Running ‘testthat.R’ [250s/125s]
Running the tests in ‘tests/testthat.R’ failed.
Complete output:
  > if (requireNamespace("testthat", quietly = TRUE)) {
  +   library("checkmate")
  +   library("testthat")
  +   library("mlr3")
  +   library("paradox")
  +   library("mlr3pipelines")
  +   test_check("mlr3pipelines")
  + }
  Starting 2 test processes.
  > test_Graph.R: Training debug.multi with input list(input_1 = 1, input_2 = 1)
  > test_PipeOp.R: Training test_autotrain
  > test_PipeOp.R: Predicting test_autotrain
  > test_PipeOp.R: Training test_autotrain
  > test_PipeOp.R: Training test_autotrain
  > test_PipeOp.R: Predicting test_autotrain
  > test_PipeOp.R: Predicting test_autotrain
  > test_PipeOp.R: Training test_autotrain
  > test_PipeOp.R: Training test_autotrain
  > test_PipeOp.R: Training test_autotrain
  > test_PipeOp.R: Predicting test_autotrain
  > test_PipeOp.R: Predicting test_autotrain
  > test_PipeOp.R: Predicting test_autotrain
  > test_PipeOp.R: Training test_autotrain
  > test_PipeOp.R: Predicting test_autotrain
  > test_PipeOp.R: Training test_autotrain
  > test_PipeOp.R: Training test_autotrain
  > test_PipeOp.R: Predicting test_autotrain
  > test_PipeOp.R: Predicting test_autotrain
  > test_PipeOp.R: Training test_autotrain
  > test_PipeOp.R: Training test_autotrain
  > test_PipeOp.R: Training test_autotrain
  > test_PipeOp.R: Predicting test_autotrain
  > test_PipeOp.R: Predicting test_autotrain
  > test_PipeOp.R: Predicting test_autotrain
  > test_PipeOp.R: Training test_autotrain
  > test_PipeOp.R: Predicting test_autotrain
  Saving _problems/test_conversion-143.R
  Saving _problems/test_conversion-165.R
  > test_filter_ensemble.R: OptimInstanceSingleCrit is deprecated. Use OptimInstanceBatchSingleCrit instead.
  Saving _problems/test_filter_ensemble-291.R
  Saving _problems/test_filter_ensemble-447.R
  Saving _problems/test_mlr_graphs_bagging-49.R
  Saving _problems/test_mlr_graphs_stacking-16.R
  > test_mlr_graphs_robustify.R: 'as(<dgCMatrix>, "dgTMatrix")' is deprecated.
  > test_mlr_graphs_robustify.R: Use 'as(., "TsparseMatrix")' instead.
  > test_mlr_graphs_robustify.R: See help("Deprecated") and help("Matrix-deprecated").
  > test_multiplicities.R: 
  > test_multiplicities.R: [[1]]
  > test_multiplicities.R: [1] 0
  > test_multiplicities.R: 
  > test_multiplicities.R: 
  > test_pipeop_blsmote.R: [1] "Borderline-SMOTE done"
  > test_pipeop_blsmote.R: [1] "Borderline-SMOTE done"
  > test_pipeop_blsmote.R: [1] "Borderline-SMOTE done"
  > test_pipeop_blsmote.R: [1] "Borderline-SMOTE done"
  > test_pipeop_isomap.R: 2025-12-22 17:19:13.694554: Isomap START
  > test_pipeop_isomap.R: 2025-12-22 17:19:13.695309: constructing knn graph
  > test_pipeop_isomap.R: 2025-12-22 17:19:13.703772: calculating geodesic distances
  > test_pipeop_isomap.R: 2025-12-22 17:19:13.718212: Classical Scaling
  > test_pipeop_isomap.R: 2025-12-22 17:19:13.75171: Isomap START
  > test_pipeop_isomap.R: 2025-12-22 17:19:13.752135: constructing knn graph
  > test_pipeop_isomap.R: 2025-12-22 17:19:13.758068: calculating geodesic distances
  > test_pipeop_isomap.R: 2025-12-22 17:19:13.771962: Classical Scaling
  > test_pipeop_isomap.R: 2025-12-22 17:19:13.79094: L-Isomap embed START
  > test_pipeop_isomap.R: 2025-12-22 17:19:13.791496: constructing knn graph
  > test_pipeop_isomap.R: 2025-12-22 17:19:13.802366: calculating geodesic distances
  > test_pipeop_isomap.R: 2025-12-22 17:19:13.834387: embedding
  > test_pipeop_isomap.R: 2025-12-22 17:19:13.835379: DONE
  > test_pipeop_isomap.R: 2025-12-22 17:19:13.851611: L-Isomap embed START
  > test_pipeop_isomap.R: 2025-12-22 17:19:13.852052: constructing knn graph
  > test_pipeop_isomap.R: 2025-12-22 17:19:13.86373: calculating geodesic distances
  > test_pipeop_isomap.R: 2025-12-22 17:19:13.896: embedding
  > test_pipeop_isomap.R: 2025-12-22 17:19:13.897153: DONE
  > test_pipeop_isomap.R: 2025-12-22 17:19:14.013631: Isomap START
  > test_pipeop_isomap.R: 2025-12-22 17:19:14.013979: constructing knn graph
  > test_pipeop_isomap.R: 2025-12-22 17:19:14.02877: calculating geodesic distances
  > test_pipeop_isomap.R: 2025-12-22 17:19:14.105453: Classical Scaling
  > test_pipeop_isomap.R: 2025-12-22 17:19:14.132438: L-Isomap embed START
  > test_pipeop_isomap.R: 2025-12-22 17:19:14.13302: constructing knn graph
  > test_pipeop_isomap.R: 2025-12-22 17:19:14.171092: calculating geodesic distances
  > test_pipeop_isomap.R: 2025-12-22 17:19:14.346651: embedding
  > test_pipeop_isomap.R: 2025-12-22 17:19:14.350074: DONE
  > test_pipeop_isomap.R: 2025-12-22 17:19:14.460308: Isomap START
  > test_pipeop_isomap.R: 2025-12-22 17:19:14.460732: constructing knn graph
  > test_pipeop_isomap.R: 2025-12-22 17:19:14.466748: calculating geodesic distances
  > test_pipeop_isomap.R: 2025-12-22 17:19:14.482522: Classical Scaling
  > test_pipeop_isomap.R: 2025-12-22 17:19:14.503583: L-Isomap embed START
  > test_pipeop_isomap.R: 2025-12-22 17:19:14.504177: constructing knn graph
  > test_pipeop_isomap.R: 2025-12-22 17:19:14.515608: calculating geodesic distances
  > test_pipeop_isomap.R: 2025-12-22 17:19:14.547952: embedding
  > test_pipeop_isomap.R: 2025-12-22 17:19:14.549251: DONE
  > test_pipeop_isomap.R: 2025-12-22 17:19:14.669898: Isomap START
  > test_pipeop_isomap.R: 2025-12-22 17:19:14.670323: constructing knn graph
  > test_pipeop_isomap.R: 2025-12-22 17:19:14.677714: calculating geodesic distances
  > test_pipeop_isomap.R: 2025-12-22 17:19:14.691103: Classical Scaling
  > test_pipeop_isomap.R: 2025-12-22 17:19:14.722205: L-Isomap embed START
  > test_pipeop_isomap.R: 2025-12-22 17:19:14.722837: constructing knn graph
  > test_pipeop_isomap.R: 2025-12-22 17:19:14.734325: calculating geodesic distances
  > test_pipeop_isomap.R: 2025-12-22 17:19:14.766851: embedding
  > test_pipeop_isomap.R: 2025-12-22 17:19:14.767872: DONE
  > test_pipeop_isomap.R: 2025-12-22 17:19:14.820849: Isomap START
  > test_pipeop_isomap.R: 2025-12-22 17:19:14.821292: constructing knn graph
  > test_pipeop_isomap.R: 2025-12-22 17:19:14.828862: calculating geodesic distances
  > test_pipeop_isomap.R: 2025-12-22 17:19:14.842909: Classical Scaling
  > test_pipeop_isomap.R: 2025-12-22 17:19:14.87596: L-Isomap embed START
  > test_pipeop_isomap.R: 2025-12-22 17:19:14.876527: constructing knn graph
  > test_pipeop_isomap.R: 2025-12-22 17:19:14.888224: calculating geodesic distances
  > test_pipeop_isomap.R: 2025-12-22 17:19:14.920611: embedding
  > test_pipeop_isomap.R: 2025-12-22 17:19:14.921573: DONE
  > test_pipeop_isomap.R: 2025-12-22 17:19:14.979495: Isomap START
  > test_pipeop_isomap.R: 2025-12-22 17:19:14.97993: constructing knn graph
  > test_pipeop_isomap.R: 2025-12-22 17:19:14.998384: calculating geodesic distances
  > test_pipeop_isomap.R: 2025-12-22 17:19:15.012737: Classical Scaling
  > test_pipeop_isomap.R: 2025-12-22 17:19:15.05878: L-Isomap embed START
  > test_pipeop_isomap.R: 2025-12-22 17:19:15.059359: constructing knn graph
  > test_pipeop_isomap.R: 2025-12-22 17:19:15.071612: calculating geodesic distances
  > test_pipeop_isomap.R: 2025-12-22 17:19:15.104153: embedding
  > test_pipeop_isomap.R: 2025-12-22 17:19:15.105324: DONE
  > test_pipeop_isomap.R: 2025-12-22 17:19:15.164378: Isomap START
  > test_pipeop_isomap.R: 2025-12-22 17:19:15.164806: constructing knn graph
  > test_pipeop_isomap.R: 2025-12-22 17:19:15.176221: calculating geodesic distances
  > test_pipeop_isomap.R: 2025-12-22 17:19:15.190325: Classical Scaling
  > test_pipeop_isomap.R: 2025-12-22 17:19:15.226433: L-Isomap embed START
  > test_pipeop_isomap.R: 2025-12-22 17:19:15.227058: constructing knn graph
  > test_pipeop_isomap.R: 2025-12-22 17:19:15.238437: calculating geodesic distances
  > test_pipeop_isomap.R: 2025-12-22 17:19:15.270741: embedding
  > test_pipeop_isomap.R: 2025-12-22 17:19:15.271724: DONE
  > test_pipeop_isomap.R: 2025-12-22 17:19:15.327781: Isomap START
