Last updated on 2025-12-23 17:49:31 CET.
| Flavor | Version | Tinstall | Tcheck | Ttotal | Status | Flags |
|---|---|---|---|---|---|---|
| r-devel-linux-x86_64-debian-clang | 0.10.0 | 39.24 | 661.00 | 700.24 | ERROR | |
| r-devel-linux-x86_64-debian-gcc | 0.10.0 | 22.62 | 421.96 | 444.58 | 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 | 62.00 | 1046.37 | 1108.37 | 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 | 133.00 | 141.00 | ERROR | |
| r-oldrel-macos-x86_64 | 0.10.0 | 29.00 | 1101.00 | 1130.00 | ERROR | |
| r-oldrel-windows-x86_64 | 0.10.0 | 50.00 | 659.00 | 709.00 | 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 [04:37:47.532] [mlr3] Resampling 'cv' is being instantiated on task 'german_credit'
INFO [04:37:47.932] [mlr3] Applying learner 'classif.rpart' on task 'german_credit' (iter 1/3)
INFO [04:37:48.010] [mlr3] Applying learner 'classif.rpart' on task 'german_credit' (iter 2/3)
INFO [04:37:48.106] [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 4.257 0.105 8.279
Flavor: r-devel-linux-x86_64-debian-clang
Version: 0.10.0
Check: tests
Result: ERROR
Running ‘testthat.R’ [394s/201s]
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-depr
> test_mlr_graphs_robustify.R: ecated").
> 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_isomap.R: 2025-12-23 04:39:20.608538: Isomap START
> test_pipeop_isomap.R: 2025-12-23 04:39:20.60928: constructing knn graph
> test_pipeop_isomap.R: 2025-12-23 04:39:20.621909: calculating geodesic distances
> test_pipeop_isomap.R: 2025-12-23 04:39:20.642521: Classical Scaling
> test_pipeop_isomap.R: 2025-12-23 04:39:20.702247: Isomap START
> test_pipeop_isomap.R: 2025-12-23 04:39:20.702737: constructing knn graph
> test_pipeop_isomap.R: 2025-12-23 04:39:20.712258: calculating geodesic distances
> test_pipeop_isomap.R: 2025-12-23 04:39:20.730727: Classical Scaling
> test_pipeop_isomap.R: 2025-12-23 04:39:20.759112: L-Isomap embed START
> test_pipeop_isomap.R: 2025-12-23 04:39:20.759799: constructing knn graph
> test_pipeop_isomap.R: 2025-12-23 04:39:20.776453: calculating geodesic distances
> test_pipeop_isomap.R: 2025-12-23 04:39:20.819317: embedding
> test_pipeop_isomap.R: 2025-12-23 04:39:20.820528: DONE
> test_pipeop_isomap.R: 2025-12-23 04:39:20.846747: L-Isomap embed START
> test_pipeop_isomap.R: 2025-12-23 04:39:20.847283: constructing knn graph
> test_pipeop_isomap.R: 2025-12-23 04:39:20.86739: calculating geodesic distances
> test_pipeop_isomap.R: 2025-12-23 04:39:20.931833: embedding
> test_pipeop_isomap.R: 2025-12-23 04:39:20.934145: DONE
> test_pipeop_isomap.R: 2025-12-23 04:39:21.026839: Isomap START
> test_pipeop_isomap.R: 2025-12-23 04:39:21.027356: constructing knn graph
> test_pipeop_isomap.R: 2025-12-23 04:39:21.04161: calculating geodesic distances
> test_pipeop_isomap.R: 2025-12-23 04:39:21.141268: Classical Scaling
> test_pipeop_isomap.R: 2025-12-23 04:39:21.176411: L-Isomap embed START
> test_pipeop_isomap.R: 2025-12-23 04:39:21.177107: constructing knn graph
> test_pipeop_isomap.R: 2025-12-23 04:39:21.204464: calculating geodesic distances
> test_pipeop_isomap.R: 2025-12-23 04:39:21.411146: embedding
> test_pipeop_isomap.R: 2025-12-23 04:39:21.416934: DONE
> test_pipeop_isomap.R: 2025-12-23 04:39:21.589291: Isomap START
> test_pipeop_isomap.R: 2025-12-23 04:39:21.589792: constructing knn graph
> test_pipeop_isomap.R: 2025-12-23 04:39:21.59751: calculating geodesic distances
> test_pipeop_isomap.R: 2025-12-23 04:39:21.616257: Classical Scaling
> test_pipeop_isomap.R: 2025-12-23 04:39:21.647016: L-Isomap embed START
> test_pipeop_isomap.R: 2025-12-23 04:39:21.649283: constructing knn graph
> test_pipeop_isomap.R: 2025-12-23 04:39:21.663508: calculating geodesic distances
> test_pipeop_isomap.R: 2025-12-23 04:39:21.708681: embedding
> test_pipeop_isomap.R: 2025-12-23 04:39:21.709929: DONE
> test_pipeop_isomap.R: 2025-12-23 04:39:21.858299: Isomap START
> test_pipeop_isomap.R: 2025-12-23 04:39:21.858802: constructing knn graph
> test_pipeop_isomap.R: 2025-12-23 04:39:21.866428: calculating geodesic distances
> test_pipeop_isomap.R: 2025-12-23 04:39:21.885361: Classical Scaling
> test_pipeop_isomap.R: 2025-12-23 04:39:21.93478: L-Isomap embed START
> test_pipeop_isomap.R: 2025-12-23 04:39:21.935477: constructing knn graph
> test_pipeop_isomap.R: 2025-12-23 04:39:21.94996: calculating geodesic distances
> test_pipeop_isomap.R: 2025-12-23 04:39:21.992188: embedding
> test_pipeop_isomap.R: 2025-12-23 04:39:21.995016: DONE
> test_pipeop_isomap.R: 2025-12-23 04:39:22.07761: Isomap START
> test_pipeop_isomap.R: 2025-12-23 04:39:22.078123: constructing knn graph
> test_pipeop_isomap.R: 2025-12-23 04:39:22.087562: calculating geodesic distances
> test_pipeop_isomap.R: 2025-12-23 04:39:22.106418: Classical Scaling
> test_pipeop_isomap.R: 2025-12-23 04:39:22.170741: L-Isomap embed START
> test_pipeop_isomap.R: 2025-12-23 04:39:22.171482: constructing knn graph
> test_pipeop_isomap.R: 2025-12-23 04:39:22.190146: calculating geodesic distances
> test_pipeop_isomap.R: 2025-12-23 04:39:22.232962: embedding
> test_pipeop_isomap.R: 2025-12-23 04:39:22.234224: DONE
> test_pipeop_isomap.R: 2025-12-23 04:39:22.315289: Isomap START
> test_pipeop_isomap.R: 2025-12-23 04:39:22.315801: constructing knn graph
> test_pipeop_isomap.R: 2025-12-23 04:39:22.325012: calculating geodesic distances
> test_pipeop_isomap.R: 2025-12-23 04:39:22.343798: Classical Scaling
> test_pipeop_isomap.R: 2025-12-23 04:39:22.396032: L-Isomap embed START
> test_pipeop_isomap.R: 2025-12-23 04:39:22.396804: constructing knn graph
> test_pipeop_isomap.R: 2025-12-23 04:39:22.413116: calculating geodesic distances
> test_pipeop_isomap.R: 2025-12-23 04:39:22.456379: embedding
> test_pipeop_isomap.R: 2025-12-23 04:39:22.457615: DONE
> test_pipeop_isomap.R: 2025-12-23 04:39:22.540136: Isomap START
> test_pipeop_isomap.R: 2025-12-23 04:39:22.542341: constructing knn graph
> test_pipeop_isomap.R: 2025-12-23 04:39:22.55025: calculating geodesic distances
> test_pipeop_isomap.R: 2025-12-23 04:39:22.56887: Classical Scaling
> test_pipeop_isomap.R: 2025-12-23 04:39:22.619932: L-Isomap embed START
> test_pipeop_isomap.R: 2025-12-23 04:39:22.620635: constructing knn graph
> test_pipeop_isomap.R: 2025-12-23 04:39:22.648017: calculating geodesic distances
> test_pipeop_isomap.R: 2025-12-23 04:39:22.690667: embedding
> test_pipeop_isomap.R: 2025-12-23 04:39:22.691956: DONE
> test_pipeop_isomap.R: 2025-12-23 04:39:22.784068: Isomap START
> test_pipeop_isomap.R: 2025-12-23 04:39:22.784555: constructing knn graph
> test_pipeop_isomap.R: 2025-12-23 04:39:22.792247: calculating geodesic distances
> test_pipeop_isomap.R: 2025-12-23 04:39:22.811429: Classical Scaling
> test_pipeop_isomap.R: 2025-12-23 04:39:22.891634: Isomap START
> test_pipeop_isomap.R: 2025-12-23 04:39:22.892084: constructing knn graph
> test_pipeop_isomap.R: 2025-12-23 04:39:22.899396: calculating geodesic distances
> test_pipeop_isomap.R: 2025-12-23 04:39:22.918288: Classical Scaling
> test_pipeop_isomap.R: 2025-12-23 04:39:22.944135: Isomap START
> test_pipeop_isomap.R: 2025-12-23 04:39:22.944616: constructing knn graph
> test_pipeop_isomap.R: 2025-12-23 04:39:22.952191: calculating geodesic distances
> test_pipeop_isomap.R: 2025-12-23 04:39:22.970898: 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: 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_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_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_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_nmf.R:6:3',
'test_pipeop_spatialsign.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_textvectorizer.R:37:3',
'test_pipeop_textvectorizer.R:186:3', 'test_pipeop_unbranch.R:10:3',
'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$train(tsk("german_credit"))
INFO [17:18:13.850] [mlr3] Resampling 'cv' is being instantiated on task 'german_credit'
INFO [17:18:14.088] [mlr3] Applying learner 'classif.rpart' on task 'german_credit' (iter 1/3)
INFO [17:18:14.234] [mlr3] Applying learner 'classif.rpart' on task 'german_credit' (iter 2/3)
INFO [17:18:14.328] [mlr3] Applying learner 'classif.rpart' on task 'german_credit' (iter 3/3)
Error in `[.data.table`(data, , `:=`("task_hash", task[[1L]]$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’ [250s/125s]
Running the tests in ‘tests/testthat.R’ failed.
Complete output:
> if (requireNamespace("testthat", quietly = TRUE)) {
+ library("checkmate")
+ library("testthat")
+ library("mlr3")
+ library("paradox")
+ library("mlr3pipelines")
+ test_check("mlr3pipelines")
+ }
Starting 2 test processes.
> test_Graph.R: Training debug.multi with input list(input_1 = 1, input_2 = 1)
> test_PipeOp.R: Training test_autotrain
> test_PipeOp.R: Predicting test_autotrain
> test_PipeOp.R: Training test_autotrain
> test_PipeOp.R: Training test_autotrain
> test_PipeOp.R: Predicting test_autotrain
> test_PipeOp.R: Predicting test_autotrain
> test_PipeOp.R: Training test_autotrain
> test_PipeOp.R: Training test_autotrain
> test_PipeOp.R: Training test_autotrain
> test_PipeOp.R: Predicting test_autotrain
> test_PipeOp.R: Predicting test_autotrain
> test_PipeOp.R: Predicting test_autotrain
> test_PipeOp.R: Training test_autotrain
> test_PipeOp.R: Predicting test_autotrain
> test_PipeOp.R: Training test_autotrain
> test_PipeOp.R: Training test_autotrain
> test_PipeOp.R: Predicting test_autotrain
> test_PipeOp.R: Predicting test_autotrain
> test_PipeOp.R: Training test_autotrain
> test_PipeOp.R: Training test_autotrain
> test_PipeOp.R: Training test_autotrain
> test_PipeOp.R: Predicting test_autotrain
> test_PipeOp.R: Predicting test_autotrain
> test_PipeOp.R: Predicting test_autotrain
> test_PipeOp.R: Training test_autotrain
> test_PipeOp.R: Predicting test_autotrain
Saving _problems/test_conversion-143.R
Saving _problems/test_conversion-165.R
> test_filter_ensemble.R: OptimInstanceSingleCrit is deprecated. Use OptimInstanceBatchSingleCrit instead.
Saving _problems/test_filter_ensemble-291.R
Saving _problems/test_filter_ensemble-447.R
Saving _problems/test_mlr_graphs_bagging-49.R
Saving _problems/test_mlr_graphs_stacking-16.R
> test_mlr_graphs_robustify.R: 'as(<dgCMatrix>, "dgTMatrix")' is deprecated.
> test_mlr_graphs_robustify.R: Use 'as(., "TsparseMatrix")' instead.
> test_mlr_graphs_robustify.R: See help("Deprecated") and help("Matrix-deprecated").
