.VALUE = .value, USE.NAMES = FALSE, ...)
    7.           └─mlr3 (local) FUN(X[[i]], ...)
    8.             └─m$score(prediction = self, task = task, learner = learner, train_set = train_set)
    9.               └─mlr3:::.__Measure__score(...)
   10.                 └─mlr3:::score_single_measure(self, task, learner, train_set, prediction)
   11.                   └─get_private(measure)$.score(...)
   12.                     └─mlr3fairness:::.__MeasureFairness__.score(...)
   13.                       └─mlr3fairness:::score_groupwise(...)
   14.                         └─mlr3misc::map_dbl(...)
   15.                           └─mlr3misc:::map_mold(.x, .f, NA_real_, ...)
   16.                             └─base::vapply(.x, .f, FUN.VALUE = .value, USE.NAMES = FALSE, ...)
   17.                               └─mlr3fairness (local) FUN(X[[i]], ...)
  ── Error ('test_measures.R:129:3'): fairness works on non-binary target ────────
  Error in `prediction$clone()$filter(rws)$score(base_measure, task = task, 
      ...)`: unused argument (weights = NULL)
  Backtrace:
       ▆
    1. ├─checkmate::expect_number(...) at test_measures.R:129:3
    2. └─prd$score(msr("fairness.acc"), task = task)
    3.   └─mlr3:::.__Prediction__score(...)
    4.     └─mlr3misc::map_dbl(...)
    5.       └─mlr3misc:::map_mold(.x, .f, NA_real_, ...)
    6.         └─base::vapply(.x, .f, FUN.VALUE = .value, USE.NAMES = FALSE, ...)
    7.           └─mlr3 (local) FUN(X[[i]], ...)
    8.             └─m$score(prediction = self, task = task, learner = learner, train_set = train_set)
    9.               └─mlr3:::.__Measure__score(...)
   10.                 └─mlr3:::score_single_measure(self, task, learner, train_set, prediction)
   11.                   └─get_private(measure)$.score(...)
   12.                     └─mlr3fairness:::.__MeasureFairness__.score(...)
   13.                       └─mlr3fairness:::score_groupwise(...)
   14.                         └─mlr3misc::map_dbl(...)
   15.                           └─mlr3misc:::map_mold(.x, .f, NA_real_, ...)
   16.                             └─base::vapply(.x, .f, FUN.VALUE = .value, USE.NAMES = FALSE, ...)
   17.                               └─mlr3fairness (local) FUN(X[[i]], ...)
  ── Error ('test_measures.R:140:3'): fairness.fpr can be loaded and work as expected ──
  Error in `prediction$clone()$filter(rws)$score(base_measure, task = task, 
      ...)`: unused argument (weights = NULL)
  Backtrace:
       ▆
    1. ├─testthat::expect_equal(...) at test_measures.R:140:3
    2. │ └─testthat::quasi_label(enquo(object), label, arg = "object")
    3. │   └─rlang::eval_bare(expr, quo_get_env(quo))
    4. ├─base::unname(round(predictions$score(msr_obj, test_data), 4))
    5. └─predictions$score(msr_obj, test_data)
    6.   └─mlr3:::.__Prediction__score(...)
    7.     └─mlr3misc::map_dbl(...)
    8.       └─mlr3misc:::map_mold(.x, .f, NA_real_, ...)
    9.         └─base::vapply(.x, .f, FUN.VALUE = .value, USE.NAMES = FALSE, ...)
   10.           └─mlr3 (local) FUN(X[[i]], ...)
   11.             └─m$score(prediction = self, task = task, learner = learner, train_set = train_set)
   12.               └─mlr3:::.__Measure__score(...)
   13.                 └─mlr3:::score_single_measure(self, task, learner, train_set, prediction)
   14.                   └─get_private(measure)$.score(...)
   15.                     └─mlr3fairness:::.__MeasureFairness__.score(...)
   16.                       └─mlr3fairness:::score_groupwise(...)
   17.                         └─mlr3misc::map_dbl(...)
   18.                           └─mlr3misc:::map_mold(.x, .f, NA_real_, ...)
   19.                             └─base::vapply(.x, .f, FUN.VALUE = .value, USE.NAMES = FALSE, ...)
   20.                               └─mlr3fairness (local) FUN(X[[i]], ...)
