Check results for 'mlr3pipelines'

using R Under development (unstable) (2026-08-14 r90406)
using platform: x86_64-pc-linux-gnu
R was compiled by gcc (GCC) 16.1.1 20260515 (Red Hat 16.1.1-2) GNU Fortran (GCC) 16.1.1 20260515 (Red Hat 16.1.1-2)
running under: Fedora Linux 44 (Server Edition)
using session charset: UTF-8 * current time: 2026-08-14 18:50:23 UTC
using option ‘--no-stop-on-test-error’
checking for file ‘mlr3pipelines/DESCRIPTION’ ... OK
this is package ‘mlr3pipelines’ version ‘0.11.0’
package encoding: UTF-8
checking package namespace information ... OK
checking package dependencies ... OK
checking if this is a source package ... OK
checking if there is a namespace ... OK
checking for executable files ... OK
checking for hidden files and directories ... OK
checking for portable file names ... OK
checking for sufficient/correct file permissions ... OK
checking whether package ‘mlr3pipelines’ can be installed ... [26s/48s] OK See the install log for details.
checking package directory ... OK
checking ‘build’ directory ... OK
checking DESCRIPTION meta-information ... OK
checking top-level files ... OK
checking for left-over files ... OK
checking index information ... OK
checking package subdirectories ... OK
checking code files for non-ASCII characters ... OK
checking R files for syntax errors ... OK
checking whether the package can be loaded ... OK
checking whether the package can be loaded with stated dependencies ... OK
checking whether the package can be unloaded cleanly ... OK
checking whether the namespace can be loaded with stated dependencies ... OK
checking whether the namespace can be unloaded cleanly ... OK
checking loading without being on the library search path ... OK
checking whether startup messages can be suppressed ... OK
checking use of S3 registration ... OK
checking dependencies in R code ... OK
checking S3 generic/method consistency ... OK
checking replacement functions ... OK
checking foreign function calls ... OK
checking R code for possible problems ... [67s/219s] NOTE Found calls to structure() using deprecated special names: mlr3pipelines/R/PipeOpFilter.R (.Names: 1) '.Names' should be changed to 'names'.
checking Rd files ... OK
checking Rd metadata ... OK
checking Rd line widths ... OK
checking Rd cross-references ... OK
checking for missing documentation entries ... OK
checking for code/documentation mismatches ... OK
checking Rd \usage sections ... OK
checking Rd contents ... OK
checking for unstated dependencies in examples ... OK
checking installed files from ‘inst/doc’ ... OK
checking files in ‘vignettes’ ... OK
checking examples ... [20s/59s] ERROR Running examples in ‘mlr3pipelines-Ex.R’ failed The error most likely occurred in: > ### Name: mlr_pipeops_imputeconstant > ### Title: Impute Features by a Constant > ### Aliases: mlr_pipeops_imputeconstant PipeOpImputeConstant > > ### ** Examples > > library("mlr3") > > task = tsk("pima") Warning in data(list = id, package = package, envir = ee) : data set ‘PimaIndiansDiabetes2’ not found Error in UseMethod("as_data_backend") : no applicable method for 'as_data_backend' applied to an object of class "NULL" Calls: tsk ... dictionary_initialize_item -> do.call -> <Anonymous> -> as_data_backend Execution halted
checking for unstated dependencies in ‘tests’ ... OK
