Last updated on 2025-06-23 03:51:14 CEST.
| Flavor | Version | Tinstall | Tcheck | Ttotal | Status | Flags |
|---|---|---|---|---|---|---|
| r-devel-linux-x86_64-debian-clang | 0.3.2 | 15.91 | 183.16 | 199.07 | ERROR | |
| r-devel-linux-x86_64-debian-gcc | 0.3.2 | 9.62 | 119.26 | 128.88 | ERROR | |
| r-devel-linux-x86_64-fedora-clang | 0.3.2 | 292.20 | ERROR | |||
| r-devel-linux-x86_64-fedora-gcc | 0.3.2 | 265.83 | ERROR | |||
| r-devel-windows-x86_64 | 0.3.2 | 15.00 | 155.00 | 170.00 | ERROR | |
| r-patched-linux-x86_64 | 0.3.2 | 13.03 | 278.37 | 291.40 | ERROR | |
| r-release-linux-x86_64 | 0.3.2 | 10.53 | 151.17 | 161.70 | ERROR | |
| r-release-macos-arm64 | 0.3.2 | 156.00 | NOTE | |||
| r-release-macos-x86_64 | 0.3.2 | 277.00 | NOTE | |||
| r-release-windows-x86_64 | 0.3.2 | 15.00 | 152.00 | 167.00 | ERROR | |
| r-oldrel-macos-arm64 | 0.3.2 | 137.00 | NOTE | |||
| r-oldrel-macos-x86_64 | 0.3.2 | 214.00 | NOTE | |||
| r-oldrel-windows-x86_64 | 0.3.2 | 21.00 | 209.00 | 230.00 | ERROR |
Version: 0.3.2
Check: Rd files
Result: NOTE
checkRd: (-1) groupdiff_tau.Rd:23: Lost braces
23 | \code{groupdiff_tau()} computes \eqn{min(x/y, y/x)}, i.e. the smallest symmetric ratio between \eqn{x} and eqn{y}
| ^
Flavors: r-devel-linux-x86_64-debian-clang, r-devel-linux-x86_64-debian-gcc, r-devel-linux-x86_64-fedora-clang, r-devel-linux-x86_64-fedora-gcc, r-devel-windows-x86_64, r-patched-linux-x86_64, r-release-linux-x86_64, r-release-macos-arm64, r-release-macos-x86_64, r-release-windows-x86_64, r-oldrel-macos-arm64, r-oldrel-macos-x86_64, r-oldrel-windows-x86_64
Version: 0.3.2
Check: Rd cross-references
Result: NOTE
Found the following Rd file(s) with Rd \link{} targets missing package
anchors:
MeasureFairness.Rd: mlr_measures_classif.ce,
mlr_measures_classif.fpr, mlr_measures, Task
MeasureFairnessComposite.Rd: Task
MeasureFairnessConstraint.Rd: Task
MeasureSubgroup.Rd: mlr_measures_classif.fpr
compas.Rd: TaskClassif
compute_metrics.Rd: Task
fairness_accuracy_tradeoff.Rd: PredictionClassif, BenchmarkResult,
ResampleResult, Task, Measure, TaskClassif
fairness_compare_metrics.Rd: PredictionClassif, BenchmarkResult,
ResampleResult, Measure, TaskClassif, Task
fairness_prediction_density.Rd: PredictionClassif, ResampleResult,
BenchmarkResult, Task, TaskClassif
fairness_tensor.Rd: data.table, PredictionClassif, ResampleResult,
TaskClassif, Task
groupdiff_tau.Rd: Task
groupwise_metrics.Rd: Task
mlr_learners_classif.fairfgrrm.Rd: Learner, mlr_learners, lrn
mlr_learners_classif.fairzlrm.Rd: Learner, mlr_learners, lrn
mlr_learners_fairness.Rd: Task
mlr_learners_regr.fairfrrm.Rd: Learner, mlr_learners, lrn
mlr_learners_regr.fairnclm.Rd: Learner, mlr_learners, lrn
mlr_learners_regr.fairzlm.Rd: Learner, mlr_learners, lrn
mlr_measures_fairness.Rd: Task
mlr_pipeops_equalized_odds.Rd: R6Class, PipeOpTaskPreproc, PipeOp
mlr_pipeops_explicit_pta.Rd: R6Class, PipeOpTaskPreproc, PipeOp
mlr_pipeops_reweighing.Rd: R6Class, PipeOpTaskPreproc, PipeOp
report_fairness.Rd: Task
task_summary.Rd: Task
Please provide package anchors for all Rd \link{} targets not in the
package itself and the base packages.
Flavors: r-devel-linux-x86_64-debian-clang, r-devel-linux-x86_64-debian-gcc, r-devel-windows-x86_64, r-patched-linux-x86_64, r-release-linux-x86_64, r-release-windows-x86_64
Version: 0.3.2
Check: examples
Result: ERROR
Running examples in ‘mlr3fairness-Ex.R’ failed
The error most likely occurred in:
> base::assign(".ptime", proc.time(), pos = "CheckExEnv")
> ### Name: MeasureFairness
> ### Title: Base Measure for Fairness
> ### Aliases: MeasureFairness
>
> ### ** Examples
>
> library("mlr3")
> # Create MeasureFairness to measure the Predictive Parity.
> t = tsk("adult_train")
> learner = lrn("classif.rpart", cp = .01)
> learner$train(t)
> measure = msr("fairness", base_measure = msr("classif.ppv"))
> predictions = learner$predict(t)
> predictions$score(measure, task = t)
Error in prediction$clone()$filter(rws)$score(base_measure, task = task, :
unused argument (weights = NULL)
Calls: <Anonymous> ... score_groupwise -> map_dbl -> map_mold -> vapply -> FUN
Execution halted
Flavors: r-devel-linux-x86_64-debian-clang, r-devel-linux-x86_64-debian-gcc, r-release-linux-x86_64
Version: 0.3.2
Check: tests
Result: ERROR
Running ‘testthat.R’ [19s/22s]
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 [03:15:42.446] [mlr3] Running benchmark with 12 resampling iterations
INFO [03:15:42.662] [mlr3] Applying learner 'classif.rpart' on task 'adult_train' (iter 1/3)
INFO [03:15:42.736] [mlr3] Applying learner 'classif.rpart' on task 'adult_train' (iter 2/3)
INFO [03:15:42.797] [mlr3] Applying learner 'classif.rpart' on task 'adult_train' (iter 3/3)
INFO [03:15:42.843] [mlr3] Applying learner 'classif.featureless' on task 'adult_train' (iter 1/3)
INFO [03:15:42.875] [mlr3] Applying learner 'classif.featureless' on task 'adult_train' (iter 2/3)
INFO [03:15:42.915] [mlr3] Applying learner 'classif.featureless' on task 'adult_train' (iter 3/3)
INFO [03:15:42.957] [mlr3] Applying learner 'classif.rpart' on task 'compas' (iter 1/3)
INFO [03:15:43.002] [mlr3] Applying learner 'classif.rpart' on task 'compas' (iter 2/3)
INFO [03:15:43.046] [mlr3] Applying learner 'classif.rpart' on task 'compas' (iter 3/3)
INFO [03:15:43.089] [mlr3] Applying learner 'classif.featureless' on task 'compas' (iter 1/3)
INFO [03:15:43.129] [mlr3] Applying learner 'classif.featureless' on task 'compas' (iter 2/3)
INFO [03:15:43.174] [mlr3] Applying learner 'classif.featureless' on task 'compas' (iter 3/3)
INFO [03:15:43.253] [mlr3] Finished benchmark
INFO [03:15:43.650] [mlr3] Running benchmark with 12 resampling iterations
INFO [03:15:43.698] [mlr3] Applying learner 'classif.rpart' on task 'adult_train' (iter 1/3)
INFO [03:15:43.750] [mlr3] Applying learner 'classif.rpart' on task 'adult_train' (iter 2/3)
INFO [03:15:43.806] [mlr3] Applying learner 'classif.rpart' on task 'adult_train' (iter 3/3)
INFO [03:15:43.852] [mlr3] Applying learner 'classif.featureless' on task 'adult_train' (iter 1/3)
INFO [03:15:43.891] [mlr3] Applying learner 'classif.featureless' on task 'adult_train' (iter 2/3)
INFO [03:15:43.962] [mlr3] Applying learner 'classif.featureless' on task 'adult_train' (iter 3/3)
INFO [03:15:44.031] [mlr3] Applying learner 'classif.rpart' on task 'compas' (iter 1/3)
INFO [03:15:44.095] [mlr3] Applying learner 'classif.rpart' on task 'compas' (iter 2/3)
INFO [03:15:44.140] [mlr3] Applying learner 'classif.rpart' on task 'compas' (iter 3/3)
INFO [03:15:44.183] [mlr3] Applying learner 'classif.featureless' on task 'compas' (iter 1/3)
INFO [03:15:44.216] [mlr3] Applying learner 'classif.featureless' on task 'compas' (iter 2/3)
INFO [03:15:44.252] [mlr3] Applying learner 'classif.featureless' on task 'compas' (iter 3/3)
INFO [03:15:44.287] [mlr3] Finished benchmark
INFO [03:15:44.538] [mlr3] Running benchmark with 12 resampling iterations
INFO [03:15:44.557] [mlr3] Applying learner 'classif.rpart' on task 'adult_train' (iter 1/3)
INFO [03:15:44.601] [mlr3] Applying learner 'classif.rpart' on task 'adult_train' (iter 2/3)
INFO [03:15:44.646] [mlr3] Applying learner 'classif.rpart' on task 'adult_train' (iter 3/3)
INFO [03:15:44.691] [mlr3] Applying learner 'classif.featureless' on task 'adult_train' (iter 1/3)
INFO [03:15:44.734] [mlr3] Applying learner 'classif.featureless' on task 'adult_train' (iter 2/3)
INFO [03:15:44.767] [mlr3] Applying learner 'classif.featureless' on task 'adult_train' (iter 3/3)
INFO [03:15:44.829] [mlr3] Applying learner 'classif.rpart' on task 'compas' (iter 1/3)
INFO [03:15:44.872] [mlr3] Applying learner 'classif.rpart' on task 'compas' (iter 2/3)
INFO [03:15:44.914] [mlr3] Applying learner 'classif.rpart' on task 'compas' (iter 3/3)
INFO [03:15:44.955] [mlr3] Applying learner 'classif.featureless' on task 'compas' (iter 1/3)
INFO [03:15:44.992] [mlr3] Applying learner 'classif.featureless' on task 'compas' (iter 2/3)
INFO [03:15:45.028] [mlr3] Applying learner 'classif.featureless' on task 'compas' (iter 3/3)
INFO [03:15:45.068] [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
Flavor: r-devel-linux-x86_64-debian-clang
Version: 0.3.2
Check: re-building of vignette outputs
Result: ERROR
Error(s) in re-building vignettes:
...
--- re-building ‘debiasing-vignette.Rmd’ using rmarkdown
Quitting from debiasing-vignette.Rmd:55-57 [unnamed-chunk-4]
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
<error/rlang_error>
Error in `task_set_roles()`:
! Assertion on 'roles' failed: Must be a subset of {'feature','target','name','order','stratum','group','offset','weights_learner','weights_measure','pta'}, but has additional elements {'weight'}.
This happened PipeOp reweighing_wts's $train()
---
Backtrace:
▆
1. ├─p1$train(list(task))
2. │ └─mlr3pipelines:::.__PipeOp__train(...)
3. │ ├─base::withCallingHandlers(...)