  > test_pipeop_isomap.R: 2025-12-22 17:19:15.328171: constructing knn graph
  > test_pipeop_isomap.R: 2025-12-22 17:19:15.345986: calculating geodesic distances
  > test_pipeop_isomap.R: 2025-12-22 17:19:15.360378: Classical Scaling
  > test_pipeop_isomap.R: 2025-12-22 17:19:15.427852: Isomap START
  > test_pipeop_isomap.R: 2025-12-22 17:19:15.428263: constructing knn graph
  > test_pipeop_isomap.R: 2025-12-22 17:19:15.434331: calculating geodesic distances
  > test_pipeop_isomap.R: 2025-12-22 17:19:15.44837: Classical Scaling
  > test_pipeop_isomap.R: 2025-12-22 17:19:15.464582: Isomap START
  > test_pipeop_isomap.R: 2025-12-22 17:19:15.464975: constructing knn graph
  > test_pipeop_isomap.R: 2025-12-22 17:19:15.47095: calculating geodesic distances
  > test_pipeop_isomap.R: 2025-12-22 17:19:15.484976: Classical Scaling
  Saving _problems/test_pipeop_learnerpicvplus-35.R
  Saving _problems/test_pipeop_learnerpicvplus-91.R
  Saving _problems/test_pipeop_learnerpicvplus-116.R
  Saving _problems/test_pipeop_learnerpicvplus-130.R
  Saving _problems/test_pipeop_learnerpicvplus-152.R
  Saving _problems/test_pipeop_learnercv-11.R
  Saving _problems/test_pipeop_learnercv-100.R
  Saving _problems/test_pipeop_learnercv-139.R
  Saving _problems/test_pipeop_learnercv-152.R
  Saving _problems/test_pipeop_learnercv-203.R
  Saving _problems/test_pipeop_learnercv-250.R
  Saving _problems/test_pipeop_learnercv-278.R
  Saving _problems/test_pipeop_learnercv-323.R
  Saving _problems/test_pipeop_learnercv-350.R
  Saving _problems/test_pipeop_learnercv-387.R
  Saving _problems/test_pipeop_learnercv-419.R
  Saving _problems/test_pipeop_learnercv-455.R
  Saving _problems/test_pipeop_learnercv-493.R
  Saving _problems/test_pipeop_learnercv-516.R
  Saving _problems/test_pipeop_learnercv-531.R
  Saving _problems/test_pipeop_learnercv-557.R
  Saving _problems/test_pipeop_learnercv-612.R
  Saving _problems/test_pipeop_learnercv-628.R
  Saving _problems/test_pipeop_learnercv-671.R
  > test_pipeop_nmf.R: [PipeOpNMFstate]
  > test_pipeop_nmf.R: [PipeOpNMFstate]
  > test_pipeop_task_preproc.R: Training debug_affectcols
  > test_pipeop_task_preproc.R: Training debug_affectcols
  > test_pipeop_task_preproc.R: Training debug_affectcols
  > test_pipeop_task_preproc.R: Training debug_affectcols
  > test_pipeop_task_preproc.R: Training debug_affectcols
  > test_pipeop_task_preproc.R: Training debug_affectcols
  > test_pipeop_task_preproc.R: Training debug_affectcols
  > test_pipeop_task_preproc.R: Training debug_affectcols
  > test_pipeop_task_preproc.R: Training debug_affectcols
  > test_pipeop_task_preproc.R: Training debug_affectcols
  > test_pipeop_task_preproc.R: Training debug_affectcols
  > test_pipeop_task_preproc.R: Training debug_affectcols
  > test_pipeop_task_preproc.R: Training debug_affectcols
  > test_pipeop_task_preproc.R: Training debug_affectcols
  > test_pipeop_task_preproc.R: Training debug_affectcols
  > test_pipeop_task_preproc.R: Training debug_affectcols
  > test_pipeop_task_preproc.R: Training debug_affectcols
  > test_pipeop_task_preproc.R: Training debug_affectcols
  > test_pipeop_textvectorizer.R: 'as(<dgCMatrix>, "dgTMatrix")' is deprecated.
  > test_pipeop_textvectorizer.R: Use 'as(., "TsparseMatrix")' instead.
  > test_pipeop_textvectorizer.R: See help("Deprecated") and help("Matrix-deprecated").
  > test_pipeop_task_preproc.R: Training debug_affectcols
  > test_pipeop_task_preproc.R: Training debug_affectcols
  > test_pipeop_task_preproc.R: Training debug_affectcols
  > test_pipeop_task_preproc.R: Training debug_affectcols
  Saving _problems/test_pipeop_tunethreshold-7.R
  Saving _problems/test_pipeop_tunethreshold-38.R
  Saving _problems/test_pipeop_tunethreshold-73.R
  Saving _problems/test_pipeop_tunethreshold-101.R
  Saving _problems/test_pipeop_tunethreshold-260.R
  Saving _problems/test_resample-13.R
  Saving _problems/test_usecases-153.R
  Saving _problems/test_ppl-73.R
  [ FAIL 38 | WARN 2 | SKIP 98 | PASS 12316 ]
  
  ══ Skipped tests (98) ══════════════════════════════════════════════════════════
  • On CRAN (95): 'test_CnfFormula_simplify.R:6:3', 'test_CnfFormula.R:591:3',
    'test_Graph.R:283:3', 'test_PipeOp.R:32:1', 'test_GraphLearner.R:5:3',
    'test_GraphLearner.R:221:3', 'test_GraphLearner.R:343:3',
    'test_GraphLearner.R:408:3', 'test_GraphLearner.R:571:3',
    'test_dictionary.R:7:3', 'test_learner_weightedaverage.R:5:3',
    'test_learner_weightedaverage.R:57:3',
    'test_learner_weightedaverage.R:105:3',
    'test_learner_weightedaverage.R:152:3', 'test_meta.R:39:3',
    'test_mlr_graphs_bagging.R:6:3', 'test_mlr_graphs_branching.R:26:3',
    'test_mlr_graphs_robustify.R:5:3', 'test_pipeop_adas.R:8:3',
    'test_pipeop_blsmote.R:8:3', 'test_pipeop_branch.R:4:3',
    'test_pipeop_chunk.R:4:3', 'test_pipeop_classbalancing.R:7:3',
    'test_pipeop_classweights.R:10:3', 'test_pipeop_boxcox.R:7:3',
    'test_pipeop_collapsefactors.R:6:3', 'test_pipeop_colapply.R:9:3',
    'test_pipeop_copy.R:5:3', 'test_pipeop_colroles.R:6:3',
    'test_pipeop_decode.R:14:3', 'test_pipeop_encode.R:21:3',
    'test_pipeop_encodeimpact.R:11:3', 'test_pipeop_datefeatures.R:10:3',
    'test_pipeop_encodepl.R:5:3', 'test_pipeop_encodepl.R:72:3',
    'test_pipeop_encodelmer.R:15:3', 'test_pipeop_encodelmer.R:37:3',
    'test_pipeop_encodelmer.R:80:3', 'test_pipeop_featureunion.R:9:3',
    'test_pipeop_featureunion.R:134:3', 'test_pipeop_filter.R:7:3',
    'test_pipeop_fixfactors.R:9:3', 'test_pipeop_histbin.R:7:3',
    'test_pipeop_ensemble.R:6:3', 'test_pipeop_ica.R:7:3',
    'test_pipeop_imputelearner.R:43:3', 'test_pipeop_impute.R:4:3',
    'test_pipeop_isomap.R:10:3', 'test_pipeop_kernelpca.R:9:3',
    'test_pipeop_learner.R:17:3', 'test_pipeop_info.R:6:3',
    'test_pipeop_learnerpicvplus.R:163:3', 'test_pipeop_modelmatrix.R:7:3',
    'test_pipeop_missind.R:6:3', 'test_pipeop_multiplicityexply.R:9:3',
    'test_pipeop_mutate.R:9:3', 'test_pipeop_nearmiss.R:7:3',
    'test_pipeop_multiplicityimply.R:9:3', 'test_pipeop_ovr.R:9:3',
    'test_pipeop_ovr.R:48:3', 'test_pipeop_pca.R:8:3',
    'test_pipeop_proxy.R:14:3', 'test_pipeop_quantilebin.R:5:3',
    'test_pipeop_randomprojection.R:6:3', 'test_pipeop_randomresponse.R:5:3',
    'test_pipeop_removeconstants.R:6:3', 'test_pipeop_renamecolumns.R:6:3',
    'test_pipeop_replicate.R:9:3', 'test_pipeop_rowapply.R:6:3',
    'test_pipeop_scale.R:6:3', 'test_pipeop_scale.R:10:3',
    'test_pipeop_scalemaxabs.R:6:3', 'test_pipeop_scalerange.R:7:3',
    'test_pipeop_select.R:9:3', 'test_pipeop_smote.R:10:3',
    'test_pipeop_smotenc.R:8:3', 'test_pipeop_spatialsign.R:6:3',
    'test_pipeop_nmf.R:6:3', 'test_pipeop_targetinvert.R:4:3',
    'test_pipeop_targetmutate.R:5:3', 'test_pipeop_targettrafo.R:4:3',
    'test_pipeop_subsample.R:6:3', 'test_pipeop_targettrafoscalerange.R:5:3',
    'test_pipeop_task_preproc.R:4:3', 'test_pipeop_task_preproc.R:14:3',
    'test_pipeop_tomek.R:7:3', 'test_pipeop_tunethreshold.R:111:3',
    'test_pipeop_tunethreshold.R:191:3', 'test_pipeop_unbranch.R:10:3',
    'test_pipeop_textvectorizer.R:37:3', 'test_pipeop_textvectorizer.R:186:3',
    'test_pipeop_vtreat.R:9:3', 'test_pipeop_updatetarget.R:89:3',
    'test_pipeop_yeojohnson.R:7:3', 'test_typecheck.R:188:3'
  • Skipping (1): 'test_GraphLearner.R:1278:3'
  • empty test (2): 'test_pipeop_isomap.R:111:1', 'test_pipeop_missind.R:101:1'
  
  ══ Failed tests ════════════════════════════════════════════════════════════════
  ── Error ('test_conversion.R:143:3'): Graph to GraphLearner ────────────────────
  Error in ``[.data.table`(data, , `:=`("task_hash", task[[1L]]$hash), by = "uhash")`: attempt access index 9/9 in VECTOR_ELT
  Backtrace:
      ▆
   1. └─mlr3::resample(task, glrn1, cv) at test_conversion.R:143:3
   2.   └─ResultData$new(data, data_extra, store_backends = store_backends)
   3.     └─mlr3 (local) initialize(...)
