Last updated on 2025-12-20 17:50:33 CET.
| Flavor | Version | Tinstall | Tcheck | Ttotal | Status | Flags |
|---|---|---|---|---|---|---|
| r-devel-linux-x86_64-debian-clang | 0.10.0 | 39.84 | 654.71 | 694.55 | ERROR | |
| r-devel-linux-x86_64-debian-gcc | 0.10.0 | 25.13 | 416.55 | 441.68 | ERROR | |
| r-devel-linux-x86_64-fedora-clang | 0.10.0 | 67.00 | 1074.39 | 1141.39 | ERROR | |
| r-devel-linux-x86_64-fedora-gcc | 0.10.0 | 60.00 | 1035.51 | 1095.51 | ERROR | |
| r-devel-windows-x86_64 | 0.10.0 | 39.00 | 562.00 | 601.00 | OK | |
| r-patched-linux-x86_64 | 0.10.0 | 53.21 | 713.01 | 766.22 | OK | |
| r-release-linux-x86_64 | 0.10.0 | 36.47 | 698.05 | 734.52 | OK | |
| r-release-macos-arm64 | 0.10.0 | OK | ||||
| r-release-macos-x86_64 | 0.10.0 | 26.00 | 542.00 | 568.00 | OK | |
| r-release-windows-x86_64 | 0.10.0 | 37.00 | 525.00 | 562.00 | OK | |
| r-oldrel-macos-arm64 | 0.10.0 | 8.00 | 106.00 | 114.00 | ERROR | |
| r-oldrel-macos-x86_64 | 0.10.0 | 27.00 | 683.00 | 710.00 | ERROR | |
| r-oldrel-windows-x86_64 | 0.10.0 | 52.00 | 637.00 | 689.00 | ERROR |
Version: 0.10.0
Check: examples
Result: ERROR
Running examples in ‘mlr3pipelines-Ex.R’ failed
The error most likely occurred in:
> base::assign(".ptime", proc.time(), pos = "CheckExEnv")
> ### Name: mlr_graphs_stacking
> ### Title: Create A Graph to Perform Stacking.
> ### Aliases: mlr_graphs_stacking pipeline_stacking
>
> ### ** Examples
>
> ## Don't show:
> if (mlr3misc::require_namespaces("rpart", quietly = TRUE)) withAutoprint({ # examplesIf
+ ## End(Don't show)
+ library(mlr3)
+ library(mlr3learners)
+
+ base_learners = list(
+ lrn("classif.rpart", predict_type = "prob"),
+ lrn("classif.nnet", predict_type = "prob")
+ )
+ super_learner = lrn("classif.log_reg")
+
+ graph_stack = pipeline_stacking(base_learners, super_learner)
+ graph_learner = as_learner(graph_stack)
+ graph_learner$train(tsk("german_credit"))
+ ## Don't show:
+ }) # examplesIf
> library(mlr3)
> library(mlr3learners)
> base_learners = list(lrn("classif.rpart", predict_type = "prob"), lrn("classif.nnet",
+ predict_type = "prob"))
> super_learner = lrn("classif.log_reg")
> graph_stack = pipeline_stacking(base_learners, super_learner)
> graph_learner = as_learner(graph_stack)
> graph_learner$train(tsk("german_credit"))
INFO [04:37:11.548] [mlr3] Resampling 'cv' is being instantiated on task 'german_credit'
INFO [04:37:11.857] [mlr3] Applying learner 'classif.rpart' on task 'german_credit' (iter 1/3)
INFO [04:37:12.009] [mlr3] Applying learner 'classif.rpart' on task 'german_credit' (iter 2/3)
INFO [04:37:12.174] [mlr3] Applying learner 'classif.rpart' on task 'german_credit' (iter 3/3)
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()
Calls: withAutoprint ... .__ResultData__initialize -> [ -> [.data.table -> .handleSimpleError -> h
Execution halted
Examples with CPU (user + system) or elapsed time > 5s
user system elapsed
mlr_graphs_ovr 4.22 0.084 7.743
Flavor: r-devel-linux-x86_64-debian-clang
Version: 0.10.0
Check: tests
Result: ERROR
Running ‘testthat.R’ [387s/197s]
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: 2025-12-20 04:38:41.349356: Isomap START
> test_pipeop_isomap.R: 2025-12-20 04:38:41.350135: constructing knn graph
> test_pipeop_isomap.R: 2025-12-20 04:38:41.361783: calculating geodesic distances
> test_pipeop_isomap.R: 2025-12-20 04:38:41.383263: Classical Scaling
> test_pipeop_isomap.R: 2025-12-20 04:38:41.440144: Isomap START
> test_pipeop_isomap.R: 2025-12-20 04:38:41.44062: constructing knn graph
> test_pipeop_isomap.R: 2025-12-20 04:38:41.44972: calculating geodesic distances
> test_pipeop_isomap.R: 2025-12-20 04:38:41.468473: Classical Scaling
> test_pipeop_isomap.R: 2025-12-20 04:38:41.497162: L-Isomap embed START
> test_pipeop_isomap.R: 2025-12-20 04:38:41.497877: constructing knn graph
> test_pipeop_isomap.R: 2025-12-20 04:38:41.51554: calculating geodesic distances
> test_pipeop_isomap.R: 2025-12-20 04:38:41.556405: embedding
> test_pipeop_isomap.R: 2025-12-20 04:38:41.557831: DONE
> test_pipeop_isomap.R: 2025-12-20 04:38:41.585397: L-Isomap embed START
> test_pipeop_isomap.R: 2025-12-20 04:38:41.585896: constructing knn graph
> test_pipeop_isomap.R: 2025-12-20 04:38:41.602679: calculating geodesic distances
> test_pipeop_isomap.R: 2025-12-20 04:38:41.649842: embedding
> test_pipeop_isomap.R: 2025-12-20 04:38:41.651086: DONE
> test_pipeop_isomap.R: 2025-12-20 04:38:41.737481: Isomap START
> test_pipeop_isomap.R: 2025-12-20 04:38:41.737886: constructing knn graph
> test_pipeop_isomap.R: 2025-12-20 04:38:41.755668: calculating geodesic distances
> test_pipeop_isomap.R: 2025-12-20 04:38:41.857957: Classical Scaling
> test_pipeop_isomap.R: 2025-12-20 04:38:41.892579: L-Isomap embed START
> test_pipeop_isomap.R: 2025-12-20 04:38:41.893292: constructing knn graph
> test_pipeop_isomap.R: 2025-12-20 04:38:41.942643: calculating geodesic distances
> test_pipeop_isomap.R: 2025-12-20 04:38:42.141735: embedding
> test_pipeop_isomap.R: 2025-12-20 04:38:42.144426: DONE
> test_pipeop_isomap.R: 2025-12-20 04:38:42.298247: Isomap START
> test_pipeop_isomap.R: 2025-12-20 04:38:42.298723: constructing knn graph
> test_pipeop_isomap.R: 2025-12-20 04:38:42.307768: calculating geodesic distances
> test_pipeop_isomap.R: 2025-12-20 04:38:42.328362: Classical Scaling
> test_pipeop_isomap.R: 2025-12-20 04:38:42.359928: L-Isomap embed START
> test_pipeop_isomap.R: 2025-12-20 04:38:42.362164: constructing knn graph
> test_pipeop_isomap.R: 2025-12-20 04:38:42.376517: calculating geodesic distances
> test_pipeop_isomap.R: 2025-12-20 04:38:42.420646: embedding
> test_pipeop_isomap.R: 2025-12-20 04:38:42.421839: DONE
> test_pipeop_isomap.R: 2025-12-20 04:38:42.562457: Isomap START
> test_pipeop_isomap.R: 2025-12-20 04:38:42.562957: constructing knn graph
> test_pipeop_isomap.R: 2025-12-20 04:38:42.571932: calculating geodesic distances
> test_pipeop_isomap.R: 2025-12-20 04:38:42.592027: Classical Scaling
> test_pipeop_isomap.R: 2025-12-20 04:38:42.640946: L-Isomap embed START
> test_pipeop_isomap.R: 2025-12-20 04:38:42.641628: constructing knn graph
> test_pipeop_isomap.R: 2025-12-20 04:38:42.657845: calculating geodesic distances
> test_pipeop_isomap.R: 2025-12-20 04:38:42.706692: embedding
> test_pipeop_isomap.R: 2025-12-20 04:38:42.721525: DONE
> test_pipeop_isomap.R: 2025-12-20 04:38:42.804056: Isomap START
> test_pipeop_isomap.R: 2025-12-20 04:38:42.804521: constructing knn graph
> test_pipeop_isomap.R: 2025-12-20 04:38:42.813685: calculating geodesic distances
> test_pipeop_isomap.R: 2025-12-20 04:38:42.835403: Classical Scaling
> test_pipeop_isomap.R: 2025-12-20 04:38:42.885841: L-Isomap embed START
> test_pipeop_isomap.R: 2025-12-20 04:38:42.886489: constructing knn graph
> test_pipeop_isomap.R: 2025-12-20 04:38:42.900442: calculating geodesic distances
> test_pipeop_isomap.R: 2025-12-20 04:38:42.942509: embedding
> test_pipeop_isomap.R: 2025-12-20 04:38:42.943672: DONE
> test_pipeop_isomap.R: 2025-12-20 04:38:43.023201: Isomap START
> test_pipeop_isomap.R: 2025-12-20 04:38:43.023651: constructing knn graph
> test_pipeop_isomap.R: 2025-12-20 04:38:43.030956: calculating geodesic distances
> test_pipeop_isomap.R: 2025-12-20 04:38:43.049271: Classical Scaling
> test_pipeop_isomap.R: 2025-12-20 04:38:43.098912: L-Isomap embed START
> test_pipeop_isomap.R: 2025-12-20 04:38:43.09958: constructing knn graph
> test_pipeop_isomap.R: 2025-12-20 04:38:43.113844: calculating geodesic distances
> test_pipeop_isomap.R: 2025-12-20 04:38:43.157686: embedding
> test_pipeop_isomap.R: 2025-12-20 04:38:43.158888: DONE
> test_pipeop_isomap.R: 2025-12-20 04:38:43.240074: Isomap START
> test_pipeop_isomap.R: 2025-12-20 04:38:43.240547: constructing knn graph
> test_pipeop_isomap.R: 2025-12-20 04:38:43.24977: calculating geodesic distances
> test_pipeop_isomap.R: 2025-12-20 04:38:43.268405: Classical Scaling
> test_pipeop_isomap.R: 2025-12-20 04:38:43.318479: L-Isomap embed START
> test_pipeop_isomap.R: 2025-12-20 04:38:43.319155: constructing knn graph
> test_pipeop_isomap.R: 2025-12-20 04:38:43.349005: calculating geodesic distances
> test_pipeop_isomap.R: 2025-12-20 04:38:43.391147: embedding
> test_pipeop_isomap.R: 2025-12-20 04:38:43.393937: DONE
> test_pipeop_isomap.R: 2025-12-20 04:38:43.482703: Isomap START
> test_pipeop_isomap.R: 2025-12-20 04:38:43.483133: constructing knn graph
> test_pipeop_isomap.R: 2025-12-20 04:38:43.491636: calculating geodesic distances
> test_pipeop_isomap.R: 2025-12-20 04:38:43.509221: Classical Scaling
> test_pipeop_isomap.R: 2025-12-20 04:38:43.587157: Isomap START
> test_pipeop_isomap.R: 2025-12-20 04:38:43.587602: constructing knn graph
> test_pipeop_isomap.R: 2025-12-20 04:38:43.596468: calculating geodesic distances
> test_pipeop_isomap.R: 2025-12-20 04:38:43.616376: Classical Scaling
> test_pipeop_isomap.R: 2025-12-20 04:38:43.639619: Isomap START
> test_pipeop_isomap.R: 2025-12-20 04:38:43.640071: constructing knn graph
> test_pipeop_isomap.R: 2025-12-20 04:38:43.647361: calculating geodesic distances
> test_pipeop_isomap.R: 2025-12-20 04:38:43.666083: Classical Scaling
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
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
> 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:
> 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").
