Last updated on 2025-12-26 17:49:15 CET.
| Package | ERROR | NOTE | OK |
|---|---|---|---|
| miesmuschel | 1 | 12 | |
| mlr | 1 | 2 | 10 |
| mlr3pipelines | 4 | 9 | |
| paradox | 13 | ||
| ParamHelpers | 13 |
Current CRAN status: ERROR: 1, OK: 12
Version: 0.0.4-3
Check: tests
Result: ERROR
Running 'tinytest.R' [168s]
Running the tests in 'tests/tinytest.R' failed.
Complete output:
>
> if (requireNamespace("tinytest", quietly = TRUE)) {
+ tinytest::test_package("miesmuschel", at_home = identical(Sys.getenv("NOT_CRAN"), "true"), ncpu = 2)
+ Sys.sleep(5) # wait for parallel workers to quit
+ }
starting worker pid=44340 on localhost:11571 at 17:03:14.263
starting worker pid=69108 on localhost:11571 at 17:03:14.266
Loading required package: paradox
Loading required package: paradox
Attaching package: 'data.table'
The following object is masked from 'package:base':
%notin%
Attaching package: 'data.table'
The following object is masked from 'package:base':
%notin%
Attaching package: 'bbotk'
The following objects are masked from 'package:miesmuschel':
OptimInstanceMultiCrit, OptimInstanceSingleCrit, Optimizer
Attaching package: 'bbotk'
The following objects are masked from 'package:miesmuschel':
OptimInstanceMultiCrit, OptimInstanceSingleCrit, Optimizer
OptimInstanceSingleCrit is deprecated. Use OptimInstanceBatchSingleCrit instead.
OptimInstanceSingleCrit is deprecated. Use OptimInstanceBatchSingleCrit instead.
OptimInstanceMultiCrit is deprecated. Use OptimInstanceBatchMultiCrit instead.
test_mies_survival_comma.R.... 46 tests <1b>[0;32mOK<1b>[0m <1b>[0;34m3.5s<1b>[0m
OptimInstanceSingleCrit is deprecated. Use OptimInstanceBatchSingleCrit instead.
OptimInstanceMultiCrit is deprecated. Use OptimInstanceBatchMultiCrit instead.
test_mies_survival_plus.R..... 28 tests <1b>[0;32mOK<1b>[0m <1b>[0;34m2.0s<1b>[0m
OptimInstanceSingleCrit is deprecated. Use OptimInstanceBatchSingleCrit instead.
test_OptimizerMies.R.......... 54 tests <1b>[0;32mOK<1b>[0m <1b>[0;34m12.1s<1b>[0m
test_ParamSetShadow.R......... 41 tests <1b>[0;32mOK<1b>[0m <1b>[0;34m0.5s<1b>[0m
OptimInstanceSingleCrit is deprecated. Use OptimInstanceBatchSingleCrit instead.
OptimInstanceMultiCrit is deprecated. Use OptimInstanceBatchMultiCrit instead.
OptimInstanceSingleCrit is deprecated. Use OptimInstanceBatchSingleCrit instead.
test_mutator_cmpmaybe.R....... 93 tests <1b>[0;32mOK<1b>[0m <1b>[0;34m8.0s<1b>[0m
test_TerminatorBudget.R....... 31 tests <1b>[0;32mOK<1b>[0m <1b>[0;34m2.9s<1b>[0m
test_TerminatorGenerationPerfReached.R 0 tests <1b>[0;36m2ms<1b>[0m
test_TerminatorGenerationStagnation.R 0 tests <1b>[0;36m2ms<1b>[0m
OptimInstanceSingleCrit is deprecated. Use OptimInstanceBatchSingleCrit instead.
OptimInstanceMultiCrit is deprecated. Use OptimInstanceBatchMultiCrit instead.
OptimInstanceSingleCrit is deprecated. Use OptimInstanceBatchSingleCrit instead.
test_TerminatorGenerations.R.. 22 tests <1b>[0;32mOK<1b>[0m <1b>[0;34m2.3s<1b>[0m
test_mutator_erase.R.......... 41 tests <1b>[0;32mOK<1b>[0m <1b>[0;34m23.4s<1b>[0m
test_mutator_gauss.R.......... 62 tests <1b>[0;32mOK<1b>[0m <1b>[0;34m5.8s<1b>[0m
test_mutator_maybe.R.......... 90 tests <1b>[0;32mOK<1b>[0m <1b>[0;34m5.0s<1b>[0m
test_mutator_null.R........... 24 tests <1b>[0;32mOK<1b>[0m <1b>[0;34m1.1s<1b>[0m
test_mutator_proxy.R.......... 69 tests <1b>[0;32mOK<1b>[0m <1b>[0;34m11.5s<1b>[0m
test_mutator_sequential.R..... 65 tests <1b>[0;32mOK<1b>[0m <1b>[0;34m3.1s<1b>[0m
test_mutator_unif.R........... 41 tests <1b>[0;32mOK<1b>[0m <1b>[0;34m31.1s<1b>[0m
test_operator.R............... 40 tests <1b>[0;32mOK<1b>[0m <1b>[0;34m0.3s<1b>[0m
test_operatorcombination.R.... 211 tests <1b>[0;32mOK<1b>[0m <1b>[0;34m7.4s<1b>[0m
test_recombinator_maybe.R..... 69 tests <1b>[0;32mOK<1b>[0m <1b>[0;34m6.6s<1b>[0m
test_recombinator_null.R...... 37 tests <1b>[0;32mOK<1b>[0m <1b>[0;34m1.7s<1b>[0m
test_recombinator_proxy.R..... 102 tests <1b>[0;32mOK<1b>[0m <1b>[0;34m6.2s<1b>[0m
test_recombinator_sbx.R....... 29 tests <1b>[0;32mOK<1b>[0m <1b>[0;34m1.2s<1b>[0m
test_recombinator_xounif.R.... 60 tests <1b>[0;32mOK<1b>[0m <1b>[0;34m4.9s<1b>[0m
test_scalor_one.R............. 35 tests <1b>[0;32mOK<1b>[0m <1b>[0;34m1.2s<1b>[0m
test_selector_best.R.......... 147 tests <1b>[0;32mOK<1b>[0m <1b>[0;34m9.5s<1b>[0m
test_selector_proxy.R......... 114 tests <1b>[0;32mOK<1b>[0m <1b>[0;34m7.2s<1b>[0m
test_selector_random.R........ 106 tests <1b>[0;32mOK<1b>[0m <1b>[0;34m2.3s<1b>[0m
test_shortforms.R............. 20 tests <1b>[0;32mOK<1b>[0m <1b>[0;34m22.0s<1b>[0m
test_utils.R.................. 5 tests <1b>[0;32mOK<1b>[0m <1b>[0;34m0.5s<1b>[0m
Error in checkForRemoteErrors(val) :
one node produced an error: attempt access index 9/9 in VECTOR_ELT
Calls: <Anonymous> ... clusterApply -> staticClusterApply -> checkForRemoteErrors
Warning message:
closing unused connection 3 (->localhost:11571)
Warning message:
closing unused connection 3 (->localhost:11571)
Execution halted
Flavor: r-devel-windows-x86_64
Current CRAN status: ERROR: 1, NOTE: 2, OK: 10
Version: 2.19.3
Check: examples
Result: ERROR
Running examples in 'mlr-Ex.R' failed
The error most likely occurred in:
> ### Name: benchmark
> ### Title: Benchmark experiment for multiple learners and tasks.
