Package: mlr3fairness Check: examples New 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: ... score_groupwise -> map_dbl -> map_mold -> vapply -> FUN Execution halted Package: mlr3fairness Check: tests New result: ERROR Running ‘testthat.R’ [13s/13s] 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 [09:53:36.754] [mlr3] Running benchmark with 12 resampling iterations INFO [09:53:37.071] [mlr3] Applying learner 'classif.rpart' on task 'adult_train' (iter 1/3) INFO [09:53:37.148] [mlr3] Applying learner 'classif.rpart' on task 'adult_train' (iter 2/3) INFO [09:53:37.182] [mlr3] Applying learner 'classif.rpart' on task 'adult_train' (iter 3/3) INFO [09:53:37.212] [mlr3] Applying learner 'classif.featureless' on task 'adult_train' (iter 1/3) INFO [09:53:37.239] [mlr3] Applying learner 'classif.featureless' on task 'adult_train' (iter 2/3) INFO [09:53:37.276] [mlr3] Applying learner 'classif.featureless' on task 'adult_train' (iter 3/3) INFO [09:53:37.296] [mlr3] Applying learner 'classif.rpart' on task 'compas' (iter 1/3) INFO [09:53:37.324] [mlr3] Applying learner 'classif.rpart' on task 'compas' (iter 2/3) INFO [09:53:37.351] [mlr3] Applying learner 'classif.rpart' on task 'compas' (iter 3/3) INFO [09:53:37.388] [mlr3] Applying learner 'classif.featureless' on task 'compas' (iter 1/3) INFO [09:53:37.408] [mlr3] Applying learner 'classif.featureless' on task 'compas' (iter 2/3) INFO [09:53:37.426] [mlr3] Applying learner 'classif.featureless' on task 'compas' (iter 3/3) INFO [09:53:37.449] [mlr3] Finished benchmark INFO [09:53:37.809] [mlr3] Running benchmark with 12 resampling iterations INFO [09:53:37.917] [mlr3] Applying learner 'classif.rpart' on task 'adult_train' (iter 1/3) INFO [09:53:37.964] [mlr3] Applying learner 'classif.rpart' on task 'adult_train' (iter 2/3) INFO [09:53:38.012] [mlr3] Applying learner 'classif.rpart' on task 'adult_train' (iter 3/3) INFO [09:53:38.059] [mlr3] Applying learner 'classif.featureless' on task 'adult_train' (iter 1/3) INFO [09:53:38.096] [mlr3] Applying learner 'classif.featureless' on task 'adult_train' (iter 2/3) INFO [09:53:38.132] [mlr3] Applying learner 'classif.featureless' on task 'adult_train' (iter 3/3) INFO [09:53:38.167] [mlr3] Applying learner 'classif.rpart' on task 'compas' (iter 1/3) INFO [09:53:38.226] [mlr3] Applying learner 'classif.rpart' on task 'compas' (iter 2/3) INFO [09:53:38.272] [mlr3] Applying learner 'classif.rpart' on task 'compas' (iter 3/3) INFO [09:53:38.319] [mlr3] Applying learner 'classif.featureless' on task 'compas' (iter 1/3) INFO [09:53:38.355] [mlr3] Applying learner 'classif.featureless' on task 'compas' (iter 2/3) INFO [09:53:38.384] [mlr3] Applying learner 'classif.featureless' on task 'compas' (iter 3/3) INFO [09:53:38.422] [mlr3] Finished benchmark INFO [09:53:38.696] [mlr3] Running benchmark with 12 resampling iterations INFO [09:53:38.716] [mlr3] Applying learner 'classif.rpart' on task 'adult_train' (iter 1/3) INFO [09:53:38.765] [mlr3] Applying learner 'classif.rpart' on task 'adult_train' (iter 2/3) INFO [09:53:38.815] [mlr3] Applying learner 'classif.rpart' on task 'adult_train' (iter 