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_boxcox.R:7:3', 'test_pipeop_classweights.R:10:3',
'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_datefeatures.R:10:3', 'test_pipeop_encodeimpact.R:11:3',
'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_ica.R:7:3', 'test_pipeop_ensemble.R:6:3',
'test_pipeop_impute.R:4:3', 'test_pipeop_imputelearner.R:43:3',
'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_missind.R:6:3',
'test_pipeop_modelmatrix.R:7:3', 'test_pipeop_multiplicityexply.R:9:3',
'test_pipeop_mutate.R:9:3', 'test_pipeop_multiplicityimply.R:9:3',
'test_pipeop_nearmiss.R:7:3', '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_targettrafoscalerange.R:5:3', 'test_pipeop_task_preproc.R:4:3',
'test_pipeop_task_preproc.R:14:3', 'test_pipeop_subsample.R:6:3',
'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]]$hash), by = "uhash"]
27. │ └─data.table:::`[.data.table`(...)
28. └─base::.handleSimpleError(...)
29. └─mlr3pipelines (local) h(simpleError(msg, call))
── Error ('test_pipeop_learnercv.R:11:3'): PipeOpLearnerCV - basic properties ──
Error in ``[.data.table`(data, , `:=`("task_hash", task[[1L]]$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_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_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 $train()
Backtrace:
▆
1. └─mlr3::benchmark(benchmark_grid(tasks, learners, rsmp("cv", folds = 2))) at test_ppl.R:73:3
2. └─mlr3:::future_map(...)
3. └─future.apply::future_mapply(...)
4. └─future.apply:::future_xapply(...)
5. └─base::tryCatch(...)
6. └─base (local) tryCatchList(expr, classes, parentenv, handlers)
7. └─base (local) tryCatchOne(...)
8. └─value[[3L]](cond)
9. └─future.apply:::onError(e, futures = fs, debug = debug)
[ FAIL 38 | WARN 2 | SKIP 98 | PASS 12316 ]
Error:
! Test failures.
Execution halted
Flavor: r-devel-linux-x86_64-debian-clang
Version: 0.10.0
Check: examples
Result: ERROR
Running examples in ‘mlr3pipelines-Ex.R’ failed
The error most likely occurred in:
> base::assign(".ptime", proc.time(), pos = "CheckExEnv")
> ### Name: mlr_graphs_stacking
> ### Title: Create A Graph to Perform Stacking.
> ### Aliases: mlr_graphs_stacking pipeline_stacking
>
> ### ** Examples
>
> ## Don't show:
> if (mlr3misc::require_namespaces("rpart", quietly = TRUE)) withAutoprint({ # examplesIf
+ ## End(Don't show)
+ library(mlr3)
+ library(mlr3learners)
+
+ base_learners = list(
+ lrn("classif.rpart", predict_type = "prob"),
+ lrn("classif.nnet", predict_type = "prob")
+ )
+ super_learner = lrn("classif.log_reg")
+
+ graph_stack = pipeline_stacking(base_learners, super_learner)
+ graph_learner = as_learner(graph_stack)
+ graph_learner$train(tsk("german_credit"))
+ ## Don't show:
+ }) # examplesIf
> library(mlr3)
> library(mlr3learners)
> base_learners = list(lrn("classif.rpart", predict_type = "prob"), lrn("classif.nnet",
+ predict_type = "prob"))
> super_learner = lrn("classif.log_reg")
> graph_stack = pipeline_stacking(base_learners, super_learner)
> graph_learner = as_learner(graph_stack)
> graph_learner$train(tsk("german_credit"))
INFO [17:14:43.228] [mlr3] Resampling 'cv' is being instantiated on task 'german_credit'
INFO [17:14:43.383] [mlr3] Applying learner 'classif.rpart' on task 'german_credit' (iter 1/3)
INFO [17:14:43.443] [mlr3] Applying learner 'classif.rpart' on task 'german_credit' (iter 2/3)
INFO [17:14:43.492] [mlr3] Applying learner 'classif.rpart' on task 'german_credit' (iter 3/3)
Error in `[.data.table`(data, , `:=`("task_hash", task[[1L]]$hash), by = "uhash") :
attempt access index 9/9 in VECTOR_ELT
This happened in PipeOp classif.rpart's $train()
Calls: withAutoprint ... .__ResultData__initialize -> [ -> [.data.table -> .handleSimpleError -> h
Execution halted
Flavor: r-devel-linux-x86_64-debian-gcc
Version: 0.10.0
Check: tests
Result: ERROR
Running ‘testthat.R’ [253s/127s]
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]
> test_pipeop_blsmote.R: "Borderline-SMOTE done"
> test_pipeop_blsmote.R: [1]
> test_pipeop_blsmote.R: "Borderline-SMOTE done"
> test_pipeop_blsmote.R: [1]
> test_pipeop_blsmote.R: "Borderline-SMOTE done"
> test_pipeop_isomap.R: 2025-12-19 17:15:43.83039: Isomap START
> test_pipeop_isomap.R: 2025-12-19 17:15:43.831067: constructing knn graph
> test_pipeop_isomap.R: 2025-12-19 17:15:43.839961: calculating geodesic distances
> test_pipeop_isomap.R: 2025-12-19 17:15:43.853818: Classical Scaling
> test_pipeop_isomap.R: 2025-12-19 17:15:43.889175: Isomap START
> test_pipeop_isomap.R: 2025-12-19 17:15:43.889578: constructing knn graph
> test_pipeop_isomap.R: 2025-12-19 17:15:43.89627: calculating geodesic distances
> test_pipeop_isomap.R: 2025-12-19 17:15:43.910526: Classical Scaling
> test_pipeop_isomap.R: 2025-12-19 17:15:43.932021: L-Isomap embed START
> test_pipeop_isomap.R: 2025-12-19 17:15:43.932627: constructing knn graph
> test_pipeop_isomap.R: 2025-12-19 17:15:43.946866: calculating geodesic distances
> test_pipeop_isomap.R: 2025-12-19 17:15:43.980239: embedding
> test_pipeop_isomap.R: 2025-12-19 17:15:43.981267: DONE
> test_pipeop_isomap.R: 2025-12-19 17:15:43.998362: L-Isomap embed START
> test_pipeop_isomap.R: 2025-12-19 17:15:43.998777: constructing knn graph
> test_pipeop_isomap.R: 2025-12-19 17:15:44.01223: calculating geodesic distances
> test_pipeop_isomap.R: 2025-12-19 17:15:44.044295: embedding
> test_pipeop_isomap.R: 2025-12-19 17:15:44.045435: DONE
> test_pipeop_isomap.R: 2025-12-19 17:15:44.101383: Isomap START
> test_pipeop_isomap.R: 2025-12-19 17:15:44.101833: constructing knn graph
> test_pipeop_isomap.R: 2025-12-19 17:15:44.115511: calculating geodesic distances
> test_pipeop_isomap.R: 2025-12-19 17:15:44.19343: Classical Scaling
> test_pipeop_isomap.R: 2025-12-19 17:15:44.232457: L-Isomap embed START
> test_pipeop_isomap.R: 2025-12-19 17:15:44.232993: constructing knn graph
> test_pipeop_isomap.R: 2025-12-19 17:15:44.253974: calculating geodesic distances
> test_pipeop_isomap.R: 2025-12-19 17:15:44.42806: embedding
> test_pipeop_isomap.R: 2025-12-19 17:15:44.430558: DONE
> test_pipeop_isomap.R: 2025-12-19 17:15:44.52747: Isomap START
> test_pipeop_isomap.R: 2025-12-19 17:15:44.527876: constructing knn graph
> test_pipeop_isomap.R: 2025-12-19 17:15:44.534206: calculating geodesic distances
> test_pipeop_isomap.R: 2025-12-19 17:15:44.549302: Classical Scaling
> test_pipeop_isomap.R: 2025-12-19 17:15:44.570282: L-Isomap embed START
> test_pipeop_isomap.R: 2025-12-19 17:15:44.570769: constructing knn graph
> test_pipeop_isomap.R: 2025-12-19 17:15:44.581781: calculating geodesic distances
> test_pipeop_isomap.R: 2025-12-19 17:15:44.611104: embedding
> test_pipeop_isomap.R: 2025-12-19 17:15:44.612177: DONE
> test_pipeop_isomap.R: 2025-12-19 17:15:44.700103: Isomap START
> test_pipeop_isomap.R: 2025-12-19 17:15:44.700441: constructing knn graph
> test_pipeop_isomap.R: 2025-12-19 17:15:44.705837: calculating geodesic distances
> test_pipeop_isomap.R: 2025-12-19 17:15:44.718205: Classical Scaling
> test_pipeop_isomap.R: 2025-12-19 17:15:44.744124: L-Isomap embed START
> test_pipeop_isomap.R: 2025-12-19 17:15:44.744662: constructing knn graph
> test_pipeop_isomap.R: 2025-12-19 17:15:44.768756: calculating geodesic distances
> test_pipeop_isomap.R: 2025-12-19 17:15:44.798648: embedding
> test_pipeop_isomap.R: 2025-12-19 17:15:44.799602: DONE
> test_pipeop_isomap.R: 2025-12-19 17:15:44.848565: Isomap START
> test_pipeop_isomap.R: 2025-12-19 17:15:44.848981: constructing knn graph
> test_pipeop_isomap.R: 2025-12-19 17:15:44.855596: calculating geodesic distances
> test_pipeop_isomap.R: 2025-12-19 17:15:44.869299: Classical Scaling
> test_pipeop_isomap.R: 2025-12-19 17:15:44.899346: L-Isomap embed START
> test_pipeop_isomap.R: 2025-12-19 17:15:44.899977: constructing knn graph
> test_pipeop_isomap.R: 2025-12-19 17:15:44.910785: calculating geodesic distances
> test_pipeop_isomap.R: 2025-12-19 17:15:44.942624: embedding
> test_pipeop_isomap.R: 2025-12-19 17:15:44.943512: DONE
> test_pipeop_isomap.R: 2025-12-19 17:15:44.990388: Isomap START
> test_pipeop_isomap.R: 2025-12-19 17:15:44.990708: constructing knn graph
> test_pipeop_isomap.R: 2025-12-19 17:15:44.996297: calculating geodesic distances
> test_pipeop_isomap.R: 2025-12-19 17:15:45.008782: Classical Scaling
> test_pipeop_isomap.R: 2025-12-19 17:15:45.034771: L-Isomap embed START
> test_pipeop_isomap.R: 2025-12-19 17:15:45.035271: constructing knn graph
> test_pipeop_isomap.R: 2025-12-19 17:15:45.045023: calculating geodesic distances
> test_pipeop_isomap.R: 2025-12-19 17:15:45.075027: embedding
> test_pipeop_isomap.R: 2025-12-19 17:15:45.075935: DONE
> test_pipeop_isomap.R: 2025-12-19 17:15:45.120813: Isomap START
> test_pipeop_isomap.R: 2025-12-19 17:15:45.121188: constructing knn graph
> test_pipeop_isomap.R: 2025-12-19 17:15:45.127157: calculating geodesic distances
> test_pipeop_isomap.R: 2025-12-19 17:15:45.140641: Classical Scaling
> test_pipeop_isomap.R: 2025-12-19 17:15:45.171592: L-Isomap embed START
> test_pipeop_isomap.R: 2025-12-19 17:15:45.172118: constructing knn graph
> test_pipeop_isomap.R: 2025-12-19 17:15:45.197442: calculating geodesic distances
> test_pipeop_isomap.R: 2025-12-19 17:15:45.229171: embedding
> test_pipeop_isomap.R: 2025-12-19 17:15:45.230153: DONE
> test_pipeop_isomap.R: 2025-12-19 17:15:45.290745: Isomap START
> test_pipeop_isomap.R: 2025-12-19 17:15:45.291111: constructing knn graph
> test_pipeop_isomap.R: 2025-12-19 17:15:45.297349: calculating geodesic distances
> test_pipeop_isomap.R: 2025-12-19 17:15:45.311331: Classical Scaling
> test_pipeop_isomap.R: 2025-12-19 17:15:45.362893: Isomap START
> test_pipeop_isomap.R: 2025-12-19 17:15:45.363255: constructing knn graph
> test_pipeop_isomap.R: 2025-12-19 17:15:45.369398: calculating geodesic distances
> test_pipeop_isomap.R: 2025-12-19 17:15:45.383227: Classical Scaling
> test_pipeop_isomap.R: 2025-12-19 17:15:45.398621: Isomap START
> test_pipeop_isomap.R: 2025-12-19 17:15:45.398992: constructing knn graph
> test_pipeop_isomap.R: 2025-12-19 17:15:45.404641: calculating geodesic distances
> test_pipeop_isomap.R: 2025-12-19 17:15:45.418597: 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_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_branching.R:26:3', 'test_mlr_graphs_bagging.R:6:3',
'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_encodelmer.R:15:3', 'test_pipeop_encodelmer.R:37:3',
'test_pipeop_encodelmer.R:80:3', 'test_pipeop_encodepl.R:5:3',
'test_pipeop_encodepl.R:72:3', '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_ica.R:7:3', 'test_pipeop_ensemble.R:6:3',
'test_pipeop_impute.R:4:3', 'test_pipeop_imputelearner.R:43:3',
'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_missind.R:6:3',
'test_pipeop_modelmatrix.R:7:3', '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_targettrafoscalerange.R:5:3', 'test_pipeop_subsample.R:6:3',
'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_updatetarget.R:89:3', 'test_pipeop_vtreat.R:9:3',
'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 $train()
Backtrace:
▆
1. └─mlr3::benchmark(benchmark_grid(tasks, learners, rsmp("cv", folds = 2))) at test_ppl.R:73:3
2. └─mlr3:::future_map(...)