4. │ └─private$.train(input)
5. │ └─mlr3pipelines:::.__PipeOpTaskPreproc__.train(...)
6. │ └─private$.train_task(intask)
7. │ └─mlr3fairness:::.__PipeOpReweighingWeights__.train_task(...)
8. │ └─task$set_col_roles(weightcolname, "weight")
9. │ └─mlr3:::.__Task__set_col_roles(...)
10. │ └─mlr3:::task_set_roles(...)
11. │ └─checkmate::assert_subset(roles, names(li))
12. │ └─checkmate::makeAssertion(x, res, .var.name, add)
13. │ └─checkmate:::mstop(...)
14. │ └─base::stop(simpleError(sprintf(msg, ...), call.))
15. └─mlr3pipelines (local) `<fn>`(`<smplErrr>`)
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
Error: processing vignette 'debiasing-vignette.Rmd' failed with diagnostics:
Assertion on 'roles' failed: Must be a subset of {'feature','target','name','order','stratum','group','offset','weights_learner','weights_measure','pta'}, but has additional elements {'weight'}.
This happened PipeOp reweighing_wts's $train()
--- failed re-building ‘debiasing-vignette.Rmd’
--- re-building ‘measures-vignette.Rmd’ using rmarkdown
Quitting from measures-vignette.Rmd:88-90 [unnamed-chunk-6]
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
<error/rlang_error>
Error:
! unused argument (weights = NULL)
---
Backtrace:
▆
1. └─prd$score(msr("fairness.tpr"), task = test)
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: processing vignette 'measures-vignette.Rmd' failed with diagnostics:
unused argument (weights = NULL)
--- failed re-building ‘measures-vignette.Rmd’
--- re-building ‘reports-vignette.Rmd’ using rmarkdown
Quitting from reports-vignette.Rmd:51-54 [build_modelcard_example_for_vignette]
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
<error/rlang_error>
Error in `loadNamespace()`:
! there is no package called 'posterdown'
---
Backtrace:
▆
1. ├─rmarkdown::render(rmdfile)
2. │ └─rmarkdown:::create_output_format(output_format$name, output_format$options)
3. │ └─rmarkdown:::create_output_format_function(name)
4. │ └─base::eval(xfun::parse_only(name))
5. │ └─base::eval(xfun::parse_only(name))
6. └─base::loadNamespace(x)
7. └─base::withRestarts(stop(cond), retry_loadNamespace = function() NULL)
8. └─base (local) withOneRestart(expr, restarts[[1L]])
9. └─base (local) doWithOneRestart(return(expr), restart)
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
Error: processing vignette 'reports-vignette.Rmd' failed with diagnostics:
there is no package called 'posterdown'
--- failed re-building ‘reports-vignette.Rmd’
--- re-building ‘visualization-vignette.Rmd’ using rmarkdown
** Processing: /home/hornik/tmp/R.check/r-devel-clang/Work/PKGS/mlr3fairness.Rcheck/vign_test/mlr3fairness/vignettes/visualization-vignette_files/figure-html/unnamed-chunk-6-1.png
288x288 pixels, 3x8 bits/pixel, RGB
Input IDAT size = 15540 bytes
Input file size = 15630 bytes
Trying:
zc = 9 zm = 8 zs = 0 f = 0 IDAT size = 12393
zc = 9 zm = 8 zs = 1 f = 0
zc = 1 zm = 8 zs = 2 f = 0
zc = 9 zm = 8 zs = 3 f = 0
zc = 9 zm = 8 zs = 0 f = 5
zc = 9 zm = 8 zs = 1 f = 5
zc = 1 zm = 8 zs = 2 f = 5
zc = 9 zm = 8 zs = 3 f = 5
Selecting parameters:
zc = 9 zm = 8 zs = 0 f = 0 IDAT size = 12393
Output IDAT size = 12393 bytes (3147 bytes decrease)
Output file size = 12471 bytes (3159 bytes = 20.21% decrease)
** Processing: /home/hornik/tmp/R.check/r-devel-clang/Work/PKGS/mlr3fairness.Rcheck/vign_test/mlr3fairness/vignettes/visualization-vignette_files/figure-html/unnamed-chunk-7-1.png
288x288 pixels, 3x8 bits/pixel, RGB
Input IDAT size = 17272 bytes
Input file size = 17374 bytes
Trying:
zc = 9 zm = 8 zs = 0 f = 0 IDAT size = 13068
zc = 9 zm = 8 zs = 1 f = 0
zc = 1 zm = 8 zs = 2 f = 0
zc = 9 zm = 8 zs = 3 f = 0
zc = 9 zm = 8 zs = 0 f = 5
zc = 9 zm = 8 zs = 1 f = 5
zc = 1 zm = 8 zs = 2 f = 5
zc = 9 zm = 8 zs = 3 f = 5
Selecting parameters:
zc = 9 zm = 8 zs = 0 f = 0 IDAT size = 13068
Output IDAT size = 13068 bytes (4204 bytes decrease)
Output file size = 13146 bytes (4228 bytes = 24.34% decrease)
Quitting from visualization-vignette.Rmd:88-90 [unnamed-chunk-8]
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
<error/rlang_error>
Error:
! unused argument (weights = NULL)
---
Backtrace:
▆
1. ├─mlr3fairness::fairness_accuracy_tradeoff(bmr, msr("fairness.fpr"))
2. └─mlr3fairness:::fairness_accuracy_tradeoff.BenchmarkResult(...)
3. └─object$aggregate(list(acc_measure, fairness_measure))
4. └─mlr3:::.__BenchmarkResult__aggregate(...)
5. └─mlr3misc::map_dtr(...)
6. ├─data.table::rbindlist(...)
7. ├─base::unname(map(.x, .f, ...))
8. └─mlr3misc::map(.x, .f, ...)
9. └─base::lapply(.x, .f, ...)
10. └─mlr3 (local) FUN(X[[i]], ...)
11. ├─base::as.list(resample_result_aggregate(rr, measures))
12. └─mlr3:::resample_result_aggregate(rr, measures)
13. ├─... %??% set_names(numeric(), character())
14. ├─base::unlist(...)
15. └─mlr3misc::map(...)
16. └─base::lapply(.x, .f, ...)
17. └─mlr3 (local) FUN(X[[i]], ...)
18. └─m$aggregate(rr)
19. └─mlr3:::.__Measure__aggregate(...)
20. └─mlr3:::score_measures(...)
21. └─mlr3misc::pmap_dbl(...)
22. └─mlr3misc:::mapply_list(.f, .x, list(...))
23. └─base::.mapply(.f, .dots, .args)
24. └─mlr3 (local) `<fn>`(...)
25. └─mlr3:::score_single_measure(...)
26. └─get_private(measure)$.score(...)
27. └─mlr3fairness:::.__MeasureFairness__.score(...)
28. └─mlr3fairness:::score_groupwise(...)
29. └─mlr3misc::map_dbl(...)
30. └─mlr3misc:::map_mold(.x, .f, NA_real_, ...)
31. └─base::vapply(.x, .f, FUN.VALUE = .value, USE.NAMES = FALSE, ...)
32. └─mlr3fairness (local) FUN(X[[i]], ...)
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
Error: processing vignette 'visualization-vignette.Rmd' failed with diagnostics:
unused argument (weights = NULL)
--- failed re-building ‘visualization-vignette.Rmd’
SUMMARY: processing the following files failed:
‘debiasing-vignette.Rmd’ ‘measures-vignette.Rmd’
‘reports-vignette.Rmd’ ‘visualization-vignette.Rmd’
Error: Vignette re-building failed.
Execution halted
Flavor: r-devel-linux-x86_64-debian-clang
Version: 0.3.2
Check: tests
Result: ERROR
Running ‘testthat.R’ [12s/17s]
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 [16:52:05.595] [mlr3] Running benchmark with 12 resampling iterations
INFO [16:52:05.819] [mlr3] Applying learner 'classif.rpart' on task 'adult_train' (iter 1/3)
INFO [16:52:06.024] [mlr3] Applying learner 'classif.rpart' on task 'adult_train' (iter 2/3)
INFO [16:52:06.078] [mlr3] Applying learner 'classif.rpart' on task 'adult_train' (iter 3/3)
INFO [16:52:06.119] [mlr3] Applying learner 'classif.featureless' on task 'adult_train' (iter 1/3)
INFO [16:52:06.194] [mlr3] Applying learner 'classif.featureless' on task 'adult_train' (iter 2/3)
INFO [16:52:06.269] [mlr3] Applying learner 'classif.featureless' on task 'adult_train' (iter 3/3)
INFO [16:52:06.342] [mlr3] Applying learner 'classif.rpart' on task 'compas' (iter 1/3)
INFO [16:52:06.364] [mlr3] Applying learner 'classif.rpart' on task 'compas' (iter 2/3)
INFO [16:52:06.387] [mlr3] Applying learner 'classif.rpart' on task 'compas' (iter 3/3)
INFO [16:52:06.412] [mlr3] Applying learner 'classif.featureless' on task 'compas' (iter 1/3)
INFO [16:52:06.481] [mlr3] Applying learner 'classif.featureless' on task 'compas' (iter 2/3)
INFO [16:52:06.562] [mlr3] Applying learner 'classif.featureless' on task 'compas' (iter 3/3)
INFO [16:52:06.633] [mlr3] Finished benchmark
INFO [16:52:06.938] [mlr3] Running benchmark with 12 resampling iterations
INFO [16:52:06.967] [mlr3] Applying learner 'classif.rpart' on task 'adult_train' (iter 1/3)
INFO [16:52:06.998] [mlr3] Applying learner 'classif.rpart' on task 'adult_train' (iter 2/3)
INFO [16:52:07.061] [mlr3] Applying learner 'classif.rpart' on task 'adult_train' (iter 3/3)
INFO [16:52:07.134] [mlr3] Applying learner 'classif.featureless' on task 'adult_train' (iter 1/3)
INFO [16:52:07.181] [mlr3] Applying learner 'classif.featureless' on task 'adult_train' (iter 2/3)
INFO [16:52:07.203] [mlr3] Applying learner 'classif.featureless' on task 'adult_train' (iter 3/3)
INFO [16:52:07.268] [mlr3] Applying learner 'classif.rpart' on task 'compas' (iter 1/3)
INFO [16:52:07.296] [mlr3] Applying learner 'classif.rpart' on task 'compas' (iter 2/3)
INFO [16:52:07.325] [mlr3] Applying learner 'classif.rpart' on task 'compas' (iter 3/3)
INFO [16:52:07.353] [mlr3] Applying learner 'classif.featureless' on task 'compas' (iter 1/3)
INFO [16:52:07.427] [mlr3] Applying learner 'classif.featureless' on task 'compas' (iter 2/3)
INFO [16:52:07.512] [mlr3] Applying learner 'classif.featureless' on task 'compas' (iter 3/3)
INFO [16:52:07.593] [mlr3] Finished benchmark
INFO [16:52:07.757] [mlr3] Running benchmark with 12 resampling iterations
INFO [16:52:07.769] [mlr3] Applying learner 'classif.rpart' on task 'adult_train' (iter 1/3)
INFO [16:52:07.805] [mlr3] Applying learner 'classif.rpart' on task 'adult_train' (iter 2/3)
INFO [16:52:07.839] [mlr3] Applying learner 'classif.rpart' on task 'adult_train' (iter 3/3)
INFO [16:52:07.869] [mlr3] Applying learner 'classif.featureless' on task 'adult_train' (iter 1/3)
INFO [16:52:07.948] [mlr3] Applying learner 'classif.featureless' on task 'adult_train' (iter 2/3)
INFO [16:52:08.057] [mlr3] Applying learner 'classif.featureless' on task 'adult_train' (iter 3/3)
INFO [16:52:08.152] [mlr3] Applying learner 'classif.rpart' on task 'compas' (iter 1/3)
INFO [16:52:08.212] [mlr3] Applying learner 'classif.rpart' on task 'compas' (iter 2/3)
INFO [16:52:08.383] [mlr3] Applying learner 'classif.rpart' on task 'compas' (iter 3/3)
INFO [16:52:08.557] [mlr3] Applying learner 'classif.featureless' on task 'compas' (iter 1/3)
INFO [16:52:08.657] [mlr3] Applying learner 'classif.featureless' on task 'compas' (iter 2/3)
INFO [16:52:08.750] [mlr3] Applying learner 'classif.featureless' on task 'compas' (iter 3/3)
INFO [16:52:08.863] [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
Flavor: r-devel-linux-x86_64-debian-gcc
Version: 0.3.2
Check: re-building of vignette outputs
Result: ERROR
Error(s) in re-building vignettes:
...