> ### Aliases: benchmark
>
> ### ** Examples
>
> ## Don't show:
> if (requireNamespace("rpart")) {
+ ## End(Don't show)
+ ## Don't show:
+ if (requireNamespace("MASS")) {
+ ## End(Don't show)
+ ## Don't show:
+ if (requireNamespace("rpart")) {
+ ## End(Don't show)
+ ## Don't show:
+ if (requireNamespace("PMCMRplus")) {
+ ## End(Don't show)
+ lrns = list(makeLearner("classif.lda"), makeLearner("classif.rpart"))
+ tasks = list(iris.task, sonar.task)
+ rdesc = makeResampleDesc("CV", iters = 2L)
+ meas = list(acc, ber)
+ bmr = benchmark(lrns, tasks, rdesc, measures = meas)
+ rmat = convertBMRToRankMatrix(bmr)
+ print(rmat)
+ plotBMRSummary(bmr)
+ plotBMRBoxplots(bmr, ber, style = "violin")
+ plotBMRRanksAsBarChart(bmr, pos = "stack")
+ friedmanTestBMR(bmr)
+ friedmanPostHocTestBMR(bmr, p.value = 0.05)
+ ## Don't show:
+ }
+ ## End(Don't show)
+ ## Don't show:
+ }
+ ## End(Don't show)
+ ## Don't show:
+ }
+ ## End(Don't show)
+ ## Don't show:
+ }
Loading required namespace: rpart
Loading required namespace: PMCMRplus
Task: iris-example, Learner: classif.lda
Resampling: cross-validation
Measures: acc ber
[Resample] iter 1: 0.9600000 0.0401235
[Resample] iter 2: 1.0000000 0.0000000
Aggregated Result: acc.test.mean=0.9800000,ber.test.mean=0.0200617
Task: Sonar-example, Learner: classif.lda
Resampling: cross-validation
Measures: acc ber
[Resample] iter 1: 0.6538462 0.3470218
[Resample] iter 2: 0.7211538 0.2972264
Aggregated Result: acc.test.mean=0.6875000,ber.test.mean=0.3221241
Task: iris-example, Learner: classif.rpart
Resampling: cross-validation
Measures: acc ber
[Resample] iter 1: 0.9333333 0.0679012
[Resample] iter 2: 0.8400000 0.1555184
Aggregated Result: acc.test.mean=0.8866667,ber.test.mean=0.1117098
Task: Sonar-example, Learner: classif.rpart
Resampling: cross-validation
Measures: acc ber
[Resample] iter 1: 0.6730769 0.3303737
[Resample] iter 2: 0.7307692 0.2953523
Aggregated Result: acc.test.mean=0.7019231,ber.test.mean=0.3128630
Error in `[.data.table`(df, , `:=`("alg.rank", rank(.SD$x, ties.method = ties.method)), :
attempt access index 3/3 in VECTOR_ELT
Calls: convertBMRToRankMatrix -> [ -> [.data.table
Execution halted
Flavor: r-devel-windows-x86_64
Version: 2.19.3
Check: package dependencies
Result: NOTE
Package suggested but not available for checking: ‘Rmpi’
Flavors: r-oldrel-macos-arm64, r-oldrel-macos-x86_64
Version: 2.19.3
Check: installed package size
Result: NOTE
installed size is 5.6Mb
sub-directories of 1Mb or more:
R 2.0Mb
data 2.3Mb
Flavors: r-oldrel-macos-arm64, r-oldrel-macos-x86_64
Current CRAN status: ERROR: 4, OK: 9
Version: 0.10.0
Check: examples
Result: ERROR
Running examples in 'mlr3pipelines-Ex.R' failed
The error most likely occurred in:
> ### Name: mlr_graphs_stacking
> ### Title: Create A Graph to Perform Stacking.
> ### Aliases: mlr_graphs_stacking pipeline_stacking
>
> ### ** Examples
>
> ## Don't show:
> if (mlr3misc::require_namespaces("rpart", quietly = TRUE)) withAutoprint({ # examplesIf
+ ## End(Don't show)
+ library(mlr3)
+ library(mlr3learners)
+
+ base_learners = list(
+ lrn("classif.rpart", predict_type = "prob"),
+ lrn("classif.nnet", predict_type = "prob")
+ )
+ super_learner = lrn("classif.log_reg")
+
+ graph_stack = pipeline_stacking(base_learners, super_learner)
+ graph_learner = as_learner(graph_stack)
+ graph_learner$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$train(tsk("german_credit"))
INFO [17:12:37.279] [mlr3] Resampling 'cv' is being instantiated on task 'german_credit'
INFO [17:12:37.420] [mlr3] Applying learner 'classif.rpart' on task 'german_credit' (iter 1/3)
INFO [17:12:37.493] [mlr3] Applying learner 'classif.rpart' on task 'german_credit' (iter 2/3)
INFO [17:12:37.529] [mlr3] Applying learner 'classif.rpart' on task 'german_credit' (iter 3/3)
Error in `[.data.table`(data, , `:=`("task_hash", task[[1L]]$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-windows-x86_64
Version: 0.10.0
Check: tests
Result: ERROR
Running 'testthat.R' [171s]
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:
> 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:
> 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"
> test_pipeop_isomap.R: 2025-12-24 17:13:55.612832: Isomap START
> test_pipeop_isomap.R: 2025-12-24 17:13:55.613655: constructing knn graph
> test_pipeop_isomap.R: 2025-12-24 17:13:55.625629: calculating geodesic distances
> test_pipeop_isomap.R: 2025-12-24 17:13:55.645388: Classical Scaling
> test_pipeop_isomap.R: 2025-12-24 17:13:55.69406: Isomap START
> test_pipeop_isomap.R: 2025-12-24 17:13:55.694632: constructing knn graph
> test_pipeop_isomap.R: 2025-12-24 17:13:55.701462: calculating geodesic distances
> test_pipeop_isomap.R: 2025-12-24 17:13:55.717804: Classical Scaling
> test_pipeop_isomap.R: 2025-12-24 17:13:55.74954: L-Isomap embed START
> test_pipeop_isomap.R: 2025-12-24 17:13:55.750264: constructing knn graph
> test_pipeop_isomap.R: 2025-12-24 17:13:55.769375: calculating geodesic distances
> test_pipeop_isomap.R: 2025-12-24 17:13:55.814251: embedding
> test_pipeop_isomap.R: 2025-12-24 17:13:55.815744: DONE
> test_pipeop_isomap.R: 2025-12-24 17:13:55.842133: L-Isomap embed START
> test_pipeop_isomap.R: 2025-12-24 17:13:55.842642: constructing knn graph
> test_pipeop_isomap.R: 2025-12-24 17:13:55.860334: calculating geodesic distances
> test_pipeop_isomap.R: 2025-12-24 17:13:55.904064: embedding
> test_pipeop_isomap.R: 2025-12-24 17:13:55.905769: DONE
> test_pipeop_isomap.R: 2025-12-24 17:13:56.005005: Isomap START
> test_pipeop_isomap.R: 2025-12-24 17:13:56.005534: constructing knn graph
> test_pipeop_isomap.R: 2025-12-24 17:13:56.02579: calculating geodesic distances
> test_pipeop_isomap.R: 2025-12-24 17:13:56.141048: Classical Scaling
> test_pipeop_isomap.R: 2025-12-24 17:13:56.189765: L-Isomap embed START
> test_pipeop_isomap.R: 2025-12-24 17:13:56.190404: constructing knn graph
> test_pipeop_isomap.R: 2025-12-24 17:13:56.214621: calculating geodesic distances
> test_pipeop_isomap.R: 2025-12-24 17:13:56.399302: embedding
> test_pipeop_isomap.R: 2025-12-24 17:13:56.40287: DONE
> test_pipeop_isomap.R: 2025-12-24 17:13:56.538362: Isomap START
> test_pipeop_isomap.R: 2025-12-24 17:13:56.538743: constructing knn graph
> test_pipeop_isomap.R: 2025-12-24 17:13:56.543986: calculating geodesic distances
> test_pipeop_isomap.R: 2025-12-24 17:13:56.556913: Classical Scaling
> test_pipeop_isomap.R: 2025-12-24 17:13:56.587082: L-Isomap embed START
> test_pipeop_isomap.R: 2025-12-24 17:13:56.587786: constructing knn graph
> test_pipeop_isomap.R: 2025-12-24 17:13:56.60268: calculating geodesic distances
> test_pipeop_isomap.R: 2025-12-24 17:13:56.645191: embedding
> test_pipeop_isomap.R: 2025-12-24 17:13:56.646387: DONE
> test_pipeop_isomap.R: 2025-12-24 17:13:56.769397: Isomap START
> test_pipeop_isomap.R: 2025-12-24 17:13:56.769779: constructing knn graph
> test_pipeop_isomap.R: 2025-12-24 17:13:56.775735: calculating geodesic distances
> test_pipeop_isomap.R: 2025-12-24 17:13:56.790188: Classical Scaling
> test_pipeop_isomap.R: 2025-12-24 17:13:56.828514: L-Isomap embed START
> test_pipeop_isomap.R: 2025-12-24 17:13:56.829004: constructing knn graph
> test_pipeop_isomap.R: 2025-12-24 17:13:56.843064: calculating geodesic distances
> test_pipeop_isomap.R: 2025-12-24 17:13:56.881509: embedding
> test_pipeop_isomap.R: 2025-12-24 17:13:56.890602: DONE
> test_pipeop_isomap.R: 2025-12-24 17:13:56.971269: Isomap START
> test_pipeop_isomap.R: 2025-12-24 17:13:56.971823: constructing knn graph
