Last updated on 2025-12-23 21:51:34 CET.
| Package | ERROR | NOTE | OK |
|---|---|---|---|
| miesmuschel | 4 | 9 | |
| mlr | 4 | 2 | 7 |
| mlr3pipelines | 7 | 6 | |
| paradox | 13 | ||
| ParamHelpers | 13 |
Current CRAN status: ERROR: 4, OK: 9
Version: 0.0.4-3
Check: tests
Result: ERROR
Running ‘tinytest.R’ [2s/311s]
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=152326 on localhost:11272 at 04:23:52.050
starting worker pid=152327 on localhost:11272 at 04:23:52.071
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;34m6.1s<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;34m3.5s<1b>[0m
OptimInstanceSingleCrit is deprecated. Use OptimInstanceBatchSingleCrit instead.
test_OptimizerMies.R.......... 54 tests <1b>[0;32mOK<1b>[0m <1b>[0;34m21.9s<1b>[0m
test_ParamSetShadow.R......... 41 tests <1b>[0;32mOK<1b>[0m <1b>[0;34m1.1s<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;34m16.7s<1b>[0m
test_TerminatorBudget.R....... 31 tests <1b>[0;32mOK<1b>[0m <1b>[0;34m6.8s<1b>[0m
test_TerminatorGenerationPerfReached.R 0 tests <1b>[0;36m6ms<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;34m3.8s<1b>[0m
test_mutator_erase.R.......... 41 tests <1b>[0;32mOK<1b>[0m <1b>[0;34m45.7s<1b>[0m
test_mutator_gauss.R.......... 62 tests <1b>[0;32mOK<1b>[0m <1b>[0;34m11.3s<1b>[0m
test_mutator_maybe.R.......... 90 tests <1b>[0;32mOK<1b>[0m <1b>[0;34m9.1s<1b>[0m
test_mutator_null.R........... 24 tests <1b>[0;32mOK<1b>[0m <1b>[0;34m1.3s<1b>[0m
test_mutator_proxy.R.......... 69 tests <1b>[0;32mOK<1b>[0m <1b>[0;34m19.0s<1b>[0m
test_mutator_sequential.R..... 65 tests <1b>[0;32mOK<1b>[0m <1b>[0;34m5.0s<1b>[0m
test_mutator_unif.R........... 41 tests <1b>[0;32mOK<1b>[0m <1b>[0;34m57.9s<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;34m12.8s<1b>[0m
test_recombinator_maybe.R..... 69 tests <1b>[0;32mOK<1b>[0m <1b>[0;34m12.0s<1b>[0m
test_recombinator_null.R...... 37 tests <1b>[0;32mOK<1b>[0m <1b>[0;34m2.1s<1b>[0m
test_recombinator_proxy.R..... 102 tests <1b>[0;32mOK<1b>[0m <1b>[0;34m11.3s<1b>[0m
test_recombinator_sbx.R....... 29 tests <1b>[0;32mOK<1b>[0m <1b>[0;34m2.0s<1b>[0m
test_recombinator_xounif.R.... 60 tests <1b>[0;32mOK<1b>[0m <1b>[0;34m7.9s<1b>[0m
test_scalor_one.R............. 35 tests <1b>[0;32mOK<1b>[0m <1b>[0;34m1.8s<1b>[0m
test_selector_best.R.......... 147 tests <1b>[0;32mOK<1b>[0m <1b>[0;34m17.9s<1b>[0m
test_selector_proxy.R......... 114 tests <1b>[0;32mOK<1b>[0m <1b>[0;34m15.7s<1b>[0m
test_selector_random.R........ 106 tests <1b>[0;32mOK<1b>[0m <1b>[0;34m4.1s<1b>[0m
test_shortforms.R............. 20 tests <1b>[0;32mOK<1b>[0m <1b>[0;34m42.4s<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:11272)
Execution halted
Warning message:
closing unused connection 3 (->localhost:11272)
Flavor: r-devel-linux-x86_64-debian-clang
Version: 0.0.4-3
Check: tests
Result: ERROR
Running ‘tinytest.R’ [2s/158s]
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=2108108 on localhost:11909 at 17:12:37.742
starting worker pid=2108109 on localhost:11909 at 17:12:37.901
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.
OptimInstanceMultiCrit is deprecated. Use OptimInstanceBatchMultiCrit instead.
OptimInstanceSingleCrit is deprecated. Use OptimInstanceBatchSingleCrit instead.
test_mies_survival_comma.R.... 46 tests <1b>[0;32mOK<1b>[0m <1b>[0;34m3.3s<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;34m1.7s<1b>[0m
test_mutator_cmpmaybe.R....... 93 tests <1b>[0;32mOK<1b>[0m <1b>[0;34m7.3s<1b>[0m
OptimInstanceSingleCrit is deprecated. Use OptimInstanceBatchSingleCrit instead.
test_OptimizerMies.R.......... 54 tests <1b>[0;32mOK<1b>[0m <1b>[0;34m16.0s<1b>[0m
test_ParamSetShadow.R......... 41 tests <1b>[0;32mOK<1b>[0m <1b>[0;34m0.6s<1b>[0m
OptimInstanceSingleCrit is deprecated. Use OptimInstanceBatchSingleCrit instead.
OptimInstanceMultiCrit is deprecated. Use OptimInstanceBatchMultiCrit instead.
OptimInstanceSingleCrit is deprecated. Use OptimInstanceBatchSingleCrit instead.
test_TerminatorBudget.R....... 31 tests <1b>[0;32mOK<1b>[0m <1b>[0;34m4.7s<1b>[0m
test_TerminatorGenerationPerfReached.R 0 tests <1b>[0;36m1ms<1b>[0m
test_TerminatorGenerationStagnation.R 0 tests <1b>[0;36m1ms<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;34m2.5s<1b>[0m
test_mutator_erase.R.......... 41 tests <1b>[0;32mOK<1b>[0m <1b>[0;34m22.2s<1b>[0m
test_mutator_gauss.R.......... 62 tests <1b>[0;32mOK<1b>[0m <1b>[0;34m5.2s<1b>[0m
test_mutator_maybe.R.......... 90 tests <1b>[0;32mOK<1b>[0m <1b>[0;34m4.7s<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;34m10.0s<1b>[0m
test_mutator_sequential.R..... 65 tests <1b>[0;32mOK<1b>[0m <1b>[0;34m2.7s<1b>[0m
test_mutator_unif.R........... 41 tests <1b>[0;32mOK<1b>[0m <1b>[0;34m26.4s<1b>[0m
test_operator.R............... 40 tests <1b>[0;32mOK<1b>[0m <1b>[0;34m0.2s<1b>[0m
test_operatorcombination.R.... 211 tests <1b>[0;32mOK<1b>[0m <1b>[0;34m6.7s<1b>[0m
test_recombinator_maybe.R..... 69 tests <1b>[0;32mOK<1b>[0m <1b>[0;34m7.1s<1b>[0m
test_recombinator_null.R...... 37 tests <1b>[0;32mOK<1b>[0m <1b>[0;34m2.6s<1b>[0m
test_recombinator_proxy.R..... 102 tests <1b>[0;32mOK<1b>[0m <1b>[0;34m5.9s<1b>[0m
test_recombinator_sbx.R....... 29 tests <1b>[0;32mOK<1b>[0m <1b>[0;34m0.9s<1b>[0m
test_recombinator_xounif.R.... 60 tests <1b>[0;32mOK<1b>[0m <1b>[0;34m5.4s<1b>[0m
test_scalor_one.R............. 35 tests <1b>[0;32mOK<1b>[0m <1b>[0;34m0.9s<1b>[0m
test_selector_best.R.......... 147 tests <1b>[0;32mOK<1b>[0m <1b>[0;34m9.9s<1b>[0m
test_selector_proxy.R......... 114 tests <1b>[0;32mOK<1b>[0m <1b>[0;34m6.8s<1b>[0m
test_selector_random.R........ 106 tests <1b>[0;32mOK<1b>[0m <1b>[0;34m3.4s<1b>[0m
test_shortforms.R............. 20 tests <1b>[0;32mOK<1b>[0m <1b>[0;34m19.1s<1b>[0m
test_utils.R.................. 5 tests <1b>[0;32mOK<1b>[0m <1b>[0;34m0.4s<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:11909)
Execution halted
Warning message:
closing unused connection 3 (->localhost:11909)
Flavor: r-devel-linux-x86_64-debian-gcc
Version: 0.0.4-3
Check: tests
Result: ERROR
Running ‘tinytest.R’ [0m/11m]
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=1567806 on localhost:11424 at 17:46:40.349
starting worker pid=1567807 on localhost:11424 at 17:46:40.531
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;34m16.5s<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;34m9.0s<1b>[0m
OptimInstanceSingleCrit is deprecated. Use OptimInstanceBatchSingleCrit instead.
test_OptimizerMies.R.......... 54 tests <1b>[0;32mOK<1b>[0m <1b>[0;34m55.1s<1b>[0m
test_ParamSetShadow.R......... 41 tests <1b>[0;32mOK<1b>[0m <1b>[0;34m2.2s<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;34m39.3s<1b>[0m
test_TerminatorBudget.R....... 31 tests <1b>[0;32mOK<1b>[0m <1b>[0;34m14.6s<1b>[0m
test_TerminatorGenerationPerfReached.R 0 tests <1b>[0;36m6ms<1b>[0m
test_TerminatorGenerationStagnation.R 0 tests <1b>[0;36m6ms<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;34m7.6s<1b>[0m
test_mutator_erase.R.......... 41 tests <1b>[0;32mOK<1b>[0m <1b>[0;34m1.9s<1b>[0m
test_mutator_gauss.R.......... 62 tests <1b>[0;32mOK<1b>[0m <1b>[0;34m23.0s<1b>[0m
test_mutator_maybe.R.......... 90 tests <1b>[0;32mOK<1b>[0m <1b>[0;34m21.7s<1b>[0m
test_mutator_null.R........... 24 tests <1b>[0;32mOK<1b>[0m <1b>[0;34m2.9s<1b>[0m
test_mutator_proxy.R.......... 69 tests <1b>[0;32mOK<1b>[0m <1b>[0;34m36.8s<1b>[0m
test_mutator_sequential.R..... 65 tests <1b>[0;32mOK<1b>[0m <1b>[0;34m7.4s<1b>[0m
test_mutator_unif.R........... 41 tests <1b>[0;32mOK<1b>[0m <1b>[0;34m2.2s<1b>[0m
test_operator.R............... 40 tests <1b>[0;32mOK<1b>[0m <1b>[0;34m1.1s<1b>[0m
test_operatorcombination.R.... 211 tests <1b>[0;32mOK<1b>[0m <1b>[0;34m31.1s<1b>[0m
test_recombinator_maybe.R..... 69 tests <1b>[0;32mOK<1b>[0m <1b>[0;34m26.7s<1b>[0m
test_recombinator_null.R...... 37 tests <1b>[0;32mOK<1b>[0m <1b>[0;34m4.6s<1b>[0m
test_recombinator_proxy.R..... 102 tests <1b>[0;32mOK<1b>[0m <1b>[0;34m26.1s<1b>[0m
test_recombinator_sbx.R....... 29 tests <1b>[0;32mOK<1b>[0m <1b>[0;34m4.7s<1b>[0m
test_recombinator_xounif.R.... 60 tests <1b>[0;32mOK<1b>[0m <1b>[0;34m16.9s<1b>[0m
test_scalor_one.R............. 35 tests <1b>[0;32mOK<1b>[0m <1b>[0;34m3.2s<1b>[0m
test_selector_best.R.......... 147 tests <1b>[0;32mOK<1b>[0m <1b>[0;34m35.4s<1b>[0m
test_selector_proxy.R......... 114 tests <1b>[0;32mOK<1b>[0m <1b>[0;34m32.3s<1b>[0m
test_selector_random.R........ 106 tests <1b>[0;32mOK<1b>[0m <1b>[0;34m7.7s<1b>[0m
test_shortforms.R............. 20 tests <1b>[0;32mOK<1b>[0m <1b>[0;34m1.5s<1b>[0m
test_utils.R.................. 5 tests <1b>[0;32mOK<1b>[0m <1b>[0;34m1.6s<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:11424)
Execution halted
Warning message:
closing unused connection 3 (->localhost:11424)
Flavor: r-devel-linux-x86_64-fedora-clang
Version: 0.0.4-3
Check: tests
Result: ERROR
Running ‘tinytest.R’ [2s/402s]
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=3049143 on localhost:11250 at 23:36:14.451
starting worker pid=3049144 on localhost:11250 at 23:36:14.481
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;34m9.3s<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;34m4.4s<1b>[0m
OptimInstanceSingleCrit is deprecated. Use OptimInstanceBatchSingleCrit instead.
test_mutator_cmpmaybe.R....... 93 tests <1b>[0;32mOK<1b>[0m <1b>[0;34m17.6s<1b>[0m
test_OptimizerMies.R.......... 54 tests <1b>[0;32mOK<1b>[0m <1b>[0;34m31.9s<1b>[0m
test_ParamSetShadow.R......... 41 tests <1b>[0;32mOK<1b>[0m <1b>[0;34m1.2s<1b>[0m
OptimInstanceSingleCrit is deprecated. Use OptimInstanceBatchSingleCrit instead.
OptimInstanceMultiCrit is deprecated. Use OptimInstanceBatchMultiCrit instead.