checking tests ... [267s/259s] ERROR Running ‘testthat.R’ [267s/259s] Running the tests in ‘tests/testthat.R’ failed. Complete output: > if (requireNamespace("testthat", quietly = TRUE)) { + library("checkmate") + library("testthat") + library("mlr3") + library("paradox") + library("mlr3pipelines") + test_check("mlr3pipelines") + } Starting 2 test processes. > test_Graph.R: Training debug.multi with input list(input_1 = 1, input_2 = 1) > test_PipeOp.R: Training test_autotrain > test_PipeOp.R: Predicting test_autotrain > test_PipeOp.R: Training test_autotrain > test_PipeOp.R: > test_PipeOp.R: Training test_autotrain > test_PipeOp.R: Predicting test_autotrain > test_PipeOp.R: Predicting test_autotrain > test_PipeOp.R: Training test_autotrain > test_PipeOp.R: > test_PipeOp.R: Training test_autotrain > test_PipeOp.R: Training test_autotrain > test_PipeOp.R: Predicting test_autotrain > test_PipeOp.R: > 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: > 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: Predicting test_autotrain > test_PipeOp.R: > test_PipeOp.R: Predicting test_autotrain > test_PipeOp.R: Training test_autotrain > test_PipeOp.R: > test_PipeOp.R: Predicting test_autotrain Saving _problems/test_mlr_graphs_robustify-106.R > test_multiplicities.R: > test_multiplicities.R: > test_multiplicities.R: [[1]] > 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_classbalancing-13.R Saving _problems/test_pipeop_classweights-17.R Saving _problems/test_pipeop_classweights-36.R Saving _problems/test_pipeop_impute-452.R > test_pipeop_isomap.R: 2026-08-14 19:59:50.144028: Isomap START > test_pipeop_isomap.R: 2026-08-14 19:59:50.144713: constructing knn graph > test_pipeop_isomap.R: 2026-08-14 19:59:50.897346: calculating geodesic distances > test_pipeop_isomap.R: 2026-08-14 19:59:50.913339: Classical Scaling > test_pipeop_isomap.R: 2026-08-14 19:59:50.950754: Isomap START > test_pipeop_isomap.R: 2026-08-14 19:59:50.951157: constructing knn graph > test_pipeop_isomap.R: 2026-08-14 19:59:50.960425: calculating geodesic distances > test_pipeop_isomap.R: 2026-08-14 19:59:50.976038: Classical Scaling > test_pipeop_isomap.R: 2026-08-14 19:59:50.999359: L-Isomap embed START > test_pipeop_isomap.R: 2026-08-14 19:59:50.999958: constructing knn graph > test_pipeop_isomap.R: 2026-08-14 19:59:51.015395: calculating geodesic distances > test_pipeop_isomap.R: 2026-08-14 19:59:51.051939: embedding > test_pipeop_isomap.R: 2026-08-14 19:59:51.05295: DONE Saving _problems/test_pipeop_imputelearner-7.R Saving _problems/test_pipeop_imputelearner-138.R > test_pipeop_isomap.R: 2026-08-14 19:59:51.115767: L-Isomap embed START > test_pipeop_isomap.R: 2026-08-14 19:59:51.116186: constructing knn graph > test_pipeop_isomap.R: 2026-08-14 19:59:51.144922: calculating geodesic distances > test_pipeop_isomap.R: 2026-08-14 19:59:51.216691: embedding > test_pipeop_isomap.R: 2026-08-14 19:59:51.217732: DONE > test_pipeop_isomap.R: 2026-08-14 19:59:51.31125: Isomap START > test_pipeop_isomap.R: 2026-08-14 19:59:51.312622: constructing knn graph > test_pipeop_isomap.R: 2026-08-14 19:59:51.336302: calculating geodesic distances > test_pipeop_isomap.R: 2026-08-14 19:59:51.419014: Classical Scaling > test_pipeop_isomap.R: 2026-08-14 19:59:51.445889: L-Isomap embed START > test_pipeop_isomap.R: 2026-08-14 19:59:51.447669: constructing knn graph > test_pipeop_isomap.R: 2026-08-14 19:59:51.475711: calculating geodesic distances > test_pipeop_isomap.R: 