3. └─future.apply::future_mapply(...)
4. └─future.apply:::future_xapply(...)
5. └─base::tryCatch(...)
6. └─base (local) tryCatchList(expr, classes, parentenv, handlers)
7. └─base (local) tryCatchOne(...)
8. └─value[[3L]](cond)
9. └─future.apply:::onError(e, futures = fs, debug = debug)
[ FAIL 38 | WARN 2 | SKIP 98 | PASS 12316 ]
Error:
! Test failures.
Execution halted
Flavor: r-devel-linux-x86_64-debian-gcc
Version: 0.10.0
Check: examples
Result: ERROR
Running examples in ‘mlr3pipelines-Ex.R’ failed
The error most likely occurred in:
> ### Name: mlr_graphs_stacking
> ### Title: Create A Graph to Perform Stacking.
> ### Aliases: mlr_graphs_stacking pipeline_stacking
>
> ### ** Examples
>
> ## Don't show:
> if (mlr3misc::require_namespaces("rpart", quietly = TRUE)) withAutoprint({ # examplesIf
+ ## End(Don't show)
+ library(mlr3)
+ library(mlr3learners)
+
+ base_learners = list(
+ lrn("classif.rpart", predict_type = "prob"),
+ lrn("classif.nnet", predict_type = "prob")
+ )
+ super_learner = lrn("classif.log_reg")
+
+ graph_stack = pipeline_stacking(base_learners, super_learner)
+ graph_learner = as_learner(graph_stack)
+ graph_learner$train(tsk("german_credit"))
+ ## Don't show:
+ }) # examplesIf
> library(mlr3)
> library(mlr3learners)
> base_learners = list(lrn("classif.rpart", predict_type = "prob"), lrn("classif.nnet",
+ predict_type = "prob"))
> super_learner = lrn("classif.log_reg")
> graph_stack = pipeline_stacking(base_learners, super_learner)
> graph_learner = as_learner(graph_stack)
> graph_learner$train(tsk("german_credit"))
INFO [17:55:07.334] [mlr3] Resampling 'cv' is being instantiated on task 'german_credit'
INFO [17:55:07.897] [mlr3] Applying learner 'classif.rpart' on task 'german_credit' (iter 1/3)
INFO [17:55:08.022] [mlr3] Applying learner 'classif.rpart' on task 'german_credit' (iter 2/3)
INFO [17:55:08.141] [mlr3] Applying learner 'classif.rpart' on task 'german_credit' (iter 3/3)
Error in `[.data.table`(data, , `:=`("task_hash", task[[1L]]$hash), by = "uhash") :
attempt access index 9/9 in VECTOR_ELT
This happened in PipeOp classif.rpart's $train()
Calls: withAutoprint ... .__ResultData__initialize -> [ -> [.data.table -> .handleSimpleError -> h
Execution halted
Flavor: r-devel-linux-x86_64-fedora-clang
Version: 0.10.0
Check: tests
Result: ERROR
Running ‘testthat.R’ [648s/590s]
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:
> 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:
> 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: 2025-12-19 17:59:36.782777: Isomap START
> test_pipeop_isomap.R: 2025-12-19 17:59:36.788131: constructing knn graph
> test_pipeop_isomap.R: 2025-12-19 17:59:36.823643: calculating geodesic distances
> test_pipeop_isomap.R: 2025-12-19 17:59:36.905406: Classical Scaling
> test_pipeop_isomap.R: 2025-12-19 17:59:37.148005: Isomap START
> test_pipeop_isomap.R: 2025-12-19 17:59:37.154927: constructing knn graph
> test_pipeop_isomap.R: 2025-12-19 17:59:37.217492: calculating geodesic distances
> test_pipeop_isomap.R: 2025-12-19 17:59:37.293734: Classical Scaling
> test_pipeop_isomap.R: 2025-12-19 17:59:37.427053: L-Isomap embed START
> test_pipeop_isomap.R: 2025-12-19 17:59:37.428331: constructing knn graph
> test_pipeop_isomap.R: 2025-12-19 17:59:37.492675: calculating geodesic distances
> test_pipeop_isomap.R: 2025-12-19 17:59:37.638962: embedding
> test_pipeop_isomap.R: 2025-12-19 17:59:37.647458: DONE
> test_pipeop_isomap.R: 2025-12-19 17:59:37.738513: L-Isomap embed START
> test_pipeop_isomap.R: 2025-12-19 17:59:37.739327: constructing knn graph
> test_pipeop_isomap.R: 2025-12-19 17:59:37.948353: calculating geodesic distances
> test_pipeop_isomap.R: 2025-12-19 17:59:38.085035: embedding
> test_pipeop_isomap.R: 2025-12-19 17:59:38.094977: DONE
> test_pipeop_isomap.R: 2025-12-19 17:59:38.420522: Isomap START
> test_pipeop_isomap.R: 2025-12-19 17:59:38.423569: constructing knn graph
> test_pipeop_isomap.R: 2025-12-19 17:59:38.473072: calculating geodesic distances
> test_pipeop_isomap.R: 2025-12-19 17:59:38.788484: Classical Scaling
> test_pipeop_isomap.R: 2025-12-19 17:59:38.914818: L-Isomap embed START
> test_pipeop_isomap.R: 2025-12-19 17:59:38.915874: constructing knn graph
> test_pipeop_isomap.R: 2025-12-19 17:59:39.272942: calculating geodesic distances
> test_pipeop_isomap.R: 2025-12-19 17:59:40.080209: embedding
> test_pipeop_isomap.R: 2025-12-19 17:59:40.095217: DONE
> test_pipeop_isomap.R: 2025-12-19 17:59:40.516095: Isomap START
> test_pipeop_isomap.R: 2025-12-19 17:59:40.51697: constructing knn graph
> test_pipeop_isomap.R: 2025-12-19 17:59:40.53553: calculating geodesic distances
> test_pipeop_isomap.R: 2025-12-19 17:59:40.587082: Classical Scaling
> test_pipeop_isomap.R: 2025-12-19 17:59:40.759665: L-Isomap embed START
> test_pipeop_isomap.R: 2025-12-19 17:59:40.766864: constructing knn graph
> test_pipeop_isomap.R: 2025-12-19 17:59:40.827477: calculating geodesic distances
> test_pipeop_isomap.R: 2025-12-19 17:59:40.971903: embedding
> test_pipeop_isomap.R: 2025-12-19 17:59:40.980267: DONE
> test_pipeop_isomap.R: 2025-12-19 17:59:41.518087: Isomap START
> test_pipeop_isomap.R: 2025-12-19 17:59:41.52384: constructing knn graph
> test_pipeop_isomap.R: 2025-12-19 17:59:41.606861: calculating geodesic distances
> test_pipeop_isomap.R: 2025-12-19 17:59:41.673903: Classical Scaling
> test_pipeop_isomap.R: 2025-12-19 17:59:41.888372: L-Isomap embed START
> test_pipeop_isomap.R: 2025-12-19 17:59:41.889426: constructing knn graph
> test_pipeop_isomap.R: 2025-12-19 17:59:41.942357: calculating geodesic distances
> test_pipeop_isomap.R: 2025-12-19 17:59:42.084522: embedding
> test_pipeop_isomap.R: 2025-12-19 17:59:42.086255: DONE
> test_pipeop_isomap.R: 2025-12-19 17:59:42.363597: Isomap START
> test_pipeop_isomap.R: 2025-12-19 17:59:42.364286: constructing knn graph
> test_pipeop_isomap.R: 2025-12-19 17:59:42.388848: calculating geodesic distances
> test_pipeop_isomap.R: 2025-12-19 17:59:42.453632: Classical Scaling
> test_pipeop_isomap.R: 2025-12-19 17:59:42.587881: L-Isomap embed START
> test_pipeop_isomap.R: 2025-12-19 17:59:42.591163: constructing knn graph
> test_pipeop_isomap.R: 2025-12-19 17:59:42.640784: calculating geodesic distances
> test_pipeop_isomap.R: 2025-12-19 17:59:42.733569: embedding
> test_pipeop_isomap.R: 2025-12-19 17:59:42.737515: DONE
> test_pipeop_isomap.R: 2025-12-19 17:59:42.919924: Isomap START
> test_pipeop_isomap.R: 2025-12-19 17:59:42.920698: constructing knn graph
> test_pipeop_isomap.R: 2025-12-19 17:59:42.95613: calculating geodesic distances
> test_pipeop_isomap.R: 2025-12-19 17:59:43.015573: Classical Scaling
> test_pipeop_isomap.R: 2025-12-19 17:59:43.186374: L-Isomap embed START
> test_pipeop_isomap.R: 2025-12-19 17:59:43.191629: constructing knn graph
> test_pipeop_isomap.R: 2025-12-19 17:59:43.241857: calculating geodesic distances
> test_pipeop_isomap.R: 2025-12-19 17:59:43.377422: embedding
> test_pipeop_isomap.R: 2025-12-19 17:59:43.381857: DONE
> test_pipeop_isomap.R: 2025-12-19 17:59:43.668476: Isomap START
> test_pipeop_isomap.R: 2025-12-19 17:59:43.669277: constructing knn graph
> test_pipeop_isomap.R: 2025-12-19 17:59:43.694776: calculating geodesic distances
> test_pipeop_isomap.R: 2025-12-19 17:59:43.794544: Classical Scaling
> test_pipeop_isomap.R: 2025-12-19 17:59:43.992826: L-Isomap embed START
> test_pipeop_isomap.R: 2025-12-19 17:59:43.993972: constructing knn graph
> test_pipeop_isomap.R: 2025-12-19 17:59:44.044239: calculating geodesic distances
> test_pipeop_isomap.R: 2025-12-19 17:59:44.185145: embedding
> test_pipeop_isomap.R: 2025-12-19 17:59:44.187031: DONE
> test_pipeop_isomap.R: 2025-12-19 17:59:44.574771: Isomap START
> test_pipeop_isomap.R: 2025-12-19 17:59:44.581328: constructing knn graph
> test_pipeop_isomap.R: 2025-12-19 17:59:44.604874: calculating geodesic distances
> test_pipeop_isomap.R: 2025-12-19 17:59:44.672687: Classical Scaling
> test_pipeop_isomap.R: 2025-12-19 17:59:45.045445: Isomap START
> test_pipeop_isomap.R: 2025-12-19 17:59:45.050264: constructing knn graph
> test_pipeop_isomap.R: 2025-12-19 17:59:45.077913: calculating geodesic distances
> test_pipeop_isomap.R: 2025-12-19 17:59:45.202453: Classical Scaling
> test_pipeop_isomap.R: 2025-12-19 17:59:45.332021: Isomap START
> test_pipeop_isomap.R: 2025-12-19 17:59:45.332778: constructing knn graph
> test_pipeop_isomap.R: 2025-12-19 17:59:45.365373: calculating geodesic distances
> test_pipeop_isomap.R: 2025-12-19 17:59:45.429788: 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:
> 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:
> 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:
> 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:
> test_pipeop_task_preproc.R: Training debug_affectcols
> test_pipeop_task_preproc.R:
> 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_ppl-73.R
Saving _problems/test_usecases-153.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_datefeatures.R:10:3', 'test_pipeop_encodeimpact.R:11:3',
'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_ica.R:7:3', 'test_pipeop_ensemble.R:6:3',
'test_pipeop_impute.R:4:3', 'test_pipeop_imputelearner.R:43:3',
'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_missind.R:6:3',
'test_pipeop_modelmatrix.R:7:3', '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_targettrafoscalerange.R:5:3', 'test_pipeop_subsample.R:6:3',
'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_textvectorizer.R:37:3',
'test_pipeop_textvectorizer.R:186:3', 'test_pipeop_unbranch.R:10:3',
'test_pipeop_updatetarget.R:89:3', 'test_pipeop_vtreat.R:9:3',
'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_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 $train()
Backtrace:
▆
1. └─mlr3::benchmark(benchmark_grid(tasks, learners, rsmp("cv", folds = 2))) at test_ppl.R:73:3
2. └─mlr3:::future_map(...)