> test_multiplicities.R:
> test_multiplicities.R: [[1]]
> test_multiplicities.R: [1] 0
> test_multiplicities.R:
> test_multiplicities.R:
> test_pipeop_blsmote.R: [1] "Borderline-SMOTE done"
> test_pipeop_blsmote.R: [1] "Borderline-SMOTE done"
> test_pipeop_blsmote.R: [1] "Borderline-SMOTE done"
> test_pipeop_blsmote.R: [1] "Borderline-SMOTE done"
> test_pipeop_isomap.R: 2025-12-22 17:19:13.694554: Isomap START
> test_pipeop_isomap.R: 2025-12-22 17:19:13.695309: constructing knn graph
> test_pipeop_isomap.R: 2025-12-22 17:19:13.703772: calculating geodesic distances
> test_pipeop_isomap.R: 2025-12-22 17:19:13.718212: Classical Scaling
> test_pipeop_isomap.R: 2025-12-22 17:19:13.75171: Isomap START
> test_pipeop_isomap.R: 2025-12-22 17:19:13.752135: constructing knn graph
> test_pipeop_isomap.R: 2025-12-22 17:19:13.758068: calculating geodesic distances
> test_pipeop_isomap.R: 2025-12-22 17:19:13.771962: Classical Scaling
> test_pipeop_isomap.R: 2025-12-22 17:19:13.79094: L-Isomap embed START
> test_pipeop_isomap.R: 2025-12-22 17:19:13.791496: constructing knn graph
> test_pipeop_isomap.R: 2025-12-22 17:19:13.802366: calculating geodesic distances
> test_pipeop_isomap.R: 2025-12-22 17:19:13.834387: embedding
> test_pipeop_isomap.R: 2025-12-22 17:19:13.835379: DONE
> test_pipeop_isomap.R: 2025-12-22 17:19:13.851611: L-Isomap embed START
> test_pipeop_isomap.R: 2025-12-22 17:19:13.852052: constructing knn graph
> test_pipeop_isomap.R: 2025-12-22 17:19:13.86373: calculating geodesic distances
> test_pipeop_isomap.R: 2025-12-22 17:19:13.896: embedding
> test_pipeop_isomap.R: 2025-12-22 17:19:13.897153: DONE
> test_pipeop_isomap.R: 2025-12-22 17:19:14.013631: Isomap START
> test_pipeop_isomap.R: 2025-12-22 17:19:14.013979: constructing knn graph
> test_pipeop_isomap.R: 2025-12-22 17:19:14.02877: calculating geodesic distances
> test_pipeop_isomap.R: 2025-12-22 17:19:14.105453: Classical Scaling
> test_pipeop_isomap.R: 2025-12-22 17:19:14.132438: L-Isomap embed START
> test_pipeop_isomap.R: 2025-12-22 17:19:14.13302: constructing knn graph
> test_pipeop_isomap.R: 2025-12-22 17:19:14.171092: calculating geodesic distances
> test_pipeop_isomap.R: 2025-12-22 17:19:14.346651: embedding
> test_pipeop_isomap.R: 2025-12-22 17:19:14.350074: DONE
> test_pipeop_isomap.R: 2025-12-22 17:19:14.460308: Isomap START
> test_pipeop_isomap.R: 2025-12-22 17:19:14.460732: constructing knn graph
> test_pipeop_isomap.R: 2025-12-22 17:19:14.466748: calculating geodesic distances
> test_pipeop_isomap.R: 2025-12-22 17:19:14.482522: Classical Scaling
> test_pipeop_isomap.R: 2025-12-22 17:19:14.503583: L-Isomap embed START
> test_pipeop_isomap.R: 2025-12-22 17:19:14.504177: constructing knn graph
> test_pipeop_isomap.R: 2025-12-22 17:19:14.515608: calculating geodesic distances
> test_pipeop_isomap.R: 2025-12-22 17:19:14.547952: embedding
> test_pipeop_isomap.R: 2025-12-22 17:19:14.549251: DONE
> test_pipeop_isomap.R: 2025-12-22 17:19:14.669898: Isomap START
> test_pipeop_isomap.R: 2025-12-22 17:19:14.670323: constructing knn graph
> test_pipeop_isomap.R: 2025-12-22 17:19:14.677714: calculating geodesic distances
> test_pipeop_isomap.R: 2025-12-22 17:19:14.691103: Classical Scaling
> test_pipeop_isomap.R: 2025-12-22 17:19:14.722205: L-Isomap embed START
> test_pipeop_isomap.R: 2025-12-22 17:19:14.722837: constructing knn graph
> test_pipeop_isomap.R: 2025-12-22 17:19:14.734325: calculating geodesic distances
> test_pipeop_isomap.R: 2025-12-22 17:19:14.766851: embedding
> test_pipeop_isomap.R: 2025-12-22 17:19:14.767872: DONE
> test_pipeop_isomap.R: 2025-12-22 17:19:14.820849: Isomap START
> test_pipeop_isomap.R: 2025-12-22 17:19:14.821292: constructing knn graph
> test_pipeop_isomap.R: 2025-12-22 17:19:14.828862: calculating geodesic distances
> test_pipeop_isomap.R: 2025-12-22 17:19:14.842909: Classical Scaling
> test_pipeop_isomap.R: 2025-12-22 17:19:14.87596: L-Isomap embed START
> test_pipeop_isomap.R: 2025-12-22 17:19:14.876527: constructing knn graph
> test_pipeop_isomap.R: 2025-12-22 17:19:14.888224: calculating geodesic distances
> test_pipeop_isomap.R: 2025-12-22 17:19:14.920611: embedding
> test_pipeop_isomap.R: 2025-12-22 17:19:14.921573: DONE
> test_pipeop_isomap.R: 2025-12-22 17:19:14.979495: Isomap START
> test_pipeop_isomap.R: 2025-12-22 17:19:14.97993: constructing knn graph
> test_pipeop_isomap.R: 2025-12-22 17:19:14.998384: calculating geodesic distances
> test_pipeop_isomap.R: 2025-12-22 17:19:15.012737: Classical Scaling
> test_pipeop_isomap.R: 2025-12-22 17:19:15.05878: L-Isomap embed START
> test_pipeop_isomap.R: 2025-12-22 17:19:15.059359: constructing knn graph
> test_pipeop_isomap.R: 2025-12-22 17:19:15.071612: calculating geodesic distances
> test_pipeop_isomap.R: 2025-12-22 17:19:15.104153: embedding
> test_pipeop_isomap.R: 2025-12-22 17:19:15.105324: DONE
> test_pipeop_isomap.R: 2025-12-22 17:19:15.164378: Isomap START
> test_pipeop_isomap.R: 2025-12-22 17:19:15.164806: constructing knn graph
> test_pipeop_isomap.R: 2025-12-22 17:19:15.176221: calculating geodesic distances
> test_pipeop_isomap.R: 2025-12-22 17:19:15.190325: Classical Scaling
> test_pipeop_isomap.R: 2025-12-22 17:19:15.226433: L-Isomap embed START
> test_pipeop_isomap.R: 2025-12-22 17:19:15.227058: constructing knn graph
> test_pipeop_isomap.R: 2025-12-22 17:19:15.238437: calculating geodesic distances
> test_pipeop_isomap.R: 2025-12-22 17:19:15.270741: embedding
> test_pipeop_isomap.R: 2025-12-22 17:19:15.271724: DONE
> test_pipeop_isomap.R: 2025-12-22 17:19:15.327781: Isomap START
> test_pipeop_isomap.R: 2025-12-22 17:19:15.328171: constructing knn graph
> test_pipeop_isomap.R: 2025-12-22 17:19:15.345986: calculating geodesic distances
> test_pipeop_isomap.R: 2025-12-22 17:19:15.360378: Classical Scaling
> test_pipeop_isomap.R: 2025-12-22 17:19:15.427852: Isomap START
> test_pipeop_isomap.R: 2025-12-22 17:19:15.428263: constructing knn graph
> test_pipeop_isomap.R: 2025-12-22 17:19:15.434331: calculating geodesic distances
> test_pipeop_isomap.R: 2025-12-22 17:19:15.44837: Classical Scaling
> test_pipeop_isomap.R: 2025-12-22 17:19:15.464582: Isomap START
> test_pipeop_isomap.R: 2025-12-22 17:19:15.464975: constructing knn graph
> test_pipeop_isomap.R: 2025-12-22 17:19:15.47095: calculating geodesic distances
> test_pipeop_isomap.R: 2025-12-22 17:19:15.484976: Classical Scaling
Saving _problems/test_pipeop_learnerpicvplus-35.R
Saving _problems/test_pipeop_learnerpicvplus-91.R
Saving _problems/test_pipeop_learnerpicvplus-116.R
Saving _problems/test_pipeop_learnerpicvplus-130.R
Saving _problems/test_pipeop_learnerpicvplus-152.R
Saving _problems/test_pipeop_learnercv-11.R
Saving _problems/test_pipeop_learnercv-100.R
Saving _problems/test_pipeop_learnercv-139.R
Saving _problems/test_pipeop_learnercv-152.R
Saving _problems/test_pipeop_learnercv-203.R
Saving _problems/test_pipeop_learnercv-250.R
Saving _problems/test_pipeop_learnercv-278.R
Saving _problems/test_pipeop_learnercv-323.R
Saving _problems/test_pipeop_learnercv-350.R
Saving _problems/test_pipeop_learnercv-387.R
Saving _problems/test_pipeop_learnercv-419.R
Saving _problems/test_pipeop_learnercv-455.R
Saving _problems/test_pipeop_learnercv-493.R
Saving _problems/test_pipeop_learnercv-516.R
Saving _problems/test_pipeop_learnercv-531.R
Saving _problems/test_pipeop_learnercv-557.R
Saving _problems/test_pipeop_learnercv-612.R
Saving _problems/test_pipeop_learnercv-628.R
Saving _problems/test_pipeop_learnercv-671.R
> test_pipeop_nmf.R: [PipeOpNMFstate]
> test_pipeop_nmf.R: [PipeOpNMFstate]
> test_pipeop_task_preproc.R: Training debug_affectcols
> test_pipeop_task_preproc.R: Training debug_affectcols
> test_pipeop_task_preproc.R: Training debug_affectcols
> test_pipeop_task_preproc.R: Training debug_affectcols
> test_pipeop_task_preproc.R: Training debug_affectcols
> test_pipeop_task_preproc.R: Training debug_affectcols
> test_pipeop_task_preproc.R: Training debug_affectcols
> test_pipeop_task_preproc.R: Training debug_affectcols
> test_pipeop_task_preproc.R: Training debug_affectcols
> test_pipeop_task_preproc.R: Training debug_affectcols
> test_pipeop_task_preproc.R: Training debug_affectcols
> test_pipeop_task_preproc.R: Training debug_affectcols
> test_pipeop_task_preproc.R: Training debug_affectcols
> test_pipeop_task_preproc.R: Training debug_affectcols
> test_pipeop_task_preproc.R: Training debug_affectcols
> test_pipeop_task_preproc.R: Training debug_affectcols
> test_pipeop_task_preproc.R: Training debug_affectcols
> test_pipeop_task_preproc.R: Training debug_affectcols
> test_pipeop_textvectorizer.R: 'as(<dgCMatrix>, "dgTMatrix")' is deprecated.
> test_pipeop_textvectorizer.R: Use 'as(., "TsparseMatrix")' instead.
> test_pipeop_textvectorizer.R: See help("Deprecated") and help("Matrix-deprecated").
> test_pipeop_task_preproc.R: Training debug_affectcols
> test_pipeop_task_preproc.R: Training debug_affectcols
> test_pipeop_task_preproc.R: Training debug_affectcols
> test_pipeop_task_preproc.R: Training debug_affectcols
Saving _problems/test_pipeop_tunethreshold-7.R
Saving _problems/test_pipeop_tunethreshold-38.R
Saving _problems/test_pipeop_tunethreshold-73.R
Saving _problems/test_pipeop_tunethreshold-101.R
Saving _problems/test_pipeop_tunethreshold-260.R
Saving _problems/test_resample-13.R
Saving _problems/test_usecases-153.R
Saving _problems/test_ppl-73.R
[ FAIL 38 | WARN 2 | SKIP 98 | PASS 12316 ]
══ Skipped tests (98) ══════════════════════════════════════════════════════════
• On CRAN (95): 'test_CnfFormula_simplify.R:6:3', 'test_CnfFormula.R:591:3',
'test_Graph.R:283:3', 'test_PipeOp.R:32:1', 'test_GraphLearner.R:5:3',
'test_GraphLearner.R:221:3', 'test_GraphLearner.R:343:3',
'test_GraphLearner.R:408:3', 'test_GraphLearner.R:571:3',
'test_dictionary.R:7:3', 'test_learner_weightedaverage.R:5:3',
'test_learner_weightedaverage.R:57:3',
'test_learner_weightedaverage.R:105:3',
'test_learner_weightedaverage.R:152:3', 'test_meta.R:39:3',
'test_mlr_graphs_bagging.R:6:3', 'test_mlr_graphs_branching.R:26:3',
'test_mlr_graphs_robustify.R:5:3', 'test_pipeop_adas.R:8:3',
'test_pipeop_blsmote.R:8:3', 'test_pipeop_branch.R:4:3',
'test_pipeop_chunk.R:4:3', 'test_pipeop_classbalancing.R:7:3',
'test_pipeop_classweights.R:10:3', 'test_pipeop_boxcox.R:7:3',
'test_pipeop_collapsefactors.R:6:3', 'test_pipeop_colapply.R:9:3',
'test_pipeop_copy.R:5:3', 'test_pipeop_colroles.R:6:3',
'test_pipeop_decode.R:14:3', 'test_pipeop_encode.R:21:3',
'test_pipeop_encodeimpact.R:11:3', 'test_pipeop_datefeatures.R:10:3',
'test_pipeop_encodepl.R:5:3', 'test_pipeop_encodepl.R:72:3',
'test_pipeop_encodelmer.R:15:3', 'test_pipeop_encodelmer.R:37:3',
'test_pipeop_encodelmer.R:80:3', 'test_pipeop_featureunion.R:9:3',
'test_pipeop_featureunion.R:134:3', 'test_pipeop_filter.R:7:3',
'test_pipeop_fixfactors.R:9:3', 'test_pipeop_histbin.R:7:3',
'test_pipeop_ensemble.R:6:3', 'test_pipeop_ica.R:7:3',
'test_pipeop_imputelearner.R:43:3', 'test_pipeop_impute.R:4:3',
'test_pipeop_isomap.R:10:3', 'test_pipeop_kernelpca.R:9:3',
'test_pipeop_learner.R:17:3', 'test_pipeop_info.R:6:3',
'test_pipeop_learnerpicvplus.R:163:3', 'test_pipeop_modelmatrix.R:7:3',
'test_pipeop_missind.R:6:3', 'test_pipeop_multiplicityexply.R:9:3',
'test_pipeop_mutate.R:9:3', 'test_pipeop_nearmiss.R:7:3',
'test_pipeop_multiplicityimply.R:9:3', 'test_pipeop_ovr.R:9:3',
'test_pipeop_ovr.R:48:3', 'test_pipeop_pca.R:8:3',
'test_pipeop_proxy.R:14:3', 'test_pipeop_quantilebin.R:5:3',
'test_pipeop_randomprojection.R:6:3', 'test_pipeop_randomresponse.R:5:3',
'test_pipeop_removeconstants.R:6:3', 'test_pipeop_renamecolumns.R:6:3',
'test_pipeop_replicate.R:9:3', 'test_pipeop_rowapply.R:6:3',
'test_pipeop_scale.R:6:3', 'test_pipeop_scale.R:10:3',
'test_pipeop_scalemaxabs.R:6:3', 'test_pipeop_scalerange.R:7:3',
'test_pipeop_select.R:9:3', 'test_pipeop_smote.R:10:3',
'test_pipeop_smotenc.R:8:3', 'test_pipeop_spatialsign.R:6:3',
'test_pipeop_nmf.R:6:3', 'test_pipeop_targetinvert.R:4:3',
'test_pipeop_targetmutate.R:5:3', 'test_pipeop_targettrafo.R:4:3',
'test_pipeop_subsample.R:6:3', 'test_pipeop_targettrafoscalerange.R:5:3',
'test_pipeop_task_preproc.R:4:3', 'test_pipeop_task_preproc.R:14:3',
'test_pipeop_tomek.R:7:3', 'test_pipeop_tunethreshold.R:111:3',
'test_pipeop_tunethreshold.R:191:3', 'test_pipeop_unbranch.R:10:3',
'test_pipeop_textvectorizer.R:37:3', 'test_pipeop_textvectorizer.R:186:3',
'test_pipeop_vtreat.R:9:3', 'test_pipeop_updatetarget.R:89:3',
'test_pipeop_yeojohnson.R:7:3', 'test_typecheck.R:188:3'
• Skipping (1): 'test_GraphLearner.R:1278:3'
• empty test (2): 'test_pipeop_isomap.R:111:1', 'test_pipeop_missind.R:101:1'
══ Failed tests ════════════════════════════════════════════════════════════════
── Error ('test_conversion.R:143:3'): Graph to GraphLearner ────────────────────
Error in ``[.data.table`(data, , `:=`("task_hash", task[[1L]]$hash), by = "uhash")`: attempt access index 9/9 in VECTOR_ELT
Backtrace:
▆
1. └─mlr3::resample(task, glrn1, cv) at test_conversion.R:143:3
2. └─ResultData$new(data, data_extra, store_backends = store_backends)
3. └─mlr3 (local) initialize(...)