OptimInstanceSingleCrit is deprecated. Use OptimInstanceBatchSingleCrit instead.
test_TerminatorBudget.R....... 31 tests <1b>[0;32mOK<1b>[0m <1b>[0;34m7.4s<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;34m5.4s<1b>[0m
test_mutator_erase.R.......... 41 tests <1b>[0;32mOK<1b>[0m <1b>[0;34m58.9s<1b>[0m
test_mutator_gauss.R.......... 62 tests <1b>[0;32mOK<1b>[0m <1b>[0;34m14.0s<1b>[0m
test_mutator_maybe.R.......... 90 tests <1b>[0;32mOK<1b>[0m <1b>[0;34m12.8s<1b>[0m
test_mutator_null.R........... 24 tests <1b>[0;32mOK<1b>[0m <1b>[0;34m2.2s<1b>[0m
test_mutator_proxy.R.......... 69 tests <1b>[0;32mOK<1b>[0m <1b>[0;34m25.8s<1b>[0m
test_mutator_sequential.R..... 65 tests <1b>[0;32mOK<1b>[0m <1b>[0;34m6.4s<1b>[0m
test_mutator_unif.R........... 41 tests <1b>[0;32mOK<1b>[0m <1b>[0;34m1.4s<1b>[0m
test_operator.R............... 40 tests <1b>[0;32mOK<1b>[0m <1b>[0;34m0.7s<1b>[0m
test_operatorcombination.R.... 211 tests <1b>[0;32mOK<1b>[0m <1b>[0;34m18.2s<1b>[0m
test_recombinator_maybe.R..... 69 tests <1b>[0;32mOK<1b>[0m <1b>[0;34m15.2s<1b>[0m
test_recombinator_null.R...... 37 tests <1b>[0;32mOK<1b>[0m <1b>[0;34m2.9s<1b>[0m
test_recombinator_proxy.R..... 102 tests <1b>[0;32mOK<1b>[0m <1b>[0;34m13.1s<1b>[0m
test_recombinator_sbx.R....... 29 tests <1b>[0;32mOK<1b>[0m <1b>[0;34m2.6s<1b>[0m
test_recombinator_xounif.R.... 60 tests <1b>[0;32mOK<1b>[0m <1b>[0;34m11.8s<1b>[0m
test_scalor_one.R............. 35 tests <1b>[0;32mOK<1b>[0m <1b>[0;34m2.2s<1b>[0m
test_selector_best.R.......... 147 tests <1b>[0;32mOK<1b>[0m <1b>[0;34m20.6s<1b>[0m
test_selector_proxy.R......... 114 tests <1b>[0;32mOK<1b>[0m <1b>[0;34m18.9s<1b>[0m
test_selector_random.R........ 106 tests <1b>[0;32mOK<1b>[0m <1b>[0;34m5.8s<1b>[0m
test_shortforms.R............. 20 tests <1b>[0;32mOK<1b>[0m <1b>[0;34m49.1s<1b>[0m
test_utils.R.................. 5 tests <1b>[0;32mOK<1b>[0m <1b>[0;34m0.8s<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:11250)
Execution halted
Warning message:
closing unused connection 3 (->localhost:11250)
Flavor: r-devel-linux-x86_64-fedora-gcc
Current CRAN status: ERROR: 4, NOTE: 2, OK: 7
Version: 2.19.3
Check: examples
Result: ERROR
Running examples in ‘mlr-Ex.R’ failed
The error most likely occurred in:
> base::assign(".ptime", proc.time(), pos = "CheckExEnv")
> ### 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
Flavors: r-devel-linux-x86_64-debian-clang, r-devel-linux-x86_64-debian-gcc
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
Flavors: r-devel-linux-x86_64-fedora-clang, r-devel-linux-x86_64-fedora-gcc
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: 7, OK: 6
Version: 0.10.0
Check: examples
Result: ERROR
Running examples in ‘mlr3pipelines-Ex.R’ failed
The error most likely occurred in:
> base::assign(".ptime", proc.time(), pos = "CheckExEnv")
> ### Name: mlr_graphs_stacking
> ### Title: Create A Graph to Perform Stacking.
> ### Aliases: mlr_graphs_stacking pipeline_stacking
>
> ### ** Examples
>
> ## Don't show:
> if (mlr3misc::require_namespaces("rpart", quietly = TRUE)) withAutoprint({ # examplesIf
+ ## End(Don't show)
+ library(mlr3)
+ library(mlr3learners)
+
+ base_learners = list(
+ lrn("classif.rpart", predict_type = "prob"),
+ lrn("classif.nnet", predict_type = "prob")
+ )
+ super_learner = lrn("classif.log_reg")
+
+ graph_stack = pipeline_stacking(base_learners, super_learner)
+ graph_learner = as_learner(graph_stack)
+ graph_learner$train(tsk("german_credit"))
+ ## Don't show:
+ }) # examplesIf
> library(mlr3)
> library(mlr3learners)
> base_learners = list(lrn("classif.rpart", predict_type = "prob"), lrn("classif.nnet",
+ predict_type = "prob"))
> super_learner = lrn("classif.log_reg")
> graph_stack = pipeline_stacking(base_learners, super_learner)
> graph_learner = as_learner(graph_stack)
> graph_learner$train(tsk("german_credit"))
INFO [04:37:47.532] [mlr3] Resampling 'cv' is being instantiated on task 'german_credit'
INFO [04:37:47.932] [mlr3] Applying learner 'classif.rpart' on task 'german_credit' (iter 1/3)
INFO [04:37:48.010] [mlr3] Applying learner 'classif.rpart' on task 'german_credit' (iter 2/3)
INFO [04:37:48.106] [mlr3] Applying learner 'classif.rpart' on task 'german_credit' (iter 3/3)
Error in `[.data.table`(data, , `:=`("task_hash", task[[1L]]$hash), by = "uhash") :
attempt access index 9/9 in VECTOR_ELT
This happened in PipeOp classif.rpart's $train()
Calls: withAutoprint ... .__ResultData__initialize -> [ -> [.data.table -> .handleSimpleError -> h
Execution halted
Examples with CPU (user + system) or elapsed time > 5s
user system elapsed
mlr_graphs_ovr 4.257 0.105 8.279
Flavor: r-devel-linux-x86_64-debian-clang
Version: 0.10.0
Check: tests
Result: ERROR
Running ‘testthat.R’ [394s/201s]
Running the tests in ‘tests/testthat.R’ failed.
Complete output:
> if (requireNamespace("testthat", quietly = TRUE)) {
+ library("checkmate")
+ library("testthat")
+ library("mlr3")
+ library("paradox")
+ library("mlr3pipelines")
+ test_check("mlr3pipelines")
+ }
Starting 2 test processes.
> test_Graph.R: Training debug.multi with input list(input_1 = 1, input_2 = 1)
> test_PipeOp.R: Training test_autotrain
> test_PipeOp.R: Predicting test_autotrain
> test_PipeOp.R: Training test_autotrain
> test_PipeOp.R: Training test_autotrain
> test_PipeOp.R: Predicting test_autotrain
> test_PipeOp.R: Predicting test_autotrain
> test_PipeOp.R: Training test_autotrain
> test_PipeOp.R: Training test_autotrain
> test_PipeOp.R: Training test_autotrain
> test_PipeOp.R: Predicting test_autotrain
> test_PipeOp.R: Predicting test_autotrain
> test_PipeOp.R: Predicting test_autotrain
> test_PipeOp.R: Training test_autotrain
> test_PipeOp.R: Predicting test_autotrain
> test_PipeOp.R: Training test_autotrain
> test_PipeOp.R: Training test_autotrain
> test_PipeOp.R: Predicting test_autotrain
> test_PipeOp.R: Predicting test_autotrain
> test_PipeOp.R: Training test_autotrain
> test_PipeOp.R: Training test_autotrain
> test_PipeOp.R: Training test_autotrain
> test_PipeOp.R: Predicting test_autotrain
> test_PipeOp.R: Predicting test_autotrain
> test_PipeOp.R: Predicting test_autotrain
> test_PipeOp.R: Training test_autotrain
> test_PipeOp.R: Predicting test_autotrain
Saving _problems/test_conversion-143.R
Saving _problems/test_conversion-165.R
> test_filter_ensemble.R: OptimInstanceSingleCrit is deprecated. Use OptimInstanceBatchSingleCrit instead.
Saving _problems/test_filter_ensemble-291.R
Saving _problems/test_filter_ensemble-447.R
Saving _problems/test_mlr_graphs_bagging-49.R
Saving _problems/test_mlr_graphs_stacking-16.R
> test_mlr_graphs_robustify.R: 'as(<dgCMatrix>, "dgTMatrix")' is deprecated.
> test_mlr_graphs_robustify.R: Use 'as(., "TsparseMatrix")' instead.
> test_mlr_graphs_robustify.R: See help("Deprecated") and help("Matrix-depr
> test_mlr_graphs_robustify.R: ecated").
> test_multiplicities.R:
> test_multiplicities.R: [[1]]
> test_multiplicities.R: [1] 0
> test_multiplicities.R:
> test_multiplicities.R:
> test_pipeop_blsmote.R: [1] "Borderline-SMOTE done"
> test_pipeop_blsmote.R: [1] "Borderline-SMOTE done"
> test_pipeop_blsmote.R: [1] "Borderline-SMOTE done"
> test_pipeop_blsmote.R: [1]
> test_pipeop_blsmote.R: "Borderline-SMOTE done"
> test_pipeop_isomap.R: 2025-12-23 04:39:20.608538: Isomap START
> test_pipeop_isomap.R: 2025-12-23 04:39:20.60928: constructing knn graph
> test_pipeop_isomap.R: 2025-12-23 04:39:20.621909: calculating geodesic distances
> test_pipeop_isomap.R: 2025-12-23 04:39:20.642521: Classical Scaling
> test_pipeop_isomap.R: 2025-12-23 04:39:20.702247: Isomap START
> test_pipeop_isomap.R: 2025-12-23 04:39:20.702737: constructing knn graph
> test_pipeop_isomap.R: 2025-12-23 04:39:20.712258: calculating geodesic distances
> test_pipeop_isomap.R: 2025-12-23 04:39:20.730727: Classical Scaling
> test_pipeop_isomap.R: 2025-12-23 04:39:20.759112: L-Isomap embed START
> test_pipeop_isomap.R: 2025-12-23 04:39:20.759799: constructing knn graph
> test_pipeop_isomap.R: 2025-12-23 04:39:20.776453: calculating geodesic distances
> test_pipeop_isomap.R: 2025-12-23 04:39:20.819317: embedding
> test_pipeop_isomap.R: 2025-12-23 04:39:20.820528: DONE
> test_pipeop_isomap.R: 2025-12-23 04:39:20.846747: L-Isomap embed START
> test_pipeop_isomap.R: 2025-12-23 04:39:20.847283: constructing knn graph
> test_pipeop_isomap.R: 2025-12-23 04:39:20.86739: calculating geodesic distances
> test_pipeop_isomap.R: 2025-12-23 04:39:20.931833: embedding
> test_pipeop_isomap.R: 2025-12-23 04:39:20.934145: DONE
> test_pipeop_isomap.R: 2025-12-23 04:39:21.026839: Isomap START
> test_pipeop_isomap.R: 2025-12-23 04:39:21.027356: constructing knn graph
> test_pipeop_isomap.R: 2025-12-23 04:39:21.04161: calculating geodesic distances
> test_pipeop_isomap.R: 2025-12-23 04:39:21.141268: Classical Scaling
> test_pipeop_isomap.R: 2025-12-23 04:39:21.176411: L-Isomap embed START
> test_pipeop_isomap.R: 2025-12-23 04:39:21.177107: constructing knn graph
> test_pipeop_isomap.R: 2025-12-23 04:39:21.204464: calculating geodesic distances
> test_pipeop_isomap.R: 2025-12-23 04:39:21.411146: embedding
> test_pipeop_isomap.R: 2025-12-23 04:39:21.416934: DONE
> test_pipeop_isomap.R: 2025-12-23 04:39:21.589291: Isomap START
> test_pipeop_isomap.R: 2025-12-23 04:39:21.589792: constructing knn graph
> test_pipeop_isomap.R: 2025-12-23 04:39:21.59751: calculating geodesic distances
> test_pipeop_isomap.R: 2025-12-23 04:39:21.616257: Classical Scaling
> test_pipeop_isomap.R: 2025-12-23 04:39:21.647016: L-Isomap embed START
> test_pipeop_isomap.R: 2025-12-23 04:39:21.649283: constructing knn graph
> test_pipeop_isomap.R: 2025-12-23 04:39:21.663508: calculating geodesic distances
> test_pipeop_isomap.R: 2025-12-23 04:39:21.708681: embedding
> test_pipeop_isomap.R: 2025-12-23 04:39:21.709929: DONE
> test_pipeop_isomap.R: 2025-12-23 04:39:21.858299: Isomap START
> test_pipeop_isomap.R: 2025-12-23 04:39:21.858802: constructing knn graph
> test_pipeop_isomap.R: 2025-12-23 04:39:21.866428: calculating geodesic distances
> test_pipeop_isomap.R: 2025-12-23 04:39:21.885361: Classical Scaling
> test_pipeop_isomap.R: 2025-12-23 04:39:21.93478: L-Isomap embed START
> test_pipeop_isomap.R: 2025-12-23 04:39:21.935477: constructing knn graph
> test_pipeop_isomap.R: 2025-12-23 04:39:21.94996: calculating geodesic distances
> test_pipeop_isomap.R: 2025-12-23 04:39:21.992188: embedding
> test_pipeop_isomap.R: 2025-12-23 04:39:21.995016: DONE
> test_pipeop_isomap.R: 2025-12-23 04:39:22.07761: Isomap START
> test_pipeop_isomap.R: 2025-12-23 04:39:22.078123: constructing knn graph
> test_pipeop_isomap.R: 2025-12-23 04:39:22.087562: calculating geodesic distances
> test_pipeop_isomap.R: 2025-12-23 04:39:22.106418: Classical Scaling
> test_pipeop_isomap.R: 2025-12-23 04:39:22.170741: L-Isomap embed START
> test_pipeop_isomap.R: 2025-12-23 04:39:22.171482: constructing knn graph
> test_pipeop_isomap.R: 2025-12-23 04:39:22.190146: calculating geodesic distances
> test_pipeop_isomap.R: 2025-12-23 04:39:22.232962: embedding
> test_pipeop_isomap.R: 2025-12-23 04:39:22.234224: DONE
> test_pipeop_isomap.R: 2025-12-23 04:39:22.315289: Isomap START
> test_pipeop_isomap.R: 2025-12-23 04:39:22.315801: constructing knn graph
> test_pipeop_isomap.R: 2025-12-23 04:39:22.325012: calculating geodesic distances