3. └─future.apply::future_mapply(...)
4. └─future.apply:::future_xapply(...)
5. └─base::tryCatch(...)
6. └─base (local) tryCatchList(expr, classes, parentenv, handlers)
7. └─base (local) tryCatchOne(...)
8. └─value[[3L]](cond)
9. └─future.apply:::onError(e, futures = fs, debug = debug)
── Error ('test_usecases.R:153:3'): stacking ───────────────────────────────────
Error in ``[.data.table`(data, , `:=`("task_hash", task[[1L]]$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))
[ FAIL 38 | WARN 2 | SKIP 98 | PASS 12316 ]
Error:
! Test failures.
Execution halted
Flavor: r-devel-linux-x86_64-fedora-clang
Version: 0.10.0
Check: examples
Result: ERROR
Running examples in ‘mlr3pipelines-Ex.R’ failed
The error most likely occurred in:
> ### Name: mlr_graphs_stacking
> ### Title: Create A Graph to Perform Stacking.
> ### Aliases: mlr_graphs_stacking pipeline_stacking
>
> ### ** Examples
>
> ## Don't show:
> if (mlr3misc::require_namespaces("rpart", quietly = TRUE)) withAutoprint({ # examplesIf
+ ## End(Don't show)
+ library(mlr3)
+ library(mlr3learners)
+
+ base_learners = list(
+ lrn("classif.rpart", predict_type = "prob"),
+ lrn("classif.nnet", predict_type = "prob")
+ )
+ super_learner = lrn("classif.log_reg")
+
+ graph_stack = pipeline_stacking(base_learners, super_learner)
+ graph_learner = as_learner(graph_stack)
+ graph_learner$train(tsk("german_credit"))
+ ## Don't show:
+ }) # examplesIf
> library(mlr3)
> library(mlr3learners)
> base_learners = list(lrn("classif.rpart", predict_type = "prob"), lrn("classif.nnet",
+ predict_type = "prob"))
> super_learner = lrn("classif.log_reg")
> graph_stack = pipeline_stacking(base_learners, super_learner)
> graph_learner = as_learner(graph_stack)
> graph_learner$train(tsk("german_credit"))
INFO [12:35:59.250] [mlr3] Resampling 'cv' is being instantiated on task 'german_credit'
INFO [12:35:59.762] [mlr3] Applying learner 'classif.rpart' on task 'german_credit' (iter 1/3)
INFO [12:35:59.962] [mlr3] Applying learner 'classif.rpart' on task 'german_credit' (iter 2/3)
INFO [12:36:00.073] [mlr3] Applying learner 'classif.rpart' on task 'german_credit' (iter 3/3)
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()
Calls: withAutoprint ... .__ResultData__initialize -> [ -> [.data.table -> .handleSimpleError -> h
Execution halted
Flavor: r-devel-linux-x86_64-fedora-gcc
Version: 0.10.0
Check: tests
Result: ERROR
Running ‘testthat.R’ [10m/10m]
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_Graph.R:
> 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:
> 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:
> 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]
> test_pipeop_blsmote.R: "Borderline-SMOTE done"
> test_pipeop_blsmote.R: [1]
> test_pipeop_blsmote.R: "Borderline-SMOTE done"
> test_pipeop_blsmote.R: [1]
> test_pipeop_blsmote.R: "Borderline-SMOTE done"
> test_pipeop_isomap.R: 2025-12-19 12:40:40.810455: Isomap START
> test_pipeop_isomap.R: 2025-12-19 12:40:40.811509: constructing knn graph
> test_pipeop_isomap.R: 2025-12-19 12:40:40.839367: calculating geodesic distances
> test_pipeop_isomap.R: 2025-12-19 12:40:40.87009: Classical Scaling
> test_pipeop_isomap.R: 2025-12-19 12:40:41.029903: Isomap START
> test_pipeop_isomap.R: 2025-12-19 12:40:41.03606: constructing knn graph
> test_pipeop_isomap.R: 2025-12-19 12:40:41.063067: calculating geodesic distances
> test_pipeop_isomap.R: 2025-12-19 12:40:41.134463: Classical Scaling
> test_pipeop_isomap.R: 2025-12-19 12:40:41.233111: L-Isomap embed START
> test_pipeop_isomap.R: 2025-12-19 12:40:41.234192: constructing knn graph
> test_pipeop_isomap.R: 2025-12-19 12:40:41.308376: calculating geodesic distances
> test_pipeop_isomap.R: 2025-12-19 12:40:41.442257: embedding
> test_pipeop_isomap.R: 2025-12-19 12:40:41.448256: DONE
> test_pipeop_isomap.R: 2025-12-19 12:40:41.524493: L-Isomap embed START
> test_pipeop_isomap.R: 2025-12-19 12:40:41.529293: constructing knn graph
> test_pipeop_isomap.R: 2025-12-19 12:40:41.578926: calculating geodesic distances
> test_pipeop_isomap.R: 2025-12-19 12:40:41.719571: embedding
> test_pipeop_isomap.R: 2025-12-19 12:40:41.721557: DONE
> test_pipeop_isomap.R: 2025-12-19 12:40:41.983984: Isomap START
> test_pipeop_isomap.R: 2025-12-19 12:40:41.984677: constructing knn graph
> test_pipeop_isomap.R: 2025-12-19 12:40:42.101086: calculating geodesic distances
> test_pipeop_isomap.R: 2025-12-19 12:40:42.512402: Classical Scaling
> test_pipeop_isomap.R: 2025-12-19 12:40:42.626629: L-Isomap embed START
> test_pipeop_isomap.R: 2025-12-19 12:40:42.633308: constructing knn graph
> test_pipeop_isomap.R: 2025-12-19 12:40:42.723963: calculating geodesic distances
> test_pipeop_isomap.R: 2025-12-19 12:40:43.462029: embedding
> test_pipeop_isomap.R: 2025-12-19 12:40:43.471896: DONE
> test_pipeop_isomap.R: 2025-12-19 12:40:43.985367: Isomap START
> test_pipeop_isomap.R: 2025-12-19 12:40:43.991899: constructing knn graph
> test_pipeop_isomap.R: 2025-12-19 12:40:44.013667: calculating geodesic distances
> test_pipeop_isomap.R: 2025-12-19 12:40:44.089733: Classical Scaling
> test_pipeop_isomap.R: 2025-12-19 12:40:44.223707: L-Isomap embed START
> test_pipeop_isomap.R: 2025-12-19 12:40:44.224686: constructing knn graph
> test_pipeop_isomap.R: 2025-12-19 12:40:44.27414: calculating geodesic distances
> test_pipeop_isomap.R: 2025-12-19 12:40:44.468682: embedding
> test_pipeop_isomap.R: 2025-12-19 12:40:44.470539: DONE
> test_pipeop_isomap.R: 2025-12-19 12:40:44.958345: Isomap START
> test_pipeop_isomap.R: 2025-12-19 12:40:44.95904: constructing knn graph
> test_pipeop_isomap.R: 2025-12-19 12:40:44.995049: calculating geodesic distances
> test_pipeop_isomap.R: 2025-12-19 12:40:45.079285: Classical Scaling
> test_pipeop_isomap.R: 2025-12-19 12:40:45.160857: L-Isomap embed START
> test_pipeop_isomap.R: 2025-12-19 12:40:45.16432: constructing knn graph
> test_pipeop_isomap.R: 2025-12-19 12:40:45.246153: calculating geodesic distances
> test_pipeop_isomap.R: 2025-12-19 12:40:45.373753: embedding
> test_pipeop_isomap.R: 2025-12-19 12:40:45.379883: DONE
> test_pipeop_isomap.R: 2025-12-19 12:40:45.640641: Isomap START
> test_pipeop_isomap.R: 2025-12-19 12:40:45.641471: constructing knn graph
> test_pipeop_isomap.R: 2025-12-19 12:40:45.667388: calculating geodesic distances
> test_pipeop_isomap.R: 2025-12-19 12:40:45.727318: Classical Scaling
> test_pipeop_isomap.R: 2025-12-19 12:40:45.879744: L-Isomap embed START
> test_pipeop_isomap.R: 2025-12-19 12:40:45.886705: constructing knn graph
> test_pipeop_isomap.R: 2025-12-19 12:40:45.940945: calculating geodesic distances
> test_pipeop_isomap.R: 2025-12-19 12:40:46.080362: embedding
> test_pipeop_isomap.R: 2025-12-19 12:40:46.088764: DONE
> test_pipeop_isomap.R: 2025-12-19 12:40:46.573868: Isomap START
> test_pipeop_isomap.R: 2025-12-19 12:40:46.574545: constructing knn graph
> test_pipeop_isomap.R: 2025-12-19 12:40:46.604222: calculating geodesic distances
> test_pipeop_isomap.R: 2025-12-19 12:40:46.691745: Classical Scaling
> test_pipeop_isomap.R: 2025-12-19 12:40:46.862719: L-Isomap embed START
> test_pipeop_isomap.R: 2025-12-19 12:40:46.863737: constructing knn graph
> test_pipeop_isomap.R: 2025-12-19 12:40:46.914001: calculating geodesic distances
> test_pipeop_isomap.R: 2025-12-19 12:40:47.055884: embedding
> test_pipeop_isomap.R: 2025-12-19 12:40:47.057964: DONE
> test_pipeop_isomap.R: 2025-12-19 12:40:47.376329: Isomap START
> test_pipeop_isomap.R: 2025-12-19 12:40:47.385907: constructing knn graph
> test_pipeop_isomap.R: 2025-12-19 12:40:47.412987: calculating geodesic distances
> test_pipeop_isomap.R: 2025-12-19 12:40:47.489905: Classical Scaling
> test_pipeop_isomap.R: 2025-12-19 12:40:47.738713: L-Isomap embed START
> test_pipeop_isomap.R: 2025-12-19 12:40:47.73972: constructing knn graph
> test_pipeop_isomap.R: 2025-12-19 12:40:47.803509: calculating geodesic distances
> test_pipeop_isomap.R: 2025-12-19 12:40:47.982624: embedding
> test_pipeop_isomap.R: 2025-12-19 12:40:47.990171: DONE
> test_pipeop_isomap.R: 2025-12-19 12:40:48.318157: Isomap START
> test_pipeop_isomap.R: 2025-12-19 12:40:48.318858: constructing knn graph
> test_pipeop_isomap.R: 2025-12-19 12:40:48.344776: calculating geodesic distances
> test_pipeop_isomap.R: 2025-12-19 12:40:48.40859: Classical Scaling
> test_pipeop_isomap.R: 2025-12-19 12:40:48.695255: Isomap START
> test_pipeop_isomap.R: 2025-12-19 12:40:48.695925: constructing knn graph
> test_pipeop_isomap.R: 2025-12-19 12:40:48.731643: calculating geodesic distances
> test_pipeop_isomap.R: 2025-12-19 12:40:48.797391: Classical Scaling
> test_pipeop_isomap.R: 2025-12-19 12:40:48.888212: Isomap START
> test_pipeop_isomap.R: 2025-12-19 12:40:48.894901: constructing knn graph
> test_pipeop_isomap.R: 2025-12-19 12:40:48.916525: calculating geodesic distances
> test_pipeop_isomap.R: 2025-12-19 12:40:48.980285: 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:
> test_pipeop_task_preproc.R: Training debug_affectcols
> test_pipeop_task_preproc.R:
> test_pipeop_task_preproc.R: Training debug_affectcols
> test_pipeop_task_preproc.R:
> 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:
> test_pipeop_task_preproc.R: Training debug_affectcols
> test_pipeop_task_preproc.R:
> 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:
> 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:
> 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_boxcox.R:7:3', 'test_pipeop_classweights.R:10:3',
'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_ica.R:7:3', 'test_pipeop_ensemble.R:6:3',
'test_pipeop_impute.R:4:3', 'test_pipeop_imputelearner.R:43:3',
'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_missind.R:6:3',
'test_pipeop_modelmatrix.R:7:3', 'test_pipeop_multiplicityexply.R:9:3',
'test_pipeop_multiplicityimply.R:9:3', 'test_pipeop_mutate.R:9:3',
'test_pipeop_nearmiss.R:7:3', '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_targettrafoscalerange.R:5:3', 'test_pipeop_subsample.R:6:3',
'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_updatetarget.R:89:3', 'test_pipeop_vtreat.R:9:3',
'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 $train()
Backtrace:
▆
1. └─mlr3::benchmark(benchmark_grid(tasks, learners, rsmp("cv", folds = 2))) at test_ppl.R:73:3
2. └─mlr3:::future_map(...)