2026-08-14 19:59:51.836012: embedding > test_pipeop_isomap.R: 2026-08-14 19:59:51.843815: DONE > test_pipeop_isomap.R: 2026-08-14 19:59:52.101323: Isomap START > test_pipeop_isomap.R: 2026-08-14 19:59:52.102438: constructing knn graph > test_pipeop_isomap.R: 2026-08-14 19:59:52.121486: calculating geodesic distances > test_pipeop_isomap.R: 2026-08-14 19:59:52.148963: Classical Scaling > test_pipeop_isomap.R: 2026-08-14 19:59:52.195061: L-Isomap embed START > test_pipeop_isomap.R: 2026-08-14 19:59:52.196795: constructing knn graph > test_pipeop_isomap.R: 2026-08-14 19:59:52.211967: calculating geodesic distances > test_pipeop_isomap.R: 2026-08-14 19:59:52.247331: embedding > test_pipeop_isomap.R: 2026-08-14 19:59:52.248375: DONE > test_pipeop_isomap.R: 2026-08-14 19:59:52.401021: Isomap START > test_pipeop_isomap.R: 2026-08-14 19:59:52.401447: constructing knn graph > test_pipeop_isomap.R: 2026-08-14 19:59:52.418706: calculating geodesic distances > test_pipeop_isomap.R: 2026-08-14 19:59:52.44742: Classical Scaling > test_pipeop_isomap.R: 2026-08-14 19:59:52.527603: L-Isomap embed START > test_pipeop_isomap.R: 2026-08-14 19:59:52.52821: constructing knn graph > test_pipeop_isomap.R: 2026-08-14 19:59:52.559116: calculating geodesic distances > test_pipeop_isomap.R: 2026-08-14 19:59:52.633345: embedding > test_pipeop_isomap.R: 2026-08-14 19:59:52.637245: DONE > test_pipeop_isomap.R: 2026-08-14 19:59:52.763013: Isomap START > test_pipeop_isomap.R: 2026-08-14 19:59:52.763423: constructing knn graph > test_pipeop_isomap.R: 2026-08-14 19:59:52.781828: calculating geodesic distances > test_pipeop_isomap.R: 2026-08-14 19:59:52.80966: Classical Scaling > test_pipeop_isomap.R: 2026-08-14 19:59:52.915605: L-Isomap embed START > test_pipeop_isomap.R: 2026-08-14 19:59:52.916191: constructing knn graph > test_pipeop_isomap.R: 2026-08-14 19:59:52.959866: calculating geodesic distances > test_pipeop_isomap.R: 2026-08-14 19:59:53.035068: embedding > test_pipeop_isomap.R: 2026-08-14 19:59:53.040234: DONE > test_pipeop_isomap.R: 2026-08-14 19:59:53.114723: Isomap START > test_pipeop_isomap.R: 2026-08-14 19:59:53.115136: constructing knn graph > test_pipeop_isomap.R: 2026-08-14 19:59:53.126791: calculating geodesic distances > test_pipeop_isomap.R: 2026-08-14 19:59:53.147269: Classical Scaling > test_pipeop_isomap.R: 2026-08-14 19:59:53.203981: L-Isomap embed START > test_pipeop_isomap.R: 2026-08-14 19:59:53.205879: constructing knn graph > test_pipeop_isomap.R: 2026-08-14 19:59:53.232958: calculating geodesic distances > test_pipeop_isomap.R: 2026-08-14 19:59:53.267711: embedding > test_pipeop_isomap.R: 2026-08-14 19:59:53.270017: DONE > test_pipeop_isomap.R: 2026-08-14 19:59:53.329046: Isomap START > test_pipeop_isomap.R: 2026-08-14 19:59:53.329436: constructing knn graph > test_pipeop_isomap.R: 2026-08-14 19:59:53.339048: calculating geodesic distances > test_pipeop_isomap.R: 2026-08-14 19:59:53.354468: Classical Scaling > test_pipeop_isomap.R: 2026-08-14 19:59:53.406163: L-Isomap embed START > test_pipeop_isomap.R: 2026-08-14 19:59:53.406777: constructing knn graph > test_pipeop_isomap.R: 2026-08-14 19:59:53.437605: calculating geodesic distances > test_pipeop_isomap.R: 2026-08-14 19:59:53.509862: embedding > test_pipeop_isomap.R: 2026-08-14 19:59:53.510918: DONE > test_pipeop_isomap.R: 2026-08-14 19:59:53.655555: Isomap START > test_pipeop_isomap.R: 