  ── Error ('test_measures.R:145:3'): fairness.acc can be loaded and work as expected ──
  Error in `prediction$clone()$filter(rws)$score(base_measure, task = task, 
      ...)`: unused argument (weights = NULL)
  Backtrace:
       ▆
    1. ├─testthat::expect_lt(...) at test_measures.R:145:3
    2. │ └─testthat::quasi_label(enquo(object), label, arg = "object")
    3. │   └─rlang::eval_bare(expr, quo_get_env(quo))
    4. └─predictions$score(msr_obj, test_data)
    5.   └─mlr3:::.__Prediction__score(...)
    6.     └─mlr3misc::map_dbl(...)
    7.       └─mlr3misc:::map_mold(.x, .f, NA_real_, ...)
    8.         └─base::vapply(.x, .f, FUN.VALUE = .value, USE.NAMES = FALSE, ...)
    9.           └─mlr3 (local) FUN(X[[i]], ...)
   10.             └─m$score(prediction = self, task = task, learner = learner, train_set = train_set)
   11.               └─mlr3:::.__Measure__score(...)
   12.                 └─mlr3:::score_single_measure(self, task, learner, train_set, prediction)
   13.                   └─get_private(measure)$.score(...)
   14.                     └─mlr3fairness:::.__MeasureFairness__.score(...)
   15.                       └─mlr3fairness:::score_groupwise(...)
   16.                         └─mlr3misc::map_dbl(...)
   17.                           └─mlr3misc:::map_mold(.x, .f, NA_real_, ...)
   18.                             └─base::vapply(.x, .f, FUN.VALUE = .value, USE.NAMES = FALSE, ...)
   19.                               └─mlr3fairness (local) FUN(X[[i]], ...)
  ── Error ('test_measures.R:150:3'): fairness.fnr can be loaded and work as expected ──
  Error in `prediction$clone()$filter(rws)$score(base_measure, task = task, 
      ...)`: unused argument (weights = NULL)
  Backtrace:
       ▆
    1. ├─testthat::expect_lt(...) at test_measures.R:150:3
    2. │ └─testthat::quasi_label(enquo(object), label, arg = "object")
    3. │   └─rlang::eval_bare(expr, quo_get_env(quo))
    4. └─predictions$score(msr_obj, test_data)
    5.   └─mlr3:::.__Prediction__score(...)
    6.     └─mlr3misc::map_dbl(...)
    7.       └─mlr3misc:::map_mold(.x, .f, NA_real_, ...)
    8.         └─base::vapply(.x, .f, FUN.VALUE = .value, USE.NAMES = FALSE, ...)
    9.           └─mlr3 (local) FUN(X[[i]], ...)
   10.             └─m$score(prediction = self, task = task, learner = learner, train_set = train_set)
   11.               └─mlr3:::.__Measure__score(...)
   12.                 └─mlr3:::score_single_measure(self, task, learner, train_set, prediction)
   13.                   └─get_private(measure)$.score(...)
   14.                     └─mlr3fairness:::.__MeasureFairness__.score(...)
   15.                       └─mlr3fairness:::score_groupwise(...)
   16.                         └─mlr3misc::map_dbl(...)
   17.                           └─mlr3misc:::map_mold(.x, .f, NA_real_, ...)
   18.                             └─base::vapply(.x, .f, FUN.VALUE = .value, USE.NAMES = FALSE, ...)
   19.                               └─mlr3fairness (local) FUN(X[[i]], ...)
  ── Error ('test_measures.R:155:3'): fairness.tpr can be loaded and work as expected ──
  Error in `prediction$clone()$filter(rws)$score(base_measure, task = task, 
      ...)`: unused argument (weights = NULL)
  Backtrace:
       ▆
    1. ├─testthat::expect_lt(...) at test_measures.R:155:3
    2. │ └─testthat::quasi_label(enquo(object), label, arg = "object")
    3. │   └─rlang::eval_bare(expr, quo_get_env(quo))
    4. └─predictions$score(msr_obj, test_data)
    5.   └─mlr3:::.__Prediction__score(...)
    6.     └─mlr3misc::map_dbl(...)
    7.       └─mlr3misc:::map_mold(.x, .f, NA_real_, ...)