3. └─future.apply::future_mapply(...)
4. └─future.apply:::future_xapply(...)
5. └─base::tryCatch(...)
6. └─base (local) tryCatchList(expr, classes, parentenv, handlers)
7. └─base (local) tryCatchOne(...)
8. └─value[[3L]](cond)
9. └─future.apply:::onError(e, futures = fs, debug = debug)
[ FAIL 38 | WARN 2 | SKIP 98 | PASS 12316 ]
Error:
! Test failures.
Execution halted
Flavor: r-devel-linux-x86_64-fedora-gcc
Version: 0.10.0
Check: examples
Result: ERROR
Running examples in ‘mlr3pipelines-Ex.R’ failed
The error most likely occurred in:
> ### Name: mlr_pipeops_nmf
> ### Title: Non-negative Matrix Factorization
> ### Aliases: mlr_pipeops_nmf PipeOpNMF
>
> ### ** Examples
>
> ## Don't show:
> if (mlr3misc::require_namespaces(c("NMF", "MASS"), quietly = TRUE)) withAutoprint({ # examplesIf
+ ## End(Don't show)
+ ## Don't show:
+ # NMF attaches these packages to search path on load, #929
+ lapply(c("package:Biobase", "package:BiocGenerics", "package:generics"), detach, character.only = TRUE)
+ ## End(Don't show)
+ library("mlr3")
+
+ task = tsk("iris")
+ pop = po("nmf")
+
+ task$data()
+ pop$train(list(task))[[1]]$data()
+
+ pop$state
+ ## Don't show:
+ # BiocGenerics overwrites printer for our tables mlr-org/mlr3#1112
+ # Necessary as detaching packages does not remove registered S3 methods
+ suppressWarnings(try(rm("format.list", envir = .BaseNamespaceEnv$.__S3MethodsTable__.), silent = TRUE))
+ ## End(Don't show)
+ ## Don't show:
+ }) # examplesIf
> lapply(c("package:Biobase", "package:BiocGenerics", "package:generics"),
+ detach, character.only = TRUE)
Error in FUN(X[[i]], ...) : invalid 'name' argument
Calls: withAutoprint ... withVisible -> eval -> eval -> lapply -> lapply -> FUN
Execution halted
Flavors: r-oldrel-macos-arm64, r-oldrel-macos-x86_64, r-oldrel-windows-x86_64
Version: 0.10.0
Check: tests
Result: ERROR
Running ‘testthat.R’ [99s/48s]
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:
> 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
> test_filter_ensemble.R: OptimInstanceSingleCrit is deprecated. Use OptimInstanceBatchSingleCrit instead.
> 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"
Saving _problems/test_pipeop_datefeatures-7.R
Saving _problems/test_pipeop_datefeatures-17.R
> test_pipeop_isomap.R: 2025-12-19 21:19:35.911659: Isomap START
> test_pipeop_isomap.R: 2025-12-19 21:19:35.911948: constructing knn graph
> test_pipeop_isomap.R: 2025-12-19 21:19:35.91494: calculating geodesic distances
> test_pipeop_isomap.R: 2025-12-19 21:19:35.921081: Classical Scaling
> test_pipeop_isomap.R: 2025-12-19 21:19:35.93169: Isomap START
> test_pipeop_isomap.R: 2025-12-19 21:19:35.931813: constructing knn graph
> test_pipeop_isomap.R: 2025-12-19 21:19:35.934013: calculating geodesic distances
> test_pipeop_isomap.R: 2025-12-19 21:19:35.939944: Classical Scaling
> test_pipeop_isomap.R: 2025-12-19 21:19:35.947109: L-Isomap embed START
> test_pipeop_isomap.R: 2025-12-19 21:19:35.947258: constructing knn graph
> test_pipeop_isomap.R: 2025-12-19 21:19:35.951705: calculating geodesic distances
> test_pipeop_isomap.R: 2025-12-19 21:19:35.966403: embedding
> test_pipeop_isomap.R: 2025-12-19 21:19:35.966743: DONE
> test_pipeop_isomap.R: 2025-12-19 21:19:35.97162: L-Isomap embed START
> test_pipeop_isomap.R: 2025-12-19 21:19:35.971755: constructing knn graph
> test_pipeop_isomap.R: 2025-12-19 21:19:35.975963: calculating geodesic distances
> test_pipeop_isomap.R: 2025-12-19 21:19:35.990659: embedding
> test_pipeop_isomap.R: 2025-12-19 21:19:35.991019: DONE
> test_pipeop_isomap.R: 2025-12-19 21:19:36.009116: Isomap START
> test_pipeop_isomap.R: 2025-12-19 21:19:36.009239: constructing knn graph
> test_pipeop_isomap.R: 2025-12-19 21:19:36.013663: calculating geodesic distances
> test_pipeop_isomap.R: 2025-12-19 21:19:36.046672: Classical Scaling
> test_pipeop_isomap.R: 2025-12-19 21:19:36.054747: L-Isomap embed START
> test_pipeop_isomap.R: 2025-12-19 21:19:36.054962: constructing knn graph
> test_pipeop_isomap.R: 2025-12-19 21:19:36.063529: calculating geodesic distances
> test_pipeop_isomap.R: 2025-12-19 21:19:36.139986: embedding
> test_pipeop_isomap.R: 2025-12-19 21:19:36.14119: DONE
> test_pipeop_isomap.R: 2025-12-19 21:19:36.179638: Isomap START
> test_pipeop_isomap.R: 2025-12-19 21:19:36.179755: constructing knn graph
> test_pipeop_isomap.R: 2025-12-19 21:19:36.18159: calculating geodesic distances
> test_pipeop_isomap.R: 2025-12-19 21:19:36.187387: Classical Scaling
> test_pipeop_isomap.R: 2025-12-19 21:19:36.193788: L-Isomap embed START
> test_pipeop_isomap.R: 2025-12-19 21:19:36.193957: constructing knn graph
> test_pipeop_isomap.R: 2025-12-19 21:19:36.199124: calculating geodesic distances
> test_pipeop_isomap.R: 2025-12-19 21:19:36.214714: embedding
> test_pipeop_isomap.R: 2025-12-19 21:19:36.215241: DONE
> test_pipeop_isomap.R: 2025-12-19 21:19:36.245952: Isomap START
> test_pipeop_isomap.R: 2025-12-19 21:19:36.246105: constructing knn graph
> test_pipeop_isomap.R: 2025-12-19 21:19:36.248463: calculating geodesic distances
> test_pipeop_isomap.R: 2025-12-19 21:19:36.254648: Classical Scaling
> test_pipeop_isomap.R: 2025-12-19 21:19:36.264979: L-Isomap embed START
> test_pipeop_isomap.R: 2025-12-19 21:19:36.265164: constructing knn graph
> test_pipeop_isomap.R: 2025-12-19 21:19:36.269644: calculating geodesic distances
> test_pipeop_isomap.R: 2025-12-19 21:19:36.28446: embedding
> test_pipeop_isomap.R: 2025-12-19 21:19:36.284807: DONE
> test_pipeop_isomap.R: 2025-12-19 21:19:36.302118: Isomap START
> test_pipeop_isomap.R: 2025-12-19 21:19:36.30224: constructing knn graph
> test_pipeop_isomap.R: 2025-12-19 21:19:36.304594: calculating geodesic distances
> test_pipeop_isomap.R: 2025-12-19 21:19:36.310604: Classical Scaling
> test_pipeop_isomap.R: 2025-12-19 21:19:36.32045: L-Isomap embed START
> test_pipeop_isomap.R: 2025-12-19 21:19:36.320627: constructing knn graph
> test_pipeop_isomap.R: 2025-12-19 21:19:36.325034: calculating geodesic distances
> test_pipeop_isomap.R: 2025-12-19 21:19:36.339206: embedding
> test_pipeop_isomap.R: 2025-12-19 21:19:36.339549: DONE
> test_pipeop_isomap.R: 2025-12-19 21:19:36.355152: Isomap START
> test_pipeop_isomap.R: 2025-12-19 21:19:36.355294: constructing knn graph
> test_pipeop_isomap.R: 2025-12-19 21:19:36.357721: calculating geodesic distances
> test_pipeop_isomap.R: 2025-12-19 21:19:36.363551: Classical Scaling
> test_pipeop_isomap.R: 2025-12-19 21:19:36.404645: L-Isomap embed START
> test_pipeop_isomap.R: 2025-12-19 21:19:36.404849: constructing knn graph
> test_pipeop_isomap.R: 2025-12-19 21:19:36.409609: calculating geodesic distances
> test_pipeop_isomap.R: 2025-12-19 21:19:36.424721: embedding
> test_pipeop_isomap.R: 2025-12-19 21:19:36.42511: DONE
> test_pipeop_isomap.R: 2025-12-19 21:19:36.441397: Isomap START
> test_pipeop_isomap.R: 2025-12-19 21:19:36.441525: constructing knn graph
> test_pipeop_isomap.R: 2025-12-19 21:19:36.443713: calculating geodesic distances
> test_pipeop_isomap.R: 2025-12-19 21:19:36.449857: Classical Scaling
> test_pipeop_isomap.R: 2025-12-19 21:19:36.459773: L-Isomap embed START
> test_pipeop_isomap.R: 2025-12-19 21:19:36.459934: constructing knn graph
> test_pipeop_isomap.R: 2025-12-19 21:19:36.464663: calculating geodesic distances
> test_pipeop_isomap.R: 2025-12-19 21:19:36.479576: embedding
> test_pipeop_isomap.R: 2025-12-19 21:19:36.479942: DONE
> test_pipeop_isomap.R: 2025-12-19 21:19:36.497536: Isomap START
> test_pipeop_isomap.R: 2025-12-19 21:19:36.497663: constructing knn graph
> test_pipeop_isomap.R: 2025-12-19 21:19:36.499727: calculating geodesic distances
> test_pipeop_isomap.R: 2025-12-19 21:19:36.505833: Classical Scaling
> test_pipeop_isomap.R: 2025-12-19 21:19:36.522571: Isomap START
> test_pipeop_isomap.R: 2025-12-19 21:19:36.52271: constructing knn graph
> test_pipeop_isomap.R: 2025-12-19 21:19:36.525017: calculating geodesic distances
> test_pipeop_isomap.R: 2025-12-19 21:19:36.531261: Classical Scaling
> test_pipeop_isomap.R: 2025-12-19 21:19:36.536585: Isomap START
> test_pipeop_isomap.R: 2025-12-19 21:19:36.536715: constructing knn graph
> test_pipeop_isomap.R: 2025-12-19 21:19:36.538962: calculating geodesic distances
> test_pipeop_isomap.R: 2025-12-19 21:19:36.545261: Classical Scaling
Saving _problems/test_pipeop_nmf-45.R
Saving _problems/test_pipeop_nmf-73.R
Saving _problems/test_pipeop_nmf-93.R
Saving _problems/test_pipeop_nmf-98.R
> 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_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_tunethreshold.R: OptimInstanceSingleCrit is deprecated. Use OptimInstanceBatchSingleCrit instead.