2026-08-14 19:59:53.655978: constructing knn graph > test_pipeop_isomap.R: 2026-08-14 19:59:53.673441: calculating geodesic distances > test_pipeop_isomap.R: 2026-08-14 19:59:53.703666: Classical Scaling > test_pipeop_isomap.R: 2026-08-14 19:59:53.778605: Isomap START > test_pipeop_isomap.R: 2026-08-14 19:59:53.778994: constructing knn graph > test_pipeop_isomap.R: 2026-08-14 19:59:53.788739: calculating geodesic distances > test_pipeop_isomap.R: 2026-08-14 19:59:53.804194: Classical Scaling > test_pipeop_isomap.R: 2026-08-14 19:59:53.824321: Isomap START > test_pipeop_isomap.R: 2026-08-14 19:59:53.824778: constructing knn graph > test_pipeop_isomap.R: 2026-08-14 19:59:53.837873: calculating geodesic distances > test_pipeop_isomap.R: 2026-08-14 19:59:53.855219: Classical Scaling Saving _problems/test_pipeop_missind-4.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: > 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: > test_pipeop_task_preproc.R: Training debug_affectcols > test_pipeop_task_preproc.R: > test_pipeop_task_preproc.R: Training debug_affectcols > test_pipeop_task_preproc.R: > test_pipeop_task_preproc.R: Training debug_affectcols > test_pipeop_task_preproc.R: Training debug_affectcols > test_pipeop_task_preproc.R: Training debug_affectcols > test_pipeop_task_preproc.R: Training debug_affectcols > test_pipeop_task_preproc.R: > test_pipeop_task_preproc.R: Training debug_affectcols > test_pipeop_task_preproc.R: Training debug_affectcols > test_pipeop_task_preproc.R: Training debug_affectcols > test_pipeop_task_preproc.R: Training debug_affectcols > test_pipeop_task_preproc.R: Training debug_affectcols > test_pipeop_task_preproc.R: Training debug_affectcols > test_pipeop_task_preproc.R: Training debug_affectcols Saving _problems/test_pipeop_unbranch-21.R Saving _problems/test_pipeop_tunethreshold-36.R Saving _problems/test_pipeop_tunethreshold-73.R Saving _problems/test_selector-6.R [ FAIL 12 | WARN 21 | SKIP 128 | PASS 8462 ] ══ Skipped tests (128) ═════════════════════════════════════════════════════════ • On CRAN (125): '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_PipeOp.R:32:1', 'test_Graph.R:283:3', 'test_doublearrow.R:2:1', 'test_gunion.R:2:1', '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_dictionary.R:7:3', 'test_mlr_graphs_robustify.R:5:3', 'test_pipeop_adas.R:8:3', 'test_pipeop_blsmote.R:8:3', 'test_pipeop_boxcox.R:7:3', '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_colapply.R:9:3', 'test_pipeop_collapsefactors.R:6:3', 'test_pipeop_colroles.R:6:3', 'test_pipeop_copy.R:5:3', 'test_pipeop_classweightsex.R:9:3', '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_ensemble.R:3:1', '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_encodelmer.R:15:3', 'test_pipeop_encodelmer.R:37:3', 'test_pipeop_encodelmer.R:80:3', 'test_pipeop_impute.R:4:3', 'test_pipeop_info.R:3:1', '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_learnerpicvplus.R:2:1', '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:3:3', 'test_pipeop_learnercv.R:43:3', 'test_pipeop_learnercv.R:73:3', 'test_pipeop_learnercv.R:92:3', 'test_pipeop_learnercv.R:141:3', 'test_pipeop_learnercv.R:157:3', 'test_pipeop_learnercv.R:203:3', 'test_pipeop_learnercv.R:249:3', 'test_pipeop_learnercv.R:278:3', 'test_pipeop_learnercv.R:332:3', 'test_pipeop_learnercv.R:359:3', 'test_pipeop_learnercv.R:389:3', 