    8.         └─base::vapply(.x, .f, FUN.VALUE = .value, USE.NAMES = FALSE, ...)
    9.           └─mlr3 (local) FUN(X[[i]], ...)
   10.             └─m$score(prediction = self, task = task, learner = learner, train_set = train_set)
   11.               └─mlr3:::.__Measure__score(...)
   12.                 └─mlr3:::score_single_measure(self, task, learner, train_set, prediction)
   13.                   └─get_private(measure)$.score(...)
   14.                     └─mlr3fairness:::.__MeasureFairness__.score(...)
   15.                       └─mlr3fairness:::score_groupwise(...)
   16.                         └─mlr3misc::map_dbl(...)
   17.                           └─mlr3misc:::map_mold(.x, .f, NA_real_, ...)
   18.                             └─base::vapply(.x, .f, FUN.VALUE = .value, USE.NAMES = FALSE, ...)
   19.                               └─mlr3fairness (local) FUN(X[[i]], ...)
  ── Error ('test_measures.R:160:3'): fairness.ppv can be loaded and work as expected ──
  Error in `prediction$clone()$filter(rws)$score(base_measure, task = task, 
      ...)`: unused argument (weights = NULL)
  Backtrace:
       ▆
    1. ├─testthat::expect_lt(...) at test_measures.R:160:3
    2. │ └─testthat::quasi_label(enquo(object), label, arg = "object")
    3. │   └─rlang::eval_bare(expr, quo_get_env(quo))
    4. └─predictions$score(msr_obj, test_data)
    5.   └─mlr3:::.__Prediction__score(...)
    6.     └─mlr3misc::map_dbl(...)
    7.       └─mlr3misc:::map_mold(.x, .f, NA_real_, ...)
    8.         └─base::vapply(.x, .f, FUN.VALUE = .value, USE.NAMES = FALSE, ...)
    9.           └─mlr3 (local) FUN(X[[i]], ...)
   10.             └─m$score(prediction = self, task = task, learner = learner, train_set = train_set)
   11.               └─mlr3:::.__Measure__score(...)
   12.                 └─mlr3:::score_single_measure(self, task, learner, train_set, prediction)
   13.                   └─get_private(measure)$.score(...)
   14.                     └─mlr3fairness:::.__MeasureFairness__.score(...)
   15.                       └─mlr3fairness:::score_groupwise(...)
   16.                         └─mlr3misc::map_dbl(...)
   17.                           └─mlr3misc:::map_mold(.x, .f, NA_real_, ...)
   18.                             └─base::vapply(.x, .f, FUN.VALUE = .value, USE.NAMES = FALSE, ...)
   19.                               └─mlr3fairness (local) FUN(X[[i]], ...)
  ── Error ('test_measures.R:165:3'): fairness.npv can be loaded and work as expected ──
  Error in `prediction$clone()$filter(rws)$score(base_measure, task = task, 
      ...)`: unused argument (weights = NULL)
  Backtrace:
       ▆
    1. ├─testthat::expect_lt(...) at test_measures.R:165:3
    2. │ └─testthat::quasi_label(enquo(object), label, arg = "object")
    3. │   └─rlang::eval_bare(expr, quo_get_env(quo))
    4. └─predictions$score(msr_obj, test_data)
    5.   └─mlr3:::.__Prediction__score(...)
    6.     └─mlr3misc::map_dbl(...)
    7.       └─mlr3misc:::map_mold(.x, .f, NA_real_, ...)
    8.         └─base::vapply(.x, .f, FUN.VALUE = .value, USE.NAMES = FALSE, ...)
    9.           └─mlr3 (local) FUN(X[[i]], ...)
   10.             └─m$score(prediction = self, task = task, learner = learner, train_set = train_set)
   11.               └─mlr3:::.__Measure__score(...)
   12.                 └─mlr3:::score_single_measure(self, task, learner, train_set, prediction)
   13.                   └─get_private(measure)$.score(...)
   14.                     └─mlr3fairness:::.__MeasureFairness__.score(...)
   15.                       └─mlr3fairness:::score_groupwise(...)
   16.                         └─mlr3misc::map_dbl(...)
   17.                           └─mlr3misc:::map_mold(.x, .f, NA_real_, ...)
   18.                             └─base::vapply(.x, .f, FUN.VALUE = .value, USE.NAMES = FALSE, ...)