   4.       └─mlr3:::.__ResultData__initialize(...)
   5.         ├─data[, `:=`("task_hash", task[[1L]]$hash), by = "uhash"]
   6.         └─data.table:::`[.data.table`(...)
  ── Error ('test_conversion.R:165:3'): PipeOp to GraphLearner ───────────────────
  Error in ``[.data.table`(data, , `:=`("task_hash", task[[1L]]$hash), by = "uhash")`: attempt access index 9/9 in VECTOR_ELT
  Backtrace:
      ▆
   1. └─mlr3::resample(task, glrn1, cv) at test_conversion.R:165:3
   2.   └─ResultData$new(data, data_extra, store_backends = store_backends)
   3.     └─mlr3 (local) initialize(...)
   4.       └─mlr3:::.__ResultData__initialize(...)
   5.         ├─data[, `:=`("task_hash", task[[1L]]$hash), by = "uhash"]
   6.         └─data.table:::`[.data.table`(...)
  ── Error ('test_filter_ensemble.R:291:3'): FilterEnsemble ignores NA scores from wrapped filters ──
  Error in ``[.data.table`(data, , `:=`("task_hash", task[[1L]]$hash), by = "uhash")`: attempt access index 9/9 in VECTOR_ELT
  Backtrace:
       ▆
    1. └─permutation_filter$calculate(task) at test_filter_ensemble.R:291:3
    2.   └─mlr3filters:::.__Filter__calculate(...)
    3.     └─private$.calculate(task, nfeat)
    4.       └─mlr3filters:::.__FilterPermutation__.calculate(...)
    5.         └─mlr3::resample(task, self$learner, self$resampling)
    6.           └─ResultData$new(data, data_extra, store_backends = store_backends)
    7.             └─mlr3 (local) initialize(...)
    8.               └─mlr3:::.__ResultData__initialize(...)
    9.                 ├─data[, `:=`("task_hash", task[[1L]]$hash), by = "uhash"]
   10.                 └─data.table:::`[.data.table`(...)
  ── Error ('test_filter_ensemble.R:447:7'): FilterEnsemble weight search space works with bbotk ──
  Error in ``[.data.table`(data, , `:=`("task_hash", task[[1L]]$hash), by = "uhash")`: attempt access index 9/9 in VECTOR_ELT
  Backtrace:
       ▆
    1. └─optimizer$optimize(instance) at test_filter_ensemble.R:463:3
    2.   └─bbotk:::.__OptimizerBatch__optimize(...)
    3.     └─bbotk::optimize_batch_default(inst, self)
    4.       ├─base::tryCatch(...)
    5.       │ └─base (local) tryCatchList(expr, classes, parentenv, handlers)
    6.       │   └─base (local) tryCatchOne(expr, names, parentenv, handlers[[1L]])
    7.       │     └─base (local) doTryCatch(return(expr), name, parentenv, handler)
    8.       └─get_private(optimizer)$.optimize(instance)
    9.         └─bbotk:::.__OptimizerBatchRandomSearch__.optimize(...)
   10.           └─inst$eval_batch(design$data)
   11.             └─bbotk:::.__OptimInstanceBatch__eval_batch(...)
   12.               └─self$objective$eval_many(xss_trafoed)
   13.                 └─bbotk:::.__Objective__eval_many(...)
   14.                   ├─mlr3misc::invoke(private$.eval_many, xss = xss, .args = self$constants$values)
   15.                   │ └─base::eval.parent(expr, n = 1L)
   16.                   │   └─base::eval(expr, p)
   17.                   │     └─base::eval(expr, p)
   18.                   └─private$.eval_many(xss = xss)
   19.                     └─bbotk:::.__Objective__.eval_many(...)
   20.                       └─mlr3misc::map_dtr(...)
   21.                         ├─data.table::rbindlist(...)
   22.                         ├─base::unname(map(.x, .f, ...))
   23.                         └─mlr3misc::map(.x, .f, ...)
   24.                           └─base::lapply(.x, .f, ...)
   25.                             └─bbotk (local) FUN(X[[i]], ...)
   26.                               └─self$eval(xs)
   27.                                 └─bbotk:::.__ObjectiveRFun__eval(...)
   28.                                   ├─mlr3misc::invoke(private$.fun, xs, .args = self$constants$values)
   29.                                   │ └─base::eval.parent(expr, n = 1L)
   30.                                   │   └─base::eval(expr, p)
   31.                                   │     └─base::eval(expr, p)
   32.                                   └─private$.fun(xs)
   33.                                     └─mlr3::resample(task, learner, resampling) at test_filter_ensemble.R:447:7
   34.                                       └─ResultData$new(data, data_extra, store_backends = store_backends)
   35.                                         └─mlr3 (local) initialize(...)
   36.                                           └─mlr3:::.__ResultData__initialize(...)
   37.                                             ├─data[, `:=`("task_hash", task[[1L]]$hash), by = "uhash"]
   38.                                             └─data.table:::`[.data.table`(...)
  ── Error ('test_mlr_graphs_bagging.R:49:3'): Bagging with replacement ──────────
  Error in ``[.data.table`(data, , `:=`("task_hash", task[[1L]]$hash), by = "uhash")`: attempt access index 9/9 in VECTOR_ELT
  Backtrace:
      ▆
   1. └─mlr3::resample(tsk, GraphLearner$new(p), rsmp("holdout")) at test_mlr_graphs_bagging.R:49:3
   2.   └─ResultData$new(data, data_extra, store_backends = store_backends)
   3.     └─mlr3 (local) initialize(...)
   4.       └─mlr3:::.__ResultData__initialize(...)
   5.         ├─data[, `:=`("task_hash", task[[1L]]$hash), by = "uhash"]
   6.         └─data.table:::`[.data.table`(...)
  ── Error ('test_mlr_graphs_stacking.R:16:3'): Stacking Pipeline ────────────────
  Error in ``[.data.table`(data, , `:=`("task_hash", task[[1L]]$hash), by = "uhash")`: attempt access index 9/9 in VECTOR_ELT
  This happened in PipeOp base.rpart's $train()
  Backtrace:
       ▆
    1. ├─graph_learner$train(tsk("iris")) at test_mlr_graphs_stacking.R:16:3
    2. │ └─mlr3:::.__Learner__train(...)
    3. │   └─mlr3:::learner_train(...)
    4. │     └─mlr3misc::encapsulate(...)
    5. │       ├─mlr3misc::invoke(...)
    6. │       │ └─base::eval.parent(expr, n = 1L)
    7. │       │   └─base::eval(expr, p)
    8. │       │     └─base::eval(expr, p)
    9. │       └─mlr3 (local) .f(learner = `<GrphLrnr>`, task = `<TskClssf>`)
   10. │         └─get_private(learner)$.train(task)
   11. │           └─mlr3pipelines:::.__GraphLearner__.train(...)
   12. │             └─self$graph$train(task)
   13. │               └─mlr3pipelines:::.__Graph__train(...)
   14. │                 └─mlr3pipelines:::graph_reduce(self, input, "train", single_input)
   15. │                   └─op[[fun]](input)
   16. │                     └─mlr3pipelines:::.__PipeOp__train(...)
   17. │                       ├─base::withCallingHandlers(...)
   18. │                       └─private$.train(input)
   19. │                         └─mlr3pipelines:::.__PipeOpTaskPreproc__.train(...)
   20. │                           └─private$.train_task(intask)
   21. │                             └─mlr3pipelines:::.__PipeOpLearnerCV__.train_task(...)
   22. │                               └─mlr3::resample(...)
   23. │                                 └─ResultData$new(data, data_extra, store_backends = store_backends)
   24. │                                   └─mlr3 (local) initialize(...)