4. └─mlr3:::.__ResultData__initialize(...)
5. ├─data[, `:=`("task_hash", task[[1L]]$hash), by = "uhash"]
6. └─data.table:::`[.data.table`(...)
── Error ('test_conversion.R:165:3'): PipeOp to GraphLearner ───────────────────
Error in ``[.data.table`(data, , `:=`("task_hash", task[[1L]]$hash), by = "uhash")`: attempt access index 9/9 in VECTOR_ELT
Backtrace:
▆
1. └─mlr3::resample(task, glrn1, cv) at test_conversion.R:165:3
2. └─ResultData$new(data, data_extra, store_backends = store_backends)
3. └─mlr3 (local) initialize(...)
4. └─mlr3:::.__ResultData__initialize(...)
5. ├─data[, `:=`("task_hash", task[[1L]]$hash), by = "uhash"]
6. └─data.table:::`[.data.table`(...)
── Error ('test_filter_ensemble.R:291:3'): FilterEnsemble ignores NA scores from wrapped filters ──
Error in ``[.data.table`(data, , `:=`("task_hash", task[[1L]]$hash), by = "uhash")`: attempt access index 9/9 in VECTOR_ELT
Backtrace:
▆
1. └─permutation_filter$calculate(task) at test_filter_ensemble.R:291:3
2. └─mlr3filters:::.__Filter__calculate(...)
3. └─private$.calculate(task, nfeat)
4. └─mlr3filters:::.__FilterPermutation__.calculate(...)
5. └─mlr3::resample(task, self$learner, self$resampling)
6. └─ResultData$new(data, data_extra, store_backends = store_backends)
7. └─mlr3 (local) initialize(...)
8. └─mlr3:::.__ResultData__initialize(...)
9. ├─data[, `:=`("task_hash", task[[1L]]$hash), by = "uhash"]
10. └─data.table:::`[.data.table`(...)
── Error ('test_filter_ensemble.R:447:7'): FilterEnsemble weight search space works with bbotk ──
Error in ``[.data.table`(data, , `:=`("task_hash", task[[1L]]$hash), by = "uhash")`: attempt access index 9/9 in VECTOR_ELT
Backtrace:
▆
1. └─optimizer$optimize(instance) at test_filter_ensemble.R:463:3
2. └─bbotk:::.__OptimizerBatch__optimize(...)
3. └─bbotk::optimize_batch_default(inst, self)
4. ├─base::tryCatch(...)
5. │ └─base (local) tryCatchList(expr, classes, parentenv, handlers)
6. │ └─base (local) tryCatchOne(expr, names, parentenv, handlers[[1L]])
7. │ └─base (local) doTryCatch(return(expr), name, parentenv, handler)
8. └─get_private(optimizer)$.optimize(instance)
9. └─bbotk:::.__OptimizerBatchRandomSearch__.optimize(...)
10. └─inst$eval_batch(design$data)
11. └─bbotk:::.__OptimInstanceBatch__eval_batch(...)
12. └─self$objective$eval_many(xss_trafoed)
13. └─bbotk:::.__Objective__eval_many(...)
14. ├─mlr3misc::invoke(private$.eval_many, xss = xss, .args = self$constants$values)
15. │ └─base::eval.parent(expr, n = 1L)
16. │ └─base::eval(expr, p)
17. │ └─base::eval(expr, p)
18. └─private$.eval_many(xss = xss)
19. └─bbotk:::.__Objective__.eval_many(...)
20. └─mlr3misc::map_dtr(...)
21. ├─data.table::rbindlist(...)
22. ├─base::unname(map(.x, .f, ...))
23. └─mlr3misc::map(.x, .f, ...)
24. └─base::lapply(.x, .f, ...)
25. └─bbotk (local) FUN(X[[i]], ...)
26. └─self$eval(xs)
27. └─bbotk:::.__ObjectiveRFun__eval(...)
28. ├─mlr3misc::invoke(private$.fun, xs, .args = self$constants$values)
29. │ └─base::eval.parent(expr, n = 1L)
30. │ └─base::eval(expr, p)
31. │ └─base::eval(expr, p)
32. └─private$.fun(xs)
33. └─mlr3::resample(task, learner, resampling) at test_filter_ensemble.R:447:7
34. └─ResultData$new(data, data_extra, store_backends = store_backends)
35. └─mlr3 (local) initialize(...)
36. └─mlr3:::.__ResultData__initialize(...)
37. ├─data[, `:=`("task_hash", task[[1L]]$hash), by = "uhash"]
38. └─data.table:::`[.data.table`(...)
── Error ('test_mlr_graphs_bagging.R:49:3'): Bagging with replacement ──────────
Error in ``[.data.table`(data, , `:=`("task_hash", task[[1L]]$hash), by = "uhash")`: attempt access index 9/9 in VECTOR_ELT
Backtrace:
▆
1. └─mlr3::resample(tsk, GraphLearner$new(p), rsmp("holdout")) at test_mlr_graphs_bagging.R:49:3
2. └─ResultData$new(data, data_extra, store_backends = store_backends)
3. └─mlr3 (local) initialize(...)
4. └─mlr3:::.__ResultData__initialize(...)
5. ├─data[, `:=`("task_hash", task[[1L]]$hash), by = "uhash"]
6. └─data.table:::`[.data.table`(...)
── Error ('test_mlr_graphs_stacking.R:16:3'): Stacking Pipeline ────────────────
Error in ``[.data.table`(data, , `:=`("task_hash", task[[1L]]$hash), by = "uhash")`: attempt access index 9/9 in VECTOR_ELT
This happened in PipeOp base.rpart's $train()
Backtrace:
▆
1. ├─graph_learner$train(tsk("iris")) at test_mlr_graphs_stacking.R:16:3
2. │ └─mlr3:::.__Learner__train(...)
3. │ └─mlr3:::learner_train(...)
4. │ └─mlr3misc::encapsulate(...)
5. │ ├─mlr3misc::invoke(...)
6. │ │ └─base::eval.parent(expr, n = 1L)
7. │ │ └─base::eval(expr, p)
8. │ │ └─base::eval(expr, p)
9. │ └─mlr3 (local) .f(learner = `<GrphLrnr>`, task = `<TskClssf>`)
10. │ └─get_private(learner)$.train(task)
11. │ └─mlr3pipelines:::.__GraphLearner__.train(...)
12. │ └─self$graph$train(task)
13. │ └─mlr3pipelines:::.__Graph__train(...)
14. │ └─mlr3pipelines:::graph_reduce(self, input, "train", single_input)
15. │ └─op[[fun]](input)
16. │ └─mlr3pipelines:::.__PipeOp__train(...)
17. │ ├─base::withCallingHandlers(...)
18. │ └─private$.train(input)
19. │ └─mlr3pipelines:::.__PipeOpTaskPreproc__.train(...)
20. │ └─private$.train_task(intask)
21. │ └─mlr3pipelines:::.__PipeOpLearnerCV__.train_task(...)
22. │ └─mlr3::resample(...)
23. │ └─ResultData$new(data, data_extra, store_backends = store_backends)
24. │ └─mlr3 (local) initialize(...)
25. │ └─mlr3:::.__ResultData__initialize(...)
26. │ ├─data[, `:=`("task_hash", task[[1L]]$hash), by = "uhash"]
27. │ └─data.table:::`[.data.table`(...)
28. └─base::.handleSimpleError(...)
29. └─mlr3pipelines (local) h(simpleError(msg, call))
── Error ('test_pipeop_learnerpicvplus.R:35:3'): PipeOpLearnerPICVPlus - basic properties ──
Error in ``[.data.table`(data, , `:=`("task_hash", task[[1L]]$hash), by = "uhash")`: attempt access index 9/9 in VECTOR_ELT
This happened in PipeOp regr.featureless's $train()
Backtrace:
▆
1. ├─global train_pipeop(po, list(task)) at test_pipeop_learnerpicvplus.R:35:3
2. │ └─po$train(inputs)
3. │ └─mlr3pipelines:::.__PipeOp__train(...)
4. │ ├─base::withCallingHandlers(...)
5. │ └─private$.train(input)
6. │ └─mlr3pipelines:::.__PipeOpLearnerPICVPlus__.train(...)
7. │ └─mlr3::resample(task, private$.learner, rdesc, store_models = TRUE)
8. │ └─ResultData$new(data, data_extra, store_backends = store_backends)
9. │ └─mlr3 (local) initialize(...)
10. │ └─mlr3:::.__ResultData__initialize(...)
11. │ ├─data[, `:=`("task_hash", task[[1L]]$hash), by = "uhash"]
12. │ └─data.table:::`[.data.table`(...)
13. └─base::.handleSimpleError(...)
14. └─mlr3pipelines (local) h(simpleError(msg, call))
── Error ('test_pipeop_learnerpicvplus.R:91:3'): PipeOpLearnerPICVPlus - model active binding to state ──
Error in ``[.data.table`(data, , `:=`("task_hash", task[[1L]]$hash), by = "uhash")`: attempt access index 9/9 in VECTOR_ELT
This happened in PipeOp regr.featureless's $train()
Backtrace:
▆
1. ├─po$train(list(task)) at test_pipeop_learnerpicvplus.R:91:3
2. │ └─mlr3pipelines:::.__PipeOp__train(...)
3. │ ├─base::withCallingHandlers(...)
4. │ └─private$.train(input)
5. │ └─mlr3pipelines:::.__PipeOpLearnerPICVPlus__.train(...)
6. │ └─mlr3::resample(task, private$.learner, rdesc, store_models = TRUE)
7. │ └─ResultData$new(data, data_extra, store_backends = store_backends)
8. │ └─mlr3 (local) initialize(...)
9. │ └─mlr3:::.__ResultData__initialize(...)
10. │ ├─data[, `:=`("task_hash", task[[1L]]$hash), by = "uhash"]
11. │ └─data.table:::`[.data.table`(...)
12. └─base::.handleSimpleError(...)
13. └─mlr3pipelines (local) h(simpleError(msg, call))
── Error ('test_pipeop_learnerpicvplus.R:116:3'): PipeOpLearnerPICVPlus - integration with larger graph ──
Error in ``[.data.table`(data, , `:=`("task_hash", task[[1L]]$hash), by = "uhash")`: attempt access index 9/9 in VECTOR_ELT
This happened in PipeOp regr.rpart's $train()
Backtrace:
▆
1. ├─graph$train(task) at test_pipeop_learnerpicvplus.R:116:3
2. │ └─mlr3pipelines:::.__Graph__train(...)
3. │ └─mlr3pipelines:::graph_reduce(self, input, "train", single_input)
4. │ └─op[[fun]](input)
5. │ └─mlr3pipelines:::.__PipeOp__train(...)
6. │ ├─base::withCallingHandlers(...)
7. │ └─private$.train(input)
8. │ └─mlr3pipelines:::.__PipeOpLearnerPICVPlus__.train(...)
9. │ └─mlr3::resample(task, private$.learner, rdesc, store_models = TRUE)
10. │ └─ResultData$new(data, data_extra, store_backends = store_backends)
11. │ └─mlr3 (local) initialize(...)
12. │ └─mlr3:::.__ResultData__initialize(...)
13. │ ├─data[, `:=`("task_hash", task[[1L]]$hash), by = "uhash"]
14. │ └─data.table:::`[.data.table`(...)
15. └─base::.handleSimpleError(...)
16. └─mlr3pipelines (local) h(simpleError(msg, call))
── Error ('test_pipeop_learnerpicvplus.R:130:3'): marshal ──────────────────────
Error in ``[.data.table`(data, , `:=`("task_hash", task[[1L]]$hash), by = "uhash")`: attempt access index 9/9 in VECTOR_ELT
This happened in PipeOp regr.debug's $train()
Backtrace:
▆
1. ├─po$train(list(task)) at test_pipeop_learnerpicvplus.R:130:3
2. │ └─mlr3pipelines:::.__PipeOp__train(...)
3. │ ├─base::withCallingHandlers(...)
4. │ └─private$.train(input)
5. │ └─mlr3pipelines:::.__PipeOpLearnerPICVPlus__.train(...)
6. │ └─mlr3::resample(task, private$.learner, rdesc, store_models = TRUE)
7. │ └─ResultData$new(data, data_extra, store_backends = store_backends)
8. │ └─mlr3 (local) initialize(...)
9. │ └─mlr3:::.__ResultData__initialize(...)
10. │ ├─data[, `:=`("task_hash", task[[1L]]$hash), by = "uhash"]
11. │ └─data.table:::`[.data.table`(...)
12. └─base::.handleSimpleError(...)
13. └─mlr3pipelines (local) h(simpleError(msg, call))
── Error ('test_pipeop_learnerpicvplus.R:152:3'): marshal multiplicity ─────────
Error in ``[.data.table`(data, , `:=`("task_hash", task[[1L]]$hash), by = "uhash")`: attempt access index 9/9 in VECTOR_ELT
This happened in PipeOp regr.debug's $train()
Backtrace:
▆
1. ├─po$train(list(Multiplicity(task1, task2))) at test_pipeop_learnerpicvplus.R:152:3
2. │ └─mlr3pipelines:::.__PipeOp__train(...)
3. │ └─mlr3pipelines:::evaluate_multiplicities(...)
4. │ └─mlr3misc::imap(...)
5. │ ├─stats::setNames(mapply_list(.f, list(.x, .nn), list(...)), names(.x))
6. │ └─mlr3misc:::mapply_list(.f, list(.x, .nn), list(...))
7. │ └─base::.mapply(.f, .dots, .args)
8. │ └─mlr3pipelines (local) `<fn>`(dots[[1L]][[1L]], dots[[2L]][[1L]])
9. │ └─self[[evalcall]](input)
10. │ └─mlr3pipelines:::.__PipeOp__train(...)
11. │ ├─base::withCallingHandlers(...)
12. │ └─private$.train(input)
13. │ └─mlr3pipelines:::.__PipeOpLearnerPICVPlus__.train(...)
14. │ └─mlr3::resample(task, private$.learner, rdesc, store_models = TRUE)
15. │ └─ResultData$new(data, data_extra, store_backends = store_backends)
16. │ └─mlr3 (local) initialize(...)
17. │ └─mlr3:::.__ResultData__initialize(...)