'test_pipeop_learnercv.R:399:3', 'test_pipeop_learnercv.R:432:3', 'test_pipeop_learnercv.R:472:3', 'test_pipeop_learnercv.R:481:3', 'test_pipeop_learnercv.R:498:3', 'test_pipeop_learnercv.R:506:3', 'test_pipeop_learnercv.R:530:3', 'test_pipeop_learnercv.R:554:3', 'test_pipeop_learnercv.R:634:3', 'test_pipeop_learnercv.R:654:3', 'test_pipeop_learnercv.R:669:3', 'test_pipeop_learnercv.R:754:3', 'test_pipeop_learnercv.R:799:3', 'test_pipeop_learnercv.R:827:3', 'test_pipeop_ovr.R:9:3', 'test_pipeop_ovr.R:48:3', 'test_pipeop_nmf.R:6:3', 'test_pipeop_proxy.R:2:1', 'test_pipeop_pca.R:8:3', 'test_pipeop_quantilebin.R:5:3', 'test_pipeop_randomresponse.R:5:3', 'test_pipeop_randomprojection.R:6:3', 'test_pipeop_renamecolumns.R:6:3', 'test_pipeop_replicate.R:9:3', 'test_pipeop_removeconstants.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_rowapply.R:6:3', 'test_pipeop_select.R:9:3', 'test_pipeop_smote.R:10:3', 'test_pipeop_spatialsign.R:3:1', 'test_pipeop_splines.R:3:1', 'test_pipeop_smotenc.R:8:3', 'test_pipeop_targetinvert.R:4:3', 'test_pipeop_subsample.R:6:3', 'test_pipeop_targettrafo.R:4:3', 'test_pipeop_targettrafoscalerange.R:5:3', 'test_pipeop_targetmutate.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_textvectorizer.R:37:3', 'test_pipeop_textvectorizer.R:186:3', 'test_pipeop_unbranch.R:10:3', 'test_pipeop_tunethreshold.R:111:3', 'test_pipeop_tunethreshold.R:191:3', 'test_pipeop_vtreat.R:9:3', 'test_pipeop_yeojohnson.R:7:3', 'test_pipeop_updatetarget.R:89:3', 'test_ppl.R:63:3', '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_mlr_graphs_robustify.R:106:3'): Robustify Pipeline Impute Missings ── Error in `UseMethod("as_data_backend")`: no applicable method for 'as_data_backend' applied to an object of class "NULL" Backtrace: ▆ 1. └─mlr3::tsk("pima") at test_mlr_graphs_robustify.R:106:3 2. └─mlr3misc::dictionary_sugar_get(dict = mlr_tasks, .key, ...) 3. └─mlr3misc:::dictionary_get(dict, .key, .dicts_suggest = .dicts_suggest) 4. └─mlr3misc:::dictionary_initialize_item(key, obj, dots) 5. ├─base::do.call(constructor, cargs) 6. └─mlr3 (local) `<fn>`() 7. └─mlr3::as_data_backend(load_dataset("PimaIndiansDiabetes2", "mlbench")) ── Error ('test_pipeop_classbalancing.R:13:3'): PipeOpClassBalancing ─────────── Error in `UseMethod("as_data_backend")`: no applicable method for 'as_data_backend' applied to an object of class "NULL" Backtrace: ▆ 1. └─mlr_tasks$get("pima") at test_pipeop_classbalancing.R:13:3 2. ├─mlr3misc::invoke(dictionary_get, self = self, key = key, .args = args) 3. │ └─base::eval.parent(expr, n = 1L) 4. │ └─base::eval(expr, p) 5. │ └─base::eval(expr, p) 6. └─mlr3misc:::dictionary_get(self = self, key = key) 7. └─mlr3misc:::dictionary_initialize_item(key, obj, dots) 8. ├─base::do.call(constructor, cargs) 9. └─mlr3 (local) `<fn>`() 10. └─mlr3::as_data_backend(load_dataset("PimaIndiansDiabetes2", "mlbench")) ── Error ('test_pipeop_classweights.R:17:3'): PipeOpClassWeights ─────────────── Error in `UseMethod("as_data_backend")`: no applicable method for 'as_data_backend' applied to an object of class "NULL" Backtrace: ▆ 1. └─mlr_tasks$get("pima") at test_pipeop_classweights.R:17:3 2. ├─mlr3misc::invoke(dictionary_get, self = self, key = key, .args = args) 3. │ └─base::eval.parent(expr, n = 1L) 4. │ └─base::eval(expr, p) 5. │ └─base::eval(expr, p) 6. └─mlr3misc:::dictionary_get(self = self, key = key) 