> test_pipeop_isomap.R: 2025-12-24 17:13:56.980644: calculating geodesic distances
> test_pipeop_isomap.R: 2025-12-24 17:13:57.001431: Classical Scaling
> test_pipeop_isomap.R: 2025-12-24 17:13:57.053573: L-Isomap embed START
> test_pipeop_isomap.R: 2025-12-24 17:13:57.054248: constructing knn graph
> test_pipeop_isomap.R: 2025-12-24 17:13:57.068706: calculating geodesic distances
> test_pipeop_isomap.R: 2025-12-24 17:13:57.11281: embedding
> test_pipeop_isomap.R: 2025-12-24 17:13:57.113938: DONE
> test_pipeop_isomap.R: 2025-12-24 17:13:57.191203: Isomap START
> test_pipeop_isomap.R: 2025-12-24 17:13:57.191909: constructing knn graph
> test_pipeop_isomap.R: 2025-12-24 17:13:57.202773: calculating geodesic distances
> test_pipeop_isomap.R: 2025-12-24 17:13:57.223766: Classical Scaling
> test_pipeop_isomap.R: 2025-12-24 17:13:57.280651: L-Isomap embed START
> test_pipeop_isomap.R: 2025-12-24 17:13:57.281372: constructing knn graph
> test_pipeop_isomap.R: 2025-12-24 17:13:57.301767: calculating geodesic distances
> test_pipeop_isomap.R: 2025-12-24 17:13:57.355475: embedding
> test_pipeop_isomap.R: 2025-12-24 17:13:57.357042: DONE
> test_pipeop_isomap.R: 2025-12-24 17:13:57.448109: Isomap START
> test_pipeop_isomap.R: 2025-12-24 17:13:57.448778: constructing knn graph
> test_pipeop_isomap.R: 2025-12-24 17:13:57.459609: calculating geodesic distances
> test_pipeop_isomap.R: 2025-12-24 17:13:57.477791: Classical Scaling
> test_pipeop_isomap.R: 2025-12-24 17:13:57.535437: L-Isomap embed START
> test_pipeop_isomap.R: 2025-12-24 17:13:57.53623: constructing knn graph
> test_pipeop_isomap.R: 2025-12-24 17:13:57.556421: calculating geodesic distances
> test_pipeop_isomap.R: 2025-12-24 17:13:57.595287: embedding
> test_pipeop_isomap.R: 2025-12-24 17:13:57.59658: DONE
> test_pipeop_isomap.R: 2025-12-24 17:13:57.678123: Isomap START
> test_pipeop_isomap.R: 2025-12-24 17:13:57.678466: constructing knn graph
> test_pipeop_isomap.R: 2025-12-24 17:13:57.691609: calculating geodesic distances
> test_pipeop_isomap.R: 2025-12-24 17:13:57.708099: Classical Scaling
> test_pipeop_isomap.R: 2025-12-24 17:13:57.767787: Isomap START
> test_pipeop_isomap.R: 2025-12-24 17:13:57.768299: constructing knn graph
> test_pipeop_isomap.R: 2025-12-24 17:13:57.775576: calculating geodesic distances
> test_pipeop_isomap.R: 2025-12-24 17:13:57.788739: Classical Scaling
> test_pipeop_isomap.R: 2025-12-24 17:13:57.805722: Isomap START
> test_pipeop_isomap.R: 2025-12-24 17:13:57.806079: constructing knn graph
> test_pipeop_isomap.R: 2025-12-24 17:13:57.811284: calculating geodesic distances
> test_pipeop_isomap.R: 2025-12-24 17:13:57.824532: Classical Scaling
Saving _problems/test_pipeop_learnerpicvplus-35.R
Saving _problems/test_pipeop_learnerpicvplus-91.R
Saving _problems/test_pipeop_learnerpicvplus-116.R
Saving _problems/test_pipeop_learnerpicvplus-130.R
Saving _problems/test_pipeop_learnerpicvplus-152.R
Saving _problems/test_pipeop_learnercv-11.R
Saving _problems/test_pipeop_learnercv-100.R
Saving _problems/test_pipeop_learnercv-139.R
Saving _problems/test_pipeop_learnercv-152.R
Saving _problems/test_pipeop_learnercv-203.R
Saving _problems/test_pipeop_learnercv-250.R
Saving _problems/test_pipeop_learnercv-278.R
Saving _problems/test_pipeop_learnercv-323.R
Saving _problems/test_pipeop_learnercv-350.R
Saving _problems/test_pipeop_learnercv-387.R
Saving _problems/test_pipeop_learnercv-419.R
Saving _problems/test_pipeop_learnercv-455.R
Saving _problems/test_pipeop_learnercv-493.R
Saving _problems/test_pipeop_learnercv-516.R
Saving _problems/test_pipeop_learnercv-531.R
Saving _problems/test_pipeop_learnercv-557.R
Saving _problems/test_pipeop_learnercv-612.R
Saving _problems/test_pipeop_learnercv-628.R
Saving _problems/test_pipeop_learnercv-671.R
> test_pipeop_nmf.R: [PipeOpNMFstate]
> test_pipeop_nmf.R: [PipeOpNMFstate]
> test_pipeop_task_preproc.R: Training debug_affectcols
> test_pipeop_task_preproc.R: Training debug_affectcols
> test_pipeop_task_preproc.R: Training debug_affectcols
> test_pipeop_task_preproc.R: Training debug_affectcols
> test_pipeop_task_preproc.R: Training debug_affectcols
> test_pipeop_task_preproc.R: Training debug_affectcols
> test_pipeop_task_preproc.R: Training debug_affectcols
> test_pipeop_task_preproc.R: Training debug_affectcols
> test_pipeop_task_preproc.R: Training debug_affectcols
> test_pipeop_task_preproc.R: Training debug_affectcols
> test_pipeop_task_preproc.R: Training debug_affectcols
> test_pipeop_task_preproc.R: Training debug_affectcols
> test_pipeop_task_preproc.R: Training debug_affectcols
> test_pipeop_task_preproc.R: Training debug_affectcols
> test_pipeop_task_preproc.R: Training debug_affectcols
> test_pipeop_task_preproc.R: Training debug_affectcols
> test_pipeop_task_preproc.R: Training debug_affectcols
> test_pipeop_task_preproc.R: Training debug_affectcols
> test_pipeop_task_preproc.R: Training debug_affectcols
> test_pipeop_task_preproc.R: Training debug_affectcols
> test_pipeop_task_preproc.R: Training debug_affectcols
> test_pipeop_task_preproc.R: Training debug_affectcols
Saving _problems/test_pipeop_tunethreshold-7.R
Saving _problems/test_pipeop_tunethreshold-38.R
Saving _problems/test_pipeop_tunethreshold-73.R
Saving _problems/test_pipeop_tunethreshold-101.R
Saving _problems/test_pipeop_tunethreshold-260.R
Saving _problems/test_resample-13.R
Saving _problems/test_usecases-153.R
Saving _problems/test_ppl-73.R
[ FAIL 38 | WARN 2 | SKIP 98 | PASS 12316 ]
══ Skipped tests (98) ══════════════════════════════════════════════════════════
• On CRAN (95): 'test_CnfFormula_simplify.R:6:3', 'test_CnfFormula.R:591:3',
'test_Graph.R:283:3', 'test_PipeOp.R:32:1', 'test_GraphLearner.R:5:3',
'test_GraphLearner.R:221:3', 'test_GraphLearner.R:343:3',
'test_GraphLearner.R:408:3', 'test_GraphLearner.R:571:3',
'test_dictionary.R:7:3', 'test_learner_weightedaverage.R:5:3',
'test_learner_weightedaverage.R:57:3',
'test_learner_weightedaverage.R:105:3',
'test_learner_weightedaverage.R:152:3', 'test_meta.R:39:3',
'test_mlr_graphs_bagging.R:6:3', 'test_mlr_graphs_branching.R:26:3',
'test_mlr_graphs_robustify.R:5:3', 'test_pipeop_adas.R:8:3',
'test_pipeop_blsmote.R:8:3', 'test_pipeop_branch.R:4:3',
'test_pipeop_chunk.R:4:3', 'test_pipeop_classbalancing.R:7:3',
'test_pipeop_boxcox.R:7:3', 'test_pipeop_classweights.R:10:3',
'test_pipeop_collapsefactors.R:6:3', 'test_pipeop_colapply.R:9:3',
'test_pipeop_copy.R:5:3', 'test_pipeop_colroles.R:6:3',
'test_pipeop_decode.R:14:3', 'test_pipeop_encode.R:21:3',
'test_pipeop_encodeimpact.R:11:3', 'test_pipeop_datefeatures.R:10:3',
'test_pipeop_encodepl.R:5:3', 'test_pipeop_encodepl.R:72:3',
'test_pipeop_encodelmer.R:15:3', 'test_pipeop_encodelmer.R:37:3',
'test_pipeop_encodelmer.R:80:3', 'test_pipeop_featureunion.R:9:3',
'test_pipeop_featureunion.R:134:3', 'test_pipeop_filter.R:7:3',
'test_pipeop_fixfactors.R:9:3', 'test_pipeop_histbin.R:7:3',
'test_pipeop_ica.R:7:3', 'test_pipeop_ensemble.R:6:3',
'test_pipeop_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_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_nmf.R:6:3',
'test_pipeop_subsample.R:6:3', 'test_pipeop_targetinvert.R:4:3',
'test_pipeop_spatialsign.R:6:3', '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_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 $train()
Backtrace:
▆
1. └─mlr3::benchmark(benchmark_grid(tasks, learners, rsmp("cv", folds = 2))) at test_ppl.R:73:3
2. └─mlr3:::future_map(...)