> test_pipeop_tunethreshold.R: OptimInstanceSingleCrit is deprecated. Use OptimInstanceBatchSingleCrit instead.
> test_pipeop_tunethreshold.R: OptimInstanceSingleCrit is deprecated. Use OptimInstanceBatchSingleCrit instead.
> test_pipeop_tunethreshold.R: OptimInstanceSingleCrit is deprecated. Use OptimInstanceBatchSingleCrit instead.
> test_pipeop_tunethreshold.R: OptimInstanceSingleCrit is deprecated. Use OptimInstanceBatchSingleCrit instead.
> test_pipeop_tunethreshold.R: OptimInstanceSingleCrit is deprecated. Use OptimInstanceBatchSingleCrit instead.
> test_pipeop_tunethreshold.R: OptimInstanceSingleCrit is deprecated. Use OptimInstanceBatchSingleCrit instead.
[ FAIL 6 | WARN 0 | SKIP 99 | PASS 13006 ]
══ Skipped tests (99) ══════════════════════════════════════════════════════════
• 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_branching.R:26:3', 'test_mlr_graphs_bagging.R:6:3',
'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_boxcox.R:7:3', 'test_pipeop_classweights.R:10:3',
'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_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_ensemble.R:6:3', 'test_pipeop_histbin.R:7:3',
'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_missind.R:6:3', 'test_pipeop_modelmatrix.R:7:3',
'test_pipeop_multiplicityexply.R:9:3', 'test_pipeop_multiplicityimply.R:9:3',
'test_pipeop_mutate.R:9:3', 'test_pipeop_nearmiss.R:7:3',
'test_pipeop_learnercv.R:31:3', '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_nmf.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_rowapply.R:6:3', 'test_pipeop_smotenc.R:8:3',
'test_pipeop_subsample.R:6:3', 'test_pipeop_targetinvert.R:4:3',
'test_pipeop_targetmutate.R:5:3', 'test_pipeop_targettrafo.R:4:3',
'test_pipeop_targettrafoscalerange.R:5:3', 'test_pipeop_task_preproc.R:4:3',
'test_pipeop_task_preproc.R:14:3', 'test_pipeop_spatialsign.R:6:3',
'test_pipeop_tomek.R:7:3', 'test_pipeop_textvectorizer.R:37:3',
'test_pipeop_textvectorizer.R:186:3', 'test_pipeop_unbranch.R:10:3',
'test_pipeop_updatetarget.R:89:3', 'test_pipeop_vtreat.R:9:3',
'test_pipeop_yeojohnson.R:7:3', 'test_pipeop_tunethreshold.R:111:3',
'test_pipeop_tunethreshold.R:191:3', 'test_typecheck.R:188:3'
• Skipping (1): 'test_GraphLearner.R:1278:3'
• empty test (3): 'test_pipeop_isomap.R:111:1', 'test_pipeop_missind.R:101:1',
'test_ppl.R:61:1'
══ Failed tests ════════════════════════════════════════════════════════════════
── Error ('test_pipeop_datefeatures.R:7:3'): PipeOpDateFeatures - basic properties ──
Error in `seq.Date(as.Date("2020-01-31"), length.out = 150L)`: exactly two of 'to', 'by' and 'length.out' / 'along.with' must be specified
Backtrace:
▆
1. ├─base::seq(as.Date("2020-01-31"), length.out = 150L) at test_pipeop_datefeatures.R:7:3
2. └─base::seq.Date(as.Date("2020-01-31"), length.out = 150L)
── Error ('test_pipeop_datefeatures.R:17:3'): PipeOpDateFeatures - finds POSIXct column ──
Error in `seq.Date(as.Date("2020-01-31"), length.out = 150L)`: exactly two of 'to', 'by' and 'length.out' / 'along.with' must be specified
Backtrace:
▆
1. ├─base::seq(as.Date("2020-01-31"), length.out = 150L) at test_pipeop_datefeatures.R:17:3
2. └─base::seq.Date(as.Date("2020-01-31"), length.out = 150L)
── Error ('test_pipeop_nmf.R:45:3'): PipeOpNMF - does not modify search path when NMF is not loaded, fix for #929 ──
Error in `detach(package:generics)`: invalid 'name' argument
Backtrace:
▆
1. └─base::detach(package:generics) at test_pipeop_nmf.R:45:3
── Failure ('test_pipeop_nmf.R:73:3'): PipeOpNMF - does not modify search path when NMF is loaded, fix for #929 ──
Expected `all(...)` to be TRUE.
Differences:
`actual`: FALSE
`expected`: TRUE
── Failure ('test_pipeop_nmf.R:93:3'): PipeOpNMF - does not modify search path when some of NMF's dependencies are loaded, fix for #929 ──
Expected `all(paste0("package:", c("BiocGenerics", "generics")) %in% search())` to be TRUE.
Differences:
`actual`: FALSE
`expected`: TRUE
── Error ('test_pipeop_nmf.R:98:3'): PipeOpNMF - does not modify search path when some of NMF's dependencies are loaded, fix for #929 ──
Error in `FUN(X[[i]], ...)`: invalid 'name' argument
This happened in PipeOp nmf's $train()
Backtrace:
▆
1. ├─op$train(list(tsk("iris"))) at test_pipeop_nmf.R:98:3
2. │ └─mlr3pipelines:::.__PipeOp__train(...)
3. │ ├─base::withCallingHandlers(...)
4. │ └─private$.train(input)
5. │ └─mlr3pipelines:::.__PipeOpTaskPreproc__.train(...)
6. │ └─private$.train_task(intask)
7. │ └─mlr3pipelines:::.__PipeOpTaskPreproc__.train_task(...)
8. │ ├─data.table::as.data.table(...)
9. │ └─private$.train_dt(dt, task$levels(cols), task$truth())
10. │ └─mlr3pipelines:::.__PipeOpNMF__.train_dt(...)
11. │ └─mlr3misc::map(to_be_detached, detach, character.only = TRUE)
12. │ └─base::lapply(.x, .f, ...)
13. │ └─base (local) FUN(X[[i]], ...)
14. │ └─base::stop("invalid 'name' argument")
15. └─base::.handleSimpleError(...)
16. └─mlr3pipelines (local) h(simpleError(msg, call))
[ FAIL 6 | WARN 0 | SKIP 99 | PASS 13006 ]
Error:
! Test failures.
Execution halted
Flavor: r-oldrel-macos-arm64
Version: 0.10.0
Check: tests
Result: ERROR
Running ‘testthat.R’ [314s/359s]
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:
> 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:
> 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
> test_filter_ensemble.R: OptimInstanceSingleCrit is deprecated. Use OptimInstanceBatchSingleCrit instead.
> 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]
> test_pipeop_blsmote.R: "Borderline-SMOTE done"
> test_pipeop_blsmote.R:
Saving _problems/test_pipeop_datefeatures-7.R
Saving _problems/test_pipeop_datefeatures-17.R
> test_pipeop_isomap.R: 2025-12-20 10:14:59.138311: Isomap START
> test_pipeop_isomap.R: 2025-12-20 10:14:59.138851: constructing knn graph
> test_pipeop_isomap.R: 2025-12-20 10:14:59.148404: calculating geodesic distances
> test_pipeop_isomap.R: 2025-12-20 10:14:59.220405: Classical Scaling
> test_pipeop_isomap.R: 2025-12-20 10:14:59.321886: Isomap START
> test_pipeop_isomap.R: 2025-12-20 10:14:59.322182: constructing knn graph
> test_pipeop_isomap.R: 2025-12-20 10:14:59.34093: calculating geodesic distances
> test_pipeop_isomap.R: 2025-12-20 10:14:59.408757: Classical Scaling
> test_pipeop_isomap.R: 2025-12-20 10:14:59.456554: L-Isomap embed START
> test_pipeop_isomap.R: 2025-12-20 10:14:59.457195: constructing knn graph
> test_pipeop_isomap.R: 2025-12-20 10:14:59.524263: calculating geodesic distances
> test_pipeop_isomap.R: 2025-12-20 10:14:59.649129: embedding
> test_pipeop_isomap.R: 2025-12-20 10:14:59.651309: DONE
> test_pipeop_isomap.R: 2025-12-20 10:14:59.69918: L-Isomap embed START
> test_pipeop_isomap.R: 2025-12-20 10:14:59.699491: constructing knn graph
> test_pipeop_isomap.R: 2025-12-20 10:14:59.770012: calculating geodesic distances
> test_pipeop_isomap.R: 2025-12-20 10:14:59.890269: embedding
> test_pipeop_isomap.R: 2025-12-20 10:14:59.891547: DONE
> test_pipeop_isomap.R: 2025-12-20 10:15:00.052877: Isomap START
> test_pipeop_isomap.R: 2025-12-20 10:15:00.053171: constructing knn graph
> test_pipeop_isomap.R: 2025-12-20 10:15:00.069498: calculating geodesic distances
> test_pipeop_isomap.R: 2025-12-20 10:15:00.366568: Classical Scaling
> test_pipeop_isomap.R: 2025-12-20 10:15:00.438265: L-Isomap embed START
> test_pipeop_isomap.R: 2025-12-20 10:15:00.438694: constructing knn graph
> test_pipeop_isomap.R: 2025-12-20 10:15:00.502303: calculating geodesic distances
> test_pipeop_isomap.R: 2025-12-20 10:15:01.118212: embedding
> test_pipeop_isomap.R: 2025-12-20 10:15:01.142726: DONE
> test_pipeop_isomap.R: 2025-12-20 10:15:01.474701: Isomap START
> test_pipeop_isomap.R: 2025-12-20 10:15:01.474992: constructing knn graph
> test_pipeop_isomap.R: 2025-12-20 10:15:01.511901: calculating geodesic distances
> test_pipeop_isomap.R: 2025-12-20 10:15:01.54158: Classical Scaling
> test_pipeop_isomap.R: 2025-12-20 10:15:01.669599: L-Isomap embed START
> test_pipeop_isomap.R: 2025-12-20 10:15:01.670039: constructing knn graph
> test_pipeop_isomap.R: 2025-12-20 10:15:01.706691: calculating geodesic distances
> test_pipeop_isomap.R: 2025-12-20 10:15:01.860959: embedding
> test_pipeop_isomap.R: 2025-12-20 10:15:01.862633: DONE
> test_pipeop_isomap.R: 2025-12-20 10:15:02.082595: Isomap START
> test_pipeop_isomap.R: 2025-12-20 10:15:02.082899: constructing knn graph
> test_pipeop_isomap.R: 2025-12-20 10:15:02.090789: calculating geodesic distances
> test_pipeop_isomap.R: 2025-12-20 10:15:02.154462: Classical Scaling
> test_pipeop_isomap.R: 2025-12-20 10:15:02.242718: L-Isomap embed START
> test_pipeop_isomap.R: 2025-12-20 10:15:02.243159: constructing knn graph
> test_pipeop_isomap.R: 2025-12-20 10:15:02.299981: calculating geodesic distances
> test_pipeop_isomap.R: 2025-12-20 10:15:02.396162: embedding
> test_pipeop_isomap.R: 2025-12-20 10:15:02.398348: DONE
> test_pipeop_isomap.R: 2025-12-20 10:15:02.519433: Isomap START
> test_pipeop_isomap.R: 2025-12-20 10:15:02.519774: constructing knn graph
> test_pipeop_isomap.R: 2025-12-20 10:15:02.526565: calculating geodesic distances
> test_pipeop_isomap.R: 2025-12-20 10:15:02.580622: Classical Scaling
> test_pipeop_isomap.R: 2025-12-20 10:15:02.648678: L-Isomap embed START
> test_pipeop_isomap.R: 2025-12-20 10:15:02.649122: constructing knn graph
> test_pipeop_isomap.R: 2025-12-20 10:15:02.673246: calculating geodesic distances
> test_pipeop_isomap.R: 2025-12-20 10:15:02.750283: embedding
> test_pipeop_isomap.R: 2025-12-20 10:15:02.751423: DONE
> test_pipeop_isomap.R: 2025-12-20 10:15:02.865941: Isomap START
> test_pipeop_isomap.R: 2025-12-20 10:15:02.867286: constructing knn graph
> test_pipeop_isomap.R: 2025-12-20 10:15:02.876832: calculating geodesic distances
> test_pipeop_isomap.R: 2025-12-20 10:15:02.916486: Classical Scaling
> test_pipeop_isomap.R: 2025-12-20 10:15:02.984826: L-Isomap embed START
> test_pipeop_isomap.R: 2025-12-20 10:15:02.98535: constructing knn graph
> test_pipeop_isomap.R: 2025-12-20 10:15:03.034908: calculating geodesic distances
> test_pipeop_isomap.R: 2025-12-20 10:15:03.125605: embedding
> test_pipeop_isomap.R: 2025-12-20 10:15:03.126806: DONE
> test_pipeop_isomap.R: 2025-12-20 10:15:03.237815: Isomap START
> test_pipeop_isomap.R: 2025-12-20 10:15:03.238117: constructing knn graph
> test_pipeop_isomap.R: 2025-12-20 10:15:03.245401: calculating geodesic distances
> test_pipeop_isomap.R: 2025-12-20 10:15:03.28529: Classical Scaling
> test_pipeop_isomap.R: 2025-12-20 10:15:03.348314: L-Isomap embed START
> test_pipeop_isomap.R: 2025-12-20 10:15:03.348768: constructing knn graph
> test_pipeop_isomap.R: 2025-12-20 10:15:03.375703: calculating geodesic distances
> test_pipeop_isomap.R: 2025-12-20 10:15:03.463027: embedding
> test_pipeop_isomap.R: 2025-12-20 10:15:03.464743: DONE
> test_pipeop_isomap.R: 2025-12-20 10:15:03.62032: Isomap START
> test_pipeop_isomap.R: 2025-12-20 10:15:03.620632: constructing knn graph
> test_pipeop_isomap.R: 2025-12-20 10:15:03.641438: calculating geodesic distances
> test_pipeop_isomap.R: 2025-12-20 10:15:03.692031: Classical Scaling
> test_pipeop_isomap.R: 2025-12-20 10:15:03.82227: Isomap START
> test_pipeop_isomap.R: 2025-12-20 10:15:03.822561: constructing knn graph
> test_pipeop_isomap.R: 2025-12-20 10:15:03.839594: calculating geodesic distances
> test_pipeop_isomap.R: 2025-12-20 10:15:03.888166: Classical Scaling
> test_pipeop_isomap.R: 2025-12-20 10:15:03.933293: Isomap START
> test_pipeop_isomap.R: 2025-12-20 10:15:03.934217: constructing knn graph
> test_pipeop_isomap.R: 2025-12-20 10:15:03.961636: calculating geodesic distances
> test_pipeop_isomap.R: 2025-12-20 10:15:04.013321: Classical Scaling
Saving _problems/test_pipeop_nmf-45.R
Saving _problems/test_pipeop_nmf-73.R
Saving _problems/test_pipeop_nmf-93.R
Saving _problems/test_pipeop_nmf-98.R
> 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_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:
> 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_tunethreshold.R: OptimInstanceSingleCrit is deprecated. Use OptimInstanceBatchSingleCrit instead.