18. │ ├─data[, `:=`("task_hash", task[[1L]]$hash), by = "uhash"]
19. │ └─data.table:::`[.data.table`(...)
20. └─base::.handleSimpleError(...)
21. └─mlr3pipelines (local) h(simpleError(msg, call))
── Error ('test_pipeop_learnercv.R:11:3'): PipeOpLearnerCV - basic properties ──
Error in ``[.data.table`(data, , `:=`("task_hash", task[[1L]]$hash), by = "uhash")`: attempt access index 9/9 in VECTOR_ELT
This happened in PipeOp classif.featureless's $train()
Backtrace:
▆
1. ├─global train_pipeop(po, list(task = task)) at test_pipeop_learnercv.R:11:3
2. │ └─po$train(inputs)
3. │ └─mlr3pipelines:::.__PipeOp__train(...)
4. │ ├─base::withCallingHandlers(...)
5. │ └─private$.train(input)
6. │ └─mlr3pipelines:::.__PipeOpTaskPreproc__.train(...)
7. │ └─private$.train_task(intask)
8. │ └─mlr3pipelines:::.__PipeOpLearnerCV__.train_task(...)
9. │ └─mlr3::resample(...)
10. │ └─ResultData$new(data, data_extra, store_backends = store_backends)
11. │ └─mlr3 (local) initialize(...)
12. │ └─mlr3:::.__ResultData__initialize(...)
13. │ ├─data[, `:=`("task_hash", task[[1L]]$hash), by = "uhash"]
14. │ └─data.table:::`[.data.table`(...)
15. └─base::.handleSimpleError(...)
16. └─mlr3pipelines (local) h(simpleError(msg, call))
── Error ('test_pipeop_learnercv.R:100:3'): PipeOpLearnerCV - cv ensemble averages fold learners ──
Error in ``[.data.table`(data, , `:=`("task_hash", task[[1L]]$hash), by = "uhash")`: attempt access index 9/9 in VECTOR_ELT
This happened in PipeOp classif.rpart's $train()
Backtrace:
▆
1. ├─po$train(list(task)) at test_pipeop_learnercv.R:100:3
2. │ └─mlr3pipelines:::.__PipeOp__train(...)
3. │ ├─base::withCallingHandlers(...)
4. │ └─private$.train(input)
5. │ └─mlr3pipelines:::.__PipeOpTaskPreproc__.train(...)
6. │ └─private$.train_task(intask)
7. │ └─mlr3pipelines:::.__PipeOpLearnerCV__.train_task(...)
8. │ └─mlr3::resample(...)
9. │ └─ResultData$new(data, data_extra, store_backends = store_backends)
10. │ └─mlr3 (local) initialize(...)
11. │ └─mlr3:::.__ResultData__initialize(...)
12. │ ├─data[, `:=`("task_hash", task[[1L]]$hash), by = "uhash"]
13. │ └─data.table:::`[.data.table`(...)
14. └─base::.handleSimpleError(...)
15. └─mlr3pipelines (local) h(simpleError(msg, call))
── Error ('test_pipeop_learnercv.R:139:3'): PipeOpLearnerCV - cv ensemble drops response when requested ──
Error in ``[.data.table`(data, , `:=`("task_hash", task[[1L]]$hash), by = "uhash")`: attempt access index 9/9 in VECTOR_ELT
This happened in PipeOp classif.rpart's $train()
Backtrace:
▆
1. ├─po$train(list(task)) at test_pipeop_learnercv.R:139:3
2. │ └─mlr3pipelines:::.__PipeOp__train(...)
3. │ ├─base::withCallingHandlers(...)
4. │ └─private$.train(input)
5. │ └─mlr3pipelines:::.__PipeOpTaskPreproc__.train(...)
6. │ └─private$.train_task(intask)
7. │ └─mlr3pipelines:::.__PipeOpLearnerCV__.train_task(...)
8. │ └─mlr3::resample(...)
9. │ └─ResultData$new(data, data_extra, store_backends = store_backends)
10. │ └─mlr3 (local) initialize(...)
11. │ └─mlr3:::.__ResultData__initialize(...)
12. │ ├─data[, `:=`("task_hash", task[[1L]]$hash), by = "uhash"]
13. │ └─data.table:::`[.data.table`(...)
14. └─base::.handleSimpleError(...)
15. └─mlr3pipelines (local) h(simpleError(msg, call))
── Error ('test_pipeop_learnercv.R:152:3'): PipeOpLearnerCV - cv ensemble averages classif responses ──
Error in ``[.data.table`(data, , `:=`("task_hash", task[[1L]]$hash), by = "uhash")`: attempt access index 9/9 in VECTOR_ELT
This happened in PipeOp classif.rpart's $train()
Backtrace:
▆
1. ├─po$train(list(task)) at test_pipeop_learnercv.R:152:3
2. │ └─mlr3pipelines:::.__PipeOp__train(...)
3. │ ├─base::withCallingHandlers(...)
4. │ └─private$.train(input)
5. │ └─mlr3pipelines:::.__PipeOpTaskPreproc__.train(...)
6. │ └─private$.train_task(intask)
7. │ └─mlr3pipelines:::.__PipeOpLearnerCV__.train_task(...)
8. │ └─mlr3::resample(...)
9. │ └─ResultData$new(data, data_extra, store_backends = store_backends)
10. │ └─mlr3 (local) initialize(...)
11. │ └─mlr3:::.__ResultData__initialize(...)
12. │ ├─data[, `:=`("task_hash", task[[1L]]$hash), by = "uhash"]
13. │ └─data.table:::`[.data.table`(...)
14. └─base::.handleSimpleError(...)
15. └─mlr3pipelines (local) h(simpleError(msg, call))
── Error ('test_pipeop_learnercv.R:203:3'): PipeOpLearnerCV - cv ensemble log prob aggregation ──
Error in ``[.data.table`(data, , `:=`("task_hash", task[[1L]]$hash), by = "uhash")`: attempt access index 9/9 in VECTOR_ELT
This happened in PipeOp classif.rpart's $train()
Backtrace:
▆
1. ├─po$train(list(task)) at test_pipeop_learnercv.R:203:3
2. │ └─mlr3pipelines:::.__PipeOp__train(...)
3. │ ├─base::withCallingHandlers(...)
4. │ └─private$.train(input)
5. │ └─mlr3pipelines:::.__PipeOpTaskPreproc__.train(...)
6. │ └─private$.train_task(intask)
7. │ └─mlr3pipelines:::.__PipeOpLearnerCV__.train_task(...)
8. │ └─mlr3::resample(...)
9. │ └─ResultData$new(data, data_extra, store_backends = store_backends)
10. │ └─mlr3 (local) initialize(...)
11. │ └─mlr3:::.__ResultData__initialize(...)
12. │ ├─data[, `:=`("task_hash", task[[1L]]$hash), by = "uhash"]
13. │ └─data.table:::`[.data.table`(...)
14. └─base::.handleSimpleError(...)
15. └─mlr3pipelines (local) h(simpleError(msg, call))
── Error ('test_pipeop_learnercv.R:250:3'): PipeOpLearnerCV - log aggregation with zeros uses epsilon ──
Error in ``[.data.table`(data, , `:=`("task_hash", task[[1L]]$hash), by = "uhash")`: attempt access index 9/9 in VECTOR_ELT
This happened in PipeOp classif.featureless's $train()
Backtrace:
▆
1. ├─po$train(list(task)) at test_pipeop_learnercv.R:250:3
2. │ └─mlr3pipelines:::.__PipeOp__train(...)
3. │ ├─base::withCallingHandlers(...)
4. │ └─private$.train(input)
5. │ └─mlr3pipelines:::.__PipeOpTaskPreproc__.train(...)
6. │ └─private$.train_task(intask)
7. │ └─mlr3pipelines:::.__PipeOpLearnerCV__.train_task(...)
8. │ └─mlr3::resample(...)
9. │ └─ResultData$new(data, data_extra, store_backends = store_backends)
10. │ └─mlr3 (local) initialize(...)
11. │ └─mlr3:::.__ResultData__initialize(...)
12. │ ├─data[, `:=`("task_hash", task[[1L]]$hash), by = "uhash"]
13. │ └─data.table:::`[.data.table`(...)
14. └─base::.handleSimpleError(...)
15. └─mlr3pipelines (local) h(simpleError(msg, call))
── Error ('test_pipeop_learnercv.R:278:3'): PipeOpLearnerCV - log aggregation epsilon controls shrinkage ──
Error in ``[.data.table`(data, , `:=`("task_hash", task[[1L]]$hash), by = "uhash")`: attempt access index 9/9 in VECTOR_ELT
This happened in PipeOp classif.featureless's $train()
Backtrace:
▆
1. ├─po$train(list(task)) at test_pipeop_learnercv.R:278:3
2. │ └─mlr3pipelines:::.__PipeOp__train(...)
3. │ ├─base::withCallingHandlers(...)
4. │ └─private$.train(input)
5. │ └─mlr3pipelines:::.__PipeOpTaskPreproc__.train(...)
6. │ └─private$.train_task(intask)
7. │ └─mlr3pipelines:::.__PipeOpLearnerCV__.train_task(...)
8. │ └─mlr3::resample(...)
9. │ └─ResultData$new(data, data_extra, store_backends = store_backends)
10. │ └─mlr3 (local) initialize(...)
11. │ └─mlr3:::.__ResultData__initialize(...)
12. │ ├─data[, `:=`("task_hash", task[[1L]]$hash), by = "uhash"]
13. │ └─data.table:::`[.data.table`(...)
14. └─base::.handleSimpleError(...)
15. └─mlr3pipelines (local) h(simpleError(msg, call))
── Error ('test_pipeop_learnercv.R:323:3'): PipeOpLearnerCV - cv ensemble averages regression predictions ──
Error in ``[.data.table`(data, , `:=`("task_hash", task[[1L]]$hash), by = "uhash")`: attempt access index 9/9 in VECTOR_ELT
This happened in PipeOp regr.rpart's $train()
Backtrace:
▆
1. ├─po$train(list(task)) at test_pipeop_learnercv.R:323:3
2. │ └─mlr3pipelines:::.__PipeOp__train(...)
3. │ ├─base::withCallingHandlers(...)
4. │ └─private$.train(input)
5. │ └─mlr3pipelines:::.__PipeOpTaskPreproc__.train(...)
6. │ └─private$.train_task(intask)
7. │ └─mlr3pipelines:::.__PipeOpLearnerCV__.train_task(...)
8. │ └─mlr3::resample(...)
9. │ └─ResultData$new(data, data_extra, store_backends = store_backends)
10. │ └─mlr3 (local) initialize(...)
11. │ └─mlr3:::.__ResultData__initialize(...)
12. │ ├─data[, `:=`("task_hash", task[[1L]]$hash), by = "uhash"]
13. │ └─data.table:::`[.data.table`(...)
14. └─base::.handleSimpleError(...)
15. └─mlr3pipelines (local) h(simpleError(msg, call))
── Error ('test_pipeop_learnercv.R:350:3'): PipeOpLearnerCV - cv ensemble handles multiplicity ──
Error in ``[.data.table`(data, , `:=`("task_hash", task[[1L]]$hash), by = "uhash")`: attempt access index 9/9 in VECTOR_ELT
This happened in PipeOp classif.rpart's $train()
Backtrace:
▆
1. ├─po$train(list(tasks)) at test_pipeop_learnercv.R:350:3
2. │ └─mlr3pipelines:::.__PipeOp__train(...)
3. │ └─mlr3pipelines:::evaluate_multiplicities(...)
4. │ └─mlr3misc::imap(...)
5. │ ├─stats::setNames(mapply_list(.f, list(.x, .nn), list(...)), names(.x))
6. │ └─mlr3misc:::mapply_list(.f, list(.x, .nn), list(...))
7. │ └─base::.mapply(.f, .dots, .args)
8. │ └─mlr3pipelines (local) `<fn>`(dots[[1L]][[1L]], dots[[2L]][[1L]])
9. │ └─self[[evalcall]](input)
10. │ └─mlr3pipelines:::.__PipeOp__train(...)
11. │ ├─base::withCallingHandlers(...)
12. │ └─private$.train(input)
13. │ └─mlr3pipelines:::.__PipeOpTaskPreproc__.train(...)
14. │ └─private$.train_task(intask)
15. │ └─mlr3pipelines:::.__PipeOpLearnerCV__.train_task(...)
16. │ └─mlr3::resample(...)
17. │ └─ResultData$new(data, data_extra, store_backends = store_backends)
18. │ └─mlr3 (local) initialize(...)
19. │ └─mlr3:::.__ResultData__initialize(...)
20. │ ├─data[, `:=`("task_hash", task[[1L]]$hash), by = "uhash"]
21. │ └─data.table:::`[.data.table`(...)
22. └─base::.handleSimpleError(...)
23. └─mlr3pipelines (local) h(simpleError(msg, call))
── Error ('test_pipeop_learnercv.R:387:3'): PipeOpLearnerCV - learner_model returns averaged ensemble ──
Error in ``[.data.table`(data, , `:=`("task_hash", task[[1L]]$hash), by = "uhash")`: attempt access index 9/9 in VECTOR_ELT
This happened in PipeOp classif.rpart's $train()
Backtrace:
▆
1. ├─po$train(list(task)) at test_pipeop_learnercv.R:387:3
2. │ └─mlr3pipelines:::.__PipeOp__train(...)
3. │ ├─base::withCallingHandlers(...)
4. │ └─private$.train(input)
5. │ └─mlr3pipelines:::.__PipeOpTaskPreproc__.train(...)
6. │ └─private$.train_task(intask)
7. │ └─mlr3pipelines:::.__PipeOpLearnerCV__.train_task(...)
8. │ └─mlr3::resample(...)
9. │ └─ResultData$new(data, data_extra, store_backends = store_backends)
10. │ └─mlr3 (local) initialize(...)
11. │ └─mlr3:::.__ResultData__initialize(...)
12. │ ├─data[, `:=`("task_hash", task[[1L]]$hash), by = "uhash"]
13. │ └─data.table:::`[.data.table`(...)
14. └─base::.handleSimpleError(...)
15. └─mlr3pipelines (local) h(simpleError(msg, call))
── Error ('test_pipeop_learnercv.R:419:3'): PipeOpLearnerCV - cv ensemble with predict_type = 'se' ──
Error in ``[.data.table`(data, , `:=`("task_hash", task[[1L]]$hash), by = "uhash")`: attempt access index 9/9 in VECTOR_ELT
This happened in PipeOp regr.lm's $train()
Backtrace:
▆
1. ├─po$train(list(task)) at test_pipeop_learnercv.R:419:3
2. │ └─mlr3pipelines:::.__PipeOp__train(...)
3. │ ├─base::withCallingHandlers(...)
4. │ └─private$.train(input)
5. │ └─mlr3pipelines:::.__PipeOpTaskPreproc__.train(...)