3. └─future.apply::future_mapply(...)
4. └─future.apply:::future_xapply(...)
5. └─base::tryCatch(...)
6. └─base (local) tryCatchList(expr, classes, parentenv, handlers)
7. └─base (local) tryCatchOne(...)
8. └─value[[3L]](cond)
9. └─future.apply:::onError(e, futures = fs, debug = debug)
[ FAIL 38 | WARN 2 | SKIP 98 | PASS 12316 ]
Error:
! Test failures.
Execution halted
Flavor: r-devel-windows-x86_64
Version: 0.10.0
Check: examples
Result: ERROR
Running examples in ‘mlr3pipelines-Ex.R’ failed
The error most likely occurred in:
> ### Name: mlr_pipeops_nmf
> ### Title: Non-negative Matrix Factorization
> ### Aliases: mlr_pipeops_nmf PipeOpNMF
>
> ### ** Examples
>
> ## Don't show:
> if (mlr3misc::require_namespaces(c("NMF", "MASS"), quietly = TRUE)) withAutoprint({ # examplesIf
+ ## End(Don't show)
+ ## Don't show:
+ # NMF attaches these packages to search path on load, #929
+ lapply(c("package:Biobase", "package:BiocGenerics", "package:generics"), detach, character.only = TRUE)
+ ## End(Don't show)
+ library("mlr3")
+
+ task = tsk("iris")
+ pop = po("nmf")
+
+ task$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’ [109s/54s]
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.
Saving _problems/test_filter_ensemble-294.R
Saving _problems/test_filter_ensemble-307.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"
Saving _problems/test_pipeop_datefeatures-7.R
Saving _problems/test_pipeop_datefeatures-17.R
> test_pipeop_isomap.R: 2025-12-27 02:11:06.93185: Isomap START
> test_pipeop_isomap.R: 2025-12-27 02:11:06.932111: constructing knn graph
> test_pipeop_isomap.R: 2025-12-27 02:11:06.93524: calculating geodesic distances
> test_pipeop_isomap.R: 2025-12-27 02:11:06.941576: Classical Scaling
> test_pipeop_isomap.R: 2025-12-27 02:11:06.952625: Isomap START
> test_pipeop_isomap.R: 2025-12-27 02:11:06.952749: constructing knn graph
> test_pipeop_isomap.R: 2025-12-27 02:11:06.955141: calculating geodesic distances
> test_pipeop_isomap.R: 2025-12-27 02:11:06.961338: Classical Scaling
> test_pipeop_isomap.R: 2025-12-27 02:11:06.967652: L-Isomap embed START
> test_pipeop_isomap.R: 2025-12-27 02:11:06.967789: constructing knn graph
> test_pipeop_isomap.R: 2025-12-27 02:11:06.972093: calculating geodesic distances
> test_pipeop_isomap.R: 2025-12-27 02:11:06.987202: embedding
> test_pipeop_isomap.R: 2025-12-27 02:11:06.987704: DONE
> test_pipeop_isomap.R: 2025-12-27 02:11:06.993529: L-Isomap embed START
> test_pipeop_isomap.R: 2025-12-27 02:11:06.993641: constructing knn graph
> test_pipeop_isomap.R: 2025-12-27 02:11:06.998371: calculating geodesic distances
> test_pipeop_isomap.R: 2025-12-27 02:11:07.013351: embedding
> test_pipeop_isomap.R: 2025-12-27 02:11:07.013859: DONE
> test_pipeop_isomap.R: 2025-12-27 02:11:07.032976: Isomap START
> test_pipeop_isomap.R: 2025-12-27 02:11:07.033094: constructing knn graph
> test_pipeop_isomap.R: 2025-12-27 02:11:07.037268: calculating geodesic distances
> test_pipeop_isomap.R: 2025-12-27 02:11:07.070664: Classical Scaling
> test_pipeop_isomap.R: 2025-12-27 02:11:07.078531: L-Isomap embed START
> test_pipeop_isomap.R: 2025-12-27 02:11:07.0787: constructing knn graph
> test_pipeop_isomap.R: 2025-12-27 02:11:07.092263: calculating geodesic distances
> test_pipeop_isomap.R: 2025-12-27 02:11:07.170591: embedding
> test_pipeop_isomap.R: 2025-12-27 02:11:07.171695: DONE
> test_pipeop_isomap.R: 2025-12-27 02:11:07.204333: Isomap START
> test_pipeop_isomap.R: 2025-12-27 02:11:07.204459: constructing knn graph
> test_pipeop_isomap.R: 2025-12-27 02:11:07.206733: calculating geodesic distances
> test_pipeop_isomap.R: 2025-12-27 02:11:07.212654: Classical Scaling
> test_pipeop_isomap.R: 2025-12-27 02:11:07.219666: L-Isomap embed START
> test_pipeop_isomap.R: 2025-12-27 02:11:07.219831: constructing knn graph
> test_pipeop_isomap.R: 2025-12-27 02:11:07.224587: calculating geodesic distances
> test_pipeop_isomap.R: 2025-12-27 02:11:07.23954: embedding
> test_pipeop_isomap.R: 2025-12-27 02:11:07.23996: DONE
> test_pipeop_isomap.R: 2025-12-27 02:11:07.271888: Isomap START
> test_pipeop_isomap.R: 2025-12-27 02:11:07.272033: constructing knn graph
> test_pipeop_isomap.R: 2025-12-27 02:11:07.274502: calculating geodesic distances
> test_pipeop_isomap.R: 2025-12-27 02:11:07.280632: Classical Scaling
> test_pipeop_isomap.R: 2025-12-27 02:11:07.499276: L-Isomap embed START
> test_pipeop_isomap.R: 2025-12-27 02:11:07.499474: constructing knn graph
> test_pipeop_isomap.R: 2025-12-27 02:11:07.503749: calculating geodesic distances
> test_pipeop_isomap.R: 2025-12-27 02:11:07.51931: embedding
> test_pipeop_isomap.R: 2025-12-27 02:11:07.519684: DONE
> test_pipeop_isomap.R: 2025-12-27 02:11:07.537951: Isomap START
> test_pipeop_isomap.R: 2025-12-27 02:11:07.538076: constructing knn graph
> test_pipeop_isomap.R: 2025-12-27 02:11:07.540499: calculating geodesic distances
> test_pipeop_isomap.R: 2025-12-27 02:11:07.546629: Classical Scaling
> test_pipeop_isomap.R: 2025-12-27 02:11:07.557687: L-Isomap embed START
> test_pipeop_isomap.R: 2025-12-27 02:11:07.557855: constructing knn graph
> test_pipeop_isomap.R: 2025-12-27 02:11:07.562671: calculating geodesic distances
> test_pipeop_isomap.R: 2025-12-27 02:11:07.577456: embedding
> test_pipeop_isomap.R: 2025-12-27 02:11:07.577816: DONE
> test_pipeop_isomap.R: 2025-12-27 02:11:07.595066: Isomap START
> test_pipeop_isomap.R: 2025-12-27 02:11:07.595205: constructing knn graph
> test_pipeop_isomap.R: 2025-12-27 02:11:07.597343: calculating geodesic distances
> test_pipeop_isomap.R: 2025-12-27 02:11:07.603387: Classical Scaling
> test_pipeop_isomap.R: 2025-12-27 02:11:07.614825: L-Isomap embed START
> test_pipeop_isomap.R: 2025-12-27 02:11:07.614993: constructing knn graph
> test_pipeop_isomap.R: 2025-12-27 02:11:07.619287: calculating geodesic distances
> test_pipeop_isomap.R: 2025-12-27 02:11:07.634129: embedding
> test_pipeop_isomap.R: 2025-12-27 02:11:07.634532: DONE
> test_pipeop_isomap.R: 2025-12-27 02:11:07.652308: Isomap START