7. └─mlr3misc:::dictionary_initialize_item(key, obj, dots) 8. ├─base::do.call(constructor, cargs) 9. └─mlr3 (local) `<fn>`() 10. └─mlr3::as_data_backend(load_dataset("PimaIndiansDiabetes2", "mlbench")) ── Error ('test_pipeop_classweights.R:36:5'): PipeOpClassWeights - weight roles assigned ── Error in `UseMethod("as_data_backend")`: no applicable method for 'as_data_backend' applied to an object of class "NULL" Backtrace: ▆ 1. └─mlr3::tsk("pima") at test_pipeop_classweights.R:36:5 2. └─mlr3misc::dictionary_sugar_get(dict = mlr_tasks, .key, ...) 3. └─mlr3misc:::dictionary_get(dict, .key, .dicts_suggest = .dicts_suggest) 4. └─mlr3misc:::dictionary_initialize_item(key, obj, dots) 5. ├─base::do.call(constructor, cargs) 6. └─mlr3 (local) `<fn>`() 7. └─mlr3::as_data_backend(load_dataset("PimaIndiansDiabetes2", "mlbench")) ── Error ('test_pipeop_impute.R:452:3'): impute, test rows and affect_columns ── Error in `UseMethod("as_data_backend")`: no applicable method for 'as_data_backend' applied to an object of class "NULL" Backtrace: ▆ 1. └─mlr3::tsk("pima") at test_pipeop_impute.R:452:3 2. └─mlr3misc::dictionary_sugar_get(dict = mlr_tasks, .key, ...) 3. └─mlr3misc:::dictionary_get(dict, .key, .dicts_suggest = .dicts_suggest) 4. └─mlr3misc:::dictionary_initialize_item(key, obj, dots) 5. ├─base::do.call(constructor, cargs) 6. └─mlr3 (local) `<fn>`() 7. └─mlr3::as_data_backend(load_dataset("PimaIndiansDiabetes2", "mlbench")) ── Error ('test_pipeop_imputelearner.R:7:3'): PipeOpImputeLearner - simple tests ── Error in `UseMethod("as_data_backend")`: no applicable method for 'as_data_backend' applied to an object of class "NULL" Backtrace: ▆ 1. └─mlr_tasks$get("pima") at test_pipeop_imputelearner.R:7:3 2. ├─mlr3misc::invoke(dictionary_get, self = self, key = key, .args = args) 3. │ └─base::eval.parent(expr, n = 1L) 4. │ └─base::eval(expr, p) 5. │ └─base::eval(expr, p) 6. └─mlr3misc:::dictionary_get(self = self, key = key) 7. └─mlr3misc:::dictionary_initialize_item(key, obj, dots) 8. ├─base::do.call(constructor, cargs) 9. └─mlr3 (local) `<fn>`() 10. └─mlr3::as_data_backend(load_dataset("PimaIndiansDiabetes2", "mlbench")) ── Error ('test_pipeop_imputelearner.R:138:3'): PipeOpImputeLearner - model active binding to state ── Error in `UseMethod("as_data_backend")`: no applicable method for 'as_data_backend' applied to an object of class "NULL" Backtrace: ▆ 1. └─mlr_tasks$get("pima") at test_pipeop_imputelearner.R:138:3 2. ├─mlr3misc::invoke(dictionary_get, self = self, key = key, .args = args) 3. │ └─base::eval.parent(expr, n = 1L) 4. │ └─base::eval(expr, p) 5. │ └─base::eval(expr, p) 6. └─mlr3misc:::dictionary_get(self = self, key = key) 7. └─mlr3misc:::dictionary_initialize_item(key, obj, dots) 8. ├─base::do.call(constructor, cargs) 9. └─mlr3 (local) `<fn>`() 10. └─mlr3::as_data_backend(load_dataset("PimaIndiansDiabetes2", "mlbench")) ── Error ('test_pipeop_missind.R:4:3'): PipeOpMissInd ────────────────────────── Error in `UseMethod("as_data_backend")`: no applicable method for 'as_data_backend' applied to an object of class "NULL" Backtrace: ▆ 1. └─mlr_tasks$get("pima") at test_pipeop_missind.R:4:3 2. ├─mlr3misc::invoke(dictionary_get, self = self, key = key, .args = args) 3. │ └─base::eval.parent(expr, n = 1L) 4. │ └─base::eval(expr, p) 5. │ └─base::eval(expr, p) 6. └─mlr3misc:::dictionary_get(self = self, key = key) 7. └─mlr3misc:::dictionary_initialize_item(key, obj, dots) 