   19.                               └─mlr3fairness (local) FUN(X[[i]], ...)
  ── Error ('test_measures.R:170:3'): fairness.fp can be loaded and work as expected ──
  Error in `prediction$clone()$filter(rws)$score(base_measure, task = task, 
      ...)`: unused argument (weights = NULL)
  Backtrace:
       ▆
    1. ├─testthat::expect_equal(...) at test_measures.R:170:3
    2. │ └─testthat::quasi_label(enquo(object), label, arg = "object")
    3. │   └─rlang::eval_bare(expr, quo_get_env(quo))
    4. ├─base::unname(predictions$score(msr_obj, test_data))
    5. └─predictions$score(msr_obj, test_data)
    6.   └─mlr3:::.__Prediction__score(...)
    7.     └─mlr3misc::map_dbl(...)
    8.       └─mlr3misc:::map_mold(.x, .f, NA_real_, ...)
    9.         └─base::vapply(.x, .f, FUN.VALUE = .value, USE.NAMES = FALSE, ...)
   10.           └─mlr3 (local) FUN(X[[i]], ...)
   11.             └─m$score(prediction = self, task = task, learner = learner, train_set = train_set)
   12.               └─mlr3:::.__Measure__score(...)
   13.                 └─mlr3:::score_single_measure(self, task, learner, train_set, prediction)
   14.                   └─get_private(measure)$.score(...)
   15.                     └─mlr3fairness:::.__MeasureFairness__.score(...)
   16.                       └─mlr3fairness:::score_groupwise(...)
   17.                         └─mlr3misc::map_dbl(...)
   18.                           └─mlr3misc:::map_mold(.x, .f, NA_real_, ...)
   19.                             └─base::vapply(.x, .f, FUN.VALUE = .value, USE.NAMES = FALSE, ...)
   20.                               └─mlr3fairness (local) FUN(X[[i]], ...)
  ── Error ('test_measures.R:175:3'): fairness.fn can be loaded and work as expected ──
  Error in `prediction$clone()$filter(rws)$score(base_measure, task = task, 
      ...)`: unused argument (weights = NULL)
  Backtrace:
       ▆
    1. ├─testthat::expect_equal(...) at test_measures.R:175:3
    2. │ └─testthat::quasi_label(enquo(object), label, arg = "object")
    3. │   └─rlang::eval_bare(expr, quo_get_env(quo))
    4. ├─base::unname(predictions$score(msr_obj, test_data))
    5. └─predictions$score(msr_obj, test_data)
    6.   └─mlr3:::.__Prediction__score(...)
    7.     └─mlr3misc::map_dbl(...)
    8.       └─mlr3misc:::map_mold(.x, .f, NA_real_, ...)
    9.         └─base::vapply(.x, .f, FUN.VALUE = .value, USE.NAMES = FALSE, ...)
   10.           └─mlr3 (local) FUN(X[[i]], ...)
   11.             └─m$score(prediction = self, task = task, learner = learner, train_set = train_set)
   12.               └─mlr3:::.__Measure__score(...)
   13.                 └─mlr3:::score_single_measure(self, task, learner, train_set, prediction)
   14.                   └─get_private(measure)$.score(...)
   15.                     └─mlr3fairness:::.__MeasureFairness__.score(...)
   16.                       └─mlr3fairness:::score_groupwise(...)
   17.                         └─mlr3misc::map_dbl(...)
   18.                           └─mlr3misc:::map_mold(.x, .f, NA_real_, ...)
   19.                             └─base::vapply(.x, .f, FUN.VALUE = .value, USE.NAMES = FALSE, ...)
   20.                               └─mlr3fairness (local) FUN(X[[i]], ...)
  ── Error ('test_measures.R:180:3'): fairness.pp (disparate impact score) can be loaded and work as expected ──
  Error in `prediction$clone()$filter(rws)$score(base_measure, task = task, 
      ...)`: unused argument (weights = NULL)
  Backtrace:
       ▆
    1. ├─testthat::expect_equal(...) at test_measures.R:180:3
    2. │ └─testthat::quasi_label(enquo(object), label, arg = "object")
    3. │   └─rlang::eval_bare(expr, quo_get_env(quo))
    4. ├─base::unname(predictions$score(msr_obj, test_data))
    5. └─predictions$score(msr_obj, test_data)
    6.   └─mlr3:::.__Prediction__score(...)