   25. │                                     └─mlr3:::.__ResultData__initialize(...)
   26. │                                       ├─data[, `:=`("task_hash", task[[1L]]$hash), by = "uhash"]
   27. │                                       └─data.table:::`[.data.table`(...)
   28. └─base::.handleSimpleError(...)
   29.   └─mlr3pipelines (local) h(simpleError(msg, call))
  ── Error ('test_pipeop_learnerpicvplus.R:35:3'): PipeOpLearnerPICVPlus - basic properties ──
  Error in ``[.data.table`(data, , `:=`("task_hash", task[[1L]]$hash), by = "uhash")`: attempt access index 9/9 in VECTOR_ELT
  This happened in PipeOp regr.featureless's $train()
  Backtrace:
       ▆
    1. ├─global train_pipeop(po, list(task)) at test_pipeop_learnerpicvplus.R:35:3
    2. │ └─po$train(inputs)
    3. │   └─mlr3pipelines:::.__PipeOp__train(...)
    4. │     ├─base::withCallingHandlers(...)
    5. │     └─private$.train(input)
    6. │       └─mlr3pipelines:::.__PipeOpLearnerPICVPlus__.train(...)
    7. │         └─mlr3::resample(task, private$.learner, rdesc, store_models = TRUE)
    8. │           └─ResultData$new(data, data_extra, store_backends = store_backends)
    9. │             └─mlr3 (local) initialize(...)
   10. │               └─mlr3:::.__ResultData__initialize(...)
   11. │                 ├─data[, `:=`("task_hash", task[[1L]]$hash), by = "uhash"]
   12. │                 └─data.table:::`[.data.table`(...)
   13. └─base::.handleSimpleError(...)
   14.   └─mlr3pipelines (local) h(simpleError(msg, call))
  ── Error ('test_pipeop_learnerpicvplus.R:91:3'): PipeOpLearnerPICVPlus - model active binding to state ──
  Error in ``[.data.table`(data, , `:=`("task_hash", task[[1L]]$hash), by = "uhash")`: attempt access index 9/9 in VECTOR_ELT
  This happened in PipeOp regr.featureless's $train()
  Backtrace:
       ▆
    1. ├─po$train(list(task)) at test_pipeop_learnerpicvplus.R:91:3
    2. │ └─mlr3pipelines:::.__PipeOp__train(...)
    3. │   ├─base::withCallingHandlers(...)
    4. │   └─private$.train(input)
    5. │     └─mlr3pipelines:::.__PipeOpLearnerPICVPlus__.train(...)
    6. │       └─mlr3::resample(task, private$.learner, rdesc, store_models = TRUE)
    7. │         └─ResultData$new(data, data_extra, store_backends = store_backends)
    8. │           └─mlr3 (local) initialize(...)
    9. │             └─mlr3:::.__ResultData__initialize(...)
   10. │               ├─data[, `:=`("task_hash", task[[1L]]$hash), by = "uhash"]
   11. │               └─data.table:::`[.data.table`(...)
   12. └─base::.handleSimpleError(...)
   13.   └─mlr3pipelines (local) h(simpleError(msg, call))
  ── Error ('test_pipeop_learnerpicvplus.R:116:3'): PipeOpLearnerPICVPlus - integration with larger graph ──
  Error in ``[.data.table`(data, , `:=`("task_hash", task[[1L]]$hash), by = "uhash")`: attempt access index 9/9 in VECTOR_ELT
  This happened in PipeOp regr.rpart's $train()
  Backtrace:
       ▆
    1. ├─graph$train(task) at test_pipeop_learnerpicvplus.R:116:3
    2. │ └─mlr3pipelines:::.__Graph__train(...)
    3. │   └─mlr3pipelines:::graph_reduce(self, input, "train", single_input)
    4. │     └─op[[fun]](input)
    5. │       └─mlr3pipelines:::.__PipeOp__train(...)
    6. │         ├─base::withCallingHandlers(...)
    7. │         └─private$.train(input)
    8. │           └─mlr3pipelines:::.__PipeOpLearnerPICVPlus__.train(...)
    9. │             └─mlr3::resample(task, private$.learner, rdesc, store_models = TRUE)
   10. │               └─ResultData$new(data, data_extra, store_backends = store_backends)
   11. │                 └─mlr3 (local) initialize(...)
   12. │                   └─mlr3:::.__ResultData__initialize(...)
   13. │                     ├─data[, `:=`("task_hash", task[[1L]]$hash), by = "uhash"]
   14. │                     └─data.table:::`[.data.table`(...)
   15. └─base::.handleSimpleError(...)
   16.   └─mlr3pipelines (local) h(simpleError(msg, call))
  ── Error ('test_pipeop_learnerpicvplus.R:130:3'): marshal ──────────────────────
  Error in ``[.data.table`(data, , `:=`("task_hash", task[[1L]]$hash), by = "uhash")`: attempt access index 9/9 in VECTOR_ELT
  This happened in PipeOp regr.debug's $train()
  Backtrace:
       ▆
    1. ├─po$train(list(task)) at test_pipeop_learnerpicvplus.R:130:3
    2. │ └─mlr3pipelines:::.__PipeOp__train(...)
    3. │   ├─base::withCallingHandlers(...)
    4. │   └─private$.train(input)
    5. │     └─mlr3pipelines:::.__PipeOpLearnerPICVPlus__.train(...)
    6. │       └─mlr3::resample(task, private$.learner, rdesc, store_models = TRUE)
    7. │         └─ResultData$new(data, data_extra, store_backends = store_backends)
    8. │           └─mlr3 (local) initialize(...)
    9. │             └─mlr3:::.__ResultData__initialize(...)
   10. │               ├─data[, `:=`("task_hash", task[[1L]]$hash), by = "uhash"]
   11. │               └─data.table:::`[.data.table`(...)
   12. └─base::.handleSimpleError(...)
   13.   └─mlr3pipelines (local) h(simpleError(msg, call))
  ── Error ('test_pipeop_learnerpicvplus.R:152:3'): marshal multiplicity ─────────
  Error in ``[.data.table`(data, , `:=`("task_hash", task[[1L]]$hash), by = "uhash")`: attempt access index 9/9 in VECTOR_ELT
  This happened in PipeOp regr.debug's $train()
  Backtrace:
       ▆
    1. ├─po$train(list(Multiplicity(task1, task2))) at test_pipeop_learnerpicvplus.R:152:3
    2. │ └─mlr3pipelines:::.__PipeOp__train(...)
    3. │   └─mlr3pipelines:::evaluate_multiplicities(...)
    4. │     └─mlr3misc::imap(...)
    5. │       ├─stats::setNames(mapply_list(.f, list(.x, .nn), list(...)), names(.x))
    6. │       └─mlr3misc:::mapply_list(.f, list(.x, .nn), list(...))
    7. │         └─base::.mapply(.f, .dots, .args)
    8. │           └─mlr3pipelines (local) `<fn>`(dots[[1L]][[1L]], dots[[2L]][[1L]])
    9. │             └─self[[evalcall]](input)
   10. │               └─mlr3pipelines:::.__PipeOp__train(...)
   11. │                 ├─base::withCallingHandlers(...)
   12. │                 └─private$.train(input)
   13. │                   └─mlr3pipelines:::.__PipeOpLearnerPICVPlus__.train(...)
   14. │                     └─mlr3::resample(task, private$.learner, rdesc, store_models = TRUE)
   15. │                       └─ResultData$new(data, data_extra, store_backends = store_backends)
   16. │                         └─mlr3 (local) initialize(...)
   17. │                           └─mlr3:::.__ResultData__initialize(...)
   18. │                             ├─data[, `:=`("task_hash", task[[1L]]$hash), by = "uhash"]
   19. │                             └─data.table:::`[.data.table`(...)
   20. └─base::.handleSimpleError(...)
   21.   └─mlr3pipelines (local) h(simpleError(msg, call))
  ── Error ('test_pipeop_learnercv.R:11:3'): PipeOpLearnerCV - basic properties ──
  Error in ``[.data.table`(data, , `:=`("task_hash", task[[1L]]$hash), by = "uhash")`: attempt access index 9/9 in VECTOR_ELT
  This happened in PipeOp classif.featureless's $train()
  Backtrace:
       ▆
    1. ├─global train_pipeop(po, list(task = task)) at test_pipeop_learnercv.R:11:3
    2. │ └─po$train(inputs)
    3. │   └─mlr3pipelines:::.__PipeOp__train(...)
    4. │     ├─base::withCallingHandlers(...)
    5. │     └─private$.train(input)
    6. │       └─mlr3pipelines:::.__PipeOpTaskPreproc__.train(...)
    7. │         └─private$.train_task(intask)
    8. │           └─mlr3pipelines:::.__PipeOpLearnerCV__.train_task(...)
    9. │             └─mlr3::resample(...)
   10. │               └─ResultData$new(data, data_extra, store_backends = store_backends)
   11. │                 └─mlr3 (local) initialize(...)
   12. │                   └─mlr3:::.__ResultData__initialize(...)
   13. │                     ├─data[, `:=`("task_hash", task[[1L]]$hash), by = "uhash"]
   14. │                     └─data.table:::`[.data.table`(...)
   15. └─base::.handleSimpleError(...)
   16.   └─mlr3pipelines (local) h(simpleError(msg, call))
  ── Error ('test_pipeop_learnercv.R:100:3'): PipeOpLearnerCV - cv ensemble averages fold learners ──
  Error in ``[.data.table`(data, , `:=`("task_hash", task[[1L]]$hash), by = "uhash")`: attempt access index 9/9 in VECTOR_ELT
  This happened in PipeOp classif.rpart's $train()
  Backtrace:
       ▆
    1. ├─po$train(list(task)) at test_pipeop_learnercv.R:100:3
    2. │ └─mlr3pipelines:::.__PipeOp__train(...)