> test_pipeop_isomap.R: 2025-12-23 04:39:22.343798: Classical Scaling
> test_pipeop_isomap.R: 2025-12-23 04:39:22.396032: L-Isomap embed START
> test_pipeop_isomap.R: 2025-12-23 04:39:22.396804: constructing knn graph
> test_pipeop_isomap.R: 2025-12-23 04:39:22.413116: calculating geodesic distances
> test_pipeop_isomap.R: 2025-12-23 04:39:22.456379: embedding
> test_pipeop_isomap.R: 2025-12-23 04:39:22.457615: DONE
> test_pipeop_isomap.R: 2025-12-23 04:39:22.540136: Isomap START
> test_pipeop_isomap.R: 2025-12-23 04:39:22.542341: constructing knn graph
> test_pipeop_isomap.R: 2025-12-23 04:39:22.55025: calculating geodesic distances
> test_pipeop_isomap.R: 2025-12-23 04:39:22.56887: Classical Scaling
> test_pipeop_isomap.R: 2025-12-23 04:39:22.619932: L-Isomap embed START
> test_pipeop_isomap.R: 2025-12-23 04:39:22.620635: constructing knn graph
> test_pipeop_isomap.R: 2025-12-23 04:39:22.648017: calculating geodesic distances
> test_pipeop_isomap.R: 2025-12-23 04:39:22.690667: embedding
> test_pipeop_isomap.R: 2025-12-23 04:39:22.691956: DONE
> test_pipeop_isomap.R: 2025-12-23 04:39:22.784068: Isomap START
> test_pipeop_isomap.R: 2025-12-23 04:39:22.784555: constructing knn graph
> test_pipeop_isomap.R: 2025-12-23 04:39:22.792247: calculating geodesic distances
> test_pipeop_isomap.R: 2025-12-23 04:39:22.811429: Classical Scaling
> test_pipeop_isomap.R: 2025-12-23 04:39:22.891634: Isomap START
> test_pipeop_isomap.R: 2025-12-23 04:39:22.892084: constructing knn graph
> test_pipeop_isomap.R: 2025-12-23 04:39:22.899396: calculating geodesic distances
> test_pipeop_isomap.R: 2025-12-23 04:39:22.918288: Classical Scaling
> test_pipeop_isomap.R: 2025-12-23 04:39:22.944135: Isomap START
> test_pipeop_isomap.R: 2025-12-23 04:39:22.944616: constructing knn graph
> test_pipeop_isomap.R: 2025-12-23 04:39:22.952191: calculating geodesic distances
> test_pipeop_isomap.R: 2025-12-23 04:39:22.970898: Classical Scaling
Saving _problems/test_pipeop_learnercv-11.R
Saving _problems/test_pipeop_learnercv-100.R
Saving _problems/test_pipeop_learnercv-139.R
Saving _problems/test_pipeop_learnercv-152.R
Saving _problems/test_pipeop_learnercv-203.R
Saving _problems/test_pipeop_learnercv-250.R
Saving _problems/test_pipeop_learnercv-278.R
Saving _problems/test_pipeop_learnercv-323.R
Saving _problems/test_pipeop_learnercv-350.R
Saving _problems/test_pipeop_learnercv-387.R
Saving _problems/test_pipeop_learnercv-419.R
Saving _problems/test_pipeop_learnercv-455.R
Saving _problems/test_pipeop_learnercv-493.R
Saving _problems/test_pipeop_learnercv-516.R
Saving _problems/test_pipeop_learnercv-531.R
Saving _problems/test_pipeop_learnercv-557.R
Saving _problems/test_pipeop_learnercv-612.R
Saving _problems/test_pipeop_learnercv-628.R
Saving _problems/test_pipeop_learnercv-671.R
Saving _problems/test_pipeop_learnerpicvplus-35.R
Saving _problems/test_pipeop_learnerpicvplus-91.R
Saving _problems/test_pipeop_learnerpicvplus-116.R
Saving _problems/test_pipeop_learnerpicvplus-130.R
Saving _problems/test_pipeop_learnerpicvplus-152.R
> test_pipeop_nmf.R: [PipeOpNMFstate]
> test_pipeop_nmf.R: [PipeOpNMFstate]
> test_pipeop_task_preproc.R: Training debug_affectcols
> test_pipeop_task_preproc.R: Training debug_affectcols
> test_pipeop_task_preproc.R: Training debug_affectcols
> test_pipeop_task_preproc.R: Training debug_affectcols
> test_pipeop_task_preproc.R: Training debug_affectcols
> test_pipeop_task_preproc.R: Training debug_affectcols
> test_pipeop_task_preproc.R: Training debug_affectcols
> test_pipeop_task_preproc.R: Training debug_affectcols
> test_pipeop_task_preproc.R: Training debug_affectcols
> test_pipeop_task_preproc.R: Training debug_affectcols
> test_pipeop_task_preproc.R: Training debug_affectcols
> test_pipeop_task_preproc.R: Training debug_affectcols
> test_pipeop_task_preproc.R: Training debug_affectcols
> test_pipeop_task_preproc.R: Training debug_affectcols
> test_pipeop_task_preproc.R: Training debug_affectcols
> test_pipeop_task_preproc.R: Training debug_affectcols
> test_pipeop_task_preproc.R: Training debug_affectcols
> test_pipeop_task_preproc.R: Training debug_affectcols
> test_pipeop_task_preproc.R: Training debug_affectcols
> test_pipeop_task_preproc.R: Training debug_affectcols
> test_pipeop_task_preproc.R: Training debug_affectcols
> test_pipeop_task_preproc.R: Training debug_affectcols
Saving _problems/test_pipeop_tunethreshold-7.R
Saving _problems/test_pipeop_tunethreshold-38.R
Saving _problems/test_pipeop_tunethreshold-73.R
Saving _problems/test_pipeop_tunethreshold-101.R
Saving _problems/test_pipeop_tunethreshold-260.R
Saving _problems/test_resample-13.R
Saving _problems/test_usecases-153.R
Saving _problems/test_ppl-73.R
[ FAIL 38 | WARN 2 | SKIP 98 | PASS 12316 ]
══ Skipped tests (98) ══════════════════════════════════════════════════════════
• On CRAN (95): 'test_CnfFormula_simplify.R:6:3', 'test_CnfFormula.R:591:3',
'test_Graph.R:283:3', 'test_PipeOp.R:32:1', 'test_GraphLearner.R:5:3',
'test_GraphLearner.R:221:3', 'test_GraphLearner.R:343:3',
'test_GraphLearner.R:408:3', 'test_GraphLearner.R:571:3',
'test_dictionary.R:7:3', 'test_learner_weightedaverage.R:5:3',
'test_learner_weightedaverage.R:57:3',
'test_learner_weightedaverage.R:105:3',
'test_learner_weightedaverage.R:152:3', 'test_meta.R:39:3',
'test_mlr_graphs_bagging.R:6:3', 'test_mlr_graphs_branching.R:26:3',
'test_mlr_graphs_robustify.R:5:3', 'test_pipeop_adas.R:8:3',
'test_pipeop_blsmote.R:8:3', 'test_pipeop_branch.R:4:3',
'test_pipeop_chunk.R:4:3', 'test_pipeop_classbalancing.R:7:3',
'test_pipeop_boxcox.R:7:3', 'test_pipeop_classweights.R:10:3',
'test_pipeop_collapsefactors.R:6:3', 'test_pipeop_colapply.R:9:3',
'test_pipeop_copy.R:5:3', 'test_pipeop_colroles.R:6:3',
'test_pipeop_decode.R:14:3', 'test_pipeop_encode.R:21:3',
'test_pipeop_encodeimpact.R:11:3', 'test_pipeop_datefeatures.R:10:3',
'test_pipeop_encodelmer.R:15:3', 'test_pipeop_encodelmer.R:37:3',
'test_pipeop_encodelmer.R:80:3', 'test_pipeop_encodepl.R:5:3',
'test_pipeop_encodepl.R:72:3', 'test_pipeop_featureunion.R:9:3',
'test_pipeop_featureunion.R:134:3', 'test_pipeop_filter.R:7:3',
'test_pipeop_fixfactors.R:9:3', 'test_pipeop_histbin.R:7:3',
'test_pipeop_ica.R:7:3', 'test_pipeop_ensemble.R:6:3',
'test_pipeop_impute.R:4:3', 'test_pipeop_imputelearner.R:43:3',
'test_pipeop_isomap.R:10:3', 'test_pipeop_kernelpca.R:9:3',
'test_pipeop_learner.R:17:3', 'test_pipeop_info.R:6:3',
'test_pipeop_learnerpicvplus.R:163:3', 'test_pipeop_missind.R:6:3',
'test_pipeop_modelmatrix.R:7:3', 'test_pipeop_multiplicityexply.R:9:3',
'test_pipeop_mutate.R:9:3', 'test_pipeop_multiplicityimply.R:9:3',
'test_pipeop_nearmiss.R:7:3', 'test_pipeop_ovr.R:9:3',
'test_pipeop_ovr.R:48:3', 'test_pipeop_pca.R:8:3',
'test_pipeop_proxy.R:14:3', 'test_pipeop_quantilebin.R:5:3',
'test_pipeop_randomprojection.R:6:3', 'test_pipeop_randomresponse.R:5:3',
'test_pipeop_removeconstants.R:6:3', 'test_pipeop_renamecolumns.R:6:3',
'test_pipeop_replicate.R:9:3', 'test_pipeop_rowapply.R:6:3',
'test_pipeop_scale.R:6:3', 'test_pipeop_scale.R:10:3',
'test_pipeop_scalemaxabs.R:6:3', 'test_pipeop_scalerange.R:7:3',
'test_pipeop_select.R:9:3', 'test_pipeop_smote.R:10:3',
'test_pipeop_smotenc.R:8:3', 'test_pipeop_nmf.R:6:3',
'test_pipeop_spatialsign.R:6:3', 'test_pipeop_targetinvert.R:4:3',
'test_pipeop_targetmutate.R:5:3', 'test_pipeop_targettrafo.R:4:3',
'test_pipeop_targettrafoscalerange.R:5:3', 'test_pipeop_task_preproc.R:4:3',
'test_pipeop_task_preproc.R:14:3', 'test_pipeop_subsample.R:6:3',
'test_pipeop_tomek.R:7:3', 'test_pipeop_tunethreshold.R:111:3',
'test_pipeop_tunethreshold.R:191:3', 'test_pipeop_textvectorizer.R:37:3',
'test_pipeop_textvectorizer.R:186:3', 'test_pipeop_unbranch.R:10:3',
'test_pipeop_vtreat.R:9:3', 'test_pipeop_updatetarget.R:89:3',
'test_pipeop_yeojohnson.R:7:3', 'test_typecheck.R:188:3'
• Skipping (1): 'test_GraphLearner.R:1278:3'
• empty test (2): 'test_pipeop_isomap.R:111:1', 'test_pipeop_missind.R:101:1'
══ Failed tests ════════════════════════════════════════════════════════════════
── Error ('test_conversion.R:143:3'): Graph to GraphLearner ────────────────────
Error in ``[.data.table`(data, , `:=`("task_hash", task[[1L]]$hash), by = "uhash")`: attempt access index 9/9 in VECTOR_ELT
Backtrace:
▆
1. └─mlr3::resample(task, glrn1, cv) at test_conversion.R:143:3
2. └─ResultData$new(data, data_extra, store_backends = store_backends)
3. └─mlr3 (local) initialize(...)
4. └─mlr3:::.__ResultData__initialize(...)
5. ├─data[, `:=`("task_hash", task[[1L]]$hash), by = "uhash"]
6. └─data.table:::`[.data.table`(...)
── Error ('test_conversion.R:165:3'): PipeOp to GraphLearner ───────────────────
Error in ``[.data.table`(data, , `:=`("task_hash", task[[1L]]$hash), by = "uhash")`: attempt access index 9/9 in VECTOR_ELT
Backtrace:
▆
1. └─mlr3::resample(task, glrn1, cv) at test_conversion.R:165:3
2. └─ResultData$new(data, data_extra, store_backends = store_backends)
3. └─mlr3 (local) initialize(...)
4. └─mlr3:::.__ResultData__initialize(...)
5. ├─data[, `:=`("task_hash", task[[1L]]$hash), by = "uhash"]
6. └─data.table:::`[.data.table`(...)
── Error ('test_filter_ensemble.R:291:3'): FilterEnsemble ignores NA scores from wrapped filters ──
Error in ``[.data.table`(data, , `:=`("task_hash", task[[1L]]$hash), by = "uhash")`: attempt access index 9/9 in VECTOR_ELT
Backtrace:
▆
1. └─permutation_filter$calculate(task) at test_filter_ensemble.R:291:3
2. └─mlr3filters:::.__Filter__calculate(...)
3. └─private$.calculate(task, nfeat)
4. └─mlr3filters:::.__FilterPermutation__.calculate(...)
5. └─mlr3::resample(task, self$learner, self$resampling)
6. └─ResultData$new(data, data_extra, store_backends = store_backends)
7. └─mlr3 (local) initialize(...)
8. └─mlr3:::.__ResultData__initialize(...)
9. ├─data[, `:=`("task_hash", task[[1L]]$hash), by = "uhash"]
10. └─data.table:::`[.data.table`(...)
── Error ('test_filter_ensemble.R:447:7'): FilterEnsemble weight search space works with bbotk ──
Error in ``[.data.table`(data, , `:=`("task_hash", task[[1L]]$hash), by = "uhash")`: attempt access index 9/9 in VECTOR_ELT
Backtrace:
▆
1. └─optimizer$optimize(instance) at test_filter_ensemble.R:463:3
2. └─bbotk:::.__OptimizerBatch__optimize(...)
3. └─bbotk::optimize_batch_default(inst, self)
4. ├─base::tryCatch(...)
5. │ └─base (local) tryCatchList(expr, classes, parentenv, handlers)
6. │ └─base (local) tryCatchOne(expr, names, parentenv, handlers[[1L]])
7. │ └─base (local) doTryCatch(return(expr), name, parentenv, handler)
8. └─get_private(optimizer)$.optimize(instance)
9. └─bbotk:::.__OptimizerBatchRandomSearch__.optimize(...)
10. └─inst$eval_batch(design$data)
11. └─bbotk:::.__OptimInstanceBatch__eval_batch(...)
12. └─self$objective$eval_many(xss_trafoed)
13. └─bbotk:::.__Objective__eval_many(...)
14. ├─mlr3misc::invoke(private$.eval_many, xss = xss, .args = self$constants$values)
15. │ └─base::eval.parent(expr, n = 1L)
16. │ └─base::eval(expr, p)
17. │ └─base::eval(expr, p)
18. └─private$.eval_many(xss = xss)
19. └─bbotk:::.__Objective__.eval_many(...)
20. └─mlr3misc::map_dtr(...)
21. ├─data.table::rbindlist(...)
22. ├─base::unname(map(.x, .f, ...))
23. └─mlr3misc::map(.x, .f, ...)
24. └─base::lapply(.x, .f, ...)
25. └─bbotk (local) FUN(X[[i]], ...)
26. └─self$eval(xs)
27. └─bbotk:::.__ObjectiveRFun__eval(...)