--- re-building ‘debiasing-vignette.Rmd’ using rmarkdown
Quitting from debiasing-vignette.Rmd:55-57 [unnamed-chunk-4]
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
<error/rlang_error>
Error in `task_set_roles()`:
! Assertion on 'roles' failed: Must be a subset of {'feature','target','name','order','stratum','group','offset','weights_learner','weights_measure','pta'}, but has additional elements {'weight'}.
This happened PipeOp reweighing_wts's $train()
---
Backtrace:
▆
1. ├─p1$train(list(task))
2. │ └─mlr3pipelines:::.__PipeOp__train(...)
3. │ ├─base::withCallingHandlers(...)
4. │ └─private$.train(input)
5. │ └─mlr3pipelines:::.__PipeOpTaskPreproc__.train(...)
6. │ └─private$.train_task(intask)
7. │ └─mlr3fairness:::.__PipeOpReweighingWeights__.train_task(...)
8. │ └─task$set_col_roles(weightcolname, "weight")
9. │ └─mlr3:::.__Task__set_col_roles(...)
10. │ └─mlr3:::task_set_roles(...)
11. │ └─checkmate::assert_subset(roles, names(li))
12. │ └─checkmate::makeAssertion(x, res, .var.name, add)
13. │ └─checkmate:::mstop(...)
14. │ └─base::stop(simpleError(sprintf(msg, ...), call.))
15. └─mlr3pipelines (local) `<fn>`(`<smplErrr>`)
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
Error: processing vignette 'debiasing-vignette.Rmd' failed with diagnostics:
Assertion on 'roles' failed: Must be a subset of {'feature','target','name','order','stratum','group','offset','weights_learner','weights_measure','pta'}, but has additional elements {'weight'}.
This happened PipeOp reweighing_wts's $train()
--- failed re-building ‘debiasing-vignette.Rmd’
--- re-building ‘measures-vignette.Rmd’ using rmarkdown
Quitting from measures-vignette.Rmd:88-90 [unnamed-chunk-6]
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
<error/rlang_error>
Error:
! unused argument (weights = NULL)
---
Backtrace:
▆
1. └─prd$score(msr("fairness.tpr"), task = test)
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: processing vignette 'measures-vignette.Rmd' failed with diagnostics:
unused argument (weights = NULL)
--- failed re-building ‘measures-vignette.Rmd’
--- re-building ‘reports-vignette.Rmd’ using rmarkdown
Quitting from reports-vignette.Rmd:51-54 [build_modelcard_example_for_vignette]
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
<error/rlang_error>
Error in `loadNamespace()`:
! there is no package called 'posterdown'
---
Backtrace:
▆
1. ├─rmarkdown::render(rmdfile)
2. │ └─rmarkdown:::create_output_format(output_format$name, output_format$options)
3. │ └─rmarkdown:::create_output_format_function(name)
4. │ └─base::eval(xfun::parse_only(name))
5. │ └─base::eval(xfun::parse_only(name))
6. └─base::loadNamespace(x)
7. └─base::withRestarts(stop(cond), retry_loadNamespace = function() NULL)
8. └─base (local) withOneRestart(expr, restarts[[1L]])
9. └─base (local) doWithOneRestart(return(expr), restart)
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
Error: processing vignette 'reports-vignette.Rmd' failed with diagnostics:
there is no package called 'posterdown'
--- failed re-building ‘reports-vignette.Rmd’
--- re-building ‘visualization-vignette.Rmd’ using rmarkdown
** Processing: /home/hornik/tmp/R.check/r-devel-gcc/Work/PKGS/mlr3fairness.Rcheck/vign_test/mlr3fairness/vignettes/visualization-vignette_files/figure-html/unnamed-chunk-6-1.png
288x288 pixels, 3x8 bits/pixel, RGB
Input IDAT size = 15325 bytes
Input file size = 15415 bytes
Trying:
zc = 9 zm = 8 zs = 0 f = 0 IDAT size = 12125
zc = 9 zm = 8 zs = 1 f = 0
zc = 1 zm = 8 zs = 2 f = 0
zc = 9 zm = 8 zs = 3 f = 0
zc = 9 zm = 8 zs = 0 f = 5
zc = 9 zm = 8 zs = 1 f = 5
zc = 1 zm = 8 zs = 2 f = 5
zc = 9 zm = 8 zs = 3 f = 5
Selecting parameters:
zc = 9 zm = 8 zs = 0 f = 0 IDAT size = 12125
Output IDAT size = 12125 bytes (3200 bytes decrease)
Output file size = 12203 bytes (3212 bytes = 20.84% decrease)
** Processing: /home/hornik/tmp/R.check/r-devel-gcc/Work/PKGS/mlr3fairness.Rcheck/vign_test/mlr3fairness/vignettes/visualization-vignette_files/figure-html/unnamed-chunk-7-1.png
288x288 pixels, 3x8 bits/pixel, RGB
Input IDAT size = 17340 bytes
Input file size = 17442 bytes
Trying:
zc = 9 zm = 8 zs = 0 f = 0 IDAT size = 12948
zc = 9 zm = 8 zs = 1 f = 0
zc = 1 zm = 8 zs = 2 f = 0
zc = 9 zm = 8 zs = 3 f = 0
zc = 9 zm = 8 zs = 0 f = 5
zc = 9 zm = 8 zs = 1 f = 5
zc = 1 zm = 8 zs = 2 f = 5
zc = 9 zm = 8 zs = 3 f = 5
Selecting parameters:
zc = 9 zm = 8 zs = 0 f = 0 IDAT size = 12948
Output IDAT size = 12948 bytes (4392 bytes decrease)
Output file size = 13026 bytes (4416 bytes = 25.32% decrease)
Quitting from visualization-vignette.Rmd:88-90 [unnamed-chunk-8]
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
<error/rlang_error>
Error:
! unused argument (weights = NULL)
---
Backtrace:
▆
1. ├─mlr3fairness::fairness_accuracy_tradeoff(bmr, msr("fairness.fpr"))
2. └─mlr3fairness:::fairness_accuracy_tradeoff.BenchmarkResult(...)
3. └─object$aggregate(list(acc_measure, fairness_measure))
4. └─mlr3:::.__BenchmarkResult__aggregate(...)
5. └─mlr3misc::map_dtr(...)
6. ├─data.table::rbindlist(...)
7. ├─base::unname(map(.x, .f, ...))
8. └─mlr3misc::map(.x, .f, ...)
9. └─base::lapply(.x, .f, ...)
10. └─mlr3 (local) FUN(X[[i]], ...)
11. ├─base::as.list(resample_result_aggregate(rr, measures))
12. └─mlr3:::resample_result_aggregate(rr, measures)
13. ├─... %??% set_names(numeric(), character())
14. ├─base::unlist(...)
15. └─mlr3misc::map(...)
16. └─base::lapply(.x, .f, ...)
17. └─mlr3 (local) FUN(X[[i]], ...)
18. └─m$aggregate(rr)
19. └─mlr3:::.__Measure__aggregate(...)
20. └─mlr3:::score_measures(...)
21. └─mlr3misc::pmap_dbl(...)
22. └─mlr3misc:::mapply_list(.f, .x, list(...))
23. └─base::.mapply(.f, .dots, .args)
24. └─mlr3 (local) `<fn>`(...)
25. └─mlr3:::score_single_measure(...)
26. └─get_private(measure)$.score(...)
27. └─mlr3fairness:::.__MeasureFairness__.score(...)
28. └─mlr3fairness:::score_groupwise(...)
29. └─mlr3misc::map_dbl(...)
30. └─mlr3misc:::map_mold(.x, .f, NA_real_, ...)
31. └─base::vapply(.x, .f, FUN.VALUE = .value, USE.NAMES = FALSE, ...)
32. └─mlr3fairness (local) FUN(X[[i]], ...)
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
Error: processing vignette 'visualization-vignette.Rmd' failed with diagnostics:
unused argument (weights = NULL)
--- failed re-building ‘visualization-vignette.Rmd’
SUMMARY: processing the following files failed:
‘debiasing-vignette.Rmd’ ‘measures-vignette.Rmd’
‘reports-vignette.Rmd’ ‘visualization-vignette.Rmd’
Error: Vignette re-building failed.
Execution halted
Flavor: r-devel-linux-x86_64-debian-gcc
Version: 0.3.2
Check: examples
Result: ERROR
Running examples in ‘mlr3fairness-Ex.R’ failed
The error most likely occurred in:
> ### Name: MeasureFairness
> ### Title: Base Measure for Fairness
> ### Aliases: MeasureFairness
>
> ### ** Examples
>
> library("mlr3")
> # Create MeasureFairness to measure the Predictive Parity.
> t = tsk("adult_train")
> learner = lrn("classif.rpart", cp = .01)
> learner$train(t)
> measure = msr("fairness", base_measure = msr("classif.ppv"))
> predictions = learner$predict(t)
> predictions$score(measure, task = t)
Error in prediction$clone()$filter(rws)$score(base_measure, task = task, :
unused argument (weights = NULL)
Calls: <Anonymous> ... score_groupwise -> map_dbl -> map_mold -> vapply -> FUN
Execution halted
Flavors: r-devel-linux-x86_64-fedora-clang, r-devel-linux-x86_64-fedora-gcc, r-devel-windows-x86_64, r-release-windows-x86_64, r-oldrel-windows-x86_64
Version: 0.3.2
Check: tests
Result: ERROR
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
Flavor: r-devel-linux-x86_64-fedora-clang
Version: 0.3.2
Check: re-building of vignette outputs
Result: ERROR
Error(s) in re-building vignettes:
--- re-building ‘debiasing-vignette.Rmd’ using rmarkdown
Quitting from debiasing-vignette.Rmd:55-57 [unnamed-chunk-4]
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
<error/rlang_error>
Error in `task_set_roles()`:
! Assertion on 'roles' failed: Must be a subset of {'feature','target','name','order','stratum','group','offset','weights_learner','weights_measure','pta'}, but has additional elements {'weight'}.
This happened PipeOp reweighing_wts's $train()
---
Backtrace:
▆
1. ├─p1$train(list(task))
2. │ └─mlr3pipelines:::.__PipeOp__train(...)
3. │ ├─base::withCallingHandlers(...)
4. │ └─private$.train(input)
5. │ └─mlr3pipelines:::.__PipeOpTaskPreproc__.train(...)