    3. │   ├─base::withCallingHandlers(...)
    4. │   └─private$.train(input)
    5. │     └─mlr3pipelines:::.__PipeOpTaskPreproc__.train(...)
    6. │       └─private$.train_task(intask)
    7. │         └─mlr3pipelines:::.__PipeOpLearnerCV__.train_task(...)
    8. │           └─mlr3::resample(...)
    9. │             └─ResultData$new(data, data_extra, store_backends = store_backends)
   10. │               └─mlr3 (local) initialize(...)
   11. │                 └─mlr3:::.__ResultData__initialize(...)
   12. │                   ├─data[, `:=`("task_hash", task[[1L]]$hash), by = "uhash"]
   13. │                   └─data.table:::`[.data.table`(...)
   14. └─base::.handleSimpleError(...)
   15.   └─mlr3pipelines (local) h(simpleError(msg, call))
  ── Error ('test_pipeop_learnercv.R:139:3'): PipeOpLearnerCV - cv ensemble drops response when requested ──
  Error in ``[.data.table`(data, , `:=`("task_hash", task[[1L]]$hash), by = "uhash")`: attempt access index 9/9 in VECTOR_ELT
  This happened in PipeOp classif.rpart's $train()
  Backtrace:
       ▆
    1. ├─po$train(list(task)) at test_pipeop_learnercv.R:139:3
    2. │ └─mlr3pipelines:::.__PipeOp__train(...)
    3. │   ├─base::withCallingHandlers(...)
    4. │   └─private$.train(input)
    5. │     └─mlr3pipelines:::.__PipeOpTaskPreproc__.train(...)
    6. │       └─private$.train_task(intask)
    7. │         └─mlr3pipelines:::.__PipeOpLearnerCV__.train_task(...)
    8. │           └─mlr3::resample(...)
    9. │             └─ResultData$new(data, data_extra, store_backends = store_backends)
   10. │               └─mlr3 (local) initialize(...)
   11. │                 └─mlr3:::.__ResultData__initialize(...)
   12. │                   ├─data[, `:=`("task_hash", task[[1L]]$hash), by = "uhash"]
   13. │                   └─data.table:::`[.data.table`(...)
   14. └─base::.handleSimpleError(...)
   15.   └─mlr3pipelines (local) h(simpleError(msg, call))
  ── Error ('test_pipeop_learnercv.R:152:3'): PipeOpLearnerCV - cv ensemble averages classif responses ──
  Error in ``[.data.table`(data, , `:=`("task_hash", task[[1L]]$hash), by = "uhash")`: attempt access index 9/9 in VECTOR_ELT
  This happened in PipeOp classif.rpart's $train()
  Backtrace:
       ▆
    1. ├─po$train(list(task)) at test_pipeop_learnercv.R:152:3
    2. │ └─mlr3pipelines:::.__PipeOp__train(...)
    3. │   ├─base::withCallingHandlers(...)
    4. │   └─private$.train(input)
    5. │     └─mlr3pipelines:::.__PipeOpTaskPreproc__.train(...)
    6. │       └─private$.train_task(intask)
    7. │         └─mlr3pipelines:::.__PipeOpLearnerCV__.train_task(...)
    8. │           └─mlr3::resample(...)
    9. │             └─ResultData$new(data, data_extra, store_backends = store_backends)
   10. │               └─mlr3 (local) initialize(...)
   11. │                 └─mlr3:::.__ResultData__initialize(...)
   12. │                   ├─data[, `:=`("task_hash", task[[1L]]$hash), by = "uhash"]
   13. │                   └─data.table:::`[.data.table`(...)
   14. └─base::.handleSimpleError(...)
   15.   └─mlr3pipelines (local) h(simpleError(msg, call))
  ── Error ('test_pipeop_learnercv.R:203:3'): PipeOpLearnerCV - cv ensemble log prob aggregation ──
  Error in ``[.data.table`(data, , `:=`("task_hash", task[[1L]]$hash), by = "uhash")`: attempt access index 9/9 in VECTOR_ELT
  This happened in PipeOp classif.rpart's $train()
  Backtrace:
       ▆
    1. ├─po$train(list(task)) at test_pipeop_learnercv.R:203:3
    2. │ └─mlr3pipelines:::.__PipeOp__train(...)
    3. │   ├─base::withCallingHandlers(...)
    4. │   └─private$.train(input)
    5. │     └─mlr3pipelines:::.__PipeOpTaskPreproc__.train(...)
    6. │       └─private$.train_task(intask)
    7. │         └─mlr3pipelines:::.__PipeOpLearnerCV__.train_task(...)
    8. │           └─mlr3::resample(...)
    9. │             └─ResultData$new(data, data_extra, store_backends = store_backends)
   10. │               └─mlr3 (local) initialize(...)
   11. │                 └─mlr3:::.__ResultData__initialize(...)
   12. │                   ├─data[, `:=`("task_hash", task[[1L]]$hash), by = "uhash"]
   13. │                   └─data.table:::`[.data.table`(...)
   14. └─base::.handleSimpleError(...)
   15.   └─mlr3pipelines (local) h(simpleError(msg, call))
  ── Error ('test_pipeop_learnercv.R:250:3'): PipeOpLearnerCV - log aggregation with zeros uses epsilon ──
  Error in ``[.data.table`(data, , `:=`("task_hash", task[[1L]]$hash), by = "uhash")`: attempt access index 9/9 in VECTOR_ELT
  This happened in PipeOp classif.featureless's $train()
  Backtrace:
       ▆
    1. ├─po$train(list(task)) at test_pipeop_learnercv.R:250:3
    2. │ └─mlr3pipelines:::.__PipeOp__train(...)
    3. │   ├─base::withCallingHandlers(...)
    4. │   └─private$.train(input)
    5. │     └─mlr3pipelines:::.__PipeOpTaskPreproc__.train(...)
    6. │       └─private$.train_task(intask)
    7. │         └─mlr3pipelines:::.__PipeOpLearnerCV__.train_task(...)
    8. │           └─mlr3::resample(...)
    9. │             └─ResultData$new(data, data_extra, store_backends = store_backends)
   10. │               └─mlr3 (local) initialize(...)
   11. │                 └─mlr3:::.__ResultData__initialize(...)
   12. │                   ├─data[, `:=`("task_hash", task[[1L]]$hash), by = "uhash"]
   13. │                   └─data.table:::`[.data.table`(...)
   14. └─base::.handleSimpleError(...)
   15.   └─mlr3pipelines (local) h(simpleError(msg, call))
  ── Error ('test_pipeop_learnercv.R:278:3'): PipeOpLearnerCV - log aggregation epsilon controls shrinkage ──
  Error in ``[.data.table`(data, , `:=`("task_hash", task[[1L]]$hash), by = "uhash")`: attempt access index 9/9 in VECTOR_ELT
  This happened in PipeOp classif.featureless's $train()
  Backtrace:
       ▆
    1. ├─po$train(list(task)) at test_pipeop_learnercv.R:278:3
    2. │ └─mlr3pipelines:::.__PipeOp__train(...)
    3. │   ├─base::withCallingHandlers(...)
    4. │   └─private$.train(input)
    5. │     └─mlr3pipelines:::.__PipeOpTaskPreproc__.train(...)
    6. │       └─private$.train_task(intask)
    7. │         └─mlr3pipelines:::.__PipeOpLearnerCV__.train_task(...)
    8. │           └─mlr3::resample(...)
    9. │             └─ResultData$new(data, data_extra, store_backends = store_backends)
   10. │               └─mlr3 (local) initialize(...)
   11. │                 └─mlr3:::.__ResultData__initialize(...)
   12. │                   ├─data[, `:=`("task_hash", task[[1L]]$hash), by = "uhash"]
   13. │                   └─data.table:::`[.data.table`(...)
   14. └─base::.handleSimpleError(...)
   15.   └─mlr3pipelines (local) h(simpleError(msg, call))
  ── Error ('test_pipeop_learnercv.R:323:3'): PipeOpLearnerCV - cv ensemble averages regression predictions ──
  Error in ``[.data.table`(data, , `:=`("task_hash", task[[1L]]$hash), by = "uhash")`: attempt access index 9/9 in VECTOR_ELT
  This happened in PipeOp regr.rpart's $train()
  Backtrace:
       ▆
    1. ├─po$train(list(task)) at test_pipeop_learnercv.R:323:3
    2. │ └─mlr3pipelines:::.__PipeOp__train(...)
    3. │   ├─base::withCallingHandlers(...)
    4. │   └─private$.train(input)
    5. │     └─mlr3pipelines:::.__PipeOpTaskPreproc__.train(...)
    6. │       └─private$.train_task(intask)
    7. │         └─mlr3pipelines:::.__PipeOpLearnerCV__.train_task(...)
    8. │           └─mlr3::resample(...)
    9. │             └─ResultData$new(data, data_extra, store_backends = store_backends)
   10. │               └─mlr3 (local) initialize(...)
   11. │                 └─mlr3:::.__ResultData__initialize(...)
   12. │                   ├─data[, `:=`("task_hash", task[[1L]]$hash), by = "uhash"]
   13. │                   └─data.table:::`[.data.table`(...)
   14. └─base::.handleSimpleError(...)