28. ├─mlr3misc::invoke(private$.fun, xs, .args = self$constants$values)
29. │ └─base::eval.parent(expr, n = 1L)
30. │ └─base::eval(expr, p)
31. │ └─base::eval(expr, p)
32. └─private$.fun(xs)
33. └─mlr3::resample(task, learner, resampling) at test_filter_ensemble.R:447:7
34. └─ResultData$new(data, data_extra, store_backends = store_backends)
35. └─mlr3 (local) initialize(...)
36. └─mlr3:::.__ResultData__initialize(...)
37. ├─data[, `:=`("task_hash", task[[1L]]$hash), by = "uhash"]
38. └─data.table:::`[.data.table`(...)
── Error ('test_mlr_graphs_bagging.R:49:3'): Bagging with replacement ──────────
Error in ``[.data.table`(data, , `:=`("task_hash", task[[1L]]$hash), by = "uhash")`: attempt access index 9/9 in VECTOR_ELT
Backtrace:
▆
1. └─mlr3::resample(tsk, GraphLearner$new(p), rsmp("holdout")) at test_mlr_graphs_bagging.R:49:3
2. └─ResultData$new(data, data_extra, store_backends = store_backends)
3. └─mlr3 (local) initialize(...)
4. └─mlr3:::.__ResultData__initialize(...)
5. ├─data[, `:=`("task_hash", task[[1L]]$hash), by = "uhash"]
6. └─data.table:::`[.data.table`(...)
── Error ('test_mlr_graphs_stacking.R:16:3'): Stacking Pipeline ────────────────
Error in ``[.data.table`(data, , `:=`("task_hash", task[[1L]]$hash), by = "uhash")`: attempt access index 9/9 in VECTOR_ELT
This happened in PipeOp base.rpart's $train()
Backtrace:
▆
1. ├─graph_learner$train(tsk("iris")) at test_mlr_graphs_stacking.R:16:3
2. │ └─mlr3:::.__Learner__train(...)
3. │ └─mlr3:::learner_train(...)
4. │ └─mlr3misc::encapsulate(...)
5. │ ├─mlr3misc::invoke(...)
6. │ │ └─base::eval.parent(expr, n = 1L)
7. │ │ └─base::eval(expr, p)
8. │ │ └─base::eval(expr, p)
9. │ └─mlr3 (local) .f(learner = `<GrphLrnr>`, task = `<TskClssf>`)
10. │ └─get_private(learner)$.train(task)
11. │ └─mlr3pipelines:::.__GraphLearner__.train(...)
12. │ └─self$graph$train(task)
13. │ └─mlr3pipelines:::.__Graph__train(...)
14. │ └─mlr3pipelines:::graph_reduce(self, input, "train", single_input)
15. │ └─op[[fun]](input)
16. │ └─mlr3pipelines:::.__PipeOp__train(...)
17. │ ├─base::withCallingHandlers(...)
18. │ └─private$.train(input)
19. │ └─mlr3pipelines:::.__PipeOpTaskPreproc__.train(...)
20. │ └─private$.train_task(intask)
21. │ └─mlr3pipelines:::.__PipeOpLearnerCV__.train_task(...)
22. │ └─mlr3::resample(...)
23. │ └─ResultData$new(data, data_extra, store_backends = store_backends)
24. │ └─mlr3 (local) initialize(...)
25. │ └─mlr3:::.__ResultData__initialize(...)
26. │ ├─data[, `:=`("task_hash", task[[1L]]$hash), by = "uhash"]
27. │ └─data.table:::`[.data.table`(...)
28. └─base::.handleSimpleError(...)
29. └─mlr3pipelines (local) h(simpleError(msg, call))
── Error ('test_pipeop_learnercv.R:11:3'): PipeOpLearnerCV - basic properties ──
Error in ``[.data.table`(data, , `:=`("task_hash", task[[1L]]$hash), by = "uhash")`: attempt access index 9/9 in VECTOR_ELT
This happened in PipeOp classif.featureless's $train()
Backtrace:
▆
1. ├─global train_pipeop(po, list(task = task)) at test_pipeop_learnercv.R:11:3
2. │ └─po$train(inputs)
3. │ └─mlr3pipelines:::.__PipeOp__train(...)
4. │ ├─base::withCallingHandlers(...)
5. │ └─private$.train(input)
6. │ └─mlr3pipelines:::.__PipeOpTaskPreproc__.train(...)
7. │ └─private$.train_task(intask)
8. │ └─mlr3pipelines:::.__PipeOpLearnerCV__.train_task(...)
9. │ └─mlr3::resample(...)
10. │ └─ResultData$new(data, data_extra, store_backends = store_backends)
11. │ └─mlr3 (local) initialize(...)
12. │ └─mlr3:::.__ResultData__initialize(...)
13. │ ├─data[, `:=`("task_hash", task[[1L]]$hash), by = "uhash"]
14. │ └─data.table:::`[.data.table`(...)
15. └─base::.handleSimpleError(...)
16. └─mlr3pipelines (local) h(simpleError(msg, call))
── Error ('test_pipeop_learnercv.R:100:3'): PipeOpLearnerCV - cv ensemble averages fold learners ──
Error in ``[.data.table`(data, , `:=`("task_hash", task[[1L]]$hash), by = "uhash")`: attempt access index 9/9 in VECTOR_ELT
This happened in PipeOp classif.rpart's $train()
Backtrace:
▆
1. ├─po$train(list(task)) at test_pipeop_learnercv.R:100:3
2. │ └─mlr3pipelines:::.__PipeOp__train(...)
3. │ ├─base::withCallingHandlers(...)
4. │ └─private$.train(input)
5. │ └─mlr3pipelines:::.__PipeOpTaskPreproc__.train(...)
6. │ └─private$.train_task(intask)
7. │ └─mlr3pipelines:::.__PipeOpLearnerCV__.train_task(...)
8. │ └─mlr3::resample(...)
9. │ └─ResultData$new(data, data_extra, store_backends = store_backends)
10. │ └─mlr3 (local) initialize(...)
11. │ └─mlr3:::.__ResultData__initialize(...)
12. │ ├─data[, `:=`("task_hash", task[[1L]]$hash), by = "uhash"]
13. │ └─data.table:::`[.data.table`(...)
14. └─base::.handleSimpleError(...)
15. └─mlr3pipelines (local) h(simpleError(msg, call))
── Error ('test_pipeop_learnercv.R:139:3'): PipeOpLearnerCV - cv ensemble drops response when requested ──
Error in ``[.data.table`(data, , `:=`("task_hash", task[[1L]]$hash), by = "uhash")`: attempt access index 9/9 in VECTOR_ELT
This happened in PipeOp classif.rpart's $train()
Backtrace:
▆
1. ├─po$train(list(task)) at test_pipeop_learnercv.R:139:3
2. │ └─mlr3pipelines:::.__PipeOp__train(...)
3. │ ├─base::withCallingHandlers(...)
4. │ └─private$.train(input)
5. │ └─mlr3pipelines:::.__PipeOpTaskPreproc__.train(...)
6. │ └─private$.train_task(intask)
7. │ └─mlr3pipelines:::.__PipeOpLearnerCV__.train_task(...)
8. │ └─mlr3::resample(...)
9. │ └─ResultData$new(data, data_extra, store_backends = store_backends)
10. │ └─mlr3 (local) initialize(...)
11. │ └─mlr3:::.__ResultData__initialize(...)
12. │ ├─data[, `:=`("task_hash", task[[1L]]$hash), by = "uhash"]
13. │ └─data.table:::`[.data.table`(...)
14. └─base::.handleSimpleError(...)
15. └─mlr3pipelines (local) h(simpleError(msg, call))
── Error ('test_pipeop_learnercv.R:152:3'): PipeOpLearnerCV - cv ensemble averages classif responses ──
Error in ``[.data.table`(data, , `:=`("task_hash", task[[1L]]$hash), by = "uhash")`: attempt access index 9/9 in VECTOR_ELT
This happened in PipeOp classif.rpart's $train()
Backtrace:
▆
1. ├─po$train(list(task)) at test_pipeop_learnercv.R:152:3
2. │ └─mlr3pipelines:::.__PipeOp__train(...)
3. │ ├─base::withCallingHandlers(...)
4. │ └─private$.train(input)
5. │ └─mlr3pipelines:::.__PipeOpTaskPreproc__.train(...)
6. │ └─private$.train_task(intask)
7. │ └─mlr3pipelines:::.__PipeOpLearnerCV__.train_task(...)
8. │ └─mlr3::resample(...)
9. │ └─ResultData$new(data, data_extra, store_backends = store_backends)
10. │ └─mlr3 (local) initialize(...)
11. │ └─mlr3:::.__ResultData__initialize(...)
12. │ ├─data[, `:=`("task_hash", task[[1L]]$hash), by = "uhash"]
13. │ └─data.table:::`[.data.table`(...)
14. └─base::.handleSimpleError(...)
15. └─mlr3pipelines (local) h(simpleError(msg, call))
── Error ('test_pipeop_learnercv.R:203:3'): PipeOpLearnerCV - cv ensemble log prob aggregation ──
Error in ``[.data.table`(data, , `:=`("task_hash", task[[1L]]$hash), by = "uhash")`: attempt access index 9/9 in VECTOR_ELT
This happened in PipeOp classif.rpart's $train()
Backtrace:
▆
1. ├─po$train(list(task)) at test_pipeop_learnercv.R:203:3
2. │ └─mlr3pipelines:::.__PipeOp__train(...)
3. │ ├─base::withCallingHandlers(...)
4. │ └─private$.train(input)
5. │ └─mlr3pipelines:::.__PipeOpTaskPreproc__.train(...)
6. │ └─private$.train_task(intask)
7. │ └─mlr3pipelines:::.__PipeOpLearnerCV__.train_task(...)
8. │ └─mlr3::resample(...)
9. │ └─ResultData$new(data, data_extra, store_backends = store_backends)
10. │ └─mlr3 (local) initialize(...)
11. │ └─mlr3:::.__ResultData__initialize(...)
12. │ ├─data[, `:=`("task_hash", task[[1L]]$hash), by = "uhash"]
13. │ └─data.table:::`[.data.table`(...)
14. └─base::.handleSimpleError(...)
15. └─mlr3pipelines (local) h(simpleError(msg, call))
── Error ('test_pipeop_learnercv.R:250:3'): PipeOpLearnerCV - log aggregation with zeros uses epsilon ──
Error in ``[.data.table`(data, , `:=`("task_hash", task[[1L]]$hash), by = "uhash")`: attempt access index 9/9 in VECTOR_ELT
This happened in PipeOp classif.featureless's $train()
Backtrace:
▆
1. ├─po$train(list(task)) at test_pipeop_learnercv.R:250:3
2. │ └─mlr3pipelines:::.__PipeOp__train(...)
3. │ ├─base::withCallingHandlers(...)
4. │ └─private$.train(input)
5. │ └─mlr3pipelines:::.__PipeOpTaskPreproc__.train(...)
6. │ └─private$.train_task(intask)
7. │ └─mlr3pipelines:::.__PipeOpLearnerCV__.train_task(...)
8. │ └─mlr3::resample(...)
9. │ └─ResultData$new(data, data_extra, store_backends = store_backends)
10. │ └─mlr3 (local) initialize(...)
11. │ └─mlr3:::.__ResultData__initialize(...)
12. │ ├─data[, `:=`("task_hash", task[[1L]]$hash), by = "uhash"]
13. │ └─data.table:::`[.data.table`(...)
14. └─base::.handleSimpleError(...)
15. └─mlr3pipelines (local) h(simpleError(msg, call))
── Error ('test_pipeop_learnercv.R:278:3'): PipeOpLearnerCV - log aggregation epsilon controls shrinkage ──
Error in ``[.data.table`(data, , `:=`("task_hash", task[[1L]]$hash), by = "uhash")`: attempt access index 9/9 in VECTOR_ELT
This happened in PipeOp classif.featureless's $train()
Backtrace:
▆
1. ├─po$train(list(task)) at test_pipeop_learnercv.R:278:3
2. │ └─mlr3pipelines:::.__PipeOp__train(...)
3. │ ├─base::withCallingHandlers(...)
4. │ └─private$.train(input)
5. │ └─mlr3pipelines:::.__PipeOpTaskPreproc__.train(...)
6. │ └─private$.train_task(intask)
7. │ └─mlr3pipelines:::.__PipeOpLearnerCV__.train_task(...)
8. │ └─mlr3::resample(...)
9. │ └─ResultData$new(data, data_extra, store_backends = store_backends)
10. │ └─mlr3 (local) initialize(...)
11. │ └─mlr3:::.__ResultData__initialize(...)
12. │ ├─data[, `:=`("task_hash", task[[1L]]$hash), by = "uhash"]
13. │ └─data.table:::`[.data.table`(...)
14. └─base::.handleSimpleError(...)
15. └─mlr3pipelines (local) h(simpleError(msg, call))
── Error ('test_pipeop_learnercv.R:323:3'): PipeOpLearnerCV - cv ensemble averages regression predictions ──
Error in ``[.data.table`(data, , `:=`("task_hash", task[[1L]]$hash), by = "uhash")`: attempt access index 9/9 in VECTOR_ELT
This happened in PipeOp regr.rpart's $train()
Backtrace:
▆
1. ├─po$train(list(task)) at test_pipeop_learnercv.R:323:3
2. │ └─mlr3pipelines:::.__PipeOp__train(...)
3. │ ├─base::withCallingHandlers(...)
4. │ └─private$.train(input)
5. │ └─mlr3pipelines:::.__PipeOpTaskPreproc__.train(...)
6. │ └─private$.train_task(intask)
7. │ └─mlr3pipelines:::.__PipeOpLearnerCV__.train_task(...)
8. │ └─mlr3::resample(...)
9. │ └─ResultData$new(data, data_extra, store_backends = store_backends)
10. │ └─mlr3 (local) initialize(...)
11. │ └─mlr3:::.__ResultData__initialize(...)
12. │ ├─data[, `:=`("task_hash", task[[1L]]$hash), by = "uhash"]
13. │ └─data.table:::`[.data.table`(...)
14. └─base::.handleSimpleError(...)
15. └─mlr3pipelines (local) h(simpleError(msg, call))
── Error ('test_pipeop_learnercv.R:350:3'): PipeOpLearnerCV - cv ensemble handles multiplicity ──
Error in ``[.data.table`(data, , `:=`("task_hash", task[[1L]]$hash), by = "uhash")`: attempt access index 9/9 in VECTOR_ELT
This happened in PipeOp classif.rpart's $train()
Backtrace:
▆
1. ├─po$train(list(tasks)) at test_pipeop_learnercv.R:350:3
2. │ └─mlr3pipelines:::.__PipeOp__train(...)