6. │ └─private$.train_task(intask)
7. │ └─mlr3pipelines:::.__PipeOpLearnerCV__.train_task(...)
8. │ └─mlr3::resample(...)
9. │ └─ResultData$new(data, data_extra, store_backends = store_backends)
10. │ └─mlr3 (local) initialize(...)
11. │ └─mlr3:::.__ResultData__initialize(...)
12. │ ├─data[, `:=`("task_hash", task[[1L]]$hash), by = "uhash"]
13. │ └─data.table:::`[.data.table`(...)
14. └─base::.handleSimpleError(...)
15. └─mlr3pipelines (local) h(simpleError(msg, call))
── Error ('test_pipeop_learnercv.R:455:3'): PipeOpLearnerCV - within resampling ──
Error in ``[.data.table`(data, , `:=`("task_hash", task[[1L]]$hash), by = "uhash")`: attempt access index 9/9 in VECTOR_ELT
This happened in PipeOp classif.rpart's $train()
Backtrace:
▆
1. └─mlr3::resample(tsk("iris"), gr, rsmp("holdout")) at test_pipeop_learnercv.R:455:3
2. └─mlr3:::future_map(...)
3. └─future.apply::future_mapply(...)
4. └─future.apply:::future_xapply(...)
5. └─base::tryCatch(...)
6. └─base (local) tryCatchList(expr, classes, parentenv, handlers)
7. └─base (local) tryCatchOne(...)
8. └─value[[3L]](cond)
9. └─future.apply:::onError(e, futures = fs, debug = debug)
── Error ('test_pipeop_learnercv.R:493:3'): PipeOpLearnerCV - model active binding to state ──
Error in ``[.data.table`(data, , `:=`("task_hash", task[[1L]]$hash), by = "uhash")`: attempt access index 9/9 in VECTOR_ELT
This happened in PipeOp classif.featureless's $train()
Backtrace:
▆
1. ├─po$train(list(task)) at test_pipeop_learnercv.R:493:3
2. │ └─mlr3pipelines:::.__PipeOp__train(...)
3. │ ├─base::withCallingHandlers(...)
4. │ └─private$.train(input)
5. │ └─mlr3pipelines:::.__PipeOpTaskPreproc__.train(...)
6. │ └─private$.train_task(intask)
7. │ └─mlr3pipelines:::.__PipeOpLearnerCV__.train_task(...)
8. │ └─mlr3::resample(...)
9. │ └─ResultData$new(data, data_extra, store_backends = store_backends)
10. │ └─mlr3 (local) initialize(...)
11. │ └─mlr3:::.__ResultData__initialize(...)
12. │ ├─data[, `:=`("task_hash", task[[1L]]$hash), by = "uhash"]
13. │ └─data.table:::`[.data.table`(...)
14. └─base::.handleSimpleError(...)
15. └─mlr3pipelines (local) h(simpleError(msg, call))
── Error ('test_pipeop_learnercv.R:516:3'): predict_type ───────────────────────
Error in ``[.data.table`(data, , `:=`("task_hash", task[[1L]]$hash), by = "uhash")`: attempt access index 9/9 in VECTOR_ELT
This happened in PipeOp classif.rpart's $train()
Backtrace:
▆
1. ├─global expect_equal(...) at test_pipeop_learnercv.R:516:3
2. │ ├─testthat::expect_true(...)
3. │ │ └─testthat::quasi_label(enquo(object), label)
4. │ │ └─rlang::eval_bare(expr, quo_get_env(quo))
5. │ └─base::all.equal(...)
6. ├─lcv$train(list(tsk("iris")))
7. │ └─mlr3pipelines:::.__PipeOp__train(...)
8. │ ├─base::withCallingHandlers(...)
9. │ └─private$.train(input)
10. │ └─mlr3pipelines:::.__PipeOpTaskPreproc__.train(...)
11. │ └─private$.train_task(intask)
12. │ └─mlr3pipelines:::.__PipeOpLearnerCV__.train_task(...)
13. │ └─mlr3::resample(...)
14. │ └─ResultData$new(data, data_extra, store_backends = store_backends)
15. │ └─mlr3 (local) initialize(...)
16. │ └─mlr3:::.__ResultData__initialize(...)
17. │ ├─data[, `:=`("task_hash", task[[1L]]$hash), by = "uhash"]
18. │ └─data.table:::`[.data.table`(...)
19. └─base::.handleSimpleError(...)
20. └─mlr3pipelines (local) h(simpleError(msg, call))
── Error ('test_pipeop_learnercv.R:531:3'): marshal ────────────────────────────
Error in ``[.data.table`(data, , `:=`("task_hash", task[[1L]]$hash), by = "uhash")`: attempt access index 9/9 in VECTOR_ELT
This happened in PipeOp classif.debug's $train()
Backtrace:
▆
1. ├─po_lrn$train(list(task)) at test_pipeop_learnercv.R:531:3
2. │ └─mlr3pipelines:::.__PipeOp__train(...)
3. │ ├─base::withCallingHandlers(...)
4. │ └─private$.train(input)
5. │ └─mlr3pipelines:::.__PipeOpTaskPreproc__.train(...)
6. │ └─private$.train_task(intask)
7. │ └─mlr3pipelines:::.__PipeOpLearnerCV__.train_task(...)
8. │ └─mlr3::resample(...)
9. │ └─ResultData$new(data, data_extra, store_backends = store_backends)
10. │ └─mlr3 (local) initialize(...)
11. │ └─mlr3:::.__ResultData__initialize(...)
12. │ ├─data[, `:=`("task_hash", task[[1L]]$hash), by = "uhash"]
13. │ └─data.table:::`[.data.table`(...)
14. └─base::.handleSimpleError(...)
15. └─mlr3pipelines (local) h(simpleError(msg, call))
── Error ('test_pipeop_learnercv.R:557:3'): marshal multiplicity ───────────────
Error in ``[.data.table`(data, , `:=`("task_hash", task[[1L]]$hash), by = "uhash")`: attempt access index 9/9 in VECTOR_ELT
This happened in PipeOp classif.debug's $train()
Backtrace:
▆
1. ├─po$train(list(Multiplicity(tsk("iris"), tsk("sonar")))) at test_pipeop_learnercv.R:557:3
2. │ └─mlr3pipelines:::.__PipeOp__train(...)
3. │ └─mlr3pipelines:::evaluate_multiplicities(...)
4. │ └─mlr3misc::imap(...)
5. │ ├─stats::setNames(mapply_list(.f, list(.x, .nn), list(...)), names(.x))
6. │ └─mlr3misc:::mapply_list(.f, list(.x, .nn), list(...))
7. │ └─base::.mapply(.f, .dots, .args)
8. │ └─mlr3pipelines (local) `<fn>`(dots[[1L]][[1L]], dots[[2L]][[1L]])
9. │ └─self[[evalcall]](input)
10. │ └─mlr3pipelines:::.__PipeOp__train(...)
11. │ ├─base::withCallingHandlers(...)
12. │ └─private$.train(input)
13. │ └─mlr3pipelines:::.__PipeOpTaskPreproc__.train(...)
14. │ └─private$.train_task(intask)
15. │ └─mlr3pipelines:::.__PipeOpLearnerCV__.train_task(...)
16. │ └─mlr3::resample(...)
17. │ └─ResultData$new(data, data_extra, store_backends = store_backends)
18. │ └─mlr3 (local) initialize(...)
19. │ └─mlr3:::.__ResultData__initialize(...)
20. │ ├─data[, `:=`("task_hash", task[[1L]]$hash), by = "uhash"]
21. │ └─data.table:::`[.data.table`(...)
22. └─base::.handleSimpleError(...)
23. └─mlr3pipelines (local) h(simpleError(msg, call))
── Error ('test_pipeop_learnercv.R:612:3'): marshal with cv ensemble ───────────
Error in ``[.data.table`(data, , `:=`("task_hash", task[[1L]]$hash), by = "uhash")`: attempt access index 9/9 in VECTOR_ELT
This happened in PipeOp classif.rpart's $train()
Backtrace:
▆
1. ├─po$train(list(task)) at test_pipeop_learnercv.R:612:3
2. │ └─mlr3pipelines:::.__PipeOp__train(...)
3. │ ├─base::withCallingHandlers(...)
4. │ └─private$.train(input)
5. │ └─mlr3pipelines:::.__PipeOpTaskPreproc__.train(...)
6. │ └─private$.train_task(intask)
7. │ └─mlr3pipelines:::.__PipeOpLearnerCV__.train_task(...)
8. │ └─mlr3::resample(...)
9. │ └─ResultData$new(data, data_extra, store_backends = store_backends)
10. │ └─mlr3 (local) initialize(...)
11. │ └─mlr3:::.__ResultData__initialize(...)
12. │ ├─data[, `:=`("task_hash", task[[1L]]$hash), by = "uhash"]
13. │ └─data.table:::`[.data.table`(...)
14. └─base::.handleSimpleError(...)
15. └─mlr3pipelines (local) h(simpleError(msg, call))
── Error ('test_pipeop_learnercv.R:628:3'): state class and multiplicity ───────
Error in ``[.data.table`(data, , `:=`("task_hash", task[[1L]]$hash), by = "uhash")`: attempt access index 9/9 in VECTOR_ELT
This happened in PipeOp classif.debug's $train()
Backtrace:
▆
1. ├─po$train(list(Multiplicity(tsk("iris")))) at test_pipeop_learnercv.R:628:3
2. │ └─mlr3pipelines:::.__PipeOp__train(...)
3. │ └─mlr3pipelines:::evaluate_multiplicities(...)
4. │ └─mlr3misc::imap(...)
5. │ ├─stats::setNames(mapply_list(.f, list(.x, .nn), list(...)), names(.x))
6. │ └─mlr3misc:::mapply_list(.f, list(.x, .nn), list(...))
7. │ └─base::.mapply(.f, .dots, .args)
8. │ └─mlr3pipelines (local) `<fn>`(dots[[1L]][[1L]], dots[[2L]][[1L]])
9. │ └─self[[evalcall]](input)
10. │ └─mlr3pipelines:::.__PipeOp__train(...)
11. │ ├─base::withCallingHandlers(...)
12. │ └─private$.train(input)
13. │ └─mlr3pipelines:::.__PipeOpTaskPreproc__.train(...)
14. │ └─private$.train_task(intask)
15. │ └─mlr3pipelines:::.__PipeOpLearnerCV__.train_task(...)
16. │ └─mlr3::resample(...)
17. │ └─ResultData$new(data, data_extra, store_backends = store_backends)
18. │ └─mlr3 (local) initialize(...)
19. │ └─mlr3:::.__ResultData__initialize(...)
20. │ ├─data[, `:=`("task_hash", task[[1L]]$hash), by = "uhash"]
21. │ └─data.table:::`[.data.table`(...)
22. └─base::.handleSimpleError(...)
23. └─mlr3pipelines (local) h(simpleError(msg, call))
── Error ('test_pipeop_learnercv.R:671:5'): PipeOpLearnerCV cv ensemble aggregates SE like PipeOpRegrAvg ──
Error in ``[.data.table`(data, , `:=`("task_hash", task[[1L]]$hash), by = "uhash")`: attempt access index 9/9 in VECTOR_ELT
This happened in PipeOp regr.debug's $train()
Backtrace:
▆
1. ├─po$train(list(task)) at test_pipeop_learnercv.R:671:5
2. │ └─mlr3pipelines:::.__PipeOp__train(...)
3. │ ├─base::withCallingHandlers(...)
4. │ └─private$.train(input)
5. │ └─mlr3pipelines:::.__PipeOpTaskPreproc__.train(...)
6. │ └─private$.train_task(intask)
7. │ └─mlr3pipelines:::.__PipeOpLearnerCV__.train_task(...)
8. │ └─mlr3::resample(...)
9. │ └─ResultData$new(data, data_extra, store_backends = store_backends)
10. │ └─mlr3 (local) initialize(...)
11. │ └─mlr3:::.__ResultData__initialize(...)
12. │ ├─data[, `:=`("task_hash", task[[1L]]$hash), by = "uhash"]
13. │ └─data.table:::`[.data.table`(...)
14. └─base::.handleSimpleError(...)
15. └─mlr3pipelines (local) h(simpleError(msg, call))
── Error ('test_pipeop_tunethreshold.R:7:3'): threshold works for multiclass ───
Error in ``[.data.table`(data, , `:=`("task_hash", task[[1L]]$hash), by = "uhash")`: attempt access index 9/9 in VECTOR_ELT
This happened in PipeOp classif.rpart's $train()
Backtrace:
▆
1. ├─po_cv$train(list(t)) at test_pipeop_tunethreshold.R:7:3
2. │ └─mlr3pipelines:::.__PipeOp__train(...)
3. │ ├─base::withCallingHandlers(...)
4. │ └─private$.train(input)
5. │ └─mlr3pipelines:::.__PipeOpTaskPreproc__.train(...)
6. │ └─private$.train_task(intask)
7. │ └─mlr3pipelines:::.__PipeOpLearnerCV__.train_task(...)
8. │ └─mlr3::resample(...)
9. │ └─ResultData$new(data, data_extra, store_backends = store_backends)
10. │ └─mlr3 (local) initialize(...)
11. │ └─mlr3:::.__ResultData__initialize(...)
12. │ ├─data[, `:=`("task_hash", task[[1L]]$hash), by = "uhash"]
13. │ └─data.table:::`[.data.table`(...)
14. └─base::.handleSimpleError(...)
15. └─mlr3pipelines (local) h(simpleError(msg, call))
── Error ('test_pipeop_tunethreshold.R:38:3'): threshold works for binary ──────
Error in ``[.data.table`(data, , `:=`("task_hash", task[[1L]]$hash), by = "uhash")`: attempt access index 9/9 in VECTOR_ELT
This happened in PipeOp classif.rpart's $train()
Backtrace:
▆
1. ├─po_cv$train(list(t)) at test_pipeop_tunethreshold.R:38:3
2. │ └─mlr3pipelines:::.__PipeOp__train(...)
3. │ ├─base::withCallingHandlers(...)
4. │ └─private$.train(input)
5. │ └─mlr3pipelines:::.__PipeOpTaskPreproc__.train(...)
6. │ └─private$.train_task(intask)
7. │ └─mlr3pipelines:::.__PipeOpLearnerCV__.train_task(...)
8. │ └─mlr3::resample(...)
9. │ └─ResultData$new(data, data_extra, store_backends = store_backends)
10. │ └─mlr3 (local) initialize(...)
11. │ └─mlr3:::.__ResultData__initialize(...)
12. │ ├─data[, `:=`("task_hash", task[[1L]]$hash), by = "uhash"]
13. │ └─data.table:::`[.data.table`(...)
14. └─base::.handleSimpleError(...)