   15.   └─mlr3pipelines (local) h(simpleError(msg, call))
  ── Error ('test_pipeop_learnercv.R:350:3'): PipeOpLearnerCV - cv ensemble handles multiplicity ──
  Error in ``[.data.table`(data, , `:=`("task_hash", task[[1L]]$hash), by = "uhash")`: attempt access index 9/9 in VECTOR_ELT
  This happened in PipeOp classif.rpart's $train()
  Backtrace:
       ▆
    1. ├─po$train(list(tasks)) at test_pipeop_learnercv.R:350:3
    2. │ └─mlr3pipelines:::.__PipeOp__train(...)
    3. │   └─mlr3pipelines:::evaluate_multiplicities(...)
    4. │     └─mlr3misc::imap(...)
    5. │       ├─stats::setNames(mapply_list(.f, list(.x, .nn), list(...)), names(.x))
    6. │       └─mlr3misc:::mapply_list(.f, list(.x, .nn), list(...))
    7. │         └─base::.mapply(.f, .dots, .args)
    8. │           └─mlr3pipelines (local) `<fn>`(dots[[1L]][[1L]], dots[[2L]][[1L]])
    9. │             └─self[[evalcall]](input)
   10. │               └─mlr3pipelines:::.__PipeOp__train(...)
   11. │                 ├─base::withCallingHandlers(...)
   12. │                 └─private$.train(input)
   13. │                   └─mlr3pipelines:::.__PipeOpTaskPreproc__.train(...)
   14. │                     └─private$.train_task(intask)
   15. │                       └─mlr3pipelines:::.__PipeOpLearnerCV__.train_task(...)
   16. │                         └─mlr3::resample(...)
   17. │                           └─ResultData$new(data, data_extra, store_backends = store_backends)
   18. │                             └─mlr3 (local) initialize(...)
   19. │                               └─mlr3:::.__ResultData__initialize(...)
   20. │                                 ├─data[, `:=`("task_hash", task[[1L]]$hash), by = "uhash"]
   21. │                                 └─data.table:::`[.data.table`(...)
   22. └─base::.handleSimpleError(...)
   23.   └─mlr3pipelines (local) h(simpleError(msg, call))
  ── Error ('test_pipeop_learnercv.R:387:3'): PipeOpLearnerCV - learner_model returns averaged ensemble ──
  Error in ``[.data.table`(data, , `:=`("task_hash", task[[1L]]$hash), by = "uhash")`: attempt access index 9/9 in VECTOR_ELT
  This happened in PipeOp classif.rpart's $train()
  Backtrace:
       ▆
    1. ├─po$train(list(task)) at test_pipeop_learnercv.R:387:3
    2. │ └─mlr3pipelines:::.__PipeOp__train(...)
    3. │   ├─base::withCallingHandlers(...)
    4. │   └─private$.train(input)
    5. │     └─mlr3pipelines:::.__PipeOpTaskPreproc__.train(...)
    6. │       └─private$.train_task(intask)
    7. │         └─mlr3pipelines:::.__PipeOpLearnerCV__.train_task(...)
    8. │           └─mlr3::resample(...)
    9. │             └─ResultData$new(data, data_extra, store_backends = store_backends)
   10. │               └─mlr3 (local) initialize(...)
   11. │                 └─mlr3:::.__ResultData__initialize(...)
   12. │                   ├─data[, `:=`("task_hash", task[[1L]]$hash), by = "uhash"]
   13. │                   └─data.table:::`[.data.table`(...)
   14. └─base::.handleSimpleError(...)
   15.   └─mlr3pipelines (local) h(simpleError(msg, call))
  ── Error ('test_pipeop_learnercv.R:419:3'): PipeOpLearnerCV - cv ensemble with predict_type = 'se' ──
  Error in ``[.data.table`(data, , `:=`("task_hash", task[[1L]]$hash), by = "uhash")`: attempt access index 9/9 in VECTOR_ELT
  This happened in PipeOp regr.lm's $train()
  Backtrace:
       ▆
    1. ├─po$train(list(task)) at test_pipeop_learnercv.R:419:3
    2. │ └─mlr3pipelines:::.__PipeOp__train(...)
    3. │   ├─base::withCallingHandlers(...)
    4. │   └─private$.train(input)
    5. │     └─mlr3pipelines:::.__PipeOpTaskPreproc__.train(...)
    6. │       └─private$.train_task(intask)
    7. │         └─mlr3pipelines:::.__PipeOpLearnerCV__.train_task(...)
    8. │           └─mlr3::resample(...)
    9. │             └─ResultData$new(data, data_extra, store_backends = store_backends)
   10. │               └─mlr3 (local) initialize(...)
   11. │                 └─mlr3:::.__ResultData__initialize(...)
   12. │                   ├─data[, `:=`("task_hash", task[[1L]]$hash), by = "uhash"]
   13. │                   └─data.table:::`[.data.table`(...)
   14. └─base::.handleSimpleError(...)
   15.   └─mlr3pipelines (local) h(simpleError(msg, call))
  ── Error ('test_pipeop_learnercv.R:455:3'): PipeOpLearnerCV - within resampling ──
  Error in ``[.data.table`(data, , `:=`("task_hash", task[[1L]]$hash), by = "uhash")`: attempt access index 9/9 in VECTOR_ELT
  This happened in PipeOp classif.rpart's $train()
  Backtrace:
      ▆
   1. └─mlr3::resample(tsk("iris"), gr, rsmp("holdout")) at test_pipeop_learnercv.R:455:3
   2.   └─mlr3:::future_map(...)
   3.     └─future.apply::future_mapply(...)
   4.       └─future.apply:::future_xapply(...)
   5.         └─base::tryCatch(...)
   6.           └─base (local) tryCatchList(expr, classes, parentenv, handlers)
   7.             └─base (local) tryCatchOne(...)
   8.               └─value[[3L]](cond)
   9.                 └─future.apply:::onError(e, futures = fs, debug = debug)
  ── Error ('test_pipeop_learnercv.R:493:3'): PipeOpLearnerCV - model active binding to state ──
  Error in ``[.data.table`(data, , `:=`("task_hash", task[[1L]]$hash), by = "uhash")`: attempt access index 9/9 in VECTOR_ELT
  This happened in PipeOp classif.featureless's $train()
  Backtrace:
       ▆
    1. ├─po$train(list(task)) at test_pipeop_learnercv.R:493:3
    2. │ └─mlr3pipelines:::.__PipeOp__train(...)
    3. │   ├─base::withCallingHandlers(...)
    4. │   └─private$.train(input)
    5. │     └─mlr3pipelines:::.__PipeOpTaskPreproc__.train(...)
    6. │       └─private$.train_task(intask)
    7. │         └─mlr3pipelines:::.__PipeOpLearnerCV__.train_task(...)
    8. │           └─mlr3::resample(...)
    9. │             └─ResultData$new(data, data_extra, store_backends = store_backends)
   10. │               └─mlr3 (local) initialize(...)
   11. │                 └─mlr3:::.__ResultData__initialize(...)
   12. │                   ├─data[, `:=`("task_hash", task[[1L]]$hash), by = "uhash"]
   13. │                   └─data.table:::`[.data.table`(...)
   14. └─base::.handleSimpleError(...)
   15.   └─mlr3pipelines (local) h(simpleError(msg, call))
  ── Error ('test_pipeop_learnercv.R:516:3'): predict_type ───────────────────────
  Error in ``[.data.table`(data, , `:=`("task_hash", task[[1L]]$hash), by = "uhash")`: attempt access index 9/9 in VECTOR_ELT
  This happened in PipeOp classif.rpart's $train()
  Backtrace:
       ▆
    1. ├─global expect_equal(...) at test_pipeop_learnercv.R:516:3
    2. │ ├─testthat::expect_true(...)
    3. │ │ └─testthat::quasi_label(enquo(object), label)
    4. │ │   └─rlang::eval_bare(expr, quo_get_env(quo))
    5. │ └─base::all.equal(...)
    6. ├─lcv$train(list(tsk("iris")))
    7. │ └─mlr3pipelines:::.__PipeOp__train(...)
    8. │   ├─base::withCallingHandlers(...)
    9. │   └─private$.train(input)
   10. │     └─mlr3pipelines:::.__PipeOpTaskPreproc__.train(...)
   11. │       └─private$.train_task(intask)
   12. │         └─mlr3pipelines:::.__PipeOpLearnerCV__.train_task(...)
   13. │           └─mlr3::resample(...)
   14. │             └─ResultData$new(data, data_extra, store_backends = store_backends)
   15. │               └─mlr3 (local) initialize(...)
   16. │                 └─mlr3:::.__ResultData__initialize(...)
   17. │                   ├─data[, `:=`("task_hash", task[[1L]]$hash), by = "uhash"]
   18. │                   └─data.table:::`[.data.table`(...)
   19. └─base::.handleSimpleError(...)
   20.   └─mlr3pipelines (local) h(simpleError(msg, call))
  ── Error ('test_pipeop_learnercv.R:531:3'): marshal ────────────────────────────
  Error in ``[.data.table`(data, , `:=`("task_hash", task[[1L]]$hash), by = "uhash")`: attempt access index 9/9 in VECTOR_ELT
  This happened in PipeOp classif.debug's $train()
  Backtrace:
       ▆
    1. ├─po_lrn$train(list(task)) at test_pipeop_learnercv.R:531:3
    2. │ └─mlr3pipelines:::.__PipeOp__train(...)
    3. │   ├─base::withCallingHandlers(...)
    4. │   └─private$.train(input)
    5. │     └─mlr3pipelines:::.__PipeOpTaskPreproc__.train(...)