> test_pipeop_isomap.R: 2025-12-27 02:11:07.652423: constructing knn graph
> test_pipeop_isomap.R: 2025-12-27 02:11:07.654759: calculating geodesic distances
> test_pipeop_isomap.R: 2025-12-27 02:11:07.660897: Classical Scaling
> test_pipeop_isomap.R: 2025-12-27 02:11:07.672042: L-Isomap embed START
> test_pipeop_isomap.R: 2025-12-27 02:11:07.672196: constructing knn graph
> test_pipeop_isomap.R: 2025-12-27 02:11:07.676471: calculating geodesic distances
> test_pipeop_isomap.R: 2025-12-27 02:11:07.691144: embedding
> test_pipeop_isomap.R: 2025-12-27 02:11:07.691492: DONE
> test_pipeop_isomap.R: 2025-12-27 02:11:07.710621: Isomap START
> test_pipeop_isomap.R: 2025-12-27 02:11:07.710743: constructing knn graph
> test_pipeop_isomap.R: 2025-12-27 02:11:07.713107: calculating geodesic distances
> test_pipeop_isomap.R: 2025-12-27 02:11:07.719172: Classical Scaling
> test_pipeop_isomap.R: 2025-12-27 02:11:07.739481: Isomap START
> test_pipeop_isomap.R: 2025-12-27 02:11:07.739624: constructing knn graph
> test_pipeop_isomap.R: 2025-12-27 02:11:07.742008: calculating geodesic distances
> test_pipeop_isomap.R: 2025-12-27 02:11:07.748026: Classical Scaling
> test_pipeop_isomap.R: 2025-12-27 02:11:07.760748: Isomap START
> test_pipeop_isomap.R: 2025-12-27 02:11:07.760867: constructing knn graph
> test_pipeop_isomap.R: 2025-12-27 02:11:07.762917: calculating geodesic distances
> test_pipeop_isomap.R: 2025-12-27 02:11:07.768997: 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 8 | WARN 0 | SKIP 99 | PASS 13004 ]
══ 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_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_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_ensemble.R:6:3',
'test_pipeop_fixfactors.R:9:3', 'test_pipeop_ica.R:7:3',
'test_pipeop_histbin.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_removeconstants.R:6:3', 'test_pipeop_nmf.R:6:3',
'test_pipeop_renamecolumns.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_spatialsign.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_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 ════════════════════════════════════════════════════════════════
── Failure ('test_filter_ensemble.R:294:3'): FilterEnsemble ignores NA scores from wrapped filters ──
Expected `all(is.nan(permutation_filter$scores[task$feature_names]))` to be TRUE.
Differences:
`actual`: FALSE
`expected`: TRUE
── Failure ('test_filter_ensemble.R:307:3'): FilterEnsemble ignores NA scores from wrapped filters ──
Expected `all.equal(object, expected, check.environment = FALSE, ...)` to be TRUE.
Differences:
`actual` is a character vector ('Mean relative difference: 0.3861214')
`expected` is a logical vector (TRUE)
Backtrace:
▆
1. └─global expect_equal(combined_scores, variance_scores * weights[["variance"]]) at test_filter_ensemble.R:307:3
2. └─testthat::expect_true(...)
── 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 8 | WARN 0 | SKIP 99 | PASS 13004 ]
Error:
! Test failures.
Execution halted
Flavor: r-oldrel-macos-arm64
Version: 0.10.0
Check: tests
Result: ERROR
Running ‘testthat.R’ [6m/12m]
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.
Saving _problems/test_filter_ensemble-294.R
Saving _problems/test_filter_ensemble-307.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:
> 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_datefeatures-7.R
Saving _problems/test_pipeop_datefeatures-17.R
> test_pipeop_isomap.R: 2025-12-26 07:58:39.228122: Isomap START
> test_pipeop_isomap.R: 2025-12-26 07:58:39.230199: constructing knn graph
> test_pipeop_isomap.R: 2025-12-26 07:58:39.289622: calculating geodesic distances
> test_pipeop_isomap.R: 2025-12-26 07:58:39.35125: Classical Scaling
> test_pipeop_isomap.R: 2025-12-26 07:58:39.450023: Isomap START
> test_pipeop_isomap.R: 2025-12-26 07:58:39.450367: constructing knn graph
> test_pipeop_isomap.R: 2025-12-26 07:58:39.487248: calculating geodesic distances
> test_pipeop_isomap.R: 2025-12-26 07:58:39.535311: Classical Scaling
> test_pipeop_isomap.R: 2025-12-26 07:58:39.612894: L-Isomap embed START
> test_pipeop_isomap.R: 2025-12-26 07:58:39.613359: constructing knn graph
> test_pipeop_isomap.R: 2025-12-26 07:58:39.669386: calculating geodesic distances
> test_pipeop_isomap.R: 2025-12-26 07:58:39.788385: embedding
> test_pipeop_isomap.R: 2025-12-26 07:58:39.789663: DONE
> test_pipeop_isomap.R: 2025-12-26 07:58:39.842095: L-Isomap embed START
> test_pipeop_isomap.R: 2025-12-26 07:58:39.842453: constructing knn graph
> test_pipeop_isomap.R: 2025-12-26 07:58:39.904606: calculating geodesic distances
> test_pipeop_isomap.R: 2025-12-26 07:58:40.025221: embedding
> test_pipeop_isomap.R: 2025-12-26 07:58:40.026544: DONE
> test_pipeop_isomap.R: 2025-12-26 07:58:40.247152: Isomap START
> test_pipeop_isomap.R: 2025-12-26 07:58:40.247515: constructing knn graph
> test_pipeop_isomap.R: 2025-12-26 07:58:40.306311: calculating geodesic distances
> test_pipeop_isomap.R: 2025-12-26 07:58:40.606394: Classical Scaling
> test_pipeop_isomap.R: 2025-12-26 07:58:40.67189: L-Isomap embed START
> test_pipeop_isomap.R: 2025-12-26 07:58:40.672338: constructing knn graph
> test_pipeop_isomap.R: 2025-12-26 07:58:40.7522: calculating geodesic distances
> test_pipeop_isomap.R: 2025-12-26 07:58:41.563292: embedding
> test_pipeop_isomap.R: 2025-12-26 07:58:41.567514: DONE
> test_pipeop_isomap.R: 2025-12-26 07:58:42.073169: Isomap START
> test_pipeop_isomap.R: 2025-12-26 07:58:42.073571: constructing knn graph
> test_pipeop_isomap.R: 2025-12-26 07:58:42.089602: calculating geodesic distances
> test_pipeop_isomap.R: 2025-12-26 07:58:42.181816: Classical Scaling
> test_pipeop_isomap.R: 2025-12-26 07:58:42.267811: L-Isomap embed START
> test_pipeop_isomap.R: 2025-12-26 07:58:42.269632: constructing knn graph
> test_pipeop_isomap.R: 2025-12-26 07:58:42.342429: calculating geodesic distances
> test_pipeop_isomap.R: 2025-12-26 07:58:42.492479: embedding
> test_pipeop_isomap.R: 2025-12-26 07:58:42.553471: DONE
> test_pipeop_isomap.R: 2025-12-26 07:58:43.016149: Isomap START