> test_pipeop_tunethreshold.R: OptimInstanceSingleCrit is deprecated. Use OptimInstanceBatchSingleCrit instead.
> test_pipeop_tunethreshold.R: OptimInstanceSingleCrit is deprecated. Use OptimInstanceBatchSingleCrit instead.
> test_pipeop_tunethreshold.R: OptimInstanceSingleCrit is deprecated. Use OptimInstanceBatchSingleCrit instead.
> test_pipeop_tunethreshold.R: OptimInstanceSingleCrit is deprecated. Use OptimInstanceBatchSingleCrit instead.
> test_pipeop_tunethreshold.R: OptimInstanceSingleCrit is deprecated. Use OptimInstanceBatchSingleCrit instead.
> test_pipeop_tunethreshold.R: OptimInstanceSingleCrit is deprecated. Use OptimInstanceBatchSingleCrit instead.
[ FAIL 6 | WARN 0 | SKIP 99 | PASS 13006 ]
══ Skipped tests (99) ══════════════════════════════════════════════════════════
• 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_branching.R:26:3', 'test_mlr_graphs_bagging.R:6:3',
'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_boxcox.R:7:3', 'test_pipeop_classweights.R:10:3',
'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_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_ica.R:7:3',
'test_pipeop_ensemble.R:6:3', 'test_pipeop_impute.R:4:3',
'test_pipeop_imputelearner.R:43:3', '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_missind.R:6:3', 'test_pipeop_modelmatrix.R:7:3',
'test_pipeop_multiplicityexply.R:9:3', 'test_pipeop_multiplicityimply.R:9:3',
'test_pipeop_mutate.R:9:3', 'test_pipeop_nearmiss.R:7:3',
'test_pipeop_learnercv.R:31:3', '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_nmf.R:6:3',
'test_pipeop_randomresponse.R:5:3', 'test_pipeop_renamecolumns.R:6:3',
'test_pipeop_removeconstants.R:6:3', 'test_pipeop_replicate.R:9:3',
'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_rowapply.R:6:3',
'test_pipeop_subsample.R:6:3', 'test_pipeop_targetinvert.R:4:3',
'test_pipeop_targetmutate.R:5:3', 'test_pipeop_targettrafo.R:4:3',
'test_pipeop_targettrafoscalerange.R:5:3', 'test_pipeop_task_preproc.R:4:3',
'test_pipeop_task_preproc.R:14:3', 'test_pipeop_spatialsign.R:6:3',
'test_pipeop_tomek.R:7:3', 'test_pipeop_textvectorizer.R:37:3',
'test_pipeop_textvectorizer.R:186:3', 'test_pipeop_unbranch.R:10:3',
'test_pipeop_updatetarget.R:89:3', 'test_pipeop_vtreat.R:9:3',
'test_pipeop_yeojohnson.R:7:3', 'test_pipeop_tunethreshold.R:111:3',
'test_pipeop_tunethreshold.R:191:3', 'test_typecheck.R:188:3'
• Skipping (1): 'test_GraphLearner.R:1278:3'
• empty test (3): 'test_pipeop_isomap.R:111:1', 'test_pipeop_missind.R:101:1',
'test_ppl.R:61:1'
══ Failed tests ════════════════════════════════════════════════════════════════
── Error ('test_pipeop_datefeatures.R:7:3'): PipeOpDateFeatures - basic properties ──
Error in `seq.Date(as.Date("2020-01-31"), length.out = 150L)`: exactly two of 'to', 'by' and 'length.out' / 'along.with' must be specified
Backtrace:
▆
1. ├─base::seq(as.Date("2020-01-31"), length.out = 150L) at test_pipeop_datefeatures.R:7:3
2. └─base::seq.Date(as.Date("2020-01-31"), length.out = 150L)
── Error ('test_pipeop_datefeatures.R:17:3'): PipeOpDateFeatures - finds POSIXct column ──
Error in `seq.Date(as.Date("2020-01-31"), length.out = 150L)`: exactly two of 'to', 'by' and 'length.out' / 'along.with' must be specified
Backtrace:
▆
1. ├─base::seq(as.Date("2020-01-31"), length.out = 150L) at test_pipeop_datefeatures.R:17:3
2. └─base::seq.Date(as.Date("2020-01-31"), length.out = 150L)
── Error ('test_pipeop_nmf.R:45:3'): PipeOpNMF - does not modify search path when NMF is not loaded, fix for #929 ──
Error in `detach(package:generics)`: invalid 'name' argument
Backtrace:
▆
1. └─base::detach(package:generics) at test_pipeop_nmf.R:45:3
── Failure ('test_pipeop_nmf.R:73:3'): PipeOpNMF - does not modify search path when NMF is loaded, fix for #929 ──
Expected `all(...)` to be TRUE.
Differences:
`actual`: FALSE
`expected`: TRUE
── Failure ('test_pipeop_nmf.R:93:3'): PipeOpNMF - does not modify search path when some of NMF's dependencies are loaded, fix for #929 ──
Expected `all(paste0("package:", c("BiocGenerics", "generics")) %in% search())` to be TRUE.
Differences:
`actual`: FALSE
`expected`: TRUE
── Error ('test_pipeop_nmf.R:98:3'): PipeOpNMF - does not modify search path when some of NMF's dependencies are loaded, fix for #929 ──
Error in `FUN(X[[i]], ...)`: invalid 'name' argument
This happened in PipeOp nmf's $train()
Backtrace:
▆
1. ├─op$train(list(tsk("iris"))) at test_pipeop_nmf.R:98:3
2. │ └─mlr3pipelines:::.__PipeOp__train(...)
3. │ ├─base::withCallingHandlers(...)
4. │ └─private$.train(input)
5. │ └─mlr3pipelines:::.__PipeOpTaskPreproc__.train(...)
6. │ └─private$.train_task(intask)
7. │ └─mlr3pipelines:::.__PipeOpTaskPreproc__.train_task(...)
8. │ ├─data.table::as.data.table(...)
9. │ └─private$.train_dt(dt, task$levels(cols), task$truth())
10. │ └─mlr3pipelines:::.__PipeOpNMF__.train_dt(...)
11. │ └─mlr3misc::map(to_be_detached, detach, character.only = TRUE)
12. │ └─base::lapply(.x, .f, ...)
13. │ └─base (local) FUN(X[[i]], ...)
14. │ └─base::stop("invalid 'name' argument")
15. └─base::.handleSimpleError(...)
16. └─mlr3pipelines (local) h(simpleError(msg, call))
[ FAIL 6 | WARN 0 | SKIP 99 | PASS 13006 ]
Error:
! Test failures.
Execution halted
Flavor: r-oldrel-macos-x86_64
Version: 0.10.0
Check: tests
Result: ERROR
Running 'testthat.R' [265s]
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:
> 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
> test_filter_ensemble.R: OptimInstanceSingleCrit is deprecated. Use OptimInstanceBatchSingleCrit instead.