3/3) INFO [09:53:38.866] [mlr3] Applying learner 'classif.featureless' on task 'adult_train' (iter 1/3) INFO [09:53:38.913] [mlr3] Applying learner 'classif.featureless' on task 'adult_train' (iter 2/3) INFO [09:53:38.958] [mlr3] Applying learner 'classif.featureless' on task 'adult_train' (iter 3/3) INFO [09:53:38.994] [mlr3] Applying learner 'classif.rpart' on task 'compas' (iter 1/3) INFO [09:53:39.041] [mlr3] Applying learner 'classif.rpart' on task 'compas' (iter 2/3) INFO [09:53:39.087] [mlr3] Applying learner 'classif.rpart' on task 'compas' (iter 3/3) INFO [09:53:39.134] [mlr3] Applying learner 'classif.featureless' on task 'compas' (iter 1/3) INFO [09:53:39.170] [mlr3] Applying learner 'classif.featureless' on task 'compas' (iter 2/3) INFO [09:53:39.206] [mlr3] Applying learner 'classif.featureless' on task 'compas' (iter 3/3) INFO [09:53:39.247] [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) ``(...) 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) ``(...) 31. └─mlr3:::score_single_measure(...) 32. └─get_private(measure)$.score(...) 33. └─mlr3fairness:::.__MeasureFairness__.score(...) 34. └─mlr3fairness:::score_groupwise(...) 35. └─mlr3misc::map_dbl(...) 36. └─mlr3misc:::map_mold(.x, .f, NA_real_, ...) 37. └─base::vapply(.x, .f, FUN.VALUE = .value, USE.NAMES = FALSE, ...) 38. └─mlr3fairness (local) FUN(X[[i]], ...) [ FAIL 21 | WARN 14 | SKIP 30 | PASS 99 ] Error: Test failures Execution halted Package: mlr3resampling Check: examples New result: ERROR Running examples in ‘mlr3resampling-Ex.R’ failed The error most likely occurred in: > base::assign(".ptime", proc.time(), pos = "CheckExEnv") > ### Name: pvalue > ### Title: P-values for comparing Same/Other/All training > ### Aliases: pvalue > > ### ** Examples > > > N <- 80 > library(data.table) > set.seed(1) > reg.dt <- data.table( + x=runif(N, -2, 2), + person=rep(1:2, each=0.5*N)) > reg.pattern.list <- list( + easy=function(x, person)x^2, + impossible=function(x, person)(x^2)*(-1)^person) > SOAK <- mlr3resampling::ResamplingSameOtherSizesCV$new() > reg.task.list <- list() > for(pattern in names(reg.pattern.list)){ + f <- reg.pattern.list[[pattern]] + yname <- paste0("y_",pattern) + reg.dt[, (yname) := f(x,person)+rnorm(N, sd=0.5)][] + task.dt <- reg.dt[, c("x","person",yname), with=FALSE] + task.obj <- mlr3::TaskRegr$new( + pattern, task.dt, target=yname) + task.obj$col_roles$stratum <- "person" + task.obj$col_roles$subset <- "person" + reg.task.list[[pattern]] <- task.obj + } > reg.learner.list <- list( + mlr3::LearnerRegrFeatureless$new()) > if(requireNamespace("rpart")){ + reg.learner.list$rpart <- mlr3::LearnerRegrRpart$new() + } Loading required namespace: rpart > (bench.grid <- mlr3::benchmark_grid( + reg.task.list, + reg.learner.list, + SOAK)) task learner resampling 1: easy regr.featureless same_other_sizes_cv 2: easy regr.rpart same_other_sizes_cv 3: impossible regr.featureless same_other_sizes_cv 4: impossible regr.rpart same_other_sizes_cv > bench.result <- mlr3::benchmark(bench.grid) Error in if (resampling$task_row_hash != task$row_hash) { : argument is of length zero Calls: -> pmap_dtr -> mapply_list -> .mapply -> Execution halted Package: mlr3resampling Check: re-building of vignette outputs New result: ERROR Error(s) in re-building vignettes: ... --- re-building ‘Newer_resamplers.Rmd’ using knitr ** Processing: figure/simulationScatter-1.png 432x432 pixels, 8 bits/pixel, 254 colors in palette Reducing image to 8 bits/pixel, grayscale Input IDAT size = 4572 bytes Input file size = 5424 bytes Trying: zc = 9 zm = 8 zs = 0 f = 0 IDAT size = 4060 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 = 4060 Output IDAT size = 4060 bytes (512 bytes decrease) Output file size = 4138 bytes (1286 bytes = 23.71% decrease) Quitting from Newer_resamplers.Rmd:109-126 [SameOtherCV] ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ Error in `if (resampling$task_row_hash != task$row_hash) ...`: ! argument is of length zero --- Backtrace: ▆ 1. └─mlr3::benchmark(same.other.grid, store_models = TRUE) 2. └─mlr3misc::pmap_dtr(...) 3. └─mlr3misc:::mapply_list(.f, .x, list(...)) 4. └─base::.mapply(.f, .dots, .args) 5. └─mlr3 (local) ``(...) ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ Error: processing vignette 'Newer_resamplers.Rmd' failed with diagnostics: argument is of length zero --- failed re-building ‘Newer_resamplers.Rmd’ --- re-building ‘Older_resamplers.Rmd’ using knitr ** Processing: figure/unnamed-chunk-3-1.png 432x432 pixels, 8 bits/pixel, 245 colors in palette Reducing image to 8 bits/pixel, grayscale Input IDAT size = 19674 bytes Input file size = 20523 bytes Trying: zc = 9 zm = 8 zs = 0 f = 0 IDAT size = 18124 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 = 18124 Output IDAT size = 18124 bytes (1550 bytes decrease) Output file size = 18202 bytes (2321 bytes = 11.31% decrease) Quitting from Older_resamplers.Rmd:160-167 [unnamed-chunk-7] ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ Error in `if (resampling$task_row_hash != task$row_hash) ...`: ! argument is of length zero --- Backtrace: ▆ 1. └─mlr3::benchmark(reg.bench.grid, store_models = TRUE) 2. └─mlr3misc::pmap_dtr(...) 3. └─mlr3misc:::mapply_list(.f, .x, list(...)) 4. └─base::.mapply(.f, .dots, .args) 5. └─mlr3 (local) ``(...) ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ Error: processing vignette 'Older_resamplers.Rmd' failed with diagnostics: argument is of length zero --- failed re-building ‘Older_resamplers.Rmd’ SUMMARY: processing the following files failed: ‘Newer_resamplers.Rmd’ ‘Older_resamplers.Rmd’ Error: Vignette re-building failed. Execution halted Package: mlr3spatiotempcv Check: tests New result: ERROR Running ‘testthat.R’ [99s/52s] Running the tests in ‘tests/testthat.R’ failed. Complete output: > if (requireNamespace("testthat", quietly = TRUE)) { + library("checkmate") + library("testthat") + library("mlr3spatiotempcv") + test_check("mlr3spatiotempcv") + } Loading required package: mlr3 Starting 2 test processes [ FAIL 1 | WARN 46 | SKIP 24 | PASS 1206 ] ══ Skipped tests (24) ══════════════════════════════════════════════════════════ • On CRAN (19): 'test-1-autoplot.R:40:3', 'test-1-autoplot.R:72:3', 'test-1-autoplot.R:98:3', 'test-1-autoplot.R:130:3', 'test-1-autoplot.R:157:3', 