6. │ └─private$.train_task(intask)
7. │ └─mlr3fairness:::.__PipeOpReweighingWeights__.train_task(...)
8. │ └─task$set_col_roles(weightcolname, "weight")
9. │ └─mlr3:::.__Task__set_col_roles(...)
10. │ └─mlr3:::task_set_roles(...)
11. │ └─checkmate::assert_subset(roles, names(li))
12. │ └─checkmate::makeAssertion(x, res, .var.name, add)
13. │ └─checkmate:::mstop(...)
14. │ └─base::stop(simpleError(sprintf(msg, ...), call.))
15. └─mlr3pipelines (local) `<fn>`(`<smplErrr>`)
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
Error: processing vignette 'debiasing-vignette.Rmd' failed with diagnostics:
Assertion on 'roles' failed: Must be a subset of {'feature','target','name','order','stratum','group','offset','weights_learner','weights_measure','pta'}, but has additional elements {'weight'}.
This happened PipeOp reweighing_wts's $train()
--- failed re-building ‘debiasing-vignette.Rmd’
--- re-building ‘measures-vignette.Rmd’ using rmarkdown
Quitting from measures-vignette.Rmd:88-90 [unnamed-chunk-6]
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
<error/rlang_error>
Error:
! unused argument (weights = NULL)
---
Backtrace:
▆
1. └─prd$score(msr("fairness.tpr"), task = test)
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: processing vignette 'measures-vignette.Rmd' failed with diagnostics:
unused argument (weights = NULL)
--- failed re-building ‘measures-vignette.Rmd’
--- re-building ‘reports-vignette.Rmd’ using rmarkdown
Quitting from reports-vignette.Rmd:51-54 [build_modelcard_example_for_vignette]
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
<error/rlang_error>
Error in `loadNamespace()`:
! there is no package called 'posterdown'
---
Backtrace:
▆
1. ├─rmarkdown::render(rmdfile)
2. │ └─rmarkdown:::create_output_format(output_format$name, output_format$options)
3. │ └─rmarkdown:::create_output_format_function(name)
4. │ └─base::eval(xfun::parse_only(name))
5. │ └─base::eval(xfun::parse_only(name))
6. └─base::loadNamespace(x)
7. └─base::withRestarts(stop(cond), retry_loadNamespace = function() NULL)
8. └─base (local) withOneRestart(expr, restarts[[1L]])
9. └─base (local) doWithOneRestart(return(expr), restart)
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
Error: processing vignette 'reports-vignette.Rmd' failed with diagnostics:
there is no package called 'posterdown'
--- failed re-building ‘reports-vignette.Rmd’
--- re-building ‘visualization-vignette.Rmd’ using rmarkdown
Quitting from visualization-vignette.Rmd:88-90 [unnamed-chunk-8]
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
<error/rlang_error>
Error:
! unused argument (weights = NULL)
---
Backtrace:
▆
1. ├─mlr3fairness::fairness_accuracy_tradeoff(bmr, msr("fairness.fpr"))
2. └─mlr3fairness:::fairness_accuracy_tradeoff.BenchmarkResult(...)
3. └─object$aggregate(list(acc_measure, fairness_measure))
4. └─mlr3:::.__BenchmarkResult__aggregate(...)
5. └─mlr3misc::map_dtr(...)
6. ├─data.table::rbindlist(...)
7. ├─base::unname(map(.x, .f, ...))
8. └─mlr3misc::map(.x, .f, ...)
9. └─base::lapply(.x, .f, ...)
10. └─mlr3 (local) FUN(X[[i]], ...)
11. ├─base::as.list(resample_result_aggregate(rr, measures))
12. └─mlr3:::resample_result_aggregate(rr, measures)
13. ├─... %??% set_names(numeric(), character())
14. ├─base::unlist(...)
15. └─mlr3misc::map(...)
16. └─base::lapply(.x, .f, ...)
17. └─mlr3 (local) FUN(X[[i]], ...)
18. └─m$aggregate(rr)
19. └─mlr3:::.__Measure__aggregate(...)
20. └─mlr3:::score_measures(...)
21. └─mlr3misc::pmap_dbl(...)
22. └─mlr3misc:::mapply_list(.f, .x, list(...))
23. └─base::.mapply(.f, .dots, .args)
24. └─mlr3 (local) `<fn>`(...)
25. └─mlr3:::score_single_measure(...)
26. └─get_private(measure)$.score(...)
27. └─mlr3fairness:::.__MeasureFairness__.score(...)
28. └─mlr3fairness:::score_groupwise(...)
29. └─mlr3misc::map_dbl(...)
30. └─mlr3misc:::map_mold(.x, .f, NA_real_, ...)
31. └─base::vapply(.x, .f, FUN.VALUE = .value, USE.NAMES = FALSE, ...)
32. └─mlr3fairness (local) FUN(X[[i]], ...)
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
Error: processing vignette 'visualization-vignette.Rmd' failed with diagnostics:
unused argument (weights = NULL)
--- failed re-building ‘visualization-vignette.Rmd’
SUMMARY: processing the following files failed:
‘debiasing-vignette.Rmd’ ‘measures-vignette.Rmd’
‘reports-vignette.Rmd’ ‘visualization-vignette.Rmd’
Error: Vignette re-building failed.
Execution halted
Flavors: r-devel-linux-x86_64-fedora-clang, r-devel-linux-x86_64-fedora-gcc
Version: 0.3.2
Check: tests
Result: ERROR
Running ‘testthat.R’ [22s/36s]
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 [08:56:32.384] [mlr3] Running benchmark with 12 resampling iterations
INFO [08:56:32.953] [mlr3] Applying learner 'classif.rpart' on task 'adult_train' (iter 1/3)
INFO [08:56:33.094] [mlr3] Applying learner 'classif.rpart' on task 'adult_train' (iter 2/3)
INFO [08:56:33.234] [mlr3] Applying learner 'classif.rpart' on task 'adult_train' (iter 3/3)
INFO [08:56:33.383] [mlr3] Applying learner 'classif.featureless' on task 'adult_train' (iter 1/3)
INFO [08:56:33.441] [mlr3] Applying learner 'classif.featureless' on task 'adult_train' (iter 2/3)
INFO [08:56:33.541] [mlr3] Applying learner 'classif.featureless' on task 'adult_train' (iter 3/3)
INFO [08:56:33.640] [mlr3] Applying learner 'classif.rpart' on task 'compas' (iter 1/3)
INFO [08:56:33.810] [mlr3] Applying learner 'classif.rpart' on task 'compas' (iter 2/3)
INFO [08:56:33.935] [mlr3] Applying learner 'classif.rpart' on task 'compas' (iter 3/3)
INFO [08:56:34.070] [mlr3] Applying learner 'classif.featureless' on task 'compas' (iter 1/3)
INFO [08:56:34.172] [mlr3] Applying learner 'classif.featureless' on task 'compas' (iter 2/3)
INFO [08:56:34.268] [mlr3] Applying learner 'classif.featureless' on task 'compas' (iter 3/3)
INFO [08:56:34.382] [mlr3] Finished benchmark
INFO [08:56:35.471] [mlr3] Running benchmark with 12 resampling iterations
INFO [08:56:35.650] [mlr3] Applying learner 'classif.rpart' on task 'adult_train' (iter 1/3)
INFO [08:56:35.828] [mlr3] Applying learner 'classif.rpart' on task 'adult_train' (iter 2/3)
INFO [08:56:35.980] [mlr3] Applying learner 'classif.rpart' on task 'adult_train' (iter 3/3)
INFO [08:56:36.055] [mlr3] Applying learner 'classif.featureless' on task 'adult_train' (iter 1/3)
INFO [08:56:36.125] [mlr3] Applying learner 'classif.featureless' on task 'adult_train' (iter 2/3)
INFO [08:56:36.232] [mlr3] Applying learner 'classif.featureless' on task 'adult_train' (iter 3/3)
INFO [08:56:36.316] [mlr3] Applying learner 'classif.rpart' on task 'compas' (iter 1/3)
INFO [08:56:36.384] [mlr3] Applying learner 'classif.rpart' on task 'compas' (iter 2/3)
INFO [08:56:36.451] [mlr3] Applying learner 'classif.rpart' on task 'compas' (iter 3/3)
INFO [08:56:36.524] [mlr3] Applying learner 'classif.featureless' on task 'compas' (iter 1/3)
INFO [08:56:36.594] [mlr3] Applying learner 'classif.featureless' on task 'compas' (iter 2/3)
INFO [08:56:36.664] [mlr3] Applying learner 'classif.featureless' on task 'compas' (iter 3/3)
INFO [08:56:36.717] [mlr3] Finished benchmark
INFO [08:56:37.111] [mlr3] Running benchmark with 12 resampling iterations
INFO [08:56:37.143] [mlr3] Applying learner 'classif.rpart' on task 'adult_train' (iter 1/3)
INFO [08:56:37.215] [mlr3] Applying learner 'classif.rpart' on task 'adult_train' (iter 2/3)
INFO [08:56:37.279] [mlr3] Applying learner 'classif.rpart' on task 'adult_train' (iter 3/3)
INFO [08:56:37.343] [mlr3] Applying learner 'classif.featureless' on task 'adult_train' (iter 1/3)
INFO [08:56:37.429] [mlr3] Applying learner 'classif.featureless' on task 'adult_train' (iter 2/3)
INFO [08:56:37.492] [mlr3] Applying learner 'classif.featureless' on task 'adult_train' (iter 3/3)
INFO [08:56:37.562] [mlr3] Applying learner 'classif.rpart' on task 'compas' (iter 1/3)
INFO [08:56:37.674] [mlr3] Applying learner 'classif.rpart' on task 'compas' (iter 2/3)
INFO [08:56:37.816] [mlr3] Applying learner 'classif.rpart' on task 'compas' (iter 3/3)
INFO [08:56:37.951] [mlr3] Applying learner 'classif.featureless' on task 'compas' (iter 1/3)
INFO [08:56:38.051] [mlr3] Applying learner 'classif.featureless' on task 'compas' (iter 2/3)
INFO [08:56:38.121] [mlr3] Applying learner 'classif.featureless' on task 'compas' (iter 3/3)
INFO [08:56:38.197] [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
Flavor: r-devel-linux-x86_64-fedora-gcc
Version: 0.3.2
Check: tests
Result: ERROR
Running 'testthat.R' [11s]
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 [15:45:19.283] [mlr3] Running benchmark with 12 resampling iterations
INFO [15:45:19.428] [mlr3] Applying learner 'classif.rpart' on task 'adult_train' (iter 1/3)
INFO [15:45:19.470] [mlr3] Applying learner 'classif.rpart' on task 'adult_train' (iter 2/3)
INFO [15:45:19.498] [mlr3] Applying learner 'classif.rpart' on task 'adult_train' (iter 3/3)
INFO [15:45:19.540] [mlr3] Applying learner 'classif.featureless' on task 'adult_train' (iter 1/3)
INFO [15:45:19.576] [mlr3] Applying learner 'classif.featureless' on task 'adult_train' (iter 2/3)
INFO [15:45:19.617] [mlr3] Applying learner 'classif.featureless' on task 'adult_train' (iter 3/3)
INFO [15:45:19.652] [mlr3] Applying learner 'classif.rpart' on task 'compas' (iter 1/3)