3. │ └─mlr3pipelines:::evaluate_multiplicities(...)
4. │ └─mlr3misc::imap(...)
5. │ ├─stats::setNames(mapply_list(.f, list(.x, .nn), list(...)), names(.x))
6. │ └─mlr3misc:::mapply_list(.f, list(.x, .nn), list(...))
7. │ └─base::.mapply(.f, .dots, .args)
8. │ └─mlr3pipelines (local) `<fn>`(dots[[1L]][[1L]], dots[[2L]][[1L]])
9. │ └─self[[evalcall]](input)
10. │ └─mlr3pipelines:::.__PipeOp__train(...)
11. │ ├─base::withCallingHandlers(...)
12. │ └─private$.train(input)
13. │ └─mlr3pipelines:::.__PipeOpTaskPreproc__.train(...)
14. │ └─private$.train_task(intask)
15. │ └─mlr3pipelines:::.__PipeOpLearnerCV__.train_task(...)
16. │ └─mlr3::resample(...)
17. │ └─ResultData$new(data, data_extra, store_backends = store_backends)
18. │ └─mlr3 (local) initialize(...)
19. │ └─mlr3:::.__ResultData__initialize(...)
20. │ ├─data[, `:=`("task_hash", task[[1L]]$hash), by = "uhash"]
21. │ └─data.table:::`[.data.table`(...)
22. └─base::.handleSimpleError(...)
23. └─mlr3pipelines (local) h(simpleError(msg, call))
── Error ('test_pipeop_learnercv.R:387:3'): PipeOpLearnerCV - learner_model returns averaged ensemble ──
Error in ``[.data.table`(data, , `:=`("task_hash", task[[1L]]$hash), by = "uhash")`: attempt access index 9/9 in VECTOR_ELT
This happened in PipeOp classif.rpart's $train()
Backtrace:
▆
1. ├─po$train(list(task)) at test_pipeop_learnercv.R:387:3
2. │ └─mlr3pipelines:::.__PipeOp__train(...)
3. │ ├─base::withCallingHandlers(...)
4. │ └─private$.train(input)
5. │ └─mlr3pipelines:::.__PipeOpTaskPreproc__.train(...)
6. │ └─private$.train_task(intask)
7. │ └─mlr3pipelines:::.__PipeOpLearnerCV__.train_task(...)
8. │ └─mlr3::resample(...)
9. │ └─ResultData$new(data, data_extra, store_backends = store_backends)
10. │ └─mlr3 (local) initialize(...)
11. │ └─mlr3:::.__ResultData__initialize(...)
12. │ ├─data[, `:=`("task_hash", task[[1L]]$hash), by = "uhash"]
13. │ └─data.table:::`[.data.table`(...)
14. └─base::.handleSimpleError(...)
15. └─mlr3pipelines (local) h(simpleError(msg, call))
── Error ('test_pipeop_learnercv.R:419:3'): PipeOpLearnerCV - cv ensemble with predict_type = 'se' ──
Error in ``[.data.table`(data, , `:=`("task_hash", task[[1L]]$hash), by = "uhash")`: attempt access index 9/9 in VECTOR_ELT
This happened in PipeOp regr.lm's $train()
Backtrace:
▆
1. ├─po$train(list(task)) at test_pipeop_learnercv.R:419:3
2. │ └─mlr3pipelines:::.__PipeOp__train(...)
3. │ ├─base::withCallingHandlers(...)
4. │ └─private$.train(input)
5. │ └─mlr3pipelines:::.__PipeOpTaskPreproc__.train(...)
6. │ └─private$.train_task(intask)
7. │ └─mlr3pipelines:::.__PipeOpLearnerCV__.train_task(...)
8. │ └─mlr3::resample(...)
9. │ └─ResultData$new(data, data_extra, store_backends = store_backends)
10. │ └─mlr3 (local) initialize(...)
11. │ └─mlr3:::.__ResultData__initialize(...)
12. │ ├─data[, `:=`("task_hash", task[[1L]]$hash), by = "uhash"]
13. │ └─data.table:::`[.data.table`(...)
14. └─base::.handleSimpleError(...)
15. └─mlr3pipelines (local) h(simpleError(msg, call))
── Error ('test_pipeop_learnercv.R:455:3'): PipeOpLearnerCV - within resampling ──
Error in ``[.data.table`(data, , `:=`("task_hash", task[[1L]]$hash), by = "uhash")`: attempt access index 9/9 in VECTOR_ELT
This happened in PipeOp classif.rpart's $train()
Backtrace:
▆
1. └─mlr3::resample(tsk("iris"), gr, rsmp("holdout")) at test_pipeop_learnercv.R:455:3
2. └─mlr3:::future_map(...)
3. └─future.apply::future_mapply(...)
4. └─future.apply:::future_xapply(...)
5. └─base::tryCatch(...)
6. └─base (local) tryCatchList(expr, classes, parentenv, handlers)
7. └─base (local) tryCatchOne(...)
8. └─value[[3L]](cond)
9. └─future.apply:::onError(e, futures = fs, debug = debug)
── Error ('test_pipeop_learnercv.R:493:3'): PipeOpLearnerCV - model active binding to state ──
Error in ``[.data.table`(data, , `:=`("task_hash", task[[1L]]$hash), by = "uhash")`: attempt access index 9/9 in VECTOR_ELT
This happened in PipeOp classif.featureless's $train()
Backtrace:
▆
1. ├─po$train(list(task)) at test_pipeop_learnercv.R:493:3
2. │ └─mlr3pipelines:::.__PipeOp__train(...)
3. │ ├─base::withCallingHandlers(...)
4. │ └─private$.train(input)
5. │ └─mlr3pipelines:::.__PipeOpTaskPreproc__.train(...)
6. │ └─private$.train_task(intask)
7. │ └─mlr3pipelines:::.__PipeOpLearnerCV__.train_task(...)
8. │ └─mlr3::resample(...)
9. │ └─ResultData$new(data, data_extra, store_backends = store_backends)
10. │ └─mlr3 (local) initialize(...)
11. │ └─mlr3:::.__ResultData__initialize(...)
12. │ ├─data[, `:=`("task_hash", task[[1L]]$hash), by = "uhash"]
13. │ └─data.table:::`[.data.table`(...)
14. └─base::.handleSimpleError(...)
15. └─mlr3pipelines (local) h(simpleError(msg, call))
── Error ('test_pipeop_learnercv.R:516:3'): predict_type ───────────────────────
Error in ``[.data.table`(data, , `:=`("task_hash", task[[1L]]$hash), by = "uhash")`: attempt access index 9/9 in VECTOR_ELT
This happened in PipeOp classif.rpart's $train()
Backtrace:
▆
1. ├─global expect_equal(...) at test_pipeop_learnercv.R:516:3
2. │ ├─testthat::expect_true(...)
3. │ │ └─testthat::quasi_label(enquo(object), label)
4. │ │ └─rlang::eval_bare(expr, quo_get_env(quo))
5. │ └─base::all.equal(...)
6. ├─lcv$train(list(tsk("iris")))
7. │ └─mlr3pipelines:::.__PipeOp__train(...)
8. │ ├─base::withCallingHandlers(...)
9. │ └─private$.train(input)
10. │ └─mlr3pipelines:::.__PipeOpTaskPreproc__.train(...)
11. │ └─private$.train_task(intask)
12. │ └─mlr3pipelines:::.__PipeOpLearnerCV__.train_task(...)
13. │ └─mlr3::resample(...)
14. │ └─ResultData$new(data, data_extra, store_backends = store_backends)
15. │ └─mlr3 (local) initialize(...)
16. │ └─mlr3:::.__ResultData__initialize(...)
17. │ ├─data[, `:=`("task_hash", task[[1L]]$hash), by = "uhash"]
18. │ └─data.table:::`[.data.table`(...)
19. └─base::.handleSimpleError(...)
20. └─mlr3pipelines (local) h(simpleError(msg, call))
── Error ('test_pipeop_learnercv.R:531:3'): marshal ────────────────────────────
Error in ``[.data.table`(data, , `:=`("task_hash", task[[1L]]$hash), by = "uhash")`: attempt access index 9/9 in VECTOR_ELT
This happened in PipeOp classif.debug's $train()
Backtrace:
▆
1. ├─po_lrn$train(list(task)) at test_pipeop_learnercv.R:531:3
2. │ └─mlr3pipelines:::.__PipeOp__train(...)
3. │ ├─base::withCallingHandlers(...)
4. │ └─private$.train(input)
5. │ └─mlr3pipelines:::.__PipeOpTaskPreproc__.train(...)
6. │ └─private$.train_task(intask)
7. │ └─mlr3pipelines:::.__PipeOpLearnerCV__.train_task(...)
8. │ └─mlr3::resample(...)
9. │ └─ResultData$new(data, data_extra, store_backends = store_backends)
10. │ └─mlr3 (local) initialize(...)
11. │ └─mlr3:::.__ResultData__initialize(...)
12. │ ├─data[, `:=`("task_hash", task[[1L]]$hash), by = "uhash"]
13. │ └─data.table:::`[.data.table`(...)
14. └─base::.handleSimpleError(...)
15. └─mlr3pipelines (local) h(simpleError(msg, call))
── Error ('test_pipeop_learnercv.R:557:3'): marshal multiplicity ───────────────
Error in ``[.data.table`(data, , `:=`("task_hash", task[[1L]]$hash), by = "uhash")`: attempt access index 9/9 in VECTOR_ELT
This happened in PipeOp classif.debug's $train()
Backtrace:
▆
1. ├─po$train(list(Multiplicity(tsk("iris"), tsk("sonar")))) at test_pipeop_learnercv.R:557:3
2. │ └─mlr3pipelines:::.__PipeOp__train(...)
3. │ └─mlr3pipelines:::evaluate_multiplicities(...)
4. │ └─mlr3misc::imap(...)
5. │ ├─stats::setNames(mapply_list(.f, list(.x, .nn), list(...)), names(.x))
6. │ └─mlr3misc:::mapply_list(.f, list(.x, .nn), list(...))
7. │ └─base::.mapply(.f, .dots, .args)
8. │ └─mlr3pipelines (local) `<fn>`(dots[[1L]][[1L]], dots[[2L]][[1L]])
9. │ └─self[[evalcall]](input)
10. │ └─mlr3pipelines:::.__PipeOp__train(...)
11. │ ├─base::withCallingHandlers(...)
12. │ └─private$.train(input)
13. │ └─mlr3pipelines:::.__PipeOpTaskPreproc__.train(...)
14. │ └─private$.train_task(intask)
15. │ └─mlr3pipelines:::.__PipeOpLearnerCV__.train_task(...)
16. │ └─mlr3::resample(...)
17. │ └─ResultData$new(data, data_extra, store_backends = store_backends)
18. │ └─mlr3 (local) initialize(...)
19. │ └─mlr3:::.__ResultData__initialize(...)
20. │ ├─data[, `:=`("task_hash", task[[1L]]$hash), by = "uhash"]
21. │ └─data.table:::`[.data.table`(...)
22. └─base::.handleSimpleError(...)
23. └─mlr3pipelines (local) h(simpleError(msg, call))
── Error ('test_pipeop_learnercv.R:612:3'): marshal with cv ensemble ───────────
Error in ``[.data.table`(data, , `:=`("task_hash", task[[1L]]$hash), by = "uhash")`: attempt access index 9/9 in VECTOR_ELT
This happened in PipeOp classif.rpart's $train()
Backtrace:
▆
1. ├─po$train(list(task)) at test_pipeop_learnercv.R:612:3
2. │ └─mlr3pipelines:::.__PipeOp__train(...)
3. │ ├─base::withCallingHandlers(...)
4. │ └─private$.train(input)
5. │ └─mlr3pipelines:::.__PipeOpTaskPreproc__.train(...)
6. │ └─private$.train_task(intask)
7. │ └─mlr3pipelines:::.__PipeOpLearnerCV__.train_task(...)
8. │ └─mlr3::resample(...)
9. │ └─ResultData$new(data, data_extra, store_backends = store_backends)
10. │ └─mlr3 (local) initialize(...)
11. │ └─mlr3:::.__ResultData__initialize(...)
12. │ ├─data[, `:=`("task_hash", task[[1L]]$hash), by = "uhash"]
13. │ └─data.table:::`[.data.table`(...)
14. └─base::.handleSimpleError(...)
15. └─mlr3pipelines (local) h(simpleError(msg, call))
── Error ('test_pipeop_learnercv.R:628:3'): state class and multiplicity ───────
Error in ``[.data.table`(data, , `:=`("task_hash", task[[1L]]$hash), by = "uhash")`: attempt access index 9/9 in VECTOR_ELT
This happened in PipeOp classif.debug's $train()
Backtrace:
▆
1. ├─po$train(list(Multiplicity(tsk("iris")))) at test_pipeop_learnercv.R:628:3
2. │ └─mlr3pipelines:::.__PipeOp__train(...)
3. │ └─mlr3pipelines:::evaluate_multiplicities(...)
4. │ └─mlr3misc::imap(...)
5. │ ├─stats::setNames(mapply_list(.f, list(.x, .nn), list(...)), names(.x))
6. │ └─mlr3misc:::mapply_list(.f, list(.x, .nn), list(...))
7. │ └─base::.mapply(.f, .dots, .args)
8. │ └─mlr3pipelines (local) `<fn>`(dots[[1L]][[1L]], dots[[2L]][[1L]])
9. │ └─self[[evalcall]](input)
10. │ └─mlr3pipelines:::.__PipeOp__train(...)
11. │ ├─base::withCallingHandlers(...)
12. │ └─private$.train(input)
13. │ └─mlr3pipelines:::.__PipeOpTaskPreproc__.train(...)
14. │ └─private$.train_task(intask)
15. │ └─mlr3pipelines:::.__PipeOpLearnerCV__.train_task(...)
16. │ └─mlr3::resample(...)
17. │ └─ResultData$new(data, data_extra, store_backends = store_backends)
18. │ └─mlr3 (local) initialize(...)
19. │ └─mlr3:::.__ResultData__initialize(...)
20. │ ├─data[, `:=`("task_hash", task[[1L]]$hash), by = "uhash"]
21. │ └─data.table:::`[.data.table`(...)
22. └─base::.handleSimpleError(...)