> 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:
> test_multiplicities.R: [1] 0
> 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"
Saving _problems/test_pipeop_datefeatures-7.R
Saving _problems/test_pipeop_datefeatures-17.R
> test_pipeop_isomap.R: 2025-12-19 17:23:05.936317: Isomap START
> test_pipeop_isomap.R: 2025-12-19 17:23:05.937393: constructing knn graph
> test_pipeop_isomap.R: 2025-12-19 17:23:05.951825: calculating geodesic distances
> test_pipeop_isomap.R: 2025-12-19 17:23:05.971952: Classical Scaling
> test_pipeop_isomap.R: 2025-12-19 17:23:06.049368: Isomap START
> test_pipeop_isomap.R: 2025-12-19 17:23:06.05002: constructing knn graph
> test_pipeop_isomap.R: 2025-12-19 17:23:06.061812: calculating geodesic distances
> test_pipeop_isomap.R: 2025-12-19 17:23:06.084792: Classical Scaling
> test_pipeop_isomap.R: 2025-12-19 17:23:06.125569: L-Isomap embed START
> test_pipeop_isomap.R: 2025-12-19 17:23:06.126494: constructing knn graph
> test_pipeop_isomap.R: 2025-12-19 17:23:06.148937: calculating geodesic distances
> test_pipeop_isomap.R: 2025-12-19 17:23:06.199243: embedding
> test_pipeop_isomap.R: 2025-12-19 17:23:06.201242: DONE
> test_pipeop_isomap.R: 2025-12-19 17:23:06.233279: L-Isomap embed START
> test_pipeop_isomap.R: 2025-12-19 17:23:06.233742: constructing knn graph
> test_pipeop_isomap.R: 2025-12-19 17:23:06.250341: calculating geodesic distances
> test_pipeop_isomap.R: 2025-12-19 17:23:06.298042: embedding
> test_pipeop_isomap.R: 2025-12-19 17:23:06.300003: DONE
> test_pipeop_isomap.R: 2025-12-19 17:23:06.414541: Isomap START
> test_pipeop_isomap.R: 2025-12-19 17:23:06.415212: constructing knn graph
> test_pipeop_isomap.R: 2025-12-19 17:23:06.439364: calculating geodesic distances
> test_pipeop_isomap.R: 2025-12-19 17:23:06.5599: Classical Scaling
> test_pipeop_isomap.R: 2025-12-19 17:23:06.60924: L-Isomap embed START
> test_pipeop_isomap.R: 2025-12-19 17:23:06.61016: constructing knn graph
> test_pipeop_isomap.R: 2025-12-19 17:23:06.657872: calculating geodesic distances
> test_pipeop_isomap.R: 2025-12-19 17:23:06.89736: embedding
> test_pipeop_isomap.R: 2025-12-19 17:23:06.902681: DONE
> test_pipeop_isomap.R: 2025-12-19 17:23:07.11848: Isomap START
> test_pipeop_isomap.R: 2025-12-19 17:23:07.11909: constructing knn graph
> test_pipeop_isomap.R: 2025-12-19 17:23:07.1286: calculating geodesic distances
> test_pipeop_isomap.R: 2025-12-19 17:23:07.148428: Classical Scaling
> test_pipeop_isomap.R: 2025-12-19 17:23:07.184986: L-Isomap embed START
> test_pipeop_isomap.R: 2025-12-19 17:23:07.185841: constructing knn graph
> test_pipeop_isomap.R: 2025-12-19 17:23:07.206029: calculating geodesic distances
> test_pipeop_isomap.R: 2025-12-19 17:23:07.256738: embedding
> test_pipeop_isomap.R: 2025-12-19 17:23:07.258362: DONE
> test_pipeop_isomap.R: 2025-12-19 17:23:07.457761: Isomap START
> test_pipeop_isomap.R: 2025-12-19 17:23:07.458467: constructing knn graph
> test_pipeop_isomap.R: 2025-12-19 17:23:07.469333: calculating geodesic distances
> test_pipeop_isomap.R: 2025-12-19 17:23:07.492086: Classical Scaling
> test_pipeop_isomap.R: 2025-12-19 17:23:07.558133: L-Isomap embed START
> test_pipeop_isomap.R: 2025-12-19 17:23:07.559008: constructing knn graph
> test_pipeop_isomap.R: 2025-12-19 17:23:07.57855: calculating geodesic distances
> test_pipeop_isomap.R: 2025-12-19 17:23:07.6265: embedding
> test_pipeop_isomap.R: 2025-12-19 17:23:07.627979: DONE
> test_pipeop_isomap.R: 2025-12-19 17:23:07.739302: Isomap START
> test_pipeop_isomap.R: 2025-12-19 17:23:07.740154: constructing knn graph
> test_pipeop_isomap.R: 2025-12-19 17:23:07.751093: calculating geodesic distances
> test_pipeop_isomap.R: 2025-12-19 17:23:07.767492: Classical Scaling
> test_pipeop_isomap.R: 2025-12-19 17:23:07.839473: L-Isomap embed START
> test_pipeop_isomap.R: 2025-12-19 17:23:07.840422: constructing knn graph
> test_pipeop_isomap.R: 2025-12-19 17:23:07.863383: calculating geodesic distances
> test_pipeop_isomap.R: 2025-12-19 17:23:07.91413: embedding
> test_pipeop_isomap.R: 2025-12-19 17:23:07.915772: DONE
> test_pipeop_isomap.R: 2025-12-19 17:23:08.059424: Isomap START
> test_pipeop_isomap.R: 2025-12-19 17:23:08.060167: constructing knn graph
> test_pipeop_isomap.R: 2025-12-19 17:23:08.071171: calculating geodesic distances
> test_pipeop_isomap.R: 2025-12-19 17:23:08.093111: Classical Scaling
> test_pipeop_isomap.R: 2025-12-19 17:23:08.16766: L-Isomap embed START
> test_pipeop_isomap.R: 2025-12-19 17:23:08.168722: constructing knn graph
> test_pipeop_isomap.R: 2025-12-19 17:23:08.189822: calculating geodesic distances
> test_pipeop_isomap.R: 2025-12-19 17:23:08.239678: embedding
> test_pipeop_isomap.R: 2025-12-19 17:23:08.241523: DONE
> test_pipeop_isomap.R: 2025-12-19 17:23:08.357123: Isomap START
> test_pipeop_isomap.R: 2025-12-19 17:23:08.35789: constructing knn graph
> test_pipeop_isomap.R: 2025-12-19 17:23:08.369877: calculating geodesic distances
> test_pipeop_isomap.R: 2025-12-19 17:23:08.392508: Classical Scaling
> test_pipeop_isomap.R: 2025-12-19 17:23:08.458547: L-Isomap embed START
> test_pipeop_isomap.R: 2025-12-19 17:23:08.459658: constructing knn graph
> test_pipeop_isomap.R: 2025-12-19 17:23:08.480118: calculating geodesic distances
> test_pipeop_isomap.R: 2025-12-19 17:23:08.53079: embedding
> test_pipeop_isomap.R: 2025-12-19 17:23:08.532906: DONE
> test_pipeop_isomap.R: 2025-12-19 17:23:08.668933: Isomap START
> test_pipeop_isomap.R: 2025-12-19 17:23:08.669658: constructing knn graph
> test_pipeop_isomap.R: 2025-12-19 17:23:08.680096: calculating geodesic distances
> test_pipeop_isomap.R: 2025-12-19 17:23:08.702942: Classical Scaling
> test_pipeop_isomap.R: 2025-12-19 17:23:08.816933: Isomap START
> test_pipeop_isomap.R: 2025-12-19 17:23:08.817419: constructing knn graph
> test_pipeop_isomap.R: 2025-12-19 17:23:08.826695: calculating geodesic distances
> test_pipeop_isomap.R: 2025-12-19 17:23:08.84853: Classical Scaling
> test_pipeop_isomap.R: 2025-12-19 17:23:08.887296: Isomap START
> test_pipeop_isomap.R: 2025-12-19 17:23:08.888031: constructing knn graph
> test_pipeop_isomap.R: 2025-12-19 17:23:08.921747: calculating geodesic distances
> test_pipeop_isomap.R: 2025-12-19 17:23:08.94397: Classical Scaling
Saving _problems/test_pipeop_nmf-45.R
Saving _problems/test_pipeop_nmf-73.R
Saving _problems/test_pipeop_nmf-93.R
Saving _problems/test_pipeop_nmf-98.R
> 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_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_tunethreshold.R: OptimInstanceSingleCrit is deprecated. Use OptimInstanceBatchSingleCrit instead.
> test_pipeop_tunethreshold.R: OptimInstanceSingleCrit is deprecated. Use OptimInstanceBatchSingleCrit instead.
> test_pipeop_tunethreshold.R: OptimInstanceSingleCrit is deprecated. Use OptimInstanceBatchSingleCrit instead.
> test_pipeop_tunethreshold.R: OptimInstanceSingleCrit is deprecated. Use OptimInstanceBatchSingleCrit instead.
> test_pipeop_tunethreshold.R: OptimInstanceSingleCrit is deprecated. Use OptimInstanceBatchSingleCrit instead.
> test_pipeop_tunethreshold.R: OptimInstanceSingleCrit is deprecated. Use OptimInstanceBatchSingleCrit instead.
> test_pipeop_tunethreshold.R: OptimInstanceSingleCrit is deprecated. Use OptimInstanceBatchSingleCrit instead.
[ FAIL 6 | WARN 0 | SKIP 99 | PASS 13006 ]
══ Skipped tests (99) ══════════════════════════════════════════════════════════
• 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_branching.R:26:3', 'test_mlr_graphs_bagging.R:6:3',
'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_boxcox.R:7:3', 'test_pipeop_classweights.R:10:3',
'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_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_ica.R:7:3',
'test_pipeop_ensemble.R:6:3', 'test_pipeop_impute.R:4:3',
'test_pipeop_imputelearner.R:43:3', '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_missind.R:6:3', 'test_pipeop_modelmatrix.R:7:3',
'test_pipeop_multiplicityexply.R:9:3', 'test_pipeop_learnercv.R:31:3',
'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_nmf.R:6:3',
'test_pipeop_smotenc.R:8:3', 'test_pipeop_smote.R:10:3',
'test_pipeop_subsample.R:6:3', 'test_pipeop_targetinvert.R:4:3',
'test_pipeop_targetmutate.R:5:3', 'test_pipeop_targettrafo.R:4:3',
'test_pipeop_targettrafoscalerange.R:5:3', 'test_pipeop_task_preproc.R:4:3',
'test_pipeop_task_preproc.R:14:3', 'test_pipeop_spatialsign.R:6:3',
'test_pipeop_tomek.R:7:3', 'test_pipeop_textvectorizer.R:37:3',
'test_pipeop_textvectorizer.R:186:3', 'test_pipeop_unbranch.R:10:3',
'test_pipeop_updatetarget.R:89:3', 'test_pipeop_vtreat.R:9:3',
'test_pipeop_yeojohnson.R:7:3', 'test_pipeop_tunethreshold.R:111:3',
'test_pipeop_tunethreshold.R:191:3', 'test_typecheck.R:188:3'
• Skipping (1): 'test_GraphLearner.R:1278:3'
• empty test (3): 'test_pipeop_isomap.R:111:1', 'test_pipeop_missind.R:101:1',
'test_ppl.R:61:1'
══ Failed tests ════════════════════════════════════════════════════════════════
── Error ('test_pipeop_datefeatures.R:7:3'): PipeOpDateFeatures - basic properties ──
Error in `seq.Date(as.Date("2020-01-31"), length.out = 150L)`: exactly two of 'to', 'by' and 'length.out' / 'along.with' must be specified
Backtrace:
▆
1. ├─base::seq(as.Date("2020-01-31"), length.out = 150L) at test_pipeop_datefeatures.R:7:3
2. └─base::seq.Date(as.Date("2020-01-31"), length.out = 150L)
── Error ('test_pipeop_datefeatures.R:17:3'): PipeOpDateFeatures - finds POSIXct column ──
Error in `seq.Date(as.Date("2020-01-31"), length.out = 150L)`: exactly two of 'to', 'by' and 'length.out' / 'along.with' must be specified
Backtrace:
▆
1. ├─base::seq(as.Date("2020-01-31"), length.out = 150L) at test_pipeop_datefeatures.R:17:3
2. └─base::seq.Date(as.Date("2020-01-31"), length.out = 150L)
── Error ('test_pipeop_nmf.R:45:3'): PipeOpNMF - does not modify search path when NMF is not loaded, fix for #929 ──
Error in `detach(package:generics)`: invalid 'name' argument
Backtrace:
▆
1. └─base::detach(package:generics) at test_pipeop_nmf.R:45:3
── Failure ('test_pipeop_nmf.R:73:3'): PipeOpNMF - does not modify search path when NMF is loaded, fix for #929 ──
Expected `all(...)` to be TRUE.
Differences:
`actual`: FALSE
`expected`: TRUE
── Failure ('test_pipeop_nmf.R:93:3'): PipeOpNMF - does not modify search path when some of NMF's dependencies are loaded, fix for #929 ──
Expected `all(paste0("package:", c("BiocGenerics", "generics")) %in% search())` to be TRUE.
Differences:
`actual`: FALSE
`expected`: TRUE
── Error ('test_pipeop_nmf.R:98:3'): PipeOpNMF - does not modify search path when some of NMF's dependencies are loaded, fix for #929 ──
Error in `FUN(X[[i]], ...)`: invalid 'name' argument
This happened in PipeOp nmf's $train()
Backtrace:
▆
1. ├─op$train(list(tsk("iris"))) at test_pipeop_nmf.R:98:3
2. │ └─mlr3pipelines:::.__PipeOp__train(...)
3. │ ├─base::withCallingHandlers(...)
4. │ └─private$.train(input)
5. │ └─mlr3pipelines:::.__PipeOpTaskPreproc__.train(...)
6. │ └─private$.train_task(intask)
7. │ └─mlr3pipelines:::.__PipeOpTaskPreproc__.train_task(...)
8. │ ├─data.table::as.data.table(...)
9. │ └─private$.train_dt(dt, task$levels(cols), task$truth())
10. │ └─mlr3pipelines:::.__PipeOpNMF__.train_dt(...)
11. │ └─mlr3misc::map(to_be_detached, detach, character.only = TRUE)
12. │ └─base::lapply(.x, .f, ...)
13. │ └─base (local) FUN(X[[i]], ...)
14. │ └─base::stop("invalid 'name' argument")
15. └─base::.handleSimpleError(...)
16. └─mlr3pipelines (local) h(simpleError(msg, call))
[ FAIL 6 | WARN 0 | SKIP 99 | PASS 13006 ]
Error:
! Test failures.
Execution halted
Flavor: r-oldrel-windows-x86_64