> test_pipeop_isomap.R: 2025-12-26 07:58:43.017554: constructing knn graph
> test_pipeop_isomap.R: 2025-12-26 07:58:43.036904: calculating geodesic distances
> test_pipeop_isomap.R: 2025-12-26 07:58:43.110233: Classical Scaling
> test_pipeop_isomap.R: 2025-12-26 07:58:43.248911: L-Isomap embed START
> test_pipeop_isomap.R: 2025-12-26 07:58:43.249508: constructing knn graph
> test_pipeop_isomap.R: 2025-12-26 07:58:43.346161: calculating geodesic distances
> test_pipeop_isomap.R: 2025-12-26 07:58:43.489057: embedding
> test_pipeop_isomap.R: 2025-12-26 07:58:43.49109: DONE
> test_pipeop_isomap.R: 2025-12-26 07:58:43.77888: Isomap START
> test_pipeop_isomap.R: 2025-12-26 07:58:43.779187: constructing knn graph
> test_pipeop_isomap.R: 2025-12-26 07:58:43.790617: calculating geodesic distances
> test_pipeop_isomap.R: 2025-12-26 07:58:43.866754: Classical Scaling
> test_pipeop_isomap.R: 2025-12-26 07:58:43.998219: L-Isomap embed START
> test_pipeop_isomap.R: 2025-12-26 07:58:43.998661: constructing knn graph
> test_pipeop_isomap.R: 2025-12-26 07:58:44.053857: calculating geodesic distances
> test_pipeop_isomap.R: 2025-12-26 07:58:44.178122: embedding
> test_pipeop_isomap.R: 2025-12-26 07:58:44.179539: DONE
> test_pipeop_isomap.R: 2025-12-26 07:58:44.360209: Isomap START
> test_pipeop_isomap.R: 2025-12-26 07:58:44.360518: constructing knn graph
> test_pipeop_isomap.R: 2025-12-26 07:58:44.389645: calculating geodesic distances
> test_pipeop_isomap.R: 2025-12-26 07:58:44.438829: Classical Scaling
> test_pipeop_isomap.R: 2025-12-26 07:58:44.566579: L-Isomap embed START
> test_pipeop_isomap.R: 2025-12-26 07:58:44.568622: constructing knn graph
> test_pipeop_isomap.R: 2025-12-26 07:58:44.61764: calculating geodesic distances
> test_pipeop_isomap.R: 2025-12-26 07:58:44.773757: embedding
> test_pipeop_isomap.R: 2025-12-26 07:58:44.77544: DONE
> test_pipeop_isomap.R: 2025-12-26 07:58:44.979089: Isomap START
> test_pipeop_isomap.R: 2025-12-26 07:58:44.979435: constructing knn graph
> test_pipeop_isomap.R: 2025-12-26 07:58:44.987892: calculating geodesic distances
> test_pipeop_isomap.R: 2025-12-26 07:58:45.049183: Classical Scaling
> test_pipeop_isomap.R: 2025-12-26 07:58:45.171001: L-Isomap embed START
> test_pipeop_isomap.R: 2025-12-26 07:58:45.171447: constructing knn graph
> test_pipeop_isomap.R: 2025-12-26 07:58:45.203877: calculating geodesic distances
> test_pipeop_isomap.R: 2025-12-26 07:58:45.316404: embedding
> test_pipeop_isomap.R: 2025-12-26 07:58:45.343556: DONE
> test_pipeop_isomap.R: 2025-12-26 07:58:45.604159: Isomap START
> test_pipeop_isomap.R: 2025-12-26 07:58:45.604479: constructing knn graph
> test_pipeop_isomap.R: 2025-12-26 07:58:45.64877: calculating geodesic distances
> test_pipeop_isomap.R: 2025-12-26 07:58:45.679223: Classical Scaling
> test_pipeop_isomap.R: 2025-12-26 07:58:45.917333: Isomap START
> test_pipeop_isomap.R: 2025-12-26 07:58:45.918009: constructing knn graph
> test_pipeop_isomap.R: 2025-12-26 07:58:45.956602: calculating geodesic distances
> test_pipeop_isomap.R: 2025-12-26 07:58:45.993193: Classical Scaling
> test_pipeop_isomap.R: 2025-12-26 07:58:46.050829: Isomap START
> test_pipeop_isomap.R: 2025-12-26 07:58:46.051133: constructing knn graph
> test_pipeop_isomap.R: 2025-12-26 07:58:46.087973: calculating geodesic distances
> test_pipeop_isomap.R: 2025-12-26 07:58:46.140666: 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:
> 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 8 | WARN 0 | SKIP 99 | PASS 13004 ]
══ 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_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_encodepl.R:5:3',
'test_pipeop_encodepl.R:72:3', 'test_pipeop_encodelmer.R:15:3',
'test_pipeop_encodelmer.R:37:3', 'test_pipeop_encodelmer.R:80:3',
'test_pipeop_featureunion.R:9:3', 'test_pipeop_featureunion.R:134:3',
'test_pipeop_filter.R:7:3', 'test_pipeop_fixfactors.R:9:3',
'test_pipeop_histbin.R:7:3', 'test_pipeop_ica.R:7:3',
'test_pipeop_ensemble.R:6:3', 'test_pipeop_impute.R:4:3',
'test_pipeop_imputelearner.R:43:3', 'test_pipeop_isomap.R:10:3',
'test_pipeop_kernelpca.R:9:3', 'test_pipeop_learner.R:17:3',
'test_pipeop_info.R:6:3', 'test_pipeop_learnerpicvplus.R:163:3',
'test_pipeop_missind.R:6:3', 'test_pipeop_modelmatrix.R:7:3',
'test_pipeop_multiplicityexply.R:9:3', 'test_pipeop_multiplicityimply.R:9:3',
'test_pipeop_mutate.R:9:3', 'test_pipeop_nearmiss.R:7:3',
'test_pipeop_learnercv.R:31:3', 'test_pipeop_ovr.R:9:3',
'test_pipeop_ovr.R:48:3', 'test_pipeop_pca.R:8:3',
'test_pipeop_proxy.R:14:3', 'test_pipeop_nmf.R:6:3',
'test_pipeop_quantilebin.R:5:3', 'test_pipeop_randomprojection.R:6:3',
'test_pipeop_randomresponse.R:5:3', 'test_pipeop_renamecolumns.R:6:3',
'test_pipeop_removeconstants.R:6:3', 'test_pipeop_replicate.R:9:3',
'test_pipeop_scale.R:6:3', 'test_pipeop_scale.R:10:3',
'test_pipeop_scalemaxabs.R:6:3', 'test_pipeop_scalerange.R:7:3',
'test_pipeop_select.R:9:3', 'test_pipeop_smote.R:10:3',
'test_pipeop_smotenc.R:8:3', 'test_pipeop_rowapply.R:6:3',
'test_pipeop_subsample.R:6:3', 'test_pipeop_targetinvert.R:4:3',
'test_pipeop_targetmutate.R:5:3', 'test_pipeop_targettrafo.R:4:3',
'test_pipeop_targettrafoscalerange.R:5:3', 'test_pipeop_task_preproc.R:4:3',
'test_pipeop_task_preproc.R:14:3', 'test_pipeop_spatialsign.R:6:3',
'test_pipeop_tomek.R:7:3', 'test_pipeop_textvectorizer.R:37:3',
'test_pipeop_textvectorizer.R:186:3', 'test_pipeop_unbranch.R:10:3',
'test_pipeop_updatetarget.R:89:3', 'test_pipeop_vtreat.R:9:3',
'test_pipeop_yeojohnson.R:7:3', 'test_pipeop_tunethreshold.R:111:3',
'test_pipeop_tunethreshold.R:191:3', 'test_typecheck.R:188:3'
• Skipping (1): 'test_GraphLearner.R:1278:3'
• empty test (3): 'test_pipeop_isomap.R:111:1', 'test_pipeop_missind.R:101:1',
'test_ppl.R:61:1'
══ Failed tests ════════════════════════════════════════════════════════════════
── Failure ('test_filter_ensemble.R:294:3'): FilterEnsemble ignores NA scores from wrapped filters ──
Expected `all(is.nan(permutation_filter$scores[task$feature_names]))` to be TRUE.