'test-1-autoplot.R:189:3', 'test-1-autoplot.R:224:3', 'test-1-autoplot.R:257:3', 'test-1-autoplot.R:268:3', 'test-1-autoplot.R:315:3', 'test-1-autoplot.R:343:3', 'test-1-autoplot.R:377:3', 'test-2-autoplot.R:129:3', 'test-2-autoplot.R:182:3', 'test-2-autoplot.R:204:3', 'test-2-autoplot.R:246:3', 'test-2-autoplot.R:300:3', 'test-2-autoplot.R:352:3', 'test-autoplot_buffer.R:19:3' • On Linux (2): 'test-2-autoplot.R:8:3', 'test-2-autoplot.R:54:3' • empty test (1): 'test-helper-DataBackend.R:1:1' • {raster} is not installed (1): 'test-mlr3pipelines-graph-integration.R:4:3' • {skmeans} is not installed (1): 'test-mlr_sptcv_generic.R:70:3' ══ Failed tests ════════════════════════════════════════════════════════════════ ── Error ('test-autotuner.R:38:3'): AutoTuner works with sptcv methods ───────── Error in `if (resampling$task_row_hash != task$row_hash) { stopf("Resampling '%s' is not instantiated on task '%s'", resampling$id, task$id) }`: missing value where TRUE/FALSE needed Backtrace: ▆ 1. └─mlr3::benchmark(grid) at test-autotuner.R:38:3 2. └─mlr3misc::pmap_dtr(...) 3. └─mlr3misc:::mapply_list(.f, .x, list(...)) 4. └─base::.mapply(.f, .dots, .args) 5. └─mlr3 (local) ``(...) [ FAIL 1 | WARN 46 | SKIP 24 | PASS 1206 ] Deleting unused snapshots: • 1-autoplot/autoplot-show-blocks-true-show-labels-true.svg • 1-autoplot/custom-cv-fold-1-2-sample-fold-n.svg • 1-autoplot/custom-cv-fold-1-2.svg • 1-autoplot/custom-cv-fold-1-sample-fold-n.svg • 1-autoplot/custom-cv-fold-1.svg • 1-autoplot/custom-cv-sample-fold-n.svg • 1-autoplot/cv-fold-1-2-groups-col-role.svg • 1-autoplot/cv-fold-1-2-sample-fold-n.svg • 1-autoplot/cv-fold-1-2.svg • 1-autoplot/cv-fold-1-groups-col-role.svg • 1-autoplot/cv-fold-1-sample-fold-n.svg • 1-autoplot/cv-fold-1.svg • 1-autoplot/cv-sample-fold-n.svg • 1-autoplot/repcv-fold-1-2-rep-1.svg • 1-autoplot/repcv-fold-1-2-rep-2.svg • 1-autoplot/repcv-fold-1-rep-1.svg • 1-autoplot/repcv-fold-1-rep-2.svg • 1-autoplot/repspcvblock-fold-1-2-rep-1.svg • 1-autoplot/repspcvblock-fold-1-2-rep-2.svg • 1-autoplot/repspcvblock-fold-1-rep-1.svg • 1-autoplot/repspcvblock-fold-1-rep-2.svg • 1-autoplot/repspcvcoords-fold-1-2-rep-1.svg • 1-autoplot/repspcvcoords-fold-1-2-rep-2.svg • 1-autoplot/repspcvcoords-fold-1-2-sample-fold-n.svg • 1-autoplot/repspcvcoords-fold-1-rep-1.svg • 1-autoplot/repspcvcoords-fold-1-rep-2.svg • 1-autoplot/repspcvcoords-fold-1-sample-fold-n.svg • 1-autoplot/repspcvcoords-sample-fold-n.svg • 1-autoplot/repspcvenv-fold-1-2-rep-1.svg • 1-autoplot/repspcvenv-fold-1-2-rep-2.svg • 1-autoplot/repspcvenv-fold-1-rep-1.svg • 1-autoplot/repspcvenv-fold-1-rep-2.svg • 1-autoplot/spcvblock-fold-1-2-sample-fold-n.svg • 1-autoplot/spcvblock-fold-1-2.svg • 1-autoplot/spcvblock-fold-1-sample-fold-n.svg • 1-autoplot/spcvblock-fold-1.svg • 1-autoplot/spcvblock-sample-fold-n.svg • 1-autoplot/spcvcoords-fold-1-2.svg • 1-autoplot/spcvcoords-fold-1.svg • 1-autoplot/spcvenv-fold-1-2-sample-fold-n.svg • 1-autoplot/spcvenv-fold-1-2.svg • 1-autoplot/spcvenv-fold-1-sample-fold-n.svg • 1-autoplot/spcvenv-fold-1.svg • 1-autoplot/spcvenv-sample-fold-n.svg • 2-autoplot/repspcvdisc-fold-1-2-rep-2.svg • 