    7.     └─mlr3misc::map_dbl(...)
    8.       └─mlr3misc:::map_mold(.x, .f, NA_real_, ...)
    9.         └─base::vapply(.x, .f, FUN.VALUE = .value, USE.NAMES = FALSE, ...)
   10.           └─mlr3 (local) FUN(X[[i]], ...)
   11.             └─m$score(prediction = self, task = task, learner = learner, train_set = train_set)
   12.               └─mlr3:::.__Measure__score(...)
   13.                 └─mlr3:::score_single_measure(self, task, learner, train_set, prediction)
   14.                   └─get_private(measure)$.score(...)
   15.                     └─mlr3fairness:::.__MeasureFairness__.score(...)
   16.                       └─mlr3fairness:::score_groupwise(...)
   17.                         └─mlr3misc::map_dbl(...)
   18.                           └─mlr3misc:::map_mold(.x, .f, NA_real_, ...)
   19.                             └─base::vapply(.x, .f, FUN.VALUE = .value, USE.NAMES = FALSE, ...)
   20.                               └─mlr3fairness (local) FUN(X[[i]], ...)
  ── Error ('test_measures.R:194:7'): fairness constraint measures - simulated data ──
  Error in `prediction$clone()$filter(rws)$score(base_measure, task = task, 
      ...)`: unused argument (weights = NULL)
  Backtrace:
       ▆
    1. └─mlr3misc::map(...) at test_measures.R:192:3
    2.   └─base::lapply(.x, .f, ...)
    3.     └─mlr3fairness (local) FUN(X[[i]], ...)
    4.       └─mlr3misc::map_dbl(...) at test_measures.R:193:5
    5.         └─mlr3misc:::map_mold(.x, .f, NA_real_, ...)
    6.           └─base::vapply(.x, .f, FUN.VALUE = .value, USE.NAMES = FALSE, ...)
    7.             └─mlr3fairness (local) FUN(X[[i]], ...)
    8.               └─prd$score(measures = msr(m), task = tsk) at test_measures.R:194:7
    9.                 └─mlr3:::.__Prediction__score(...)
   10.                   └─mlr3misc::map_dbl(...)
   11.                     └─mlr3misc:::map_mold(.x, .f, NA_real_, ...)
   12.                       └─base::vapply(.x, .f, FUN.VALUE = .value, USE.NAMES = FALSE, ...)
   13.                         └─mlr3 (local) FUN(X[[i]], ...)
   14.                           └─m$score(prediction = self, task = task, learner = learner, train_set = train_set)
   15.                             └─mlr3:::.__Measure__score(...)
   16.                               └─mlr3:::score_single_measure(self, task, learner, train_set, prediction)
   17.                                 └─get_private(measure)$.score(...)
   18.                                   └─mlr3fairness:::.__MeasureFairness__.score(...)
   19.                                     └─mlr3fairness:::score_groupwise(...)
   20.                                       └─mlr3misc::map_dbl(...)
   21.                                         └─mlr3misc:::map_mold(.x, .f, NA_real_, ...)
   22.                                           └─base::vapply(.x, .f, FUN.VALUE = .value, USE.NAMES = FALSE, ...)
   23.                                             └─mlr3fairness (local) FUN(X[[i]], ...)
  ── Error ('test_measures.R:249:3'): Args are passed on correctly ───────────────
  Error in `prediction$clone()$filter(rws)$score(base_measure, task = task, 
      ...)`: unused argument (weights = NULL)
  Backtrace:
       ▆
    1. └─prd$score(mfa, task = t, train_set = 1:10) at test_measures.R:249:3
    2.   └─mlr3:::.__Prediction__score(...)
    3.     └─mlr3misc::map_dbl(...)
    4.       └─mlr3misc:::map_mold(.x, .f, NA_real_, ...)
    5.         └─base::vapply(.x, .f, FUN.VALUE = .value, USE.NAMES = FALSE, ...)
    6.           └─mlr3 (local) FUN(X[[i]], ...)
    7.             └─m$score(prediction = self, task = task, learner = learner, train_set = train_set)
    8.               └─mlr3:::.__Measure__score(...)