Differences:
`actual`: FALSE
`expected`: TRUE
── Failure ('test_filter_ensemble.R:307:3'): FilterEnsemble ignores NA scores from wrapped filters ──
Expected `all.equal(object, expected, check.environment = FALSE, ...)` to be TRUE.
Differences:
`actual` is a character vector ('Mean relative difference: 0.3518106')
`expected` is a logical vector (TRUE)
Backtrace:
▆
1. └─global expect_equal(combined_scores, variance_scores * weights[["variance"]]) at test_filter_ensemble.R:307:3
2. └─testthat::expect_true(...)
── 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 8 | WARN 0 | SKIP 99 | PASS 13004 ]
Error:
! Test failures.
Execution halted
Flavor: r-oldrel-macos-x86_64
Version: 0.10.0
Check: tests
Result: ERROR
Running 'testthat.R' [269s]
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:
> 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_datefeatures-7.R
Saving _problems/test_pipeop_datefeatures-17.R
> test_pipeop_isomap.R: 2025-12-24 19:44:00.8847: Isomap START
> test_pipeop_isomap.R: 2025-12-24 19:44:00.885721: constructing knn graph
> test_pipeop_isomap.R: 2025-12-24 19:44:00.89953: calculating geodesic distances
> test_pipeop_isomap.R: 2025-12-24 19:44:00.922647: Classical Scaling
> test_pipeop_isomap.R: 2025-12-24 19:44:01.005968: Isomap START
> test_pipeop_isomap.R: 2025-12-24 19:44:01.006631: constructing knn graph
> test_pipeop_isomap.R: 2025-12-24 19:44:01.017267: calculating geodesic distances
> test_pipeop_isomap.R: 2025-12-24 19:44:01.039418: Classical Scaling
> test_pipeop_isomap.R: 2025-12-24 19:44:01.083344: L-Isomap embed START
> test_pipeop_isomap.R: 2025-12-24 19:44:01.084265: constructing knn graph
> test_pipeop_isomap.R: 2025-12-24 19:44:01.106502: calculating geodesic distances
> test_pipeop_isomap.R: 2025-12-24 19:44:01.160127: embedding
> test_pipeop_isomap.R: 2025-12-24 19:44:01.16247: DONE
> test_pipeop_isomap.R: 2025-12-24 19:44:01.20625: L-Isomap embed START
> test_pipeop_isomap.R: 2025-12-24 19:44:01.206883: constructing knn graph
> test_pipeop_isomap.R: 2025-12-24 19:44:01.231468: calculating geodesic distances
> test_pipeop_isomap.R: 2025-12-24 19:44:01.281787: embedding
> test_pipeop_isomap.R: 2025-12-24 19:44:01.283685: DONE
> test_pipeop_isomap.R: 2025-12-24 19:44:01.407489: Isomap START
> test_pipeop_isomap.R: 2025-12-24 19:44:01.408145: constructing knn graph
> test_pipeop_isomap.R: 2025-12-24 19:44:01.432668: calculating geodesic distances
> test_pipeop_isomap.R: 2025-12-24 19:44:01.549787: Classical Scaling
> test_pipeop_isomap.R: 2025-12-24 19:44:01.605741: L-Isomap embed START
> test_pipeop_isomap.R: 2025-12-24 19:44:01.606885: constructing knn graph
> test_pipeop_isomap.R: 2025-12-24 19:44:01.659569: calculating geodesic distances
> test_pipeop_isomap.R: 2025-12-24 19:44:01.889314: embedding
> test_pipeop_isomap.R: 2025-12-24 19:44:01.896023: DONE
> test_pipeop_isomap.R: 2025-12-24 19:44:02.164539: Isomap START
> test_pipeop_isomap.R: 2025-12-24 19:44:02.165331: constructing knn graph
> test_pipeop_isomap.R: 2025-12-24 19:44:02.173079: calculating geodesic distances
> test_pipeop_isomap.R: 2025-12-24 19:44:02.196576: Classical Scaling
> test_pipeop_isomap.R: 2025-12-24 19:44:02.248705: L-Isomap embed START
> test_pipeop_isomap.R: 2025-12-24 19:44:02.249781: constructing knn graph
> test_pipeop_isomap.R: 2025-12-24 19:44:02.270687: calculating geodesic distances
> test_pipeop_isomap.R: 2025-12-24 19:44:02.322682: embedding
> test_pipeop_isomap.R: 2025-12-24 19:44:02.324615: DONE
> test_pipeop_isomap.R: 2025-12-24 19:44:02.552554: Isomap START
> test_pipeop_isomap.R: 2025-12-24 19:44:02.553231: constructing knn graph
> test_pipeop_isomap.R: 2025-12-24 19:44:02.564091: calculating geodesic distances
> test_pipeop_isomap.R: 2025-12-24 19:44:02.585377: Classical Scaling
> test_pipeop_isomap.R: 2025-12-24 19:44:02.65997: L-Isomap embed START
> test_pipeop_isomap.R: 2025-12-24 19:44:02.661195: constructing knn graph
> test_pipeop_isomap.R: 2025-12-24 19:44:02.684129: calculating geodesic distances
> test_pipeop_isomap.R: 2025-12-24 19:44:02.736933: embedding
> test_pipeop_isomap.R: 2025-12-24 19:44:02.738703: DONE
> test_pipeop_isomap.R: 2025-12-24 19:44:02.839728: Isomap START
> test_pipeop_isomap.R: 2025-12-24 19:44:02.840493: constructing knn graph
> test_pipeop_isomap.R: 2025-12-24 19:44:02.851464: calculating geodesic distances
> test_pipeop_isomap.R: 2025-12-24 19:44:02.873809: Classical Scaling
> test_pipeop_isomap.R: 2025-12-24 19:44:02.947746: L-Isomap embed START
> test_pipeop_isomap.R: 2025-12-24 19:44:02.948878: constructing knn graph
> test_pipeop_isomap.R: 2025-12-24 19:44:02.976366: calculating geodesic distances
> test_pipeop_isomap.R: 2025-12-24 19:44:03.025596: embedding
> test_pipeop_isomap.R: 2025-12-24 19:44:03.027969: DONE
> test_pipeop_isomap.R: 2025-12-24 19:44:03.17419: Isomap START
> test_pipeop_isomap.R: 2025-12-24 19:44:03.174806: constructing knn graph
> test_pipeop_isomap.R: 2025-12-24 19:44:03.184778: calculating geodesic distances
> test_pipeop_isomap.R: 2025-12-24 19:44:03.206519: Classical Scaling
> test_pipeop_isomap.R: 2025-12-24 19:44:03.272558: L-Isomap embed START
> test_pipeop_isomap.R: 2025-12-24 19:44:03.273449: constructing knn graph
> test_pipeop_isomap.R: 2025-12-24 19:44:03.292544: calculating geodesic distances
> test_pipeop_isomap.R: 2025-12-24 19:44:03.340467: embedding
> test_pipeop_isomap.R: 2025-12-24 19:44:03.342014: DONE
> test_pipeop_isomap.R: 2025-12-24 19:44:03.456471: Isomap START
> test_pipeop_isomap.R: 2025-12-24 19:44:03.457097: constructing knn graph
> test_pipeop_isomap.R: 2025-12-24 19:44:03.467468: calculating geodesic distances
> test_pipeop_isomap.R: 2025-12-24 19:44:03.48764: Classical Scaling
> test_pipeop_isomap.R: 2025-12-24 19:44:03.561047: L-Isomap embed START
> test_pipeop_isomap.R: 2025-12-24 19:44:03.562372: constructing knn graph