    6. │       └─private$.train_task(intask)
    7. │         └─mlr3pipelines:::.__PipeOpLearnerCV__.train_task(...)
    8. │           └─mlr3::resample(...)
    9. │             └─ResultData$new(data, data_extra, store_backends = store_backends)
   10. │               └─mlr3 (local) initialize(...)
   11. │                 └─mlr3:::.__ResultData__initialize(...)
   12. │                   ├─data[, `:=`("task_hash", task[[1L]]$hash), by = "uhash"]
   13. │                   └─data.table:::`[.data.table`(...)
   14. └─base::.handleSimpleError(...)
   15.   └─mlr3pipelines (local) h(simpleError(msg, call))
  ── Error ('test_pipeop_learnercv.R:557:3'): marshal multiplicity ───────────────
  Error in ``[.data.table`(data, , `:=`("task_hash", task[[1L]]$hash), by = "uhash")`: attempt access index 9/9 in VECTOR_ELT
  This happened in PipeOp classif.debug's $train()
  Backtrace:
       ▆
    1. ├─po$train(list(Multiplicity(tsk("iris"), tsk("sonar")))) at test_pipeop_learnercv.R:557:3
    2. │ └─mlr3pipelines:::.__PipeOp__train(...)
    3. │   └─mlr3pipelines:::evaluate_multiplicities(...)
    4. │     └─mlr3misc::imap(...)
    5. │       ├─stats::setNames(mapply_list(.f, list(.x, .nn), list(...)), names(.x))
    6. │       └─mlr3misc:::mapply_list(.f, list(.x, .nn), list(...))
    7. │         └─base::.mapply(.f, .dots, .args)
    8. │           └─mlr3pipelines (local) `<fn>`(dots[[1L]][[1L]], dots[[2L]][[1L]])
    9. │             └─self[[evalcall]](input)
   10. │               └─mlr3pipelines:::.__PipeOp__train(...)
   11. │                 ├─base::withCallingHandlers(...)
   12. │                 └─private$.train(input)
   13. │                   └─mlr3pipelines:::.__PipeOpTaskPreproc__.train(...)
   14. │                     └─private$.train_task(intask)
   15. │                       └─mlr3pipelines:::.__PipeOpLearnerCV__.train_task(...)
   16. │                         └─mlr3::resample(...)
   17. │                           └─ResultData$new(data, data_extra, store_backends = store_backends)
   18. │                             └─mlr3 (local) initialize(...)
   19. │                               └─mlr3:::.__ResultData__initialize(...)
   20. │                                 ├─data[, `:=`("task_hash", task[[1L]]$hash), by = "uhash"]
   21. │                                 └─data.table:::`[.data.table`(...)
   22. └─base::.handleSimpleError(...)
   23.   └─mlr3pipelines (local) h(simpleError(msg, call))
  ── Error ('test_pipeop_learnercv.R:612:3'): marshal with cv ensemble ───────────
  Error in ``[.data.table`(data, , `:=`("task_hash", task[[1L]]$hash), by = "uhash")`: attempt access index 9/9 in VECTOR_ELT
  This happened in PipeOp classif.rpart's $train()
  Backtrace:
       ▆
    1. ├─po$train(list(task)) at test_pipeop_learnercv.R:612:3
    2. │ └─mlr3pipelines:::.__PipeOp__train(...)
    3. │   ├─base::withCallingHandlers(...)
    4. │   └─private$.train(input)
    5. │     └─mlr3pipelines:::.__PipeOpTaskPreproc__.train(...)
    6. │       └─private$.train_task(intask)
    7. │         └─mlr3pipelines:::.__PipeOpLearnerCV__.train_task(...)
    8. │           └─mlr3::resample(...)
    9. │             └─ResultData$new(data, data_extra, store_backends = store_backends)
   10. │               └─mlr3 (local) initialize(...)
   11. │                 └─mlr3:::.__ResultData__initialize(...)
   12. │                   ├─data[, `:=`("task_hash", task[[1L]]$hash), by = "uhash"]
   13. │                   └─data.table:::`[.data.table`(...)
   14. └─base::.handleSimpleError(...)
   15.   └─mlr3pipelines (local) h(simpleError(msg, call))
  ── Error ('test_pipeop_learnercv.R:628:3'): state class and multiplicity ───────
  Error in ``[.data.table`(data, , `:=`("task_hash", task[[1L]]$hash), by = "uhash")`: attempt access index 9/9 in VECTOR_ELT
  This happened in PipeOp classif.debug's $train()
  Backtrace:
       ▆
    1. ├─po$train(list(Multiplicity(tsk("iris")))) at test_pipeop_learnercv.R:628:3
    2. │ └─mlr3pipelines:::.__PipeOp__train(...)
    3. │   └─mlr3pipelines:::evaluate_multiplicities(...)
    4. │     └─mlr3misc::imap(...)
    5. │       ├─stats::setNames(mapply_list(.f, list(.x, .nn), list(...)), names(.x))
    6. │       └─mlr3misc:::mapply_list(.f, list(.x, .nn), list(...))
    7. │         └─base::.mapply(.f, .dots, .args)
    8. │           └─mlr3pipelines (local) `<fn>`(dots[[1L]][[1L]], dots[[2L]][[1L]])
    9. │             └─self[[evalcall]](input)
   10. │               └─mlr3pipelines:::.__PipeOp__train(...)
   11. │                 ├─base::withCallingHandlers(...)
   12. │                 └─private$.train(input)
   13. │                   └─mlr3pipelines:::.__PipeOpTaskPreproc__.train(...)
   14. │                     └─private$.train_task(intask)
   15. │                       └─mlr3pipelines:::.__PipeOpLearnerCV__.train_task(...)
   16. │                         └─mlr3::resample(...)
   17. │                           └─ResultData$new(data, data_extra, store_backends = store_backends)
   18. │                             └─mlr3 (local) initialize(...)
   19. │                               └─mlr3:::.__ResultData__initialize(...)
   20. │                                 ├─data[, `:=`("task_hash", task[[1L]]$hash), by = "uhash"]
   21. │                                 └─data.table:::`[.data.table`(...)
   22. └─base::.handleSimpleError(...)
   23.   └─mlr3pipelines (local) h(simpleError(msg, call))
  ── Error ('test_pipeop_learnercv.R:671:5'): PipeOpLearnerCV cv ensemble aggregates SE like PipeOpRegrAvg ──
  Error in ``[.data.table`(data, , `:=`("task_hash", task[[1L]]$hash), by = "uhash")`: attempt access index 9/9 in VECTOR_ELT
  This happened in PipeOp regr.debug's $train()
  Backtrace:
       ▆
    1. ├─po$train(list(task)) at test_pipeop_learnercv.R:671:5
    2. │ └─mlr3pipelines:::.__PipeOp__train(...)
    3. │   ├─base::withCallingHandlers(...)
    4. │   └─private$.train(input)
    5. │     └─mlr3pipelines:::.__PipeOpTaskPreproc__.train(...)
    6. │       └─private$.train_task(intask)
    7. │         └─mlr3pipelines:::.__PipeOpLearnerCV__.train_task(...)
    8. │           └─mlr3::resample(...)
    9. │             └─ResultData$new(data, data_extra, store_backends = store_backends)
   10. │               └─mlr3 (local) initialize(...)
   11. │                 └─mlr3:::.__ResultData__initialize(...)
   12. │                   ├─data[, `:=`("task_hash", task[[1L]]$hash), by = "uhash"]
   13. │                   └─data.table:::`[.data.table`(...)
   14. └─base::.handleSimpleError(...)
   15.   └─mlr3pipelines (local) h(simpleError(msg, call))
  ── Error ('test_pipeop_tunethreshold.R:7:3'): threshold works for multiclass ───
  Error in ``[.data.table`(data, , `:=`("task_hash", task[[1L]]$hash), by = "uhash")`: attempt access index 9/9 in VECTOR_ELT
  This happened in PipeOp classif.rpart's $train()
  Backtrace:
       ▆
    1. ├─po_cv$train(list(t)) at test_pipeop_tunethreshold.R:7:3
    2. │ └─mlr3pipelines:::.__PipeOp__train(...)
    3. │   ├─base::withCallingHandlers(...)
    4. │   └─private$.train(input)
    5. │     └─mlr3pipelines:::.__PipeOpTaskPreproc__.train(...)
    6. │       └─private$.train_task(intask)
    7. │         └─mlr3pipelines:::.__PipeOpLearnerCV__.train_task(...)
    8. │           └─mlr3::resample(...)
    9. │             └─ResultData$new(data, data_extra, store_backends = store_backends)
   10. │               └─mlr3 (local) initialize(...)
   11. │                 └─mlr3:::.__ResultData__initialize(...)
   12. │                   ├─data[, `:=`("task_hash", task[[1L]]$hash), by = "uhash"]
   13. │                   └─data.table:::`[.data.table`(...)
   14. └─base::.handleSimpleError(...)
   15.   └─mlr3pipelines (local) h(simpleError(msg, call))
  ── Error ('test_pipeop_tunethreshold.R:38:3'): threshold works for binary ──────
  Error in ``[.data.table`(data, , `:=`("task_hash", task[[1L]]$hash), by = "uhash")`: attempt access index 9/9 in VECTOR_ELT
  This happened in PipeOp classif.rpart's $train()
  Backtrace:
       ▆
    1. ├─po_cv$train(list(t)) at test_pipeop_tunethreshold.R:38:3
    2. │ └─mlr3pipelines:::.__PipeOp__train(...)
    3. │   ├─base::withCallingHandlers(...)
    4. │   └─private$.train(input)
    5. │     └─mlr3pipelines:::.__PipeOpTaskPreproc__.train(...)