2-autoplot/repspcvdisc-fold-1-2-sample-fold-n.svg • 2-autoplot/repspcvdisc-fold-1-rep-1-sample-n-fold.svg • 2-autoplot/repspcvdisc-fold-1-rep-2.svg • 2-autoplot/repspcvdisc-fold-1-sample-fold-n.svg • 2-autoplot/repspcvdisc-sample-fold-n.svg • 2-autoplot/repspcvknndm-fold-1-2-rep-2.svg • 2-autoplot/repspcvknndm-fold-1-2-sample-fold-n.svg • 2-autoplot/repspcvknndm-fold-1-rep-1-sample-n-fold.svg • 2-autoplot/repspcvknndm-fold-1-rep-2.svg • 2-autoplot/repspcvknndm-fold-1-sample-fold-n.svg • 2-autoplot/repspcvknndm-sample-fold-n.svg • 2-autoplot/repspcvtiles-fold-1-2-rep-2.svg • 2-autoplot/repspcvtiles-fold-1-2-sample-fold-n.svg • 2-autoplot/repspcvtiles-fold-1-2.svg • 2-autoplot/repspcvtiles-fold-1-rep-2.svg • 2-autoplot/repspcvtiles-fold-1-sample-fold-n.svg • 2-autoplot/repspcvtiles-fold-1.svg • 2-autoplot/repspcvtiles-sample-fold-n.svg • 2-autoplot/repspcvtiles-show-omitted.svg • 2-autoplot/repsptcvcstf-2d-space-var-fold-1-2-rep-2.svg • 2-autoplot/repsptcvcstf-2d-space-var-fold-1-rep-2.svg • 2-autoplot/repsptcvcstf-fold-1-2-rep-1.svg • 2-autoplot/repsptcvcstf-fold-1-2-rep-2.svg • 2-autoplot/repsptcvcstf-fold-1-rep-1-sample-fold-n.svg • 2-autoplot/repsptcvcstf-fold-1-rep-2.svg • 2-autoplot/spcvdisc-fold-1-2.svg • 2-autoplot/spcvdisc-fold-1.svg • 2-autoplot/spcvdisc-show-omitted.svg • 2-autoplot/spcvknndm-fold-1-2.svg • 2-autoplot/spcvknndm-fold-1.svg • 2-autoplot/sptcvcstf-2d-space-var-all-test-sets.svg • 2-autoplot/sptcvcstf-2d-space-var-fold-1-2.svg • 2-autoplot/sptcvcstf-2d-space-var-fold-1.svg • 2-autoplot/sptcvcstf-2d-time-var-all-test-sets.svg • 2-autoplot/sptcvcstf-2d-time-var-fold-1-2-rep-2.svg • 2-autoplot/sptcvcstf-2d-time-var-fold-1-2-sample-fold-n.svg • 2-autoplot/sptcvcstf-2d-time-var-fold-1-2.svg • 2-autoplot/sptcvcstf-2d-time-var-fold-1-rep-2.svg • 2-autoplot/sptcvcstf-2d-time-var-fold-1-sample-fold-n.svg • 2-autoplot/sptcvcstf-2d-time-var-fold-1.svg • 2-autoplot/sptcvcstf-2d-time-var-sample-fold-n.svg • 2-autoplot/sptcvcstf-3d-time-var-fold-1-2-sample-fold-n.svg • 2-autoplot/sptcvcstf-3d-time-var-fold-1-2.svg • 2-autoplot/sptcvcstf-3d-time-var-fold-1-sample-fold-n.svg • autoplot_buffer/spcvbuffer-fold-1-2.svg Error: Test failures Execution halted Package: mlr3torch Check: examples New result: ERROR Running examples in ‘mlr3torch-Ex.R’ failed The error most likely occurred in: > base::assign(".ptime", proc.time(), pos = "CheckExEnv") > ### Name: mlr_tasks_cifar > ### Title: CIFAR Classification Tasks > ### Aliases: mlr_tasks_cifar mlr_tasks_cifar10 mlr_tasks_cifar100 > > ### ** Examples > > task_cifar10 = tsk("cifar10") > task_cifar100 = tsk("cifar100") > print(task_cifar10) ── (60000x2): CIFAR-10 Classification ──────────────────────────── • Target: class trying URL 'https://www.cs.toronto.edu/~kriz/cifar-10-binary.tar.gz' Content type 'application/x-gzip' length 170052171 bytes (162.2 MB) ================================================== downloaded 162.2 MB Error in torch_tensor_cpp(data, dtype, device, requires_grad, pin_memory) : Lantern is not loaded. Please use `install_torch()` to install additional dependencies. Calls: print ... torch_tensor -> -> -> torch_tensor_cpp Execution halted