23. └─mlr3pipelines (local) h(simpleError(msg, call))
── Error ('test_pipeop_learnercv.R:671:5'): PipeOpLearnerCV cv ensemble aggregates SE like PipeOpRegrAvg ──
Error in ``[.data.table`(data, , `:=`("task_hash", task[[1L]]$hash), by = "uhash")`: attempt access index 9/9 in VECTOR_ELT
This happened in PipeOp regr.debug's $train()
Backtrace:
▆
1. ├─po$train(list(task)) at test_pipeop_learnercv.R:671:5
2. │ └─mlr3pipelines:::.__PipeOp__train(...)
3. │ ├─base::withCallingHandlers(...)
4. │ └─private$.train(input)
5. │ └─mlr3pipelines:::.__PipeOpTaskPreproc__.train(...)
6. │ └─private$.train_task(intask)
7. │ └─mlr3pipelines:::.__PipeOpLearnerCV__.train_task(...)
8. │ └─mlr3::resample(...)
9. │ └─ResultData$new(data, data_extra, store_backends = store_backends)
10. │ └─mlr3 (local) initialize(...)
11. │ └─mlr3:::.__ResultData__initialize(...)
12. │ ├─data[, `:=`("task_hash", task[[1L]]$hash), by = "uhash"]
13. │ └─data.table:::`[.data.table`(...)
14. └─base::.handleSimpleError(...)
15. └─mlr3pipelines (local) h(simpleError(msg, call))
── Error ('test_pipeop_learnerpicvplus.R:35:3'): PipeOpLearnerPICVPlus - basic properties ──
Error in ``[.data.table`(data, , `:=`("task_hash", task[[1L]]$hash), by = "uhash")`: attempt access index 9/9 in VECTOR_ELT
This happened in PipeOp regr.featureless's $train()
Backtrace:
▆
1. ├─global train_pipeop(po, list(task)) at test_pipeop_learnerpicvplus.R:35:3
2. │ └─po$train(inputs)
3. │ └─mlr3pipelines:::.__PipeOp__train(...)
4. │ ├─base::withCallingHandlers(...)
5. │ └─private$.train(input)
6. │ └─mlr3pipelines:::.__PipeOpLearnerPICVPlus__.train(...)
7. │ └─mlr3::resample(task, private$.learner, rdesc, store_models = TRUE)
8. │ └─ResultData$new(data, data_extra, store_backends = store_backends)
9. │ └─mlr3 (local) initialize(...)
10. │ └─mlr3:::.__ResultData__initialize(...)
11. │ ├─data[, `:=`("task_hash", task[[1L]]$hash), by = "uhash"]
12. │ └─data.table:::`[.data.table`(...)
13. └─base::.handleSimpleError(...)
14. └─mlr3pipelines (local) h(simpleError(msg, call))
── Error ('test_pipeop_learnerpicvplus.R:91:3'): PipeOpLearnerPICVPlus - model active binding to state ──
Error in ``[.data.table`(data, , `:=`("task_hash", task[[1L]]$hash), by = "uhash")`: attempt access index 9/9 in VECTOR_ELT
This happened in PipeOp regr.featureless's $train()
Backtrace:
▆
1. ├─po$train(list(task)) at test_pipeop_learnerpicvplus.R:91:3
2. │ └─mlr3pipelines:::.__PipeOp__train(...)
3. │ ├─base::withCallingHandlers(...)
4. │ └─private$.train(input)
5. │ └─mlr3pipelines:::.__PipeOpLearnerPICVPlus__.train(...)
6. │ └─mlr3::resample(task, private$.learner, rdesc, store_models = TRUE)
7. │ └─ResultData$new(data, data_extra, store_backends = store_backends)
8. │ └─mlr3 (local) initialize(...)
9. │ └─mlr3:::.__ResultData__initialize(...)
10. │ ├─data[, `:=`("task_hash", task[[1L]]$hash), by = "uhash"]
11. │ └─data.table:::`[.data.table`(...)
12. └─base::.handleSimpleError(...)
13. └─mlr3pipelines (local) h(simpleError(msg, call))
── Error ('test_pipeop_learnerpicvplus.R:116:3'): PipeOpLearnerPICVPlus - integration with larger graph ──
Error in ``[.data.table`(data, , `:=`("task_hash", task[[1L]]$hash), by = "uhash")`: attempt access index 9/9 in VECTOR_ELT
This happened in PipeOp regr.rpart's $train()
Backtrace:
▆
1. ├─graph$train(task) at test_pipeop_learnerpicvplus.R:116:3
2. │ └─mlr3pipelines:::.__Graph__train(...)
3. │ └─mlr3pipelines:::graph_reduce(self, input, "train", single_input)
4. │ └─op[[fun]](input)
5. │ └─mlr3pipelines:::.__PipeOp__train(...)
6. │ ├─base::withCallingHandlers(...)
7. │ └─private$.train(input)
8. │ └─mlr3pipelines:::.__PipeOpLearnerPICVPlus__.train(...)
9. │ └─mlr3::resample(task, private$.learner, rdesc, store_models = TRUE)
10. │ └─ResultData$new(data, data_extra, store_backends = store_backends)
11. │ └─mlr3 (local) initialize(...)
12. │ └─mlr3:::.__ResultData__initialize(...)
13. │ ├─data[, `:=`("task_hash", task[[1L]]$hash), by = "uhash"]
14. │ └─data.table:::`[.data.table`(...)
15. └─base::.handleSimpleError(...)
16. └─mlr3pipelines (local) h(simpleError(msg, call))
── Error ('test_pipeop_learnerpicvplus.R:130:3'): marshal ──────────────────────
Error in ``[.data.table`(data, , `:=`("task_hash", task[[1L]]$hash), by = "uhash")`: attempt access index 9/9 in VECTOR_ELT
This happened in PipeOp regr.debug's $train()
Backtrace:
▆
1. ├─po$train(list(task)) at test_pipeop_learnerpicvplus.R:130:3
2. │ └─mlr3pipelines:::.__PipeOp__train(...)
3. │ ├─base::withCallingHandlers(...)
4. │ └─private$.train(input)
5. │ └─mlr3pipelines:::.__PipeOpLearnerPICVPlus__.train(...)
6. │ └─mlr3::resample(task, private$.learner, rdesc, store_models = TRUE)
7. │ └─ResultData$new(data, data_extra, store_backends = store_backends)
8. │ └─mlr3 (local) initialize(...)
9. │ └─mlr3:::.__ResultData__initialize(...)
10. │ ├─data[, `:=`("task_hash", task[[1L]]$hash), by = "uhash"]
11. │ └─data.table:::`[.data.table`(...)
12. └─base::.handleSimpleError(...)
13. └─mlr3pipelines (local) h(simpleError(msg, call))
── Error ('test_pipeop_learnerpicvplus.R:152:3'): marshal multiplicity ─────────
Error in ``[.data.table`(data, , `:=`("task_hash", task[[1L]]$hash), by = "uhash")`: attempt access index 9/9 in VECTOR_ELT
This happened in PipeOp regr.debug's $train()
Backtrace:
▆
1. ├─po$train(list(Multiplicity(task1, task2))) at test_pipeop_learnerpicvplus.R:152:3
2. │ └─mlr3pipelines:::.__PipeOp__train(...)
3. │ └─mlr3pipelines:::evaluate_multiplicities(...)
4. │ └─mlr3misc::imap(...)
5. │ ├─stats::setNames(mapply_list(.f, list(.x, .nn), list(...)), names(.x))
6. │ └─mlr3misc:::mapply_list(.f, list(.x, .nn), list(...))
7. │ └─base::.mapply(.f, .dots, .args)
8. │ └─mlr3pipelines (local) `<fn>`(dots[[1L]][[1L]], dots[[2L]][[1L]])
9. │ └─self[[evalcall]](input)
10. │ └─mlr3pipelines:::.__PipeOp__train(...)
11. │ ├─base::withCallingHandlers(...)
12. │ └─private$.train(input)
13. │ └─mlr3pipelines:::.__PipeOpLearnerPICVPlus__.train(...)
14. │ └─mlr3::resample(task, private$.learner, rdesc, store_models = TRUE)
15. │ └─ResultData$new(data, data_extra, store_backends = store_backends)
16. │ └─mlr3 (local) initialize(...)
17. │ └─mlr3:::.__ResultData__initialize(...)
18. │ ├─data[, `:=`("task_hash", task[[1L]]$hash), by = "uhash"]
19. │ └─data.table:::`[.data.table`(...)
20. └─base::.handleSimpleError(...)
21. └─mlr3pipelines (local) h(simpleError(msg, call))
── Error ('test_pipeop_tunethreshold.R:7:3'): threshold works for multiclass ───
Error in ``[.data.table`(data, , `:=`("task_hash", task[[1L]]$hash), by = "uhash")`: attempt access index 9/9 in VECTOR_ELT
This happened in PipeOp classif.rpart's $train()
Backtrace:
▆
1. ├─po_cv$train(list(t)) at test_pipeop_tunethreshold.R:7:3
2. │ └─mlr3pipelines:::.__PipeOp__train(...)
3. │ ├─base::withCallingHandlers(...)
4. │ └─private$.train(input)
5. │ └─mlr3pipelines:::.__PipeOpTaskPreproc__.train(...)
6. │ └─private$.train_task(intask)
7. │ └─mlr3pipelines:::.__PipeOpLearnerCV__.train_task(...)
8. │ └─mlr3::resample(...)
9. │ └─ResultData$new(data, data_extra, store_backends = store_backends)
10. │ └─mlr3 (local) initialize(...)
11. │ └─mlr3:::.__ResultData__initialize(...)
12. │ ├─data[, `:=`("task_hash", task[[1L]]$hash), by = "uhash"]
13. │ └─data.table:::`[.data.table`(...)
14. └─base::.handleSimpleError(...)
15. └─mlr3pipelines (local) h(simpleError(msg, call))
── Error ('test_pipeop_tunethreshold.R:38:3'): threshold works for binary ──────
Error in ``[.data.table`(data, , `:=`("task_hash", task[[1L]]$hash), by = "uhash")`: attempt access index 9/9 in VECTOR_ELT
This happened in PipeOp classif.rpart's $train()
Backtrace:
▆
1. ├─po_cv$train(list(t)) at test_pipeop_tunethreshold.R:38:3
2. │ └─mlr3pipelines:::.__PipeOp__train(...)
3. │ ├─base::withCallingHandlers(...)
4. │ └─private$.train(input)
5. │ └─mlr3pipelines:::.__PipeOpTaskPreproc__.train(...)
6. │ └─private$.train_task(intask)
7. │ └─mlr3pipelines:::.__PipeOpLearnerCV__.train_task(...)
8. │ └─mlr3::resample(...)
9. │ └─ResultData$new(data, data_extra, store_backends = store_backends)
10. │ └─mlr3 (local) initialize(...)
11. │ └─mlr3:::.__ResultData__initialize(...)
12. │ ├─data[, `:=`("task_hash", task[[1L]]$hash), by = "uhash"]
13. │ └─data.table:::`[.data.table`(...)
14. └─base::.handleSimpleError(...)
15. └─mlr3pipelines (local) h(simpleError(msg, call))
── Error ('test_pipeop_tunethreshold.R:73:3'): tunethreshold graph works ───────
Error in ``[.data.table`(data, , `:=`("task_hash", task[[1L]]$hash), by = "uhash")`: attempt access index 9/9 in VECTOR_ELT
This happened in PipeOp classif.rpart's $train()
Backtrace:
▆
1. ├─graph$train(tsk("pima")) at test_pipeop_tunethreshold.R:73:3
2. │ └─mlr3pipelines:::.__Graph__train(...)
3. │ └─mlr3pipelines:::graph_reduce(self, input, "train", single_input)
4. │ └─op[[fun]](input)
5. │ └─mlr3pipelines:::.__PipeOp__train(...)
6. │ ├─base::withCallingHandlers(...)
7. │ └─private$.train(input)
8. │ └─mlr3pipelines:::.__PipeOpTaskPreproc__.train(...)
9. │ └─private$.train_task(intask)
10. │ └─mlr3pipelines:::.__PipeOpLearnerCV__.train_task(...)
11. │ └─mlr3::resample(...)
12. │ └─ResultData$new(data, data_extra, store_backends = store_backends)
13. │ └─mlr3 (local) initialize(...)
14. │ └─mlr3:::.__ResultData__initialize(...)
15. │ ├─data[, `:=`("task_hash", task[[1L]]$hash), by = "uhash"]
16. │ └─data.table:::`[.data.table`(...)
17. └─base::.handleSimpleError(...)
18. └─mlr3pipelines (local) h(simpleError(msg, call))
── Error ('test_pipeop_tunethreshold.R:101:3'): threshold works for classes that are not valid R names ──
Error in ``[.data.table`(data, , `:=`("task_hash", task[[1L]]$hash), by = "uhash")`: attempt access index 9/9 in VECTOR_ELT
This happened in PipeOp classif.rpart's $train()
Backtrace:
▆
1. ├─ppl$train(testtask) at test_pipeop_tunethreshold.R:101:3
2. │ └─mlr3pipelines:::.__Graph__train(...)
3. │ └─mlr3pipelines:::graph_reduce(self, input, "train", single_input)
4. │ └─op[[fun]](input)
5. │ └─mlr3pipelines:::.__PipeOp__train(...)
6. │ ├─base::withCallingHandlers(...)
7. │ └─private$.train(input)
8. │ └─mlr3pipelines:::.__PipeOpTaskPreproc__.train(...)
9. │ └─private$.train_task(intask)
10. │ └─mlr3pipelines:::.__PipeOpLearnerCV__.train_task(...)
11. │ └─mlr3::resample(...)
12. │ └─ResultData$new(data, data_extra, store_backends = store_backends)
13. │ └─mlr3 (local) initialize(...)
14. │ └─mlr3:::.__ResultData__initialize(...)
15. │ ├─data[, `:=`("task_hash", task[[1L]]$hash), by = "uhash"]
16. │ └─data.table:::`[.data.table`(...)
17. └─base::.handleSimpleError(...)
18. └─mlr3pipelines (local) h(simpleError(msg, call))
── Error ('test_pipeop_tunethreshold.R:260:3'): threshold graph transparency ───
Error in ``[.data.table`(data, , `:=`("task_hash", task[[1L]]$hash), by = "uhash")`: attempt access index 9/9 in VECTOR_ELT
This happened in PipeOp classif.rpart's $train()
Backtrace:
▆
1. ├─lrn_prob$train(t) at test_pipeop_tunethreshold.R:260:3
2. │ └─mlr3:::.__Learner__train(...)