INFO [15:45:19.679] [mlr3] Applying learner 'classif.rpart' on task 'compas' (iter 2/3)
INFO [15:45:19.702] [mlr3] Applying learner 'classif.rpart' on task 'compas' (iter 3/3)
INFO [15:45:19.725] [mlr3] Applying learner 'classif.featureless' on task 'compas' (iter 1/3)
INFO [15:45:19.751] [mlr3] Applying learner 'classif.featureless' on task 'compas' (iter 2/3)
INFO [15:45:19.783] [mlr3] Applying learner 'classif.featureless' on task 'compas' (iter 3/3)
INFO [15:45:19.813] [mlr3] Finished benchmark
INFO [15:45:20.063] [mlr3] Running benchmark with 12 resampling iterations
INFO [15:45:20.087] [mlr3] Applying learner 'classif.rpart' on task 'adult_train' (iter 1/3)
INFO [15:45:20.122] [mlr3] Applying learner 'classif.rpart' on task 'adult_train' (iter 2/3)
INFO [15:45:20.166] [mlr3] Applying learner 'classif.rpart' on task 'adult_train' (iter 3/3)
INFO [15:45:20.203] [mlr3] Applying learner 'classif.featureless' on task 'adult_train' (iter 1/3)
INFO [15:45:20.232] [mlr3] Applying learner 'classif.featureless' on task 'adult_train' (iter 2/3)
INFO [15:45:20.265] [mlr3] Applying learner 'classif.featureless' on task 'adult_train' (iter 3/3)
INFO [15:45:20.292] [mlr3] Applying learner 'classif.rpart' on task 'compas' (iter 1/3)
INFO [15:45:20.318] [mlr3] Applying learner 'classif.rpart' on task 'compas' (iter 2/3)
INFO [15:45:20.353] [mlr3] Applying learner 'classif.rpart' on task 'compas' (iter 3/3)
INFO [15:45:20.395] [mlr3] Applying learner 'classif.featureless' on task 'compas' (iter 1/3)
INFO [15:45:20.422] [mlr3] Applying learner 'classif.featureless' on task 'compas' (iter 2/3)
INFO [15:45:20.450] [mlr3] Applying learner 'classif.featureless' on task 'compas' (iter 3/3)
INFO [15:45:20.478] [mlr3] Finished benchmark
INFO [15:45:20.635] [mlr3] Running benchmark with 12 resampling iterations
INFO [15:45:20.653] [mlr3] Applying learner 'classif.rpart' on task 'adult_train' (iter 1/3)
INFO [15:45:20.684] [mlr3] Applying learner 'classif.rpart' on task 'adult_train' (iter 2/3)
INFO [15:45:20.717] [mlr3] Applying learner 'classif.rpart' on task 'adult_train' (iter 3/3)
INFO [15:45:20.751] [mlr3] Applying learner 'classif.featureless' on task 'adult_train' (iter 1/3)
INFO [15:45:20.788] [mlr3] Applying learner 'classif.featureless' on task 'adult_train' (iter 2/3)
INFO [15:45:20.819] [mlr3] Applying learner 'classif.featureless' on task 'adult_train' (iter 3/3)
INFO [15:45:20.850] [mlr3] Applying learner 'classif.rpart' on task 'compas' (iter 1/3)
INFO [15:45:20.890] [mlr3] Applying learner 'classif.rpart' on task 'compas' (iter 2/3)
INFO [15:45:20.929] [mlr3] Applying learner 'classif.rpart' on task 'compas' (iter 3/3)
INFO [15:45:20.967] [mlr3] Applying learner 'classif.featureless' on task 'compas' (iter 1/3)
INFO [15:45:20.996] [mlr3] Applying learner 'classif.featureless' on task 'compas' (iter 2/3)
INFO [15:45:21.035] [mlr3] Applying learner 'classif.featureless' on task 'compas' (iter 3/3)
INFO [15:45:21.062] [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 D:\RCompile\CRANpkg\local\4.6\mlr3fairness.Rcheck\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 D:\RCompile\CRANpkg\local\4.6\mlr3fairness.Rcheck\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
Flavor: r-devel-windows-x86_64
Version: 0.3.2
Check: re-building of vignette outputs
Result: ERROR
Error(s) in re-building vignettes:
--- re-building 'debiasing-vignette.Rmd' using rmarkdown
Quitting from debiasing-vignette.Rmd:55-57 [unnamed-chunk-4]
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
<error/rlang_error>
Error in `task_set_roles()`:
! Assertion on 'roles' failed: Must be a subset of {'feature','target','name','order','stratum','group','offset','weights_learner','weights_measure','pta'}, but has additional elements {'weight'}.
This happened PipeOp reweighing_wts's $train()
---
Backtrace:
▆
1. ├─p1$train(list(task))
2. │ └─mlr3pipelines:::.__PipeOp__train(...)
3. │ ├─base::withCallingHandlers(...)
4. │ └─private$.train(input)
5. │ └─mlr3pipelines:::.__PipeOpTaskPreproc__.train(...)
6. │ └─private$.train_task(intask)
7. │ └─mlr3fairness:::.__PipeOpReweighingWeights__.train_task(...)
8. │ └─task$set_col_roles(weightcolname, "weight")
9. │ └─mlr3:::.__Task__set_col_roles(...)
10. │ └─mlr3:::task_set_roles(...)
11. │ └─checkmate::assert_subset(roles, names(li))
12. │ └─checkmate::makeAssertion(x, res, .var.name, add)
13. │ └─checkmate:::mstop(...)
14. │ └─base::stop(simpleError(sprintf(msg, ...), call.))
15. └─mlr3pipelines (local) `<fn>`(`<smplErrr>`)
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
Error: processing vignette 'debiasing-vignette.Rmd' failed with diagnostics:
Assertion on 'roles' failed: Must be a subset of {'feature','target','name','order','stratum','group','offset','weights_learner','weights_measure','pta'}, but has additional elements {'weight'}.
This happened PipeOp reweighing_wts's $train()
--- failed re-building 'debiasing-vignette.Rmd'
--- re-building 'measures-vignette.Rmd' using rmarkdown
Quitting from measures-vignette.Rmd:88-90 [unnamed-chunk-6]
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
<error/rlang_error>
Error:
! unused argument (weights = NULL)
---
Backtrace:
▆
1. └─prd$score(msr("fairness.tpr"), task = test)
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: processing vignette 'measures-vignette.Rmd' failed with diagnostics:
unused argument (weights = NULL)
--- failed re-building 'measures-vignette.Rmd'
--- re-building 'reports-vignette.Rmd' using rmarkdown
Quitting from reports-vignette.Rmd:69-75 [build_fairness_example_for_vignette]
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
<error/rlang_error>
Error:
! unused argument (weights = NULL)
---
Backtrace:
▆
1. ├─... %>% kable_paper("hover", full_width = F)
2. ├─kableExtra::kable_paper(., "hover", full_width = F)
3. │ └─kableExtra:::kable_light(...)
4. │ └─kableExtra::kable_styling(...)
5. ├─kableExtra::kbl(., col.names = c("value"))
6. │ └─knitr::kable(...)
7. └─resample_result$aggregate(fair_metrics)
8. └─mlr3:::.__ResampleResult__aggregate(...)
9. └─mlr3:::resample_result_aggregate(self, measures)
10. ├─... %??% set_names(numeric(), character())
11. ├─base::unlist(...)
12. └─mlr3misc::map(...)
13. └─base::lapply(.x, .f, ...)
14. └─mlr3 (local) FUN(X[[i]], ...)
15. └─m$aggregate(rr)
16. └─mlr3:::.__Measure__aggregate(...)
17. └─mlr3:::score_measures(...)
18. └─mlr3misc::pmap_dbl(...)
19. └─mlr3misc:::mapply_list(.f, .x, list(...))
20. └─base::.mapply(.f, .dots, .args)
21. └─mlr3 (local) `<fn>`(...)
22. └─mlr3:::score_single_measure(...)
23. └─get_private(measure)$.score(...)
24. └─mlr3fairness:::.__MeasureFairness__.score(...)
25. └─mlr3fairness:::score_groupwise(...)
26. └─mlr3misc::map_dbl(...)
27. └─mlr3misc:::map_mold(.x, .f, NA_real_, ...)
28. └─base::vapply(.x, .f, FUN.VALUE = .value, USE.NAMES = FALSE, ...)
29. └─mlr3fairness (local) FUN(X[[i]], ...)
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
Error: processing vignette 'reports-vignette.Rmd' failed with diagnostics:
unused argument (weights = NULL)
--- failed re-building 'reports-vignette.Rmd'
--- re-building 'visualization-vignette.Rmd' using rmarkdown
Quitting from visualization-vignette.Rmd:88-90 [unnamed-chunk-8]
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
<error/rlang_error>
Error:
! unused argument (weights = NULL)
---
Backtrace:
▆
1. ├─mlr3fairness::fairness_accuracy_tradeoff(bmr, msr("fairness.fpr"))
2. └─mlr3fairness:::fairness_accuracy_tradeoff.BenchmarkResult(...)
3. └─object$aggregate(list(acc_measure, fairness_measure))
4. └─mlr3:::.__BenchmarkResult__aggregate(...)
5. └─mlr3misc::map_dtr(...)
6. ├─data.table::rbindlist(...)
7. ├─base::unname(map(.x, .f, ...))
8. └─mlr3misc::map(.x, .f, ...)
9. └─base::lapply(.x, .f, ...)
10. └─mlr3 (local) FUN(X[[i]], ...)
11. ├─base::as.list(resample_result_aggregate(rr, measures))
12. └─mlr3:::resample_result_aggregate(rr, measures)
13. ├─... %??% set_names(numeric(), character())
14. ├─base::unlist(...)
15. └─mlr3misc::map(...)
16. └─base::lapply(.x, .f, ...)
17. └─mlr3 (local) FUN(X[[i]], ...)
18. └─m$aggregate(rr)
19. └─mlr3:::.__Measure__aggregate(...)
20. └─mlr3:::score_measures(...)
21. └─mlr3misc::pmap_dbl(...)
22. └─mlr3misc:::mapply_list(.f, .x, list(...))
23. └─base::.mapply(.f, .dots, .args)
24. └─mlr3 (local) `<fn>`(...)
25. └─mlr3:::score_single_measure(...)
26. └─get_private(measure)$.score(...)
27. └─mlr3fairness:::.__MeasureFairness__.score(...)
28. └─mlr3fairness:::score_groupwise(...)
29. └─mlr3misc::map_dbl(...)
30. └─mlr3misc:::map_mold(.x, .f, NA_real_, ...)
31. └─base::vapply(.x, .f, FUN.VALUE = .value, USE.NAMES = FALSE, ...)
32. └─mlr3fairness (local) FUN(X[[i]], ...)
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
Error: processing vignette 'visualization-vignette.Rmd' failed with diagnostics:
unused argument (weights = NULL)
--- failed re-building 'visualization-vignette.Rmd'
SUMMARY: processing the following files failed:
'debiasing-vignette.Rmd' 'measures-vignette.Rmd'
'reports-vignette.Rmd' 'visualization-vignette.Rmd'
Error: Vignette re-building failed.
Execution halted
Flavors: r-devel-windows-x86_64, r-release-windows-x86_64, r-oldrel-windows-x86_64
Version: 0.3.2
Check: re-building of vignette outputs
Result: ERROR
Error(s) in re-building vignettes:
...