15. └─mlr3pipelines (local) h(simpleError(msg, call))
── Error ('test_pipeop_tunethreshold.R:73:3'): tunethreshold graph works ───────
Error in ``[.data.table`(data, , `:=`("task_hash", task[[1L]]$hash), by = "uhash")`: attempt access index 9/9 in VECTOR_ELT
This happened in PipeOp classif.rpart's $train()
Backtrace:
▆
1. ├─graph$train(tsk("pima")) at test_pipeop_tunethreshold.R:73:3
2. │ └─mlr3pipelines:::.__Graph__train(...)
3. │ └─mlr3pipelines:::graph_reduce(self, input, "train", single_input)
4. │ └─op[[fun]](input)
5. │ └─mlr3pipelines:::.__PipeOp__train(...)
6. │ ├─base::withCallingHandlers(...)
7. │ └─private$.train(input)
8. │ └─mlr3pipelines:::.__PipeOpTaskPreproc__.train(...)
9. │ └─private$.train_task(intask)
10. │ └─mlr3pipelines:::.__PipeOpLearnerCV__.train_task(...)
11. │ └─mlr3::resample(...)
12. │ └─ResultData$new(data, data_extra, store_backends = store_backends)
13. │ └─mlr3 (local) initialize(...)
14. │ └─mlr3:::.__ResultData__initialize(...)
15. │ ├─data[, `:=`("task_hash", task[[1L]]$hash), by = "uhash"]
16. │ └─data.table:::`[.data.table`(...)
17. └─base::.handleSimpleError(...)
18. └─mlr3pipelines (local) h(simpleError(msg, call))
── Error ('test_pipeop_tunethreshold.R:101:3'): threshold works for classes that are not valid R names ──
Error in ``[.data.table`(data, , `:=`("task_hash", task[[1L]]$hash), by = "uhash")`: attempt access index 9/9 in VECTOR_ELT
This happened in PipeOp classif.rpart's $train()
Backtrace:
▆
1. ├─ppl$train(testtask) at test_pipeop_tunethreshold.R:101:3
2. │ └─mlr3pipelines:::.__Graph__train(...)
3. │ └─mlr3pipelines:::graph_reduce(self, input, "train", single_input)
4. │ └─op[[fun]](input)
5. │ └─mlr3pipelines:::.__PipeOp__train(...)
6. │ ├─base::withCallingHandlers(...)
7. │ └─private$.train(input)
8. │ └─mlr3pipelines:::.__PipeOpTaskPreproc__.train(...)
9. │ └─private$.train_task(intask)
10. │ └─mlr3pipelines:::.__PipeOpLearnerCV__.train_task(...)
11. │ └─mlr3::resample(...)
12. │ └─ResultData$new(data, data_extra, store_backends = store_backends)
13. │ └─mlr3 (local) initialize(...)
14. │ └─mlr3:::.__ResultData__initialize(...)
15. │ ├─data[, `:=`("task_hash", task[[1L]]$hash), by = "uhash"]
16. │ └─data.table:::`[.data.table`(...)
17. └─base::.handleSimpleError(...)
18. └─mlr3pipelines (local) h(simpleError(msg, call))
── Error ('test_pipeop_tunethreshold.R:260:3'): threshold graph transparency ───
Error in ``[.data.table`(data, , `:=`("task_hash", task[[1L]]$hash), by = "uhash")`: attempt access index 9/9 in VECTOR_ELT
This happened in PipeOp classif.rpart's $train()
Backtrace:
▆
1. ├─lrn_prob$train(t) at test_pipeop_tunethreshold.R:260:3
2. │ └─mlr3:::.__Learner__train(...)
3. │ └─mlr3:::learner_train(...)
4. │ └─mlr3misc::encapsulate(...)
5. │ ├─mlr3misc::invoke(...)
6. │ │ └─base::eval.parent(expr, n = 1L)
7. │ │ └─base::eval(expr, p)
8. │ │ └─base::eval(expr, p)
9. │ └─mlr3 (local) .f(learner = `<GrphLrnr>`, task = `<TskClssf>`)
10. │ └─get_private(learner)$.train(task)
11. │ └─mlr3pipelines:::.__GraphLearner__.train(...)
12. │ └─self$graph$train(task)
13. │ └─mlr3pipelines:::.__Graph__train(...)
14. │ └─mlr3pipelines:::graph_reduce(self, input, "train", single_input)
15. │ └─op[[fun]](input)
16. │ └─mlr3pipelines:::.__PipeOp__train(...)
17. │ ├─base::withCallingHandlers(...)
18. │ └─private$.train(input)
19. │ └─mlr3pipelines:::.__PipeOpTaskPreproc__.train(...)
20. │ └─private$.train_task(intask)
21. │ └─mlr3pipelines:::.__PipeOpLearnerCV__.train_task(...)
22. │ └─mlr3::resample(...)
23. │ └─ResultData$new(data, data_extra, store_backends = store_backends)
24. │ └─mlr3 (local) initialize(...)
25. │ └─mlr3:::.__ResultData__initialize(...)
26. │ ├─data[, `:=`("task_hash", task[[1L]]$hash), by = "uhash"]
27. │ └─data.table:::`[.data.table`(...)
28. └─base::.handleSimpleError(...)
29. └─mlr3pipelines (local) h(simpleError(msg, call))
── Error ('test_resample.R:13:3'): PipeOp - Resample ───────────────────────────
Error in ``[.data.table`(data, , `:=`("task_hash", task[[1L]]$hash), by = "uhash")`: attempt access index 9/9 in VECTOR_ELT
Backtrace:
▆
1. └─mlr3::resample(task, pp, resa) at test_resample.R:13:3
2. └─ResultData$new(data, data_extra, store_backends = store_backends)
3. └─mlr3 (local) initialize(...)
4. └─mlr3:::.__ResultData__initialize(...)
5. ├─data[, `:=`("task_hash", task[[1L]]$hash), by = "uhash"]
6. └─data.table:::`[.data.table`(...)
── Error ('test_usecases.R:153:3'): stacking ───────────────────────────────────
Error in ``[.data.table`(data, , `:=`("task_hash", task[[1L]]$hash), by = "uhash")`: attempt access index 9/9 in VECTOR_ELT
This happened in PipeOp classif.rpart's $train()
Backtrace:
▆
1. ├─pipe$train(task) at test_usecases.R:153:3
2. │ └─mlr3pipelines:::.__Graph__train(...)
3. │ └─mlr3pipelines:::graph_reduce(self, input, "train", single_input)
4. │ └─op[[fun]](input)
5. │ └─mlr3pipelines:::.__PipeOp__train(...)
6. │ ├─base::withCallingHandlers(...)
7. │ └─private$.train(input)
8. │ └─mlr3pipelines:::.__PipeOpTaskPreproc__.train(...)
9. │ └─private$.train_task(intask)
10. │ └─mlr3pipelines:::.__PipeOpLearnerCV__.train_task(...)
11. │ └─mlr3::resample(...)
12. │ └─ResultData$new(data, data_extra, store_backends = store_backends)
13. │ └─mlr3 (local) initialize(...)
14. │ └─mlr3:::.__ResultData__initialize(...)
15. │ ├─data[, `:=`("task_hash", task[[1L]]$hash), by = "uhash"]
16. │ └─data.table:::`[.data.table`(...)
17. └─base::.handleSimpleError(...)
18. └─mlr3pipelines (local) h(simpleError(msg, call))
── Error ('test_ppl.R:73:3'): mlr3book authors don't sleepwalk through life ────
Error in ``[.data.table`(data, , `:=`("task_hash", task[[1L]]$hash), by = "uhash")`: attempt access index 9/9 in VECTOR_ELT
This happened in PipeOp classif.rpart.classif.rpart's $train()
Backtrace:
▆
1. └─mlr3::benchmark(benchmark_grid(tasks, learners, rsmp("cv", folds = 2))) at test_ppl.R:73:3
2. └─mlr3:::future_map(...)
3. └─future.apply::future_mapply(...)
4. └─future.apply:::future_xapply(...)
5. └─base::tryCatch(...)
6. └─base (local) tryCatchList(expr, classes, parentenv, handlers)
7. └─base (local) tryCatchOne(...)
8. └─value[[3L]](cond)
9. └─future.apply:::onError(e, futures = fs, debug = debug)
[ FAIL 38 | WARN 2 | SKIP 98 | PASS 12316 ]
Error:
! Test failures.
Execution halted
Flavor: r-devel-linux-x86_64-debian-gcc
Version: 0.10.0
Check: examples
Result: ERROR
Running examples in ‘mlr3pipelines-Ex.R’ failed
The error most likely occurred in:
> ### Name: mlr_graphs_stacking
> ### Title: Create A Graph to Perform Stacking.
> ### Aliases: mlr_graphs_stacking pipeline_stacking
>
> ### ** Examples
>
> ## Don't show:
> if (mlr3misc::require_namespaces("rpart", quietly = TRUE)) withAutoprint({ # examplesIf
+ ## End(Don't show)
+ library(mlr3)
+ library(mlr3learners)
+
+ base_learners = list(
+ lrn("classif.rpart", predict_type = "prob"),
+ lrn("classif.nnet", predict_type = "prob")
+ )
+ super_learner = lrn("classif.log_reg")
+
+ graph_stack = pipeline_stacking(base_learners, super_learner)
+ graph_learner = as_learner(graph_stack)
+ graph_learner$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 [00:12:04.066] [mlr3] Resampling 'cv' is being instantiated on task 'german_credit'
INFO [00:12:04.465] [mlr3] Applying learner 'classif.rpart' on task 'german_credit' (iter 1/3)
INFO [00:12:04.696] [mlr3] Applying learner 'classif.rpart' on task 'german_credit' (iter 2/3)
INFO [00:12:04.814] [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’ [619s/355s]
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:
> 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:
> 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:
> 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-depr
> test_mlr_graphs_robustify.R: ecated").
> test_multiplicities.R:
> test_multiplicities.R: [[1]]
> test_multiplicities.R: [1] 0
> test_multiplicities.R:
> test_multiplicities.R:
> test_pipeop_blsmote.R: [1]
> test_pipeop_blsmote.R: "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_isomap.R: 2025-12-22 00:14:46.194992: Isomap START
> test_pipeop_isomap.R: 2025-12-22 00:14:46.196115: constructing knn graph
> test_pipeop_isomap.R: 2025-12-22 00:14:46.223586: calculating geodesic distances
> test_pipeop_isomap.R: 2025-12-22 00:14:46.255276: Classical Scaling
> test_pipeop_isomap.R: 2025-12-22 00:14:46.338729: Isomap START
> test_pipeop_isomap.R: 2025-12-22 00:14:46.339449: constructing knn graph
> test_pipeop_isomap.R: 2025-12-22 00:14:46.355589: calculating geodesic distances
> test_pipeop_isomap.R: 2025-12-22 00:14:46.391414: Classical Scaling
> test_pipeop_isomap.R: 2025-12-22 00:14:46.44009: L-Isomap embed START
> test_pipeop_isomap.R: 2025-12-22 00:14:46.4432: constructing knn graph
> test_pipeop_isomap.R: 2025-12-22 00:14:46.467103: calculating geodesic distances
> test_pipeop_isomap.R: 2025-12-22 00:14:46.531911: embedding
> test_pipeop_isomap.R: 2025-12-22 00:14:46.535963: DONE
> test_pipeop_isomap.R: 2025-12-22 00:14:46.572513: L-Isomap embed START
> test_pipeop_isomap.R: 2025-12-22 00:14:46.575602: constructing knn graph
> test_pipeop_isomap.R: 2025-12-22 00:14:46.629767: calculating geodesic distances
> test_pipeop_isomap.R: 2025-12-22 00:14:46.696064: embedding
> test_pipeop_isomap.R: 2025-12-22 00:14:46.70023: DONE
> test_pipeop_isomap.R: 2025-12-22 00:14:46.868734: Isomap START
> test_pipeop_isomap.R: 2025-12-22 00:14:46.869844: constructing knn graph
> test_pipeop_isomap.R: 2025-12-22 00:14:46.893008: calculating geodesic distances
> test_pipeop_isomap.R: 2025-12-22 00:14:47.044152: Classical Scaling
> test_pipeop_isomap.R: 2025-12-22 00:14:47.098104: L-Isomap embed START
> test_pipeop_isomap.R: 2025-12-22 00:14:47.10175: constructing knn graph
> test_pipeop_isomap.R: 2025-12-22 00:14:47.172462: calculating geodesic distances
> test_pipeop_isomap.R: 2025-12-22 00:14:47.496292: embedding
> test_pipeop_isomap.R: 2025-12-22 00:14:47.502979: DONE
> test_pipeop_isomap.R: 2025-12-22 00:14:47.736426: Isomap START
> test_pipeop_isomap.R: 2025-12-22 00:14:47.737119: constructing knn graph
> test_pipeop_isomap.R: 2025-12-22 00:14:47.750529: calculating geodesic distances
> test_pipeop_isomap.R: 2025-12-22 00:14:47.781252: Classical Scaling
> test_pipeop_isomap.R: 2025-12-22 00:14:47.828796: L-Isomap embed START
> test_pipeop_isomap.R: 2025-12-22 00:14:47.832161: constructing knn graph
> test_pipeop_isomap.R: 2025-12-22 00:14:47.858837: calculating geodesic distances
> test_pipeop_isomap.R: 2025-12-22 00:14:47.923917: embedding
> test_pipeop_isomap.R: 2025-12-22 00:14:47.928197: DONE
> test_pipeop_isomap.R: 2025-12-22 00:14:48.17312: Isomap START
> test_pipeop_isomap.R: 2025-12-22 00:14:48.175875: constructing knn graph
> test_pipeop_isomap.R: 2025-12-22 00:14:48.190323: calculating geodesic distances
> test_pipeop_isomap.R: 2025-12-22 00:14:48.221833: Classical Scaling
> test_pipeop_isomap.R: 2025-12-22 00:14:48.301948: L-Isomap embed START
> test_pipeop_isomap.R: 2025-12-22 00:14:48.305237: constructing knn graph
> test_pipeop_isomap.R: 2025-12-22 00:14:48.331094: calculating geodesic distances
> test_pipeop_isomap.R: 2025-12-22 00:14:48.39641: embedding
> test_pipeop_isomap.R: 2025-12-22 00:14:48.402595: DONE
> test_pipeop_isomap.R: 2025-12-22 00:14:48.536938: Isomap START
> test_pipeop_isomap.R: 2025-12-22 00:14:48.537648: constructing knn graph
> test_pipeop_isomap.R: 2025-12-22 00:14:48.551397: calculating geodesic distances
> test_pipeop_isomap.R: 2025-12-22 00:14:48.590179: Classical Scaling
> test_pipeop_isomap.R: 2025-12-22 00:14:48.838233: L-Isomap embed START
> test_pipeop_isomap.R: 2025-12-22 00:14:48.839369: constructing knn graph
> test_pipeop_isomap.R: 2025-12-22 00:14:48.867209: calculating geodesic distances
> test_pipeop_isomap.R: 2025-12-22 00:14:48.94989: embedding