3. │ └─mlr3:::learner_train(...)
4. │ └─mlr3misc::encapsulate(...)
5. │ ├─mlr3misc::invoke(...)
6. │ │ └─base::eval.parent(expr, n = 1L)
7. │ │ └─base::eval(expr, p)
8. │ │ └─base::eval(expr, p)
9. │ └─mlr3 (local) .f(learner = `<GrphLrnr>`, task = `<TskClssf>`)
10. │ └─get_private(learner)$.train(task)
11. │ └─mlr3pipelines:::.__GraphLearner__.train(...)
12. │ └─self$graph$train(task)
13. │ └─mlr3pipelines:::.__Graph__train(...)
14. │ └─mlr3pipelines:::graph_reduce(self, input, "train", single_input)
15. │ └─op[[fun]](input)
16. │ └─mlr3pipelines:::.__PipeOp__train(...)
17. │ ├─base::withCallingHandlers(...)
18. │ └─private$.train(input)
19. │ └─mlr3pipelines:::.__PipeOpTaskPreproc__.train(...)
20. │ └─private$.train_task(intask)
21. │ └─mlr3pipelines:::.__PipeOpLearnerCV__.train_task(...)
22. │ └─mlr3::resample(...)
23. │ └─ResultData$new(data, data_extra, store_backends = store_backends)
24. │ └─mlr3 (local) initialize(...)
25. │ └─mlr3:::.__ResultData__initialize(...)
26. │ ├─data[, `:=`("task_hash", task[[1L]]$hash), by = "uhash"]
27. │ └─data.table:::`[.data.table`(...)
28. └─base::.handleSimpleError(...)
29. └─mlr3pipelines (local) h(simpleError(msg, call))
── Error ('test_resample.R:13:3'): PipeOp - Resample ───────────────────────────
Error in ``[.data.table`(data, , `:=`("task_hash", task[[1L]]$hash), by = "uhash")`: attempt access index 9/9 in VECTOR_ELT
Backtrace:
▆
1. └─mlr3::resample(task, pp, resa) at test_resample.R:13:3
2. └─ResultData$new(data, data_extra, store_backends = store_backends)
3. └─mlr3 (local) initialize(...)
4. └─mlr3:::.__ResultData__initialize(...)
5. ├─data[, `:=`("task_hash", task[[1L]]$hash), by = "uhash"]
6. └─data.table:::`[.data.table`(...)
── Error ('test_usecases.R:153:3'): stacking ───────────────────────────────────
Error in ``[.data.table`(data, , `:=`("task_hash", task[[1L]]$hash), by = "uhash")`: attempt access index 9/9 in VECTOR_ELT
This happened in PipeOp classif.rpart's $train()
Backtrace:
▆
1. ├─pipe$train(task) at test_usecases.R:153:3
2. │ └─mlr3pipelines:::.__Graph__train(...)
3. │ └─mlr3pipelines:::graph_reduce(self, input, "train", single_input)
4. │ └─op[[fun]](input)
5. │ └─mlr3pipelines:::.__PipeOp__train(...)
6. │ ├─base::withCallingHandlers(...)
7. │ └─private$.train(input)
8. │ └─mlr3pipelines:::.__PipeOpTaskPreproc__.train(...)
9. │ └─private$.train_task(intask)
10. │ └─mlr3pipelines:::.__PipeOpLearnerCV__.train_task(...)
11. │ └─mlr3::resample(...)
12. │ └─ResultData$new(data, data_extra, store_backends = store_backends)
13. │ └─mlr3 (local) initialize(...)
14. │ └─mlr3:::.__ResultData__initialize(...)
15. │ ├─data[, `:=`("task_hash", task[[1L]]$hash), by = "uhash"]
16. │ └─data.table:::`[.data.table`(...)
17. └─base::.handleSimpleError(...)
18. └─mlr3pipelines (local) h(simpleError(msg, call))
── Error ('test_ppl.R:73:3'): mlr3book authors don't sleepwalk through life ────
Error in ``[.data.table`(data, , `:=`("task_hash", task[[1L]]$hash), by = "uhash")`: attempt access index 9/9 in VECTOR_ELT
This happened in PipeOp classif.rpart.classif.rpart's $train()
Backtrace:
▆
1. └─mlr3::benchmark(benchmark_grid(tasks, learners, rsmp("cv", folds = 2))) at test_ppl.R:73:3
2. └─mlr3:::future_map(...)
3. └─future.apply::future_mapply(...)
4. └─future.apply:::future_xapply(...)
5. └─base::tryCatch(...)
6. └─base (local) tryCatchList(expr, classes, parentenv, handlers)
7. └─base (local) tryCatchOne(...)
8. └─value[[3L]](cond)
9. └─future.apply:::onError(e, futures = fs, debug = debug)
[ FAIL 38 | WARN 2 | SKIP 98 | PASS 12316 ]
Error:
! Test failures.
Execution halted
Flavor: r-devel-linux-x86_64-debian-clang
Version: 0.10.0
Check: examples
Result: ERROR
Running examples in ‘mlr3pipelines-Ex.R’ failed
The error most likely occurred in:
> base::assign(".ptime", proc.time(), pos = "CheckExEnv")
> ### Name: mlr_graphs_stacking
> ### Title: Create A Graph to Perform Stacking.
> ### Aliases: mlr_graphs_stacking pipeline_stacking
>
> ### ** Examples
>
> ## Don't show:
> if (mlr3misc::require_namespaces("rpart", quietly = TRUE)) withAutoprint({ # examplesIf
+ ## End(Don't show)
+ library(mlr3)
+ library(mlr3learners)
+
+ base_learners = list(
+ lrn("classif.rpart", predict_type = "prob"),
+ lrn("classif.nnet", predict_type = "prob")
+ )
+ super_learner = lrn("classif.log_reg")
+
+ graph_stack = pipeline_stacking(base_learners, super_learner)
+ graph_learner = as_learner(graph_stack)
+ graph_learner$train(tsk("german_credit"))
+ ## Don't show:
+ }) # examplesIf
> library(mlr3)
> library(mlr3learners)
> base_learners = list(lrn("classif.rpart", predict_type = "prob"), lrn("classif.nnet",
+ predict_type = "prob"))
> super_learner = lrn("classif.log_reg")
> graph_stack = pipeline_stacking(base_learners, super_learner)
> graph_learner = as_learner(graph_stack)
> graph_learner$train(tsk("german_credit"))
INFO [17:16:54.479] [mlr3] Resampling 'cv' is being instantiated on task 'german_credit'
INFO [17:16:54.704] [mlr3] Applying learner 'classif.rpart' on task 'german_credit' (iter 1/3)
INFO [17:16:54.806] [mlr3] Applying learner 'classif.rpart' on task 'german_credit' (iter 2/3)
INFO [17:16:54.851] [mlr3] Applying learner 'classif.rpart' on task 'german_credit' (iter 3/3)
Error in `[.data.table`(data, , `:=`("task_hash", task[[1L]]$hash), by = "uhash") :
attempt access index 9/9 in VECTOR_ELT
This happened in PipeOp classif.rpart's $train()
Calls: withAutoprint ... .__ResultData__initialize -> [ -> [.data.table -> .handleSimpleError -> h
Execution halted
Flavor: r-devel-linux-x86_64-debian-gcc
Version: 0.10.0
Check: tests
Result: ERROR
Running ‘testthat.R’ [268s/131s]
Running the tests in ‘tests/testthat.R’ failed.
Complete output:
> if (requireNamespace("testthat", quietly = TRUE)) {
+ library("checkmate")
+ library("testthat")
+ library("mlr3")
+ library("paradox")
+ library("mlr3pipelines")
+ test_check("mlr3pipelines")
+ }
Starting 2 test processes.
> test_Graph.R: Training debug.multi with input list(input_1 = 1, input_2 = 1)
> test_PipeOp.R: Training test_autotrain
> test_PipeOp.R: Predicting test_autotrain
> test_PipeOp.R: Training test_autotrain
> test_PipeOp.R: Training test_autotrain
> test_PipeOp.R: Predicting test_autotrain
> test_PipeOp.R: Predicting test_autotrain
> test_PipeOp.R: Training test_autotrain
> test_PipeOp.R: Training test_autotrain
> test_PipeOp.R: Training test_autotrain
> test_PipeOp.R: Predicting test_autotrain
> test_PipeOp.R: Predicting test_autotrain
> test_PipeOp.R: Predicting test_autotrain
> test_PipeOp.R: Training test_autotrain
> test_PipeOp.R: Predicting test_autotrain
> test_PipeOp.R: Training test_autotrain
> test_PipeOp.R: Training test_autotrain
> test_PipeOp.R: Predicting test_autotrain
> test_PipeOp.R: Predicting test_autotrain
> test_PipeOp.R: Training test_autotrain
> test_PipeOp.R: Training test_autotrain
> test_PipeOp.R: Training test_autotrain
> test_PipeOp.R: Predicting test_autotrain
> test_PipeOp.R: Predicting test_autotrain
> test_PipeOp.R: Predicting test_autotrain
> test_PipeOp.R: Training test_autotrain
> test_PipeOp.R: Predicting test_autotrain
Saving _problems/test_conversion-143.R
Saving _problems/test_conversion-165.R
> test_filter_ensemble.R: OptimInstanceSingleCrit is deprecated. Use OptimInstanceBatchSingleCrit instead.
Saving _problems/test_filter_ensemble-291.R
Saving _problems/test_filter_ensemble-447.R
Saving _problems/test_mlr_graphs_bagging-49.R
Saving _problems/test_mlr_graphs_stacking-16.R
> test_mlr_graphs_robustify.R: 'as(<dgCMatrix>, "dgTMatrix")' is deprecated.
> test_mlr_graphs_robustify.R: Use 'as(., "TsparseMatrix")' instead.
> test_mlr_graphs_robustify.R: See help("Deprecated") and help("Matrix-depr
> test_mlr_graphs_robustify.R: ecated").
> test_multiplicities.R:
> test_multiplicities.R: [[1]]
> test_multiplicities.R: [1] 0
> test_multiplicities.R:
> test_multiplicities.R:
> test_pipeop_blsmote.R: [1] "Borderline-SMOTE done"
> test_pipeop_blsmote.R: [1] "Borderline-SMOTE done"
> test_pipeop_blsmote.R: [1] "Borderline-SMOTE done"
> test_pipeop_blsmote.R: [1] "Borderline-SMOTE done"
> test_pipeop_isomap.R: 2025-12-23 17:17:54.672851: Isomap START
> test_pipeop_isomap.R: 2025-12-23 17:17:54.673589: constructing knn graph
> test_pipeop_isomap.R: 2025-12-23 17:17:54.684494: calculating geodesic distances
> test_pipeop_isomap.R: 2025-12-23 17:17:54.699504: Classical Scaling
> test_pipeop_isomap.R: 2025-12-23 17:17:54.732475: Isomap START
> test_pipeop_isomap.R: 2025-12-23 17:17:54.732888: constructing knn graph
> test_pipeop_isomap.R: 2025-12-23 17:17:54.739253: calculating geodesic distances
> test_pipeop_isomap.R: 2025-12-23 17:17:54.75234: Classical Scaling
> test_pipeop_isomap.R: 2025-12-23 17:17:54.768773: L-Isomap embed START
> test_pipeop_isomap.R: 2025-12-23 17:17:54.769297: constructing knn graph
> test_pipeop_isomap.R: 2025-12-23 17:17:54.780684: calculating geodesic distances
> test_pipeop_isomap.R: 2025-12-23 17:17:54.810232: embedding
> test_pipeop_isomap.R: 2025-12-23 17:17:54.811125: DONE
> test_pipeop_isomap.R: 2025-12-23 17:17:54.840496: L-Isomap embed START
> test_pipeop_isomap.R: 2025-12-23 17:17:54.841004: constructing knn graph
> test_pipeop_isomap.R: 2025-12-23 17:17:54.859112: calculating geodesic distances
> test_pipeop_isomap.R: 2025-12-23 17:17:54.890909: embedding
> test_pipeop_isomap.R: 2025-12-23 17:17:54.892121: DONE
> test_pipeop_isomap.R: 2025-12-23 17:17:54.958309: Isomap START
> test_pipeop_isomap.R: 2025-12-23 17:17:54.958745: constructing knn graph
> test_pipeop_isomap.R: 2025-12-23 17:17:54.972453: calculating geodesic distances
> test_pipeop_isomap.R: 2025-12-23 17:17:55.047251: Classical Scaling
> test_pipeop_isomap.R: 2025-12-23 17:17:55.076885: L-Isomap embed START
> test_pipeop_isomap.R: 2025-12-23 17:17:55.077512: constructing knn graph
> test_pipeop_isomap.R: 2025-12-23 17:17:55.107137: calculating geodesic distances
> test_pipeop_isomap.R: 2025-12-23 17:17:55.306535: embedding
> test_pipeop_isomap.R: 2025-12-23 17:17:55.311115: DONE
> test_pipeop_isomap.R: 2025-12-23 17:17:55.430129: Isomap START
> test_pipeop_isomap.R: 2025-12-23 17:17:55.430538: constructing knn graph
> test_pipeop_isomap.R: 2025-12-23 17:17:55.43779: calculating geodesic distances
> test_pipeop_isomap.R: 2025-12-23 17:17:55.454951: Classical Scaling
> test_pipeop_isomap.R: 2025-12-23 17:17:55.477498: L-Isomap embed START
> test_pipeop_isomap.R: 2025-12-23 17:17:55.478074: constructing knn graph
> test_pipeop_isomap.R: 2025-12-23 17:17:55.491292: calculating geodesic distances
> test_pipeop_isomap.R: 2025-12-23 17:17:55.52447: embedding
> test_pipeop_isomap.R: 2025-12-23 17:17:55.525577: DONE
> test_pipeop_isomap.R: 2025-12-23 17:17:55.62195: Isomap START
> test_pipeop_isomap.R: 2025-12-23 17:17:55.622389: constructing knn graph
> test_pipeop_isomap.R: 2025-12-23 17:17:55.629362: calculating geodesic distances
> test_pipeop_isomap.R: 2025-12-23 17:17:55.643615: Classical Scaling
> test_pipeop_isomap.R: 2025-12-23 17:17:55.679188: L-Isomap embed START
> test_pipeop_isomap.R: 2025-12-23 17:17:55.679802: constructing knn graph
> test_pipeop_isomap.R: 2025-12-23 17:17:55.691752: calculating geodesic distances
> test_pipeop_isomap.R: 2025-12-23 17:17:55.724597: embedding
> test_pipeop_isomap.R: 2025-12-23 17:17:55.72559: DONE
> test_pipeop_isomap.R: 2025-12-23 17:17:55.782929: Isomap START
> test_pipeop_isomap.R: 2025-12-23 17:17:55.78331: constructing knn graph
> test_pipeop_isomap.R: 2025-12-23 17:17:55.789918: calculating geodesic distances
> test_pipeop_isomap.R: 2025-12-23 17:17:55.803876: Classical Scaling
> test_pipeop_isomap.R: 2025-12-23 17:17:55.856672: L-Isomap embed START
> test_pipeop_isomap.R: 2025-12-23 17:17:55.857393: constructing knn graph
> test_pipeop_isomap.R: 2025-12-23 17:17:55.869532: calculating geodesic distances
> test_pipeop_isomap.R: 2025-12-23 17:17:55.902623: embedding
> test_pipeop_isomap.R: 2025-12-23 17:17:55.903677: DONE
> test_pipeop_isomap.R: 2025-12-23 17:17:55.95722: Isomap START
> test_pipeop_isomap.R: 2025-12-23 17:17:55.957591: constructing knn graph
> test_pipeop_isomap.R: 2025-12-23 17:17:55.96374: calculating geodesic distances
> test_pipeop_isomap.R: 2025-12-23 17:17:55.977802: Classical Scaling
> test_pipeop_isomap.R: 2025-12-23 17:17:56.01355: L-Isomap embed START
> test_pipeop_isomap.R: 2025-12-23 17:17:56.014119: constructing knn graph
> test_pipeop_isomap.R: 2025-12-23 17:17:56.02603: calculating geodesic distances
> test_pipeop_isomap.R: 2025-12-23 17:17:56.058411: embedding
> test_pipeop_isomap.R: 2025-12-23 17:17:56.059306: DONE
> test_pipeop_isomap.R: 2025-12-23 17:17:56.1113: Isomap START
> test_pipeop_isomap.R: 2025-12-23 17:17:56.111747: constructing knn graph
> test_pipeop_isomap.R: 2025-12-23 17:17:56.118132: calculating geodesic distances
> test_pipeop_isomap.R: 2025-12-23 17:17:56.13232: Classical Scaling
> test_pipeop_isomap.R: 2025-12-23 17:17:56.165102: L-Isomap embed START
> test_pipeop_isomap.R: 2025-12-23 17:17:56.165663: constructing knn graph
> test_pipeop_isomap.R: 2025-12-23 17:17:56.177414: calculating geodesic distances
> test_pipeop_isomap.R: 2025-12-23 17:17:56.210193: embedding
> test_pipeop_isomap.R: 2025-12-23 17:17:56.211251: DONE
> test_pipeop_isomap.R: 2025-12-23 17:17:56.271642: Isomap START
> test_pipeop_isomap.R: 2025-12-23 17:17:56.271991: constructing knn graph
> test_pipeop_isomap.R: 2025-12-23 17:17:56.28994: calculating geodesic distances
> test_pipeop_isomap.R: 2025-12-23 17:17:56.306154: Classical Scaling
> test_pipeop_isomap.R: 2025-12-23 17:17:56.36962: Isomap START
> test_pipeop_isomap.R: 2025-12-23 17:17:56.370035: constructing knn graph
> test_pipeop_isomap.R: 2025-12-23 17:17:56.376426: calculating geodesic distances
> test_pipeop_isomap.R: 2025-12-23 17:17:56.390361: Classical Scaling
> test_pipeop_isomap.R: 2025-12-23 17:17:56.407629: Isomap START
> test_pipeop_isomap.R: 2025-12-23 17:17:56.408023: constructing knn graph
> test_pipeop_isomap.R: 2025-12-23 17:17:56.41439: calculating geodesic distances
> test_pipeop_isomap.R: 2025-12-23 17:17:56.428446: 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
> test_pipeop_textvectorizer.R: 'as(<dgCMatrix>, "dgTMatrix")' is deprecated.