8. ├─base::do.call(constructor, cargs) 9. └─mlr3 (local) `<fn>`() 10. └─mlr3::as_data_backend(load_dataset("PimaIndiansDiabetes2", "mlbench")) ── Error ('test_pipeop_unbranch.R:21:3'): PipeOpUnbranch - train and predict ─── Error in `UseMethod("as_data_backend")`: no applicable method for 'as_data_backend' applied to an object of class "NULL" Backtrace: ▆ 1. └─mlr_tasks$get("pima") at test_pipeop_unbranch.R:21:3 2. ├─mlr3misc::invoke(dictionary_get, self = self, key = key, .args = args) 3. │ └─base::eval.parent(expr, n = 1L) 4. │ └─base::eval(expr, p) 5. │ └─base::eval(expr, p) 6. └─mlr3misc:::dictionary_get(self = self, key = key) 7. └─mlr3misc:::dictionary_initialize_item(key, obj, dots) 8. ├─base::do.call(constructor, cargs) 9. └─mlr3 (local) `<fn>`() 10. └─mlr3::as_data_backend(load_dataset("PimaIndiansDiabetes2", "mlbench")) ── Error ('test_pipeop_tunethreshold.R:36:3'): threshold works for binary ────── Error in `UseMethod("as_data_backend")`: no applicable method for 'as_data_backend' applied to an object of class "NULL" Backtrace: ▆ 1. └─mlr3::tsk("pima") at test_pipeop_tunethreshold.R:36:3 2. └─mlr3misc::dictionary_sugar_get(dict = mlr_tasks, .key, ...) 3. └─mlr3misc:::dictionary_get(dict, .key, .dicts_suggest = .dicts_suggest) 4. └─mlr3misc:::dictionary_initialize_item(key, obj, dots) 5. ├─base::do.call(constructor, cargs) 6. └─mlr3 (local) `<fn>`() 7. └─mlr3::as_data_backend(load_dataset("PimaIndiansDiabetes2", "mlbench")) ── Error ('test_pipeop_tunethreshold.R:73:3'): tunethreshold graph works ─────── Error in `UseMethod("as_data_backend")`: no applicable method for 'as_data_backend' applied to an object of class "NULL" Backtrace: ▆ 1. ├─graph$train(tsk("pima")) at test_pipeop_tunethreshold.R:73:3 2. │ └─mlr3pipelines:::.__Graph__train(...) 3. │ └─mlr3pipelines:::graph_reduce(self, input, "train", single_input) 4. └─mlr3::tsk("pima") 5. └─mlr3misc::dictionary_sugar_get(dict = mlr_tasks, .key, ...) 6. └─mlr3misc:::dictionary_get(dict, .key, .dicts_suggest = .dicts_suggest) 7. └─mlr3misc:::dictionary_initialize_item(key, obj, dots) 8. ├─base::do.call(constructor, cargs) 9. └─mlr3 (local) `<fn>`() 10. └─mlr3::as_data_backend(load_dataset("PimaIndiansDiabetes2", "mlbench")) ── Error ('test_selector.R:6:3'): Selectors work ─────────────────────────────── Error in `UseMethod("as_data_backend")`: no applicable method for 'as_data_backend' applied to an object of class "NULL" Backtrace: ▆ 1. └─mlr3::mlr_tasks$get("pima") at test_selector.R:6:3 2. ├─mlr3misc::invoke(dictionary_get, self = self, key = key, .args = args) 3. │ └─base::eval.parent(expr, n = 1L) 4. │ └─base::eval(expr, p) 5. │ └─base::eval(expr, p) 6. └─mlr3misc:::dictionary_get(self = self, key = key) 7. └─mlr3misc:::dictionary_initialize_item(key, obj, dots) 8. ├─base::do.call(constructor, cargs) 9. └─mlr3 (local) `<fn>`() 10. └─mlr3::as_data_backend(load_dataset("PimaIndiansDiabetes2", "mlbench")) [ FAIL 12 | WARN 21 | SKIP 128 | PASS 8462 ] Error: ! Test failures. Execution halted
checking for unstated dependencies in vignettes ... OK
checking package vignettes ... OK
checking re-building of vignette outputs ... [10s/28s] OK
checking PDF version of manual ... [24s/69s] OK
checking HTML version of manual ... [23s/63s] OK
checking for non-standard things in the check directory ... OK
checking for detritus in the temp directory ... OK
DONE

Status: 2 ERRORs, 1 NOTE