> test_pipeop_isomap.R: 2025-12-22 00:14:48.955322: DONE
> test_pipeop_isomap.R: 2025-12-22 00:14:49.104003: Isomap START
> test_pipeop_isomap.R: 2025-12-22 00:14:49.104746: constructing knn graph
> test_pipeop_isomap.R: 2025-12-22 00:14:49.119502: calculating geodesic distances
> test_pipeop_isomap.R: 2025-12-22 00:14:49.15744: Classical Scaling
> test_pipeop_isomap.R: 2025-12-22 00:14:49.231536: L-Isomap embed START
> test_pipeop_isomap.R: 2025-12-22 00:14:49.232549: constructing knn graph
> test_pipeop_isomap.R: 2025-12-22 00:14:49.260679: calculating geodesic distances
> test_pipeop_isomap.R: 2025-12-22 00:14:49.325991: embedding
> test_pipeop_isomap.R: 2025-12-22 00:14:49.330358: DONE
> test_pipeop_isomap.R: 2025-12-22 00:14:49.460304: Isomap START
> test_pipeop_isomap.R: 2025-12-22 00:14:49.461009: constructing knn graph
> test_pipeop_isomap.R: 2025-12-22 00:14:49.479462: calculating geodesic distances
> test_pipeop_isomap.R: 2025-12-22 00:14:49.519416: Classical Scaling
> test_pipeop_isomap.R: 2025-12-22 00:14:49.635107: L-Isomap embed START
> test_pipeop_isomap.R: 2025-12-22 00:14:49.636754: constructing knn graph
> test_pipeop_isomap.R: 2025-12-22 00:14:49.668386: calculating geodesic distances
> test_pipeop_isomap.R: 2025-12-22 00:14:49.745751: embedding
> test_pipeop_isomap.R: 2025-12-22 00:14:49.750515: DONE
> test_pipeop_isomap.R: 2025-12-22 00:14:49.897416: Isomap START
> test_pipeop_isomap.R: 2025-12-22 00:14:49.900436: constructing knn graph
> test_pipeop_isomap.R: 2025-12-22 00:14:49.917072: calculating geodesic distances
> test_pipeop_isomap.R: 2025-12-22 00:14:49.950853: Classical Scaling
> test_pipeop_isomap.R: 2025-12-22 00:14:50.091607: Isomap START
> test_pipeop_isomap.R: 2025-12-22 00:14:50.093216: constructing knn graph
> test_pipeop_isomap.R: 2025-12-22 00:14:50.108793: calculating geodesic distances
> test_pipeop_isomap.R: 2025-12-22 00:14:50.149088: Classical Scaling
> test_pipeop_isomap.R: 2025-12-22 00:14:50.188545: Isomap START
> test_pipeop_isomap.R: 2025-12-22 00:14:50.189249: constructing knn graph
> test_pipeop_isomap.R: 2025-12-22 00:14:50.202983: calculating geodesic distances
> test_pipeop_isomap.R: 2025-12-22 00:14:50.233355: 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: 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:
> 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: 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_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_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_Graph.R:283:3', 'test_PipeOp.R:32:1',
'test_dictionary.R:7:3', 'test_learner_weightedaverage.R:5:3',
'test_learner_weightedaverage.R:57:3',
'test_learner_weightedaverage.R:105:3',
'test_learner_weightedaverage.R:152:3', 'test_meta.R:39:3',
'test_mlr_graphs_bagging.R:6:3', 'test_mlr_graphs_branching.R:26:3',
'test_mlr_graphs_robustify.R:5:3', 'test_pipeop_adas.R:8:3',
'test_pipeop_blsmote.R:8:3', 'test_pipeop_branch.R:4:3',
'test_pipeop_chunk.R:4:3', 'test_pipeop_classbalancing.R:7:3',
'test_pipeop_classweights.R:10:3', 'test_pipeop_boxcox.R:7:3',
'test_pipeop_collapsefactors.R:6:3', 'test_pipeop_colapply.R:9:3',
'test_pipeop_copy.R:5:3', 'test_pipeop_colroles.R:6:3',
'test_pipeop_decode.R:14:3', 'test_pipeop_encode.R:21:3',
'test_pipeop_encodeimpact.R:11:3', 'test_pipeop_datefeatures.R:10:3',
'test_pipeop_encodepl.R:5:3', 'test_pipeop_encodepl.R:72:3',
'test_pipeop_encodelmer.R:15:3', 'test_pipeop_encodelmer.R:37:3',
'test_pipeop_encodelmer.R:80:3', 'test_pipeop_featureunion.R:9:3',
'test_pipeop_featureunion.R:134:3', 'test_pipeop_filter.R:7:3',
'test_pipeop_fixfactors.R:9:3', 'test_pipeop_histbin.R:7:3',
'test_pipeop_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_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]]$hash), by = "uhash"]
27. │ └─data.table:::`[.data.table`(...)
28. └─base::.handleSimpleError(...)
29. └─mlr3pipelines (local) h(simpleError(msg, call))
── Error ('test_pipeop_learnerpicvplus.R:35:3'): PipeOpLearnerPICVPlus - basic properties ──
Error in ``[.data.table`(data, , `:=`("task_hash", task[[1L]]$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’ [113s/60s]
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
> 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: 2025-12-24 03:32:14.847535: Isomap START
> test_pipeop_isomap.R: 2025-12-24 03:32:14.85651: constructing knn graph
> test_pipeop_isomap.R: 2025-12-24 03:32:14.862975: calculating geodesic distances
> test_pipeop_isomap.R: 2025-12-24 03:32:14.87027: Classical Scaling
> test_pipeop_isomap.R: 2025-12-24 03:32:14.883064: Isomap START
> test_pipeop_isomap.R: 2025-12-24 03:32:14.883251: constructing knn graph
> test_pipeop_isomap.R: 2025-12-24 03:32:14.885955: calculating geodesic distances
> test_pipeop_isomap.R: 2025-12-24 03:32:14.892097: Classical Scaling
> test_pipeop_isomap.R: 2025-12-24 03:32:14.900649: L-Isomap embed START
> test_pipeop_isomap.R: 2025-12-24 03:32:14.900859: constructing knn graph
> test_pipeop_isomap.R: 2025-12-24 03:32:14.90582: calculating geodesic distances
> test_pipeop_isomap.R: 2025-12-24 03:32:14.921214: embedding
> test_pipeop_isomap.R: 2025-12-24 03:32:14.921676: DONE
> test_pipeop_isomap.R: 2025-12-24 03:32:14.926757: L-Isomap embed START
> test_pipeop_isomap.R: 2025-12-24 03:32:14.926869: constructing knn graph
> test_pipeop_isomap.R: 2025-12-24 03:32:14.93139: calculating geodesic distances
> test_pipeop_isomap.R: 2025-12-24 03:32:14.947342: embedding
> test_pipeop_isomap.R: 2025-12-24 03:32:14.947833: DONE
> test_pipeop_isomap.R: 2025-12-24 03:32:14.972075: Isomap START
> test_pipeop_isomap.R: 2025-12-24 03:32:14.97221: constructing knn graph
> test_pipeop_isomap.R: 2025-12-24 03:32:14.977002: calculating geodesic distances
> test_pipeop_isomap.R: 2025-12-24 03:32:15.012294: Classical Scaling
> test_pipeop_isomap.R: 2025-12-24 03:32:15.021809: L-Isomap embed START
> test_pipeop_isomap.R: 2025-12-24 03:32:15.022007: constructing knn graph
> test_pipeop_isomap.R: 2025-12-24 03:32:15.031522: calculating geodesic distances
> test_pipeop_isomap.R: 2025-12-24 03:32:15.117257: embedding
> test_pipeop_isomap.R: 2025-12-24 03:32:15.118702: DONE
> test_pipeop_isomap.R: 2025-12-24 03:32:15.161539: Isomap START
> test_pipeop_isomap.R: 2025-12-24 03:32:15.161716: constructing knn graph
> test_pipeop_isomap.R: 2025-12-24 03:32:15.164676: calculating geodesic distances
> test_pipeop_isomap.R: 2025-12-24 03:32:15.172032: Classical Scaling
> test_pipeop_isomap.R: 2025-12-24 03:32:15.179231: L-Isomap embed START
> test_pipeop_isomap.R: 2025-12-24 03:32:15.179423: constructing knn graph
> test_pipeop_isomap.R: 2025-12-24 03:32:15.185356: calculating geodesic distances
> test_pipeop_isomap.R: 2025-12-24 03:32:15.201396: embedding
> test_pipeop_isomap.R: 2025-12-24 03:32:15.201937: DONE
> test_pipeop_isomap.R: 2025-12-24 03:32:15.238099: Isomap START
> test_pipeop_isomap.R: 2025-12-24 03:32:15.238322: constructing knn graph
> test_pipeop_isomap.R: 2025-12-24 03:32:15.241083: calculating geodesic distances
> test_pipeop_isomap.R: 2025-12-24 03:32:15.247643: Classical Scaling
> test_pipeop_isomap.R: 2025-12-24 03:32:15.2613: L-Isomap embed START
> test_pipeop_isomap.R: 2025-12-24 03:32:15.261603: constructing knn graph
> test_pipeop_isomap.R: 2025-12-24 03:32:15.267359: calculating geodesic distances
> test_pipeop_isomap.R: 2025-12-24 03:32:15.283386: embedding
> test_pipeop_isomap.R: 2025-12-24 03:32:15.283974: DONE
> test_pipeop_isomap.R: 2025-12-24 03:32:15.304311: Isomap START
> test_pipeop_isomap.R: 2025-12-24 03:32:15.304445: constructing knn graph
> test_pipeop_isomap.R: 2025-12-24 03:32:15.307019: calculating geodesic distances
> test_pipeop_isomap.R: 2025-12-24 03:32:15.313562: Classical Scaling
> test_pipeop_isomap.R: 2025-12-24 03:32:15.32613: L-Isomap embed START
> test_pipeop_isomap.R: 2025-12-24 03:32:15.326349: constructing knn graph
> test_pipeop_isomap.R: 2025-12-24 03:32:15.331215: calculating geodesic distances
> test_pipeop_isomap.R: 2025-12-24 03:32:15.348405: embedding
> test_pipeop_isomap.R: 2025-12-24 03:32:15.348996: DONE
> test_pipeop_isomap.R: 2025-12-24 03:32:15.368559: Isomap START
> test_pipeop_isomap.R: 2025-12-24 03:32:15.374884: constructing knn graph
> test_pipeop_isomap.R: 2025-12-24 03:32:15.377549: calculating geodesic distances
> test_pipeop_isomap.R: 2025-12-24 03:32:15.384078: Classical Scaling
> test_pipeop_isomap.R: 2025-12-24 03:32:15.397495: L-Isomap embed START
> test_pipeop_isomap.R: 2025-12-24 03:32:15.397703: constructing knn graph
> test_pipeop_isomap.R: 2025-12-24 03:32:15.402839: calculating geodesic distances
> test_pipeop_isomap.R: 2025-12-24 03:32:15.418732: embedding
> test_pipeop_isomap.R: 2025-12-24 03:32:15.419163: DONE
> test_pipeop_isomap.R: 2025-12-24 03:32:15.434236: Isomap START
> test_pipeop_isomap.R: 2025-12-24 03:32:15.434341: constructing knn graph
> test_pipeop_isomap.R: 2025-12-24 03:32:15.436472: calculating geodesic distances
> test_pipeop_isomap.R: 2025-12-24 03:32:15.443185: Classical Scaling
> test_pipeop_isomap.R: 2025-12-24 03:32:15.455316: L-Isomap embed START
> test_pipeop_isomap.R: 2025-12-24 03:32:15.4555: constructing knn graph
> test_pipeop_isomap.R: 2025-12-24 03:32:15.460528: calculating geodesic distances
> test_pipeop_isomap.R: 2025-12-24 03:32:15.476726: embedding
> test_pipeop_isomap.R: 2025-12-24 03:32:15.477303: DONE
> test_pipeop_isomap.R: 2025-12-24 03:32:15.497758: Isomap START
> test_pipeop_isomap.R: 2025-12-24 03:32:15.497907: constructing knn graph
> test_pipeop_isomap.R: 2025-12-24 03:32:15.500371: calculating geodesic distances
> test_pipeop_isomap.R: 2025-12-24 03:32:15.506735: Classical Scaling
> test_pipeop_isomap.R: 2025-12-24 03:32:15.528268: Isomap START
> test_pipeop_isomap.R: 2025-12-24 03:32:15.528439: constructing knn graph
> test_pipeop_isomap.R: 2025-12-24 03:32:15.531121: calculating geodesic distances
> test_pipeop_isomap.R: 2025-12-24 03:32:15.537585: Classical Scaling
> test_pipeop_isomap.R: 2025-12-24 03:32:15.543365: Isomap START
> test_pipeop_isomap.R: 2025-12-24 03:32:15.543488: constructing knn graph
> test_pipeop_isomap.R: 2025-12-24 03:32:15.582856: calculating geodesic distances
> test_pipeop_isomap.R: 2025-12-24 03:32:15.589911: 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_boxcox.R:7:3',
'test_pipeop_classbalancing.R:7:3', 'test_pipeop_classweights.R:10:3',
'test_pipeop_colapply.R:9:3', 'test_pipeop_collapsefactors.R:6:3',
'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_nmf.R:6:3', '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_learnercv.R:31:3',
'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_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’ [361s/461s]
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:
> test_PipeOp.R: Training test_autotrain
> test_PipeOp.R:
> 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: 2025-12-23 07:35:20.252459: Isomap START
> test_pipeop_isomap.R: 2025-12-23 07:35:20.254098: constructing knn graph
> test_pipeop_isomap.R: 2025-12-23 07:35:20.271048: calculating geodesic distances
> test_pipeop_isomap.R: 2025-12-23 07:35:20.358693: Classical Scaling
> test_pipeop_isomap.R: 2025-12-23 07:35:20.489892: Isomap START
> test_pipeop_isomap.R: 2025-12-23 07:35:20.490191: constructing knn graph
> test_pipeop_isomap.R: 2025-12-23 07:35:20.497595: calculating geodesic distances
> test_pipeop_isomap.R: 2025-12-23 07:35:20.544739: Classical Scaling
> test_pipeop_isomap.R: 2025-12-23 07:35:20.594509: L-Isomap embed START
> test_pipeop_isomap.R: 2025-12-23 07:35:20.595114: constructing knn graph
> test_pipeop_isomap.R: 2025-12-23 07:35:20.695322: calculating geodesic distances
> test_pipeop_isomap.R: 2025-12-23 07:35:20.760873: embedding
> test_pipeop_isomap.R: 2025-12-23 07:35:20.844842: DONE