--- re-building ‘debiasing-vignette.Rmd’ using rmarkdown
** Processing: /home/hornik/tmp/R.check/r-patched-gcc/Work/PKGS/mlr3fairness.Rcheck/vign_test/mlr3fairness/vignettes/debiasing-vignette_files/figure-html/unnamed-chunk-7-1.png
288x288 pixels, 3x8 bits/pixel, RGB
Input IDAT size = 13804 bytes
Input file size = 13894 bytes
Trying:
zc = 9 zm = 8 zs = 0 f = 0 IDAT size = 10650
zc = 9 zm = 8 zs = 1 f = 0
zc = 1 zm = 8 zs = 2 f = 0
zc = 9 zm = 8 zs = 3 f = 0
zc = 9 zm = 8 zs = 0 f = 5
zc = 9 zm = 8 zs = 1 f = 5
zc = 1 zm = 8 zs = 2 f = 5
zc = 9 zm = 8 zs = 3 f = 5
Selecting parameters:
zc = 9 zm = 8 zs = 0 f = 0 IDAT size = 10650
Output IDAT size = 10650 bytes (3154 bytes decrease)
Output file size = 10728 bytes (3166 bytes = 22.79% decrease)
--- finished re-building ‘debiasing-vignette.Rmd’
--- re-building ‘measures-vignette.Rmd’ using rmarkdown
--- finished re-building ‘measures-vignette.Rmd’
--- re-building ‘reports-vignette.Rmd’ using rmarkdown
Quitting from reports-vignette.Rmd:51-54 [build_modelcard_example_for_vignette]
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
<error/rlang_error>
Error in `loadNamespace()`:
! there is no package called 'posterdown'
---
Backtrace:
▆
1. ├─rmarkdown::render(rmdfile)
2. │ └─rmarkdown:::create_output_format(output_format$name, output_format$options)
3. │ └─rmarkdown:::create_output_format_function(name)
4. │ └─base::eval(xfun::parse_only(name))
5. │ └─base::eval(xfun::parse_only(name))
6. └─base::loadNamespace(x)
7. └─base::withRestarts(stop(cond), retry_loadNamespace = function() NULL)
8. └─base (local) withOneRestart(expr, restarts[[1L]])
9. └─base (local) doWithOneRestart(return(expr), restart)
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
Error: processing vignette 'reports-vignette.Rmd' failed with diagnostics:
there is no package called 'posterdown'
--- failed re-building ‘reports-vignette.Rmd’
--- re-building ‘visualization-vignette.Rmd’ using rmarkdown
** Processing: /home/hornik/tmp/R.check/r-patched-gcc/Work/PKGS/mlr3fairness.Rcheck/vign_test/mlr3fairness/vignettes/visualization-vignette_files/figure-html/unnamed-chunk-6-1.png
288x288 pixels, 3x8 bits/pixel, RGB
Input IDAT size = 15644 bytes
Input file size = 15734 bytes
Trying:
zc = 9 zm = 8 zs = 0 f = 0 IDAT size = 12463
zc = 9 zm = 8 zs = 1 f = 0
zc = 1 zm = 8 zs = 2 f = 0
zc = 9 zm = 8 zs = 3 f = 0
zc = 9 zm = 8 zs = 0 f = 5
zc = 9 zm = 8 zs = 1 f = 5
zc = 1 zm = 8 zs = 2 f = 5
zc = 9 zm = 8 zs = 3 f = 5
Selecting parameters:
zc = 9 zm = 8 zs = 0 f = 0 IDAT size = 12463
Output IDAT size = 12463 bytes (3181 bytes decrease)
Output file size = 12541 bytes (3193 bytes = 20.29% decrease)
** Processing: /home/hornik/tmp/R.check/r-patched-gcc/Work/PKGS/mlr3fairness.Rcheck/vign_test/mlr3fairness/vignettes/visualization-vignette_files/figure-html/unnamed-chunk-7-1.png
288x288 pixels, 3x8 bits/pixel, RGB
Input IDAT size = 17998 bytes
Input file size = 18100 bytes
Trying:
zc = 9 zm = 8 zs = 0 f = 0 IDAT size = 13421
zc = 9 zm = 8 zs = 1 f = 0
zc = 1 zm = 8 zs = 2 f = 0
zc = 9 zm = 8 zs = 3 f = 0
zc = 9 zm = 8 zs = 0 f = 5
zc = 9 zm = 8 zs = 1 f = 5
zc = 1 zm = 8 zs = 2 f = 5
zc = 9 zm = 8 zs = 3 f = 5
Selecting parameters:
zc = 9 zm = 8 zs = 0 f = 0 IDAT size = 13421
Output IDAT size = 13421 bytes (4577 bytes decrease)
Output file size = 13499 bytes (4601 bytes = 25.42% decrease)
** Processing: /home/hornik/tmp/R.check/r-patched-gcc/Work/PKGS/mlr3fairness.Rcheck/vign_test/mlr3fairness/vignettes/visualization-vignette_files/figure-html/unnamed-chunk-8-1.png
288x288 pixels, 3x8 bits/pixel, RGB
Input IDAT size = 17899 bytes
Input file size = 18001 bytes
Trying:
zc = 9 zm = 8 zs = 0 f = 0 IDAT size = 13031
zc = 9 zm = 8 zs = 1 f = 0
zc = 1 zm = 8 zs = 2 f = 0
zc = 9 zm = 8 zs = 3 f = 0
zc = 9 zm = 8 zs = 0 f = 5
zc = 9 zm = 8 zs = 1 f = 5
zc = 1 zm = 8 zs = 2 f = 5
zc = 9 zm = 8 zs = 3 f = 5
Selecting parameters:
zc = 9 zm = 8 zs = 0 f = 0 IDAT size = 13031
Output IDAT size = 13031 bytes (4868 bytes decrease)
Output file size = 13109 bytes (4892 bytes = 27.18% decrease)
** Processing: /home/hornik/tmp/R.check/r-patched-gcc/Work/PKGS/mlr3fairness.Rcheck/vign_test/mlr3fairness/vignettes/visualization-vignette_files/figure-html/unnamed-chunk-9-1.png
288x288 pixels, 8 bits/pixel, 234 colors in palette
Reducing image to 8 bits/pixel, grayscale
Input IDAT size = 2764 bytes
Input file size = 3556 bytes
Trying:
zc = 9 zm = 8 zs = 0 f = 0 IDAT size = 2595
zc = 9 zm = 8 zs = 1 f = 0
zc = 1 zm = 8 zs = 2 f = 0
zc = 9 zm = 8 zs = 3 f = 0
zc = 9 zm = 8 zs = 0 f = 5
zc = 9 zm = 8 zs = 1 f = 5
zc = 1 zm = 8 zs = 2 f = 5
zc = 9 zm = 8 zs = 3 f = 5
Selecting parameters:
zc = 9 zm = 8 zs = 0 f = 0 IDAT size = 2595
Output IDAT size = 2595 bytes (169 bytes decrease)
Output file size = 2673 bytes (883 bytes = 24.83% decrease)
** Processing: /home/hornik/tmp/R.check/r-patched-gcc/Work/PKGS/mlr3fairness.Rcheck/vign_test/mlr3fairness/vignettes/visualization-vignette_files/figure-html/unnamed-chunk-10-1.png
288x288 pixels, 3x8 bits/pixel, RGB
Input IDAT size = 10082 bytes
Input file size = 10172 bytes
Trying:
zc = 9 zm = 8 zs = 0 f = 0 IDAT size = 8053
zc = 9 zm = 8 zs = 1 f = 0
zc = 1 zm = 8 zs = 2 f = 0
zc = 9 zm = 8 zs = 3 f = 0
zc = 9 zm = 8 zs = 0 f = 5
zc = 9 zm = 8 zs = 1 f = 5
zc = 1 zm = 8 zs = 2 f = 5
zc = 9 zm = 8 zs = 3 f = 5
Selecting parameters:
zc = 9 zm = 8 zs = 0 f = 0 IDAT size = 8053
Output IDAT size = 8053 bytes (2029 bytes decrease)
Output file size = 8131 bytes (2041 bytes = 20.06% decrease)
** Processing: /home/hornik/tmp/R.check/r-patched-gcc/Work/PKGS/mlr3fairness.Rcheck/vign_test/mlr3fairness/vignettes/visualization-vignette_files/figure-html/unnamed-chunk-12-1.png
288x288 pixels, 3x8 bits/pixel, RGB
Input IDAT size = 12711 bytes
Input file size = 12801 bytes
Trying:
zc = 9 zm = 8 zs = 0 f = 0 IDAT size = 9267
zc = 9 zm = 8 zs = 1 f = 0
zc = 1 zm = 8 zs = 2 f = 0
zc = 9 zm = 8 zs = 3 f = 0
zc = 9 zm = 8 zs = 0 f = 5
zc = 9 zm = 8 zs = 1 f = 5
zc = 1 zm = 8 zs = 2 f = 5
zc = 9 zm = 8 zs = 3 f = 5
Selecting parameters:
zc = 9 zm = 8 zs = 0 f = 0 IDAT size = 9267
Output IDAT size = 9267 bytes (3444 bytes decrease)
Output file size = 9345 bytes (3456 bytes = 27.00% decrease)
--- finished re-building ‘visualization-vignette.Rmd’
SUMMARY: processing the following file failed:
‘reports-vignette.Rmd’
Error: Vignette re-building failed.