    9.                 └─mlr3:::score_single_measure(self, task, learner, train_set, prediction)
   10.                   └─get_private(measure)$.score(...)
   11.                     └─mlr3fairness:::.__MeasureFairness__.score(...)
   12.                       └─mlr3fairness:::score_groupwise(...)
   13.                         └─mlr3misc::map_dbl(...)
   14.                           └─mlr3misc:::map_mold(.x, .f, NA_real_, ...)
   15.                             └─base::vapply(.x, .f, FUN.VALUE = .value, USE.NAMES = FALSE, ...)
   16.                               └─mlr3fairness (local) FUN(X[[i]], ...)
  ── Error ('test_measures.R:271:11'): fairness measures work as expected - simulated data ──
  Error in `prediction$clone()$filter(rws)$score(base_measure, task = task, 
      ...)`: unused argument (weights = NULL)
  Backtrace:
       ▆
    1. └─prd$score(measures = ms, task = tsk) at test_measures.R:271:11
    2.   └─mlr3:::.__Prediction__score(...)
    3.     └─mlr3misc::map_dbl(...)
    4.       └─mlr3misc:::map_mold(.x, .f, NA_real_, ...)
    5.         └─base::vapply(.x, .f, FUN.VALUE = .value, USE.NAMES = FALSE, ...)
    6.           └─mlr3 (local) FUN(X[[i]], ...)
    7.             └─m$score(prediction = self, task = task, learner = learner, train_set = train_set)
    8.               └─mlr3:::.__Measure__score(...)
    9.                 └─mlr3:::score_single_measure(self, task, learner, train_set, prediction)
   10.                   └─get_private(measure)$.score(...)
   11.                     └─mlr3fairness:::.__MeasureFairness__.score(...)
   12.                       └─mlr3fairness:::score_groupwise(...)
   13.                         └─mlr3misc::map_dbl(...)
   14.                           └─mlr3misc:::map_mold(.x, .f, NA_real_, ...)
   15.                             └─base::vapply(.x, .f, FUN.VALUE = .value, USE.NAMES = FALSE, ...)
   16.                               └─mlr3fairness (local) FUN(X[[i]], ...)
  ── Error ('test_visualizations.R:10:5'): fairness_accuracy_tradeoff ────────────
  Error in `prediction$clone()$filter(rws)$score(base_measure, task = task, 
      ...)`: unused argument (weights = NULL)
  Backtrace:
       ▆
    1. └─mlr3misc::map(...) at test_visualizations.R:9:3
    2.   └─base::lapply(.x, .f, ...)
    3.     └─mlr3fairness (local) FUN(X[[i]], ...)
    4.       ├─mlr3fairness:::check_plots(fairness_accuracy_tradeoff(bmr, fmsr)) at test_visualizations.R:10:5
    5.       │ ├─testthat::expect_true(is.ggplot(ggplot_obj)) at tests/testthat/helper_test.R:4:3
    6.       │ │ └─testthat::quasi_label(enquo(object), label, arg = "object")
    7.       │ │   └─rlang::eval_bare(expr, quo_get_env(quo))
    8.       │ └─ggplot2::is.ggplot(ggplot_obj)
    9.       │   └─ggplot2::is_ggplot(x)
   10.       ├─mlr3fairness::fairness_accuracy_tradeoff(bmr, fmsr)
   11.       └─mlr3fairness:::fairness_accuracy_tradeoff.BenchmarkResult(...)
   12.         └─object$aggregate(list(acc_measure, fairness_measure))
   13.           └─mlr3:::.__BenchmarkResult__aggregate(...)
   14.             └─mlr3misc::map_dtr(...)
   15.               ├─data.table::rbindlist(...)
   16.               ├─base::unname(map(.x, .f, ...))
   17.               └─mlr3misc::map(.x, .f, ...)
   18.                 └─base::lapply(.x, .f, ...)
   19.                   └─mlr3 (local) FUN(X[[i]], ...)
   20.                     ├─base::as.list(resample_result_aggregate(rr, measures))
   21.                     └─mlr3:::resample_result_aggregate(rr, measures)
   22.                       ├─... %??% set_names(numeric(), character())
   23.                       ├─base::unlist(...)
   24.                       └─mlr3misc::map(...)
   25.                         └─base::lapply(.x, .f, ...)