> test_pipeop_isomap.R: 2025-12-24 19:44:03.583107: calculating geodesic distances
> test_pipeop_isomap.R: 2025-12-24 19:44:03.631105: embedding
> test_pipeop_isomap.R: 2025-12-24 19:44:03.632928: DONE
> test_pipeop_isomap.R: 2025-12-24 19:44:03.761525: Isomap START
> test_pipeop_isomap.R: 2025-12-24 19:44:03.762171: constructing knn graph
> test_pipeop_isomap.R: 2025-12-24 19:44:03.772532: calculating geodesic distances
> test_pipeop_isomap.R: 2025-12-24 19:44:03.794509: Classical Scaling
> test_pipeop_isomap.R: 2025-12-24 19:44:03.906476: Isomap START
> test_pipeop_isomap.R: 2025-12-24 19:44:03.90705: constructing knn graph
> test_pipeop_isomap.R: 2025-12-24 19:44:03.917833: calculating geodesic distances
> test_pipeop_isomap.R: 2025-12-24 19:44:03.939201: Classical Scaling
> test_pipeop_isomap.R: 2025-12-24 19:44:04.000915: Isomap START
> test_pipeop_isomap.R: 2025-12-24 19:44:04.001602: constructing knn graph
> test_pipeop_isomap.R: 2025-12-24 19:44:04.011507: calculating geodesic distances
> test_pipeop_isomap.R: 2025-12-24 19:44:04.034371: Classical Scaling
Saving _problems/test_pipeop_nmf-45.R
Saving _problems/test_pipeop_nmf-73.R
Saving _problems/test_pipeop_nmf-93.R
Saving _problems/test_pipeop_nmf-98.R
> test_pipeop_task_preproc.R: Training debug_affectcols
> test_pipeop_task_preproc.R: Training debug_affectcols
> test_pipeop_task_preproc.R: Training debug_affectcols
> test_pipeop_task_preproc.R: Training debug_affectcols
> test_pipeop_task_preproc.R: Training debug_affectcols
> test_pipeop_task_preproc.R: Training debug_affectcols
> test_pipeop_task_preproc.R: Training debug_affectcols
> test_pipeop_task_preproc.R: Training debug_affectcols
> test_pipeop_task_preproc.R: Training debug_affectcols
> test_pipeop_task_preproc.R: Training debug_affectcols
> test_pipeop_task_preproc.R: Training debug_affectcols
> test_pipeop_task_preproc.R: Training debug_affectcols
> test_pipeop_task_preproc.R: Training debug_affectcols
> test_pipeop_task_preproc.R: Training debug_affectcols
> test_pipeop_task_preproc.R: Training debug_affectcols
> test_pipeop_task_preproc.R: Training debug_affectcols
> test_pipeop_task_preproc.R: Training debug_affectcols
> test_pipeop_task_preproc.R: Training debug_affectcols
> test_pipeop_task_preproc.R: Training debug_affectcols
> test_pipeop_task_preproc.R: Training debug_affectcols
> test_pipeop_task_preproc.R: Training debug_affectcols
> test_pipeop_task_preproc.R: Training debug_affectcols
> test_pipeop_tunethreshold.R: OptimInstanceSingleCrit is deprecated. Use OptimInstanceBatchSingleCrit instead.
> test_pipeop_tunethreshold.R: OptimInstanceSingleCrit is deprecated. Use OptimInstanceBatchSingleCrit instead.
> test_pipeop_tunethreshold.R: OptimInstanceSingleCrit is deprecated. Use OptimInstanceBatchSingleCrit instead.
> test_pipeop_tunethreshold.R: OptimInstanceSingleCrit is deprecated. Use OptimInstanceBatchSingleCrit instead.
> test_pipeop_tunethreshold.R: OptimInstanceSingleCrit is deprecated. Use OptimInstanceBatchSingleCrit instead.
> test_pipeop_tunethreshold.R: OptimInstanceSingleCrit is deprecated. Use OptimInstanceBatchSingleCrit instead.
> test_pipeop_tunethreshold.R: OptimInstanceSingleCrit is deprecated. Use OptimInstanceBatchSingleCrit instead.
[ FAIL 6 | WARN 0 | SKIP 99 | PASS 13006 ]
══ Skipped tests (99) ══════════════════════════════════════════════════════════
• On CRAN (95): 'test_CnfFormula_simplify.R:6:3', 'test_CnfFormula.R:591:3',
'test_Graph.R:283:3', 'test_PipeOp.R:32:1', 'test_GraphLearner.R:5:3',
'test_GraphLearner.R:221:3', 'test_GraphLearner.R:343:3',
'test_GraphLearner.R:408:3', 'test_GraphLearner.R:571:3',
'test_dictionary.R:7:3', 'test_learner_weightedaverage.R:5:3',
'test_learner_weightedaverage.R:57:3',
'test_learner_weightedaverage.R:105:3',
'test_learner_weightedaverage.R:152:3', 'test_meta.R:39:3',
'test_mlr_graphs_branching.R:26:3', 'test_mlr_graphs_bagging.R:6:3',
'test_mlr_graphs_robustify.R:5:3', 'test_pipeop_adas.R:8:3',
'test_pipeop_blsmote.R:8:3', 'test_pipeop_branch.R:4:3',
'test_pipeop_chunk.R:4:3', 'test_pipeop_classbalancing.R:7:3',
'test_pipeop_boxcox.R:7:3', 'test_pipeop_classweights.R:10:3',
'test_pipeop_collapsefactors.R:6:3', 'test_pipeop_colapply.R:9:3',
'test_pipeop_copy.R:5:3', 'test_pipeop_colroles.R:6:3',
'test_pipeop_decode.R:14:3', 'test_pipeop_encode.R:21:3',
'test_pipeop_encodeimpact.R:11:3', 'test_pipeop_encodepl.R:5:3',
'test_pipeop_encodepl.R:72:3', 'test_pipeop_encodelmer.R:15:3',
'test_pipeop_encodelmer.R:37:3', 'test_pipeop_encodelmer.R:80:3',
'test_pipeop_featureunion.R:9:3', 'test_pipeop_featureunion.R:134:3',
'test_pipeop_filter.R:7:3', 'test_pipeop_fixfactors.R:9:3',
'test_pipeop_histbin.R:7:3', 'test_pipeop_ica.R:7:3',
'test_pipeop_ensemble.R:6:3', 'test_pipeop_impute.R:4:3',
'test_pipeop_imputelearner.R:43:3', 'test_pipeop_isomap.R:10:3',
'test_pipeop_kernelpca.R:9:3', 'test_pipeop_learner.R:17:3',
'test_pipeop_info.R:6:3', 'test_pipeop_learnerpicvplus.R:163:3',
'test_pipeop_missind.R:6:3', 'test_pipeop_modelmatrix.R:7:3',
'test_pipeop_multiplicityexply.R:9:3', 'test_pipeop_learnercv.R:31:3',
'test_pipeop_mutate.R:9:3', 'test_pipeop_nearmiss.R:7:3',
'test_pipeop_multiplicityimply.R:9:3', 'test_pipeop_ovr.R:9:3',
'test_pipeop_ovr.R:48:3', 'test_pipeop_pca.R:8:3',
'test_pipeop_proxy.R:14:3', 'test_pipeop_quantilebin.R:5:3',
'test_pipeop_randomprojection.R:6:3', 'test_pipeop_randomresponse.R:5:3',
'test_pipeop_removeconstants.R:6:3', 'test_pipeop_renamecolumns.R:6:3',
'test_pipeop_replicate.R:9:3', 'test_pipeop_rowapply.R:6:3',
'test_pipeop_scale.R:6:3', 'test_pipeop_scale.R:10:3',
'test_pipeop_scalemaxabs.R:6:3', 'test_pipeop_scalerange.R:7:3',
'test_pipeop_nmf.R:6:3', 'test_pipeop_select.R:9:3',
'test_pipeop_smote.R:10:3', '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-windows-x86_64
Current CRAN status: OK: 13
Current CRAN status: OK: 13