Execution halted
Flavor: r-patched-linux-x86_64
Version: 0.3.2
Check: tests
Result: ERROR
Running ‘testthat.R’ [15s/26s]
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 [14:50:42.855] [mlr3] Running benchmark with 12 resampling iterations
INFO [14:50:43.163] [mlr3] Applying learner 'classif.rpart' on task 'adult_train' (iter 1/3)
INFO [14:50:43.306] [mlr3] Applying learner 'classif.rpart' on task 'adult_train' (iter 2/3)
INFO [14:50:43.395] [mlr3] Applying learner 'classif.rpart' on task 'adult_train' (iter 3/3)
INFO [14:50:43.498] [mlr3] Applying learner 'classif.featureless' on task 'adult_train' (iter 1/3)
INFO [14:50:43.593] [mlr3] Applying learner 'classif.featureless' on task 'adult_train' (iter 2/3)
INFO [14:50:43.707] [mlr3] Applying learner 'classif.featureless' on task 'adult_train' (iter 3/3)
INFO [14:50:43.786] [mlr3] Applying learner 'classif.rpart' on task 'compas' (iter 1/3)
INFO [14:50:43.914] [mlr3] Applying learner 'classif.rpart' on task 'compas' (iter 2/3)
INFO [14:50:43.999] [mlr3] Applying learner 'classif.rpart' on task 'compas' (iter 3/3)
INFO [14:50:44.047] [mlr3] Applying learner 'classif.featureless' on task 'compas' (iter 1/3)
INFO [14:50:44.096] [mlr3] Applying learner 'classif.featureless' on task 'compas' (iter 2/3)
INFO [14:50:44.144] [mlr3] Applying learner 'classif.featureless' on task 'compas' (iter 3/3)
INFO [14:50:44.239] [mlr3] Finished benchmark
INFO [14:50:45.005] [mlr3] Running benchmark with 12 resampling iterations
INFO [14:50:45.096] [mlr3] Applying learner 'classif.rpart' on task 'adult_train' (iter 1/3)
INFO [14:50:45.180] [mlr3] Applying learner 'classif.rpart' on task 'adult_train' (iter 2/3)
INFO [14:50:45.267] [mlr3] Applying learner 'classif.rpart' on task 'adult_train' (iter 3/3)
INFO [14:50:45.361] [mlr3] Applying learner 'classif.featureless' on task 'adult_train' (iter 1/3)
INFO [14:50:45.452] [mlr3] Applying learner 'classif.featureless' on task 'adult_train' (iter 2/3)
INFO [14:50:45.536] [mlr3] Applying learner 'classif.featureless' on task 'adult_train' (iter 3/3)
INFO [14:50:45.622] [mlr3] Applying learner 'classif.rpart' on task 'compas' (iter 1/3)
INFO [14:50:45.768] [mlr3] Applying learner 'classif.rpart' on task 'compas' (iter 2/3)
INFO [14:50:45.853] [mlr3] Applying learner 'classif.rpart' on task 'compas' (iter 3/3)
INFO [14:50:45.928] [mlr3] Applying learner 'classif.featureless' on task 'compas' (iter 1/3)
INFO [14:50:46.059] [mlr3] Applying learner 'classif.featureless' on task 'compas' (iter 2/3)
INFO [14:50:46.146] [mlr3] Applying learner 'classif.featureless' on task 'compas' (iter 3/3)
INFO [14:50:46.241] [mlr3] Finished benchmark
INFO [14:50:46.656] [mlr3] Running benchmark with 12 resampling iterations
INFO [14:50:46.686] [mlr3] Applying learner 'classif.rpart' on task 'adult_train' (iter 1/3)
INFO [14:50:46.774] [mlr3] Applying learner 'classif.rpart' on task 'adult_train' (iter 2/3)
INFO [14:50:46.874] [mlr3] Applying learner 'classif.rpart' on task 'adult_train' (iter 3/3)
INFO [14:50:46.973] [mlr3] Applying learner 'classif.featureless' on task 'adult_train' (iter 1/3)
INFO [14:50:47.081] [mlr3] Applying learner 'classif.featureless' on task 'adult_train' (iter 2/3)
INFO [14:50:47.171] [mlr3] Applying learner 'classif.featureless' on task 'adult_train' (iter 3/3)
INFO [14:50:47.250] [mlr3] Applying learner 'classif.rpart' on task 'compas' (iter 1/3)
INFO [14:50:47.325] [mlr3] Applying learner 'classif.rpart' on task 'compas' (iter 2/3)
INFO [14:50:47.365] [mlr3] Applying learner 'classif.rpart' on task 'compas' (iter 3/3)
INFO [14:50:47.406] [mlr3] Applying learner 'classif.featureless' on task 'compas' (iter 1/3)
INFO [14:50:47.478] [mlr3] Applying learner 'classif.featureless' on task 'compas' (iter 2/3)
INFO [14:50:47.543] [mlr3] Applying learner 'classif.featureless' on task 'compas' (iter 3/3)
INFO [14:50:47.599] [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
Flavor: r-release-linux-x86_64
Version: 0.3.2
Check: re-building of vignette outputs
Result: ERROR
Error(s) in re-building vignettes:
...
--- re-building ‘debiasing-vignette.Rmd’ using rmarkdown
Quitting from debiasing-vignette.Rmd:55-57 [unnamed-chunk-4]
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
<error/rlang_error>
Error in `task_set_roles()`:
! Assertion on 'roles' failed: Must be a subset of {'feature','target','name','order','stratum','group','offset','weights_learner','weights_measure','pta'}, but has additional elements {'weight'}.
This happened PipeOp reweighing_wts's $train()
---
Backtrace:
▆
1. ├─p1$train(list(task))
2. │ └─mlr3pipelines:::.__PipeOp__train(...)
3. │ ├─base::withCallingHandlers(...)
4. │ └─private$.train(input)
5. │ └─mlr3pipelines:::.__PipeOpTaskPreproc__.train(...)
6. │ └─private$.train_task(intask)
7. │ └─mlr3fairness:::.__PipeOpReweighingWeights__.train_task(...)
8. │ └─task$set_col_roles(weightcolname, "weight")
9. │ └─mlr3:::.__Task__set_col_roles(...)
10. │ └─mlr3:::task_set_roles(...)
11. │ └─checkmate::assert_subset(roles, names(li))
12. │ └─checkmate::makeAssertion(x, res, .var.name, add)
13. │ └─checkmate:::mstop(...)
14. │ └─base::stop(simpleError(sprintf(msg, ...), call.))
15. └─mlr3pipelines (local) `<fn>`(`<smplErrr>`)
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
Error: processing vignette 'debiasing-vignette.Rmd' failed with diagnostics:
Assertion on 'roles' failed: Must be a subset of {'feature','target','name','order','stratum','group','offset','weights_learner','weights_measure','pta'}, but has additional elements {'weight'}.
This happened PipeOp reweighing_wts's $train()
--- failed re-building ‘debiasing-vignette.Rmd’
--- re-building ‘measures-vignette.Rmd’ using rmarkdown
Quitting from measures-vignette.Rmd:88-90 [unnamed-chunk-6]
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
<error/rlang_error>
Error:
! unused argument (weights = NULL)
---
Backtrace:
▆
1. └─prd$score(msr("fairness.tpr"), task = test)
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: processing vignette 'measures-vignette.Rmd' failed with diagnostics:
unused argument (weights = NULL)
--- failed re-building ‘measures-vignette.Rmd’
--- re-building ‘reports-vignette.Rmd’ using rmarkdown
Quitting from reports-vignette.Rmd:51-54 [build_modelcard_example_for_vignette]
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
<error/rlang_error>
Error in `loadNamespace()`:
! there is no package called 'posterdown'
---
Backtrace:
▆
1. ├─rmarkdown::render(rmdfile)
2. │ └─rmarkdown:::create_output_format(output_format$name, output_format$options)
3. │ └─rmarkdown:::create_output_format_function(name)
4. │ └─base::eval(xfun::parse_only(name))
5. │ └─base::eval(xfun::parse_only(name))
6. └─base::loadNamespace(x)
7. └─base::withRestarts(stop(cond), retry_loadNamespace = function() NULL)
8. └─base (local) withOneRestart(expr, restarts[[1L]])
9. └─base (local) doWithOneRestart(return(expr), restart)
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
Error: processing vignette 'reports-vignette.Rmd' failed with diagnostics:
there is no package called 'posterdown'
--- failed re-building ‘reports-vignette.Rmd’
--- re-building ‘visualization-vignette.Rmd’ using rmarkdown
** Processing: /home/hornik/tmp/R.check/r-release-gcc/Work/PKGS/mlr3fairness.Rcheck/vign_test/mlr3fairness/vignettes/visualization-vignette_files/figure-html/unnamed-chunk-6-1.png
288x288 pixels, 3x8 bits/pixel, RGB
Input IDAT size = 15294 bytes
Input file size = 15384 bytes
Trying:
zc = 9 zm = 8 zs = 0 f = 0 IDAT size = 12085
zc = 9 zm = 8 zs = 1 f = 0
zc = 1 zm = 8 zs = 2 f = 0
zc = 9 zm = 8 zs = 3 f = 0
zc = 9 zm = 8 zs = 0 f = 5
zc = 9 zm = 8 zs = 1 f = 5
zc = 1 zm = 8 zs = 2 f = 5
zc = 9 zm = 8 zs = 3 f = 5
Selecting parameters:
zc = 9 zm = 8 zs = 0 f = 0 IDAT size = 12085
Output IDAT size = 12085 bytes (3209 bytes decrease)
Output file size = 12163 bytes (3221 bytes = 20.94% decrease)
** Processing: /home/hornik/tmp/R.check/r-release-gcc/Work/PKGS/mlr3fairness.Rcheck/vign_test/mlr3fairness/vignettes/visualization-vignette_files/figure-html/unnamed-chunk-7-1.png
288x288 pixels, 3x8 bits/pixel, RGB
Input IDAT size = 16912 bytes
Input file size = 17014 bytes
Trying:
zc = 9 zm = 8 zs = 0 f = 0 IDAT size = 12906
zc = 9 zm = 8 zs = 1 f = 0
zc = 1 zm = 8 zs = 2 f = 0
zc = 9 zm = 8 zs = 3 f = 0
zc = 9 zm = 8 zs = 0 f = 5
zc = 9 zm = 8 zs = 1 f = 5
zc = 1 zm = 8 zs = 2 f = 5
zc = 9 zm = 8 zs = 3 f = 5
Selecting parameters:
zc = 9 zm = 8 zs = 0 f = 0 IDAT size = 12906
Output IDAT size = 12906 bytes (4006 bytes decrease)
Output file size = 12984 bytes (4030 bytes = 23.69% decrease)
Quitting from visualization-vignette.Rmd:88-90 [unnamed-chunk-8]
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
<error/rlang_error>
Error:
! unused argument (weights = NULL)
---
Backtrace:
▆
1. ├─mlr3fairness::fairness_accuracy_tradeoff(bmr, msr("fairness.fpr"))
2. └─mlr3fairness:::fairness_accuracy_tradeoff.BenchmarkResult(...)
3. └─object$aggregate(list(acc_measure, fairness_measure))
4. └─mlr3:::.__BenchmarkResult__aggregate(...)
5. └─mlr3misc::map_dtr(...)
6. ├─data.table::rbindlist(...)
7. ├─base::unname(map(.x, .f, ...))
8. └─mlr3misc::map(.x, .f, ...)
9. └─base::lapply(.x, .f, ...)
10. └─mlr3 (local) FUN(X[[i]], ...)
11. ├─base::as.list(resample_result_aggregate(rr, measures))
12. └─mlr3:::resample_result_aggregate(rr, measures)
13. ├─... %??% set_names(numeric(), character())
14. ├─base::unlist(...)
15. └─mlr3misc::map(...)
16. └─base::lapply(.x, .f, ...)
17. └─mlr3 (local) FUN(X[[i]], ...)
18. └─m$aggregate(rr)
19. └─mlr3:::.__Measure__aggregate(...)
20. └─mlr3:::score_measures(...)
21. └─mlr3misc::pmap_dbl(...)
22. └─mlr3misc:::mapply_list(.f, .x, list(...))
23. └─base::.mapply(.f, .dots, .args)
24. └─mlr3 (local) `<fn>`(...)
25. └─mlr3:::score_single_measure(...)
26. └─get_private(measure)$.score(...)
27. └─mlr3fairness:::.__MeasureFairness__.score(...)
28. └─mlr3fairness:::score_groupwise(...)
29. └─mlr3misc::map_dbl(...)
30. └─mlr3misc:::map_mold(.x, .f, NA_real_, ...)
31. └─base::vapply(.x, .f, FUN.VALUE = .value, USE.NAMES = FALSE, ...)
32. └─mlr3fairness (local) FUN(X[[i]], ...)
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
Error: processing vignette 'visualization-vignette.Rmd' failed with diagnostics:
unused argument (weights = NULL)
--- failed re-building ‘visualization-vignette.Rmd’
SUMMARY: processing the following files failed:
‘debiasing-vignette.Rmd’ ‘measures-vignette.Rmd’
‘reports-vignette.Rmd’ ‘visualization-vignette.Rmd’
Error: Vignette re-building failed.