   26.                           └─mlr3 (local) FUN(X[[i]], ...)
   27.                             └─m$aggregate(rr)
   28.                               └─mlr3:::.__Measure__aggregate(...)
   29.                                 └─mlr3:::score_measures(...)
   30.                                   └─mlr3misc::pmap_dbl(...)
   31.                                     └─mlr3misc:::mapply_list(.f, .x, list(...))
   32.                                       └─base::.mapply(.f, .dots, .args)
   33.                                         └─mlr3 (local) `<fn>`(...)
   34.                                           └─mlr3:::score_single_measure(...)
   35.                                             └─get_private(measure)$.score(...)
   36.                                               └─mlr3fairness:::.__MeasureFairness__.score(...)
   37.                                                 └─mlr3fairness:::score_groupwise(...)
   38.                                                   └─mlr3misc::map_dbl(...)
   39.                                                     └─mlr3misc:::map_mold(.x, .f, NA_real_, ...)
   40.                                                       └─base::vapply(.x, .f, FUN.VALUE = .value, USE.NAMES = FALSE, ...)
   41.                                                         └─mlr3fairness (local) FUN(X[[i]], ...)
  ── Error ('test_visualizations.R:33:3'): compare_metrics ───────────────────────
  Error in `prediction$clone()$filter(rws)$score(base_measure, task = task, 
      ...)`: unused argument (weights = NULL)
  Backtrace:
       ▆
    1. ├─mlr3fairness:::check_plots(compare_metrics(bmr, fairness_measures)) at test_visualizations.R:33:3
    2. │ ├─testthat::expect_true(is.ggplot(ggplot_obj)) at tests/testthat/helper_test.R:4:3
    3. │ │ └─testthat::quasi_label(enquo(object), label, arg = "object")
    4. │ │   └─rlang::eval_bare(expr, quo_get_env(quo))
    5. │ └─ggplot2::is.ggplot(ggplot_obj)
    6. │   └─ggplot2::is_ggplot(x)
    7. ├─mlr3fairness::compare_metrics(bmr, fairness_measures)
    8. └─mlr3fairness:::compare_metrics.BenchmarkResult(bmr, fairness_measures)
    9.   └─object$aggregate(measures, ...)
   10.     └─mlr3:::.__BenchmarkResult__aggregate(...)
   11.       └─mlr3misc::map_dtr(...)
   12.         ├─data.table::rbindlist(...)
   13.         ├─base::unname(map(.x, .f, ...))
   14.         └─mlr3misc::map(.x, .f, ...)
   15.           └─base::lapply(.x, .f, ...)
   16.             └─mlr3 (local) FUN(X[[i]], ...)
   17.               ├─base::as.list(resample_result_aggregate(rr, measures))
   18.               └─mlr3:::resample_result_aggregate(rr, measures)
   19.                 ├─... %??% set_names(numeric(), character())
   20.                 ├─base::unlist(...)
   21.                 └─mlr3misc::map(...)
   22.                   └─base::lapply(.x, .f, ...)
   23.                     └─mlr3 (local) FUN(X[[i]], ...)
   24.                       └─m$aggregate(rr)
   25.                         └─mlr3:::.__Measure__aggregate(...)
   26.                           └─mlr3:::score_measures(...)
   27.                             └─mlr3misc::pmap_dbl(...)
   28.                               └─mlr3misc:::mapply_list(.f, .x, list(...))
   29.                                 └─base::.mapply(.f, .dots, .args)
   30.                                   └─mlr3 (local) `<fn>`(...)
   31.                                     └─mlr3:::score_single_measure(...)
   32.                                       └─get_private(measure)$.score(...)
   33.                                         └─mlr3fairness:::.__MeasureFairness__.score(...)
   34.                                           └─mlr3fairness:::score_groupwise(...)
   35.                                             └─mlr3misc::map_dbl(...)
   36.                                               └─mlr3misc:::map_mold(.x, .f, NA_real_, ...)
   37.                                                 └─base::vapply(.x, .f, FUN.VALUE = .value, USE.NAMES = FALSE, ...)
   38.                                                   └─mlr3fairness (local) FUN(X[[i]], ...)
  
  [ FAIL 21 | WARN 14 | SKIP 30 | PASS 99 ]
  Error: Test failures
  Execution halted