    6. │       └─private$.train_task(intask)
    7. │         └─mlr3pipelines:::.__PipeOpLearnerCV__.train_task(...)
    8. │           └─mlr3::resample(...)
    9. │             └─ResultData$new(data, data_extra, store_backends = store_backends)
   10. │               └─mlr3 (local) initialize(...)
   11. │                 └─mlr3:::.__ResultData__initialize(...)
   12. │                   ├─data[, `:=`("task_hash", task[[1L]]$hash), by = "uhash"]
   13. │                   └─data.table:::`[.data.table`(...)
   14. └─base::.handleSimpleError(...)
   15.   └─mlr3pipelines (local) h(simpleError(msg, call))
  ── Error ('test_pipeop_tunethreshold.R:73:3'): tunethreshold graph works ───────
  Error in ``[.data.table`(data, , `:=`("task_hash", task[[1L]]$hash), by = "uhash")`: attempt access index 9/9 in VECTOR_ELT
  This happened in PipeOp classif.rpart's $train()
  Backtrace:
       ▆
    1. ├─graph$train(tsk("pima")) at test_pipeop_tunethreshold.R:73:3
    2. │ └─mlr3pipelines:::.__Graph__train(...)
    3. │   └─mlr3pipelines:::graph_reduce(self, input, "train", single_input)
    4. │     └─op[[fun]](input)
    5. │       └─mlr3pipelines:::.__PipeOp__train(...)
    6. │         ├─base::withCallingHandlers(...)
    7. │         └─private$.train(input)
    8. │           └─mlr3pipelines:::.__PipeOpTaskPreproc__.train(...)
    9. │             └─private$.train_task(intask)
   10. │               └─mlr3pipelines:::.__PipeOpLearnerCV__.train_task(...)
   11. │                 └─mlr3::resample(...)
   12. │                   └─ResultData$new(data, data_extra, store_backends = store_backends)
   13. │                     └─mlr3 (local) initialize(...)
   14. │                       └─mlr3:::.__ResultData__initialize(...)
   15. │                         ├─data[, `:=`("task_hash", task[[1L]]$hash), by = "uhash"]
   16. │                         └─data.table:::`[.data.table`(...)
   17. └─base::.handleSimpleError(...)
   18.   └─mlr3pipelines (local) h(simpleError(msg, call))
  ── Error ('test_pipeop_tunethreshold.R:101:3'): threshold works for classes that are not valid R names ──
  Error in ``[.data.table`(data, , `:=`("task_hash", task[[1L]]$hash), by = "uhash")`: attempt access index 9/9 in VECTOR_ELT
  This happened in PipeOp classif.rpart's $train()
  Backtrace:
       ▆
    1. ├─ppl$train(testtask) at test_pipeop_tunethreshold.R:101:3
    2. │ └─mlr3pipelines:::.__Graph__train(...)
    3. │   └─mlr3pipelines:::graph_reduce(self, input, "train", single_input)
    4. │     └─op[[fun]](input)
    5. │       └─mlr3pipelines:::.__PipeOp__train(...)
    6. │         ├─base::withCallingHandlers(...)
    7. │         └─private$.train(input)
    8. │           └─mlr3pipelines:::.__PipeOpTaskPreproc__.train(...)
    9. │             └─private$.train_task(intask)
   10. │               └─mlr3pipelines:::.__PipeOpLearnerCV__.train_task(...)
   11. │                 └─mlr3::resample(...)
   12. │                   └─ResultData$new(data, data_extra, store_backends = store_backends)
   13. │                     └─mlr3 (local) initialize(...)
   14. │                       └─mlr3:::.__ResultData__initialize(...)
   15. │                         ├─data[, `:=`("task_hash", task[[1L]]$hash), by = "uhash"]
   16. │                         └─data.table:::`[.data.table`(...)
   17. └─base::.handleSimpleError(...)
   18.   └─mlr3pipelines (local) h(simpleError(msg, call))
  ── Error ('test_pipeop_tunethreshold.R:260:3'): threshold graph transparency ───
  Error in ``[.data.table`(data, , `:=`("task_hash", task[[1L]]$hash), by = "uhash")`: attempt access index 9/9 in VECTOR_ELT
  This happened in PipeOp classif.rpart's $train()
  Backtrace:
       ▆
    1. ├─lrn_prob$train(t) at test_pipeop_tunethreshold.R:260:3
    2. │ └─mlr3:::.__Learner__train(...)
    3. │   └─mlr3:::learner_train(...)
    4. │     └─mlr3misc::encapsulate(...)
    5. │       ├─mlr3misc::invoke(...)
    6. │       │ └─base::eval.parent(expr, n = 1L)
    7. │       │   └─base::eval(expr, p)
    8. │       │     └─base::eval(expr, p)
    9. │       └─mlr3 (local) .f(learner = `<GrphLrnr>`, task = `<TskClssf>`)
   10. │         └─get_private(learner)$.train(task)
   11. │           └─mlr3pipelines:::.__GraphLearner__.train(...)
   12. │             └─self$graph$train(task)
   13. │               └─mlr3pipelines:::.__Graph__train(...)
   14. │                 └─mlr3pipelines:::graph_reduce(self, input, "train", single_input)
   15. │                   └─op[[fun]](input)
   16. │                     └─mlr3pipelines:::.__PipeOp__train(...)
   17. │                       ├─base::withCallingHandlers(...)
   18. │                       └─private$.train(input)
   19. │                         └─mlr3pipelines:::.__PipeOpTaskPreproc__.train(...)
   20. │                           └─private$.train_task(intask)
   21. │                             └─mlr3pipelines:::.__PipeOpLearnerCV__.train_task(...)
   22. │                               └─mlr3::resample(...)
   23. │                                 └─ResultData$new(data, data_extra, store_backends = store_backends)
   24. │                                   └─mlr3 (local) initialize(...)
   25. │                                     └─mlr3:::.__ResultData__initialize(...)
   26. │                                       ├─data[, `:=`("task_hash", task[[1L]]$hash), by = "uhash"]
   27. │                                       └─data.table:::`[.data.table`(...)
   28. └─base::.handleSimpleError(...)
   29.   └─mlr3pipelines (local) h(simpleError(msg, call))
  ── Error ('test_resample.R:13:3'): PipeOp - Resample ───────────────────────────
  Error in ``[.data.table`(data, , `:=`("task_hash", task[[1L]]$hash), by = "uhash")`: attempt access index 9/9 in VECTOR_ELT
  Backtrace:
      ▆
   1. └─mlr3::resample(task, pp, resa) at test_resample.R:13:3
   2.   └─ResultData$new(data, data_extra, store_backends = store_backends)
   3.     └─mlr3 (local) initialize(...)
   4.       └─mlr3:::.__ResultData__initialize(...)
   5.         ├─data[, `:=`("task_hash", task[[1L]]$hash), by = "uhash"]
   6.         └─data.table:::`[.data.table`(...)
  ── Error ('test_usecases.R:153:3'): stacking ───────────────────────────────────
  Error in ``[.data.table`(data, , `:=`("task_hash", task[[1L]]$hash), by = "uhash")`: attempt access index 9/9 in VECTOR_ELT
  This happened in PipeOp classif.rpart's $train()
  Backtrace:
       ▆
    1. ├─pipe$train(task) at test_usecases.R:153:3
    2. │ └─mlr3pipelines:::.__Graph__train(...)
    3. │   └─mlr3pipelines:::graph_reduce(self, input, "train", single_input)
    4. │     └─op[[fun]](input)
    5. │       └─mlr3pipelines:::.__PipeOp__train(...)
    6. │         ├─base::withCallingHandlers(...)
    7. │         └─private$.train(input)
    8. │           └─mlr3pipelines:::.__PipeOpTaskPreproc__.train(...)
    9. │             └─private$.train_task(intask)
   10. │               └─mlr3pipelines:::.__PipeOpLearnerCV__.train_task(...)
   11. │                 └─mlr3::resample(...)
   12. │                   └─ResultData$new(data, data_extra, store_backends = store_backends)
   13. │                     └─mlr3 (local) initialize(...)
   14. │                       └─mlr3:::.__ResultData__initialize(...)
   15. │                         ├─data[, `:=`("task_hash", task[[1L]]$hash), by = "uhash"]
   16. │                         └─data.table:::`[.data.table`(...)
   17. └─base::.handleSimpleError(...)
   18.   └─mlr3pipelines (local) h(simpleError(msg, call))
  ── Error ('test_ppl.R:73:3'): mlr3book authors don't sleepwalk through life ────
  Error in ``[.data.table`(data, , `:=`("task_hash", task[[1L]]$hash), by = "uhash")`: attempt access index 9/9 in VECTOR_ELT
  This happened in PipeOp classif.rpart.classif.rpart's $train()
  Backtrace:
      ▆
   1. └─mlr3::benchmark(benchmark_grid(tasks, learners, rsmp("cv", folds = 2))) at test_ppl.R:73:3
   2.   └─mlr3:::future_map(...)
   3.     └─future.apply::future_mapply(...)
   4.       └─future.apply:::future_xapply(...)
   5.         └─base::tryCatch(...)
   6.           └─base (local) tryCatchList(expr, classes, parentenv, handlers)
   7.             └─base (local) tryCatchOne(...)
   8.               └─value[[3L]](cond)
   9.                 └─future.apply:::onError(e, futures = fs, debug = debug)
  
  [ FAIL 38 | WARN 2 | SKIP 98 | PASS 12316 ]
  Error:
  ! Test failures.
  Execution halted