> test_pipeop_isomap.R: 2025-12-23 07:35:20.869576: L-Isomap embed START
> test_pipeop_isomap.R: 2025-12-23 07:35:20.869891: constructing knn graph
> test_pipeop_isomap.R: 2025-12-23 07:35:20.903551: calculating geodesic distances
> test_pipeop_isomap.R: 2025-12-23 07:35:20.964058: embedding
> test_pipeop_isomap.R: 2025-12-23 07:35:20.995783: DONE
> test_pipeop_isomap.R: 2025-12-23 07:35:21.162701: Isomap START
> test_pipeop_isomap.R: 2025-12-23 07:35:21.163238: constructing knn graph
> test_pipeop_isomap.R: 2025-12-23 07:35:21.186941: calculating geodesic distances
> test_pipeop_isomap.R: 2025-12-23 07:35:21.526211: Classical Scaling
> test_pipeop_isomap.R: 2025-12-23 07:35:21.635103: L-Isomap embed START
> test_pipeop_isomap.R: 2025-12-23 07:35:21.636209: constructing knn graph
> test_pipeop_isomap.R: 2025-12-23 07:35:21.708391: calculating geodesic distances
> test_pipeop_isomap.R: 2025-12-23 07:35:22.447791: embedding
> test_pipeop_isomap.R: 2025-12-23 07:35:22.480345: DONE
> test_pipeop_isomap.R: 2025-12-23 07:35:22.812883: Isomap START
> test_pipeop_isomap.R: 2025-12-23 07:35:22.813239: constructing knn graph
> test_pipeop_isomap.R: 2025-12-23 07:35:22.822395: calculating geodesic distances
> test_pipeop_isomap.R: 2025-12-23 07:35:22.881573: Classical Scaling
> test_pipeop_isomap.R: 2025-12-23 07:35:22.919155: L-Isomap embed START
> test_pipeop_isomap.R: 2025-12-23 07:35:22.919739: constructing knn graph
> test_pipeop_isomap.R: 2025-12-23 07:35:22.984323: calculating geodesic distances
> test_pipeop_isomap.R: 2025-12-23 07:35:23.070066: embedding
> test_pipeop_isomap.R: 2025-12-23 07:35:23.071351: DONE
> test_pipeop_isomap.R: 2025-12-23 07:35:23.329862: Isomap START
> test_pipeop_isomap.R: 2025-12-23 07:35:23.330299: constructing knn graph
> test_pipeop_isomap.R: 2025-12-23 07:35:23.340522: calculating geodesic distances
> test_pipeop_isomap.R: 2025-12-23 07:35:23.408347: Classical Scaling
> test_pipeop_isomap.R: 2025-12-23 07:35:23.472758: L-Isomap embed START
> test_pipeop_isomap.R: 2025-12-23 07:35:23.473208: constructing knn graph
> test_pipeop_isomap.R: 2025-12-23 07:35:23.512005: calculating geodesic distances
> test_pipeop_isomap.R: 2025-12-23 07:35:23.651617: embedding
> test_pipeop_isomap.R: 2025-12-23 07:35:23.653285: DONE
> test_pipeop_isomap.R: 2025-12-23 07:35:23.926891: Isomap START
> test_pipeop_isomap.R: 2025-12-23 07:35:23.928929: constructing knn graph
> test_pipeop_isomap.R: 2025-12-23 07:35:23.939399: calculating geodesic distances
> test_pipeop_isomap.R: 2025-12-23 07:35:24.021659: Classical Scaling
> test_pipeop_isomap.R: 2025-12-23 07:35:24.093543: L-Isomap embed START
> test_pipeop_isomap.R: 2025-12-23 07:35:24.113261: constructing knn graph
> test_pipeop_isomap.R: 2025-12-23 07:35:24.15315: calculating geodesic distances
> test_pipeop_isomap.R: 2025-12-23 07:35:24.252751: embedding
> test_pipeop_isomap.R: 2025-12-23 07:35:24.255499: DONE
> test_pipeop_isomap.R: 2025-12-23 07:35:24.403532: Isomap START
> test_pipeop_isomap.R: 2025-12-23 07:35:24.403819: constructing knn graph
> test_pipeop_isomap.R: 2025-12-23 07:35:24.418673: calculating geodesic distances
> test_pipeop_isomap.R: 2025-12-23 07:35:24.46426: Classical Scaling
> test_pipeop_isomap.R: 2025-12-23 07:35:24.548401: L-Isomap embed START
> test_pipeop_isomap.R: 2025-12-23 07:35:24.550077: constructing knn graph
> test_pipeop_isomap.R: 2025-12-23 07:35:24.606664: calculating geodesic distances
> test_pipeop_isomap.R: 2025-12-23 07:35:24.708521: embedding
> test_pipeop_isomap.R: 2025-12-23 07:35:24.710676: DONE
> test_pipeop_isomap.R: 2025-12-23 07:35:24.804513: Isomap START
> test_pipeop_isomap.R: 2025-12-23 07:35:24.816049: constructing knn graph
> test_pipeop_isomap.R: 2025-12-23 07:35:24.844933: calculating geodesic distances
> test_pipeop_isomap.R: 2025-12-23 07:35:24.875777: Classical Scaling
> test_pipeop_isomap.R: 2025-12-23 07:35:24.977592: L-Isomap embed START
> test_pipeop_isomap.R: 2025-12-23 07:35:24.978044: constructing knn graph
> test_pipeop_isomap.R: 2025-12-23 07:35:25.032357: calculating geodesic distances
> test_pipeop_isomap.R: 2025-12-23 07:35:25.124594: embedding
> test_pipeop_isomap.R: 2025-12-23 07:35:25.126701: DONE
> test_pipeop_isomap.R: 2025-12-23 07:35:25.312603: Isomap START
> test_pipeop_isomap.R: 2025-12-23 07:35:25.312928: constructing knn graph
> test_pipeop_isomap.R: 2025-12-23 07:35:25.320394: calculating geodesic distances
> test_pipeop_isomap.R: 2025-12-23 07:35:25.396262: Classical Scaling
> test_pipeop_isomap.R: 2025-12-23 07:35:25.573646: Isomap START
> test_pipeop_isomap.R: 2025-12-23 07:35:25.574106: constructing knn graph
> test_pipeop_isomap.R: 2025-12-23 07:35:25.667845: calculating geodesic distances
> test_pipeop_isomap.R: 2025-12-23 07:35:25.744838: Classical Scaling
> test_pipeop_isomap.R: 2025-12-23 07:35:25.779407: Isomap START
> test_pipeop_isomap.R: 2025-12-23 07:35:25.779748: constructing knn graph
> test_pipeop_isomap.R: 2025-12-23 07:35:25.789646: calculating geodesic distances
> test_pipeop_isomap.R: 2025-12-23 07:35:25.827128: 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_boxcox.R:7:3',
'test_pipeop_classbalancing.R:7:3', 'test_pipeop_classweights.R:10:3',
'test_pipeop_colapply.R:9:3', 'test_pipeop_collapsefactors.R:6:3',
'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_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_randomresponse.R:5:3',
'test_pipeop_nmf.R:6:3', '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' [283s]
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
> 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: 2025-12-22 15:20:13.299257: Isomap START
> test_pipeop_isomap.R: 2025-12-22 15:20:13.300242: constructing knn graph
> test_pipeop_isomap.R: 2025-12-22 15:20:13.313809: calculating geodesic distances
> test_pipeop_isomap.R: 2025-12-22 15:20:13.336109: Classical Scaling
> test_pipeop_isomap.R: 2025-12-22 15:20:13.416001: Isomap START
> test_pipeop_isomap.R: 2025-12-22 15:20:13.41666: constructing knn graph
> test_pipeop_isomap.R: 2025-12-22 15:20:13.427963: calculating geodesic distances
> test_pipeop_isomap.R: 2025-12-22 15:20:13.450985: Classical Scaling
> test_pipeop_isomap.R: 2025-12-22 15:20:13.493861: L-Isomap embed START
> test_pipeop_isomap.R: 2025-12-22 15:20:13.494781: constructing knn graph
> test_pipeop_isomap.R: 2025-12-22 15:20:13.516055: calculating geodesic distances
> test_pipeop_isomap.R: 2025-12-22 15:20:13.564643: embedding
> test_pipeop_isomap.R: 2025-12-22 15:20:13.56627: DONE
> test_pipeop_isomap.R: 2025-12-22 15:20:13.606333: L-Isomap embed START
> test_pipeop_isomap.R: 2025-12-22 15:20:13.607032: constructing knn graph
> test_pipeop_isomap.R: 2025-12-22 15:20:13.62741: calculating geodesic distances
> test_pipeop_isomap.R: 2025-12-22 15:20:13.677365: embedding
> test_pipeop_isomap.R: 2025-12-22 15:20:13.678945: DONE
> test_pipeop_isomap.R: 2025-12-22 15:20:13.803843: Isomap START
> test_pipeop_isomap.R: 2025-12-22 15:20:13.804476: constructing knn graph
> test_pipeop_isomap.R: 2025-12-22 15:20:13.829882: calculating geodesic distances
> test_pipeop_isomap.R: 2025-12-22 15:20:13.942266: Classical Scaling
> test_pipeop_isomap.R: 2025-12-22 15:20:13.994147: L-Isomap embed START
> test_pipeop_isomap.R: 2025-12-22 15:20:13.995056: constructing knn graph
> test_pipeop_isomap.R: 2025-12-22 15:20:14.057107: calculating geodesic distances
> test_pipeop_isomap.R: 2025-12-22 15:20:14.278284: embedding
> test_pipeop_isomap.R: 2025-12-22 15:20:14.283452: DONE
> test_pipeop_isomap.R: 2025-12-22 15:20:14.512786: Isomap START
> test_pipeop_isomap.R: 2025-12-22 15:20:14.513557: constructing knn graph
> test_pipeop_isomap.R: 2025-12-22 15:20:14.524763: calculating geodesic distances
> test_pipeop_isomap.R: 2025-12-22 15:20:14.547382: Classical Scaling
> test_pipeop_isomap.R: 2025-12-22 15:20:14.599349: L-Isomap embed START
> test_pipeop_isomap.R: 2025-12-22 15:20:14.600375: constructing knn graph
> test_pipeop_isomap.R: 2025-12-22 15:20:14.62077: calculating geodesic distances
> test_pipeop_isomap.R: 2025-12-22 15:20:14.661497: embedding
> test_pipeop_isomap.R: 2025-12-22 15:20:14.662764: DONE
> test_pipeop_isomap.R: 2025-12-22 15:20:14.845617: Isomap START
> test_pipeop_isomap.R: 2025-12-22 15:20:14.846285: constructing knn graph
> test_pipeop_isomap.R: 2025-12-22 15:20:14.858026: calculating geodesic distances
> test_pipeop_isomap.R: 2025-12-22 15:20:14.880703: Classical Scaling
> test_pipeop_isomap.R: 2025-12-22 15:20:14.960588: L-Isomap embed START
> test_pipeop_isomap.R: 2025-12-22 15:20:14.961684: constructing knn graph
> test_pipeop_isomap.R: 2025-12-22 15:20:14.9852: calculating geodesic distances
> test_pipeop_isomap.R: 2025-12-22 15:20:15.03418: embedding
> test_pipeop_isomap.R: 2025-12-22 15:20:15.057644: DONE
> test_pipeop_isomap.R: 2025-12-22 15:20:15.184788: Isomap START
> test_pipeop_isomap.R: 2025-12-22 15:20:15.185558: constructing knn graph
> test_pipeop_isomap.R: 2025-12-22 15:20:15.196751: calculating geodesic distances
> test_pipeop_isomap.R: 2025-12-22 15:20:15.219105: Classical Scaling
> test_pipeop_isomap.R: 2025-12-22 15:20:15.2964: L-Isomap embed START
> test_pipeop_isomap.R: 2025-12-22 15:20:15.297343: constructing knn graph
> test_pipeop_isomap.R: 2025-12-22 15:20:15.316794: calculating geodesic distances
> test_pipeop_isomap.R: 2025-12-22 15:20:15.366068: embedding
> test_pipeop_isomap.R: 2025-12-22 15:20:15.367332: DONE
> test_pipeop_isomap.R: 2025-12-22 15:20:15.474467: Isomap START
> test_pipeop_isomap.R: 2025-12-22 15:20:15.475143: constructing knn graph
> test_pipeop_isomap.R: 2025-12-22 15:20:15.485318: calculating geodesic distances
> test_pipeop_isomap.R: 2025-12-22 15:20:15.507591: Classical Scaling
> test_pipeop_isomap.R: 2025-12-22 15:20:15.572011: L-Isomap embed START
> test_pipeop_isomap.R: 2025-12-22 15:20:15.572655: constructing knn graph
> test_pipeop_isomap.R: 2025-12-22 15:20:15.592848: calculating geodesic distances
> test_pipeop_isomap.R: 2025-12-22 15:20:15.640865: embedding
> test_pipeop_isomap.R: 2025-12-22 15:20:15.642868: DONE
> test_pipeop_isomap.R: 2025-12-22 15:20:15.754993: Isomap START
> test_pipeop_isomap.R: 2025-12-22 15:20:15.755698: constructing knn graph
> test_pipeop_isomap.R: 2025-12-22 15:20:15.766362: calculating geodesic distances
> test_pipeop_isomap.R: 2025-12-22 15:20:15.788108: Classical Scaling
> test_pipeop_isomap.R: 2025-12-22 15:20:15.881901: L-Isomap embed START
> test_pipeop_isomap.R: 2025-12-22 15:20:15.883002: constructing knn graph
> test_pipeop_isomap.R: 2025-12-22 15:20:15.904368: calculating geodesic distances
> test_pipeop_isomap.R: 2025-12-22 15:20:15.953444: embedding
> test_pipeop_isomap.R: 2025-12-22 15:20:15.955361: DONE
> test_pipeop_isomap.R: 2025-12-22 15:20:16.0885: Isomap START
> test_pipeop_isomap.R: 2025-12-22 15:20:16.089165: constructing knn graph
> test_pipeop_isomap.R: 2025-12-22 15:20:16.101325: calculating geodesic distances
> test_pipeop_isomap.R: 2025-12-22 15:20:16.123926: Classical Scaling
> test_pipeop_isomap.R: 2025-12-22 15:20:16.242435: Isomap START
> test_pipeop_isomap.R: 2025-12-22 15:20:16.243082: constructing knn graph
> test_pipeop_isomap.R: 2025-12-22 15:20:16.255289: calculating geodesic distances
> test_pipeop_isomap.R: 2025-12-22 15:20:16.277987: Classical Scaling
> test_pipeop_isomap.R: 2025-12-22 15:20:16.312319: Isomap START
> test_pipeop_isomap.R: 2025-12-22 15:20:16.313095: constructing knn graph
> test_pipeop_isomap.R: 2025-12-22 15:20:16.326189: calculating geodesic distances
> test_pipeop_isomap.R: 2025-12-22 15:20:16.348884: 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_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_nmf.R:6:3',
'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_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