Execution halted
Flavor: r-release-linux-x86_64
Version: 0.3.2
Check: tests
Result: ERROR
Running 'testthat.R' [11s]
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 [06:44:48.649] [mlr3] Running benchmark with 12 resampling iterations
INFO [06:44:48.832] [mlr3] Applying learner 'classif.rpart' on task 'adult_train' (iter 1/3)
INFO [06:44:48.900] [mlr3] Applying learner 'classif.rpart' on task 'adult_train' (iter 2/3)
INFO [06:44:48.952] [mlr3] Applying learner 'classif.rpart' on task 'adult_train' (iter 3/3)
INFO [06:44:49.006] [mlr3] Applying learner 'classif.featureless' on task 'adult_train' (iter 1/3)
INFO [06:44:49.037] [mlr3] Applying learner 'classif.featureless' on task 'adult_train' (iter 2/3)
INFO [06:44:49.069] [mlr3] Applying learner 'classif.featureless' on task 'adult_train' (iter 3/3)
INFO [06:44:49.098] [mlr3] Applying learner 'classif.rpart' on task 'compas' (iter 1/3)
INFO [06:44:49.133] [mlr3] Applying learner 'classif.rpart' on task 'compas' (iter 2/3)
INFO [06:44:49.166] [mlr3] Applying learner 'classif.rpart' on task 'compas' (iter 3/3)
INFO [06:44:49.202] [mlr3] Applying learner 'classif.featureless' on task 'compas' (iter 1/3)
INFO [06:44:49.235] [mlr3] Applying learner 'classif.featureless' on task 'compas' (iter 2/3)
INFO [06:44:49.265] [mlr3] Applying learner 'classif.featureless' on task 'compas' (iter 3/3)
INFO [06:44:49.298] [mlr3] Finished benchmark
INFO [06:44:49.540] [mlr3] Running benchmark with 12 resampling iterations
INFO [06:44:49.580] [mlr3] Applying learner 'classif.rpart' on task 'adult_train' (iter 1/3)
INFO [06:44:49.621] [mlr3] Applying learner 'classif.rpart' on task 'adult_train' (iter 2/3)
INFO [06:44:49.667] [mlr3] Applying learner 'classif.rpart' on task 'adult_train' (iter 3/3)
INFO [06:44:49.708] [mlr3] Applying learner 'classif.featureless' on task 'adult_train' (iter 1/3)
INFO [06:44:49.738] [mlr3] Applying learner 'classif.featureless' on task 'adult_train' (iter 2/3)
INFO [06:44:49.770] [mlr3] Applying learner 'classif.featureless' on task 'adult_train' (iter 3/3)
INFO [06:44:49.797] [mlr3] Applying learner 'classif.rpart' on task 'compas' (iter 1/3)
INFO [06:44:49.840] [mlr3] Applying learner 'classif.rpart' on task 'compas' (iter 2/3)
INFO [06:44:49.885] [mlr3] Applying learner 'classif.rpart' on task 'compas' (iter 3/3)
INFO [06:44:49.940] [mlr3] Applying learner 'classif.featureless' on task 'compas' (iter 1/3)
INFO [06:44:49.974] [mlr3] Applying learner 'classif.featureless' on task 'compas' (iter 2/3)
INFO [06:44:50.010] [mlr3] Applying learner 'classif.featureless' on task 'compas' (iter 3/3)
INFO [06:44:50.037] [mlr3] Finished benchmark
INFO [06:44:50.216] [mlr3] Running benchmark with 12 resampling iterations
INFO [06:44:50.239] [mlr3] Applying learner 'classif.rpart' on task 'adult_train' (iter 1/3)
INFO [06:44:50.283] [mlr3] Applying learner 'classif.rpart' on task 'adult_train' (iter 2/3)
INFO [06:44:50.331] [mlr3] Applying learner 'classif.rpart' on task 'adult_train' (iter 3/3)
INFO [06:44:50.382] [mlr3] Applying learner 'classif.featureless' on task 'adult_train' (iter 1/3)
INFO [06:44:50.430] [mlr3] Applying learner 'classif.featureless' on task 'adult_train' (iter 2/3)
INFO [06:44:50.459] [mlr3] Applying learner 'classif.featureless' on task 'adult_train' (iter 3/3)
INFO [06:44:50.487] [mlr3] Applying learner 'classif.rpart' on task 'compas' (iter 1/3)
INFO [06:44:50.522] [mlr3] Applying learner 'classif.rpart' on task 'compas' (iter 2/3)
INFO [06:44:50.564] [mlr3] Applying learner 'classif.rpart' on task 'compas' (iter 3/3)
INFO [06:44:50.609] [mlr3] Applying learner 'classif.featureless' on task 'compas' (iter 1/3)
INFO [06:44:50.640] [mlr3] Applying learner 'classif.featureless' on task 'compas' (iter 2/3)
INFO [06:44:50.668] [mlr3] Applying learner 'classif.featureless' on task 'compas' (iter 3/3)
INFO [06:44:50.700] [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)$score(base_measure, task = task,
...)`: unused argument (weights = NULL)
Backtrace:
▆
1. └─mlr3misc::map(...) at test_visualizations.R:9:3
2. └─base::lapply(.x, .f, ...)
3. └─mlr3fairness (local) FUN(X[[i]], ...)
4. ├─mlr3fairness:::check_plots(fairness_accuracy_tradeoff(bmr, fmsr)) at test_visualizations.R:10:5
5. │ ├─testthat::expect_true(is.ggplot(ggplot_obj)) at D:\RCompile\CRANpkg\local\4.5\mlr3fairness.Rcheck\tests\testthat\helper_test.R:4:3
6. │ │ └─testthat::quasi_label(enquo(object), label, arg = "object")
7. │ │ └─rlang::eval_bare(expr, quo_get_env(quo))
8. │ └─ggplot2::is.ggplot(ggplot_obj)
9. │ └─ggplot2::is_ggplot(x)
10. ├─mlr3fairness::fairness_accuracy_tradeoff(bmr, fmsr)
11. └─mlr3fairness:::fairness_accuracy_tradeoff.BenchmarkResult(...)
12. └─object$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)$score(base_measure, task = task,
...)`: unused argument (weights = NULL)
Backtrace:
▆
1. ├─mlr3fairness:::check_plots(compare_metrics(bmr, fairness_measures)) at test_visualizations.R:33:3
2. │ ├─testthat::expect_true(is.ggplot(ggplot_obj)) at D:\RCompile\CRANpkg\local\4.5\mlr3fairness.Rcheck\tests\testthat\helper_test.R:4:3
3. │ │ └─testthat::quasi_label(enquo(object), label, arg = "object")
4. │ │ └─rlang::eval_bare(expr, quo_get_env(quo))
5. │ └─ggplot2::is.ggplot(ggplot_obj)
6. │ └─ggplot2::is_ggplot(x)
7. ├─mlr3fairness::compare_metrics(bmr, fairness_measures)
8. └─mlr3fairness:::compare_metrics.BenchmarkResult(bmr, fairness_measures)
9. └─object$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
Flavor: r-release-windows-x86_64
Version: 0.3.2
Check: tests
Result: ERROR
Running 'testthat.R' [18s]
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 [17:05:38.678] [mlr3] Running benchmark with 12 resampling iterations
INFO [17:05:38.920] [mlr3] Applying learner 'classif.rpart' on task 'adult_train' (iter 1/3)
INFO [17:05:39.030] [mlr3] Applying learner 'classif.rpart' on task 'adult_train' (iter 2/3)
INFO [17:05:39.088] [mlr3] Applying learner 'classif.rpart' on task 'adult_train' (iter 3/3)
INFO [17:05:39.148] [mlr3] Applying learner 'classif.featureless' on task 'adult_train' (iter 1/3)
INFO [17:05:39.192] [mlr3] Applying learner 'classif.featureless' on task 'adult_train' (iter 2/3)
INFO [17:05:39.237] [mlr3] Applying learner 'classif.featureless' on task 'adult_train' (iter 3/3)
INFO [17:05:39.282] [mlr3] Applying learner 'classif.rpart' on task 'compas' (iter 1/3)
INFO [17:05:39.341] [mlr3] Applying learner 'classif.rpart' on task 'compas' (iter 2/3)
INFO [17:05:39.400] [mlr3] Applying learner 'classif.rpart' on task 'compas' (iter 3/3)
INFO [17:05:39.458] [mlr3] Applying learner 'classif.featureless' on task 'compas' (iter 1/3)
INFO [17:05:39.513] [mlr3] Applying learner 'classif.featureless' on task 'compas' (iter 2/3)
INFO [17:05:39.559] [mlr3] Applying learner 'classif.featureless' on task 'compas' (iter 3/3)
INFO [17:05:39.607] [mlr3] Finished benchmark
INFO [17:05:40.076] [mlr3] Running benchmark with 12 resampling iterations
INFO [17:05:40.140] [mlr3] Applying learner 'classif.rpart' on task 'adult_train' (iter 1/3)
INFO [17:05:40.200] [mlr3] Applying learner 'classif.rpart' on task 'adult_train' (iter 2/3)
INFO [17:05:40.260] [mlr3] Applying learner 'classif.rpart' on task 'adult_train' (iter 3/3)
INFO [17:05:40.335] [mlr3] Applying learner 'classif.featureless' on task 'adult_train' (iter 1/3)
INFO [17:05:40.399] [mlr3] Applying learner 'classif.featureless' on task 'adult_train' (iter 2/3)
INFO [17:05:40.448] [mlr3] Applying learner 'classif.featureless' on task 'adult_train' (iter 3/3)
INFO [17:05:40.496] [mlr3] Applying learner 'classif.rpart' on task 'compas' (iter 1/3)
INFO [17:05:40.563] [mlr3] Applying learner 'classif.rpart' on task 'compas' (iter 2/3)
INFO [17:05:40.634] [mlr3] Applying learner 'classif.rpart' on task 'compas' (iter 3/3)
INFO [17:05:40.704] [mlr3] Applying learner 'classif.featureless' on task 'compas' (iter 1/3)
INFO [17:05:40.754] [mlr3] Applying learner 'classif.featureless' on task 'compas' (iter 2/3)
INFO [17:05:40.801] [mlr3] Applying learner 'classif.featureless' on task 'compas' (iter 3/3)
INFO [17:05:40.850] [mlr3] Finished benchmark
INFO [17:05:41.184] [mlr3] Running benchmark with 12 resampling iterations
INFO [17:05:41.211] [mlr3] Applying learner 'classif.rpart' on task 'adult_train' (iter 1/3)
INFO [17:05:41.262] [mlr3] Applying learner 'classif.rpart' on task 'adult_train' (iter 2/3)
INFO [17:05:41.329] [mlr3] Applying learner 'classif.rpart' on task 'adult_train' (iter 3/3)
INFO [17:05:41.400] [mlr3] Applying learner 'classif.featureless' on task 'adult_train' (iter 1/3)
INFO [17:05:41.454] [mlr3] Applying learner 'classif.featureless' on task 'adult_train' (iter 2/3)
INFO [17:05:41.504] [mlr3] Applying learner 'classif.featureless' on task 'adult_train' (iter 3/3)
INFO [17:05:41.555] [mlr3] Applying learner 'classif.rpart' on task 'compas' (iter 1/3)
INFO [17:05:41.622] [mlr3] Applying learner 'classif.rpart' on task 'compas' (iter 2/3)
INFO [17:05:41.694] [mlr3] Applying learner 'classif.rpart' on task 'compas' (iter 3/3)
INFO [17:05:41.758] [mlr3] Applying learner 'classif.featureless' on task 'compas' (iter 1/3)
INFO [17:05:41.810] [mlr3] Applying learner 'classif.featureless' on task 'compas' (iter 2/3)
INFO [17:05:41.876] [mlr3] Applying learner 'classif.featureless' on task 'compas' (iter 3/3)
INFO [17:05:41.936] [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 D:\RCompile\CRANpkg\local\4.4\mlr3fairness.Rcheck\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 D:\RCompile\CRANpkg\local\4.4\mlr3fairness.Rcheck\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
Flavor: r-oldrel-windows-x86_64