> test_pipeop_textvectorizer.R: Use 'as(., "TsparseMatrix")' instead.
> test_pipeop_textvectorizer.R: See help("Deprecated") and help("Matrix-deprecated").
Saving _problems/test_pipeop_tunethreshold-7.R
Saving _problems/test_pipeop_tunethreshold-38.R
Saving _problems/test_pipeop_tunethreshold-73.R
Saving _problems/test_pipeop_tunethreshold-101.R
Saving _problems/test_pipeop_tunethreshold-260.R
Saving _problems/test_resample-13.R
Saving _problems/test_ppl-73.R
Saving _problems/test_usecases-153.R
[ FAIL 38 | WARN 2 | SKIP 98 | PASS 12316 ]
══ Skipped tests (98) ══════════════════════════════════════════════════════════
• On CRAN (95): 'test_CnfFormula_simplify.R:6:3', 'test_CnfFormula.R:591:3',
'test_Graph.R:283:3', 'test_PipeOp.R:32:1', 'test_GraphLearner.R:5:3',
'test_GraphLearner.R:221:3', 'test_GraphLearner.R:343:3',
'test_GraphLearner.R:408:3', 'test_GraphLearner.R:571:3',
'test_dictionary.R:7:3', 'test_learner_weightedaverage.R:5:3',
'test_learner_weightedaverage.R:57:3',
'test_learner_weightedaverage.R:105:3',
'test_learner_weightedaverage.R:152:3', 'test_meta.R:39:3',
'test_mlr_graphs_bagging.R:6:3', 'test_mlr_graphs_branching.R:26:3',
'test_mlr_graphs_robustify.R:5:3', 'test_pipeop_adas.R:8:3',
'test_pipeop_blsmote.R:8:3', 'test_pipeop_branch.R:4:3',
'test_pipeop_chunk.R:4:3', 'test_pipeop_boxcox.R:7:3',
'test_pipeop_classbalancing.R:7:3', 'test_pipeop_classweights.R:10:3',
'test_pipeop_collapsefactors.R:6:3', 'test_pipeop_colapply.R:9:3',
'test_pipeop_copy.R:5:3', 'test_pipeop_colroles.R:6:3',
'test_pipeop_decode.R:14:3', 'test_pipeop_encode.R:21:3',
'test_pipeop_encodeimpact.R:11:3', 'test_pipeop_datefeatures.R:10:3',
'test_pipeop_encodepl.R:5:3', 'test_pipeop_encodepl.R:72:3',
'test_pipeop_encodelmer.R:15:3', 'test_pipeop_encodelmer.R:37:3',
'test_pipeop_encodelmer.R:80:3', 'test_pipeop_featureunion.R:9:3',
'test_pipeop_featureunion.R:134:3', 'test_pipeop_filter.R:7:3',
'test_pipeop_fixfactors.R:9:3', 'test_pipeop_histbin.R:7:3',
'test_pipeop_ica.R:7:3', 'test_pipeop_ensemble.R:6:3',
'test_pipeop_impute.R:4:3', 'test_pipeop_imputelearner.R:43:3',
'test_pipeop_isomap.R:10:3', 'test_pipeop_kernelpca.R:9:3',
'test_pipeop_learner.R:17:3', 'test_pipeop_info.R:6:3',
'test_pipeop_learnerpicvplus.R:163:3', 'test_pipeop_modelmatrix.R:7:3',
'test_pipeop_missind.R:6:3', 'test_pipeop_multiplicityexply.R:9:3',
'test_pipeop_mutate.R:9:3', 'test_pipeop_nearmiss.R:7:3',
'test_pipeop_multiplicityimply.R:9:3', 'test_pipeop_ovr.R:9:3',
'test_pipeop_ovr.R:48:3', 'test_pipeop_pca.R:8:3',
'test_pipeop_proxy.R:14:3', 'test_pipeop_quantilebin.R:5:3',
'test_pipeop_randomprojection.R:6:3', 'test_pipeop_randomresponse.R:5:3',
'test_pipeop_removeconstants.R:6:3', 'test_pipeop_renamecolumns.R:6:3',
'test_pipeop_replicate.R:9:3', 'test_pipeop_rowapply.R:6:3',
'test_pipeop_scale.R:6:3', 'test_pipeop_scale.R:10:3',
'test_pipeop_scalemaxabs.R:6:3', 'test_pipeop_scalerange.R:7:3',
'test_pipeop_select.R:9:3', 'test_pipeop_smote.R:10:3',
'test_pipeop_smotenc.R:8:3', 'test_pipeop_spatialsign.R:6:3',
'test_pipeop_nmf.R:6:3', 'test_pipeop_targetinvert.R:4:3',
'test_pipeop_targetmutate.R:5:3', 'test_pipeop_targettrafo.R:4:3',
'test_pipeop_targettrafoscalerange.R:5:3', 'test_pipeop_task_preproc.R:4:3',
'test_pipeop_task_preproc.R:14:3', 'test_pipeop_subsample.R:6:3',
'test_pipeop_tomek.R:7:3', 'test_pipeop_tunethreshold.R:111:3',
'test_pipeop_tunethreshold.R:191:3', 'test_pipeop_textvectorizer.R:37:3',
'test_pipeop_textvectorizer.R:186:3', 'test_pipeop_unbranch.R:10:3',
'test_pipeop_updatetarget.R:89:3', 'test_pipeop_vtreat.R:9:3',
'test_pipeop_yeojohnson.R:7:3', 'test_typecheck.R:188:3'
• Skipping (1): 'test_GraphLearner.R:1278:3'
• empty test (2): 'test_pipeop_isomap.R:111:1', 'test_pipeop_missind.R:101:1'
══ Failed tests ════════════════════════════════════════════════════════════════
── Error ('test_conversion.R:143:3'): Graph to GraphLearner ────────────────────
Error in ``[.data.table`(data, , `:=`("task_hash", task[[1L]]$hash), by = "uhash")`: attempt access index 9/9 in VECTOR_ELT
Backtrace:
▆
1. └─mlr3::resample(task, glrn1, cv) at test_conversion.R:143:3
2. └─ResultData$new(data, data_extra, store_backends = store_backends)
3. └─mlr3 (local) initialize(...)
4. └─mlr3:::.__ResultData__initialize(...)
5. ├─data[, `:=`("task_hash", task[[1L]]$hash), by = "uhash"]
6. └─data.table:::`[.data.table`(...)
── Error ('test_conversion.R:165:3'): PipeOp to GraphLearner ───────────────────
Error in ``[.data.table`(data, , `:=`("task_hash", task[[1L]]$hash), by = "uhash")`: attempt access index 9/9 in VECTOR_ELT
Backtrace:
▆
1. └─mlr3::resample(task, glrn1, cv) at test_conversion.R:165:3
2. └─ResultData$new(data, data_extra, store_backends = store_backends)
3. └─mlr3 (local) initialize(...)
4. └─mlr3:::.__ResultData__initialize(...)
5. ├─data[, `:=`("task_hash", task[[1L]]$hash), by = "uhash"]
6. └─data.table:::`[.data.table`(...)
── Error ('test_filter_ensemble.R:291:3'): FilterEnsemble ignores NA scores from wrapped filters ──
Error in ``[.data.table`(data, , `:=`("task_hash", task[[1L]]$hash), by = "uhash")`: attempt access index 9/9 in VECTOR_ELT
Backtrace:
▆
1. └─permutation_filter$calculate(task) at test_filter_ensemble.R:291:3
2. └─mlr3filters:::.__Filter__calculate(...)
3. └─private$.calculate(task, nfeat)
4. └─mlr3filters:::.__FilterPermutation__.calculate(...)
5. └─mlr3::resample(task, self$learner, self$resampling)
6. └─ResultData$new(data, data_extra, store_backends = store_backends)
7. └─mlr3 (local) initialize(...)
8. └─mlr3:::.__ResultData__initialize(...)
9. ├─data[, `:=`("task_hash", task[[1L]]$hash), by = "uhash"]
10. └─data.table:::`[.data.table`(...)
── Error ('test_filter_ensemble.R:447:7'): FilterEnsemble weight search space works with bbotk ──
Error in ``[.data.table`(data, , `:=`("task_hash", task[[1L]]$hash), by = "uhash")`: attempt access index 9/9 in VECTOR_ELT
Backtrace:
▆
1. └─optimizer$optimize(instance) at test_filter_ensemble.R:463:3
2. └─bbotk:::.__OptimizerBatch__optimize(...)
3. └─bbotk::optimize_batch_default(inst, self)
4. ├─base::tryCatch(...)
5. │ └─base (local) tryCatchList(expr, classes, parentenv, handlers)
6. │ └─base (local) tryCatchOne(expr, names, parentenv, handlers[[1L]])
7. │ └─base (local) doTryCatch(return(expr), name, parentenv, handler)
8. └─get_private(optimizer)$.optimize(instance)
9. └─bbotk:::.__OptimizerBatchRandomSearch__.optimize(...)
10. └─inst$eval_batch(design$data)
11. └─bbotk:::.__OptimInstanceBatch__eval_batch(...)
12. └─self$objective$eval_many(xss_trafoed)
13. └─bbotk:::.__Objective__eval_many(...)
14. ├─mlr3misc::invoke(private$.eval_many, xss = xss, .args = self$constants$values)
15. │ └─base::eval.parent(expr, n = 1L)
16. │ └─base::eval(expr, p)
17. │ └─base::eval(expr, p)
18. └─private$.eval_many(xss = xss)
19. └─bbotk:::.__Objective__.eval_many(...)
20. └─mlr3misc::map_dtr(...)
21. ├─data.table::rbindlist(...)
22. ├─base::unname(map(.x, .f, ...))
23. └─mlr3misc::map(.x, .f, ...)
24. └─base::lapply(.x, .f, ...)
25. └─bbotk (local) FUN(X[[i]], ...)
26. └─self$eval(xs)
27. └─bbotk:::.__ObjectiveRFun__eval(...)
28. ├─mlr3misc::invoke(private$.fun, xs, .args = self$constants$values)
29. │ └─base::eval.parent(expr, n = 1L)
30. │ └─base::eval(expr, p)
31. │ └─base::eval(expr, p)
32. └─private$.fun(xs)
33. └─mlr3::resample(task, learner, resampling) at test_filter_ensemble.R:447:7
34. └─ResultData$new(data, data_extra, store_backends = store_backends)
35. └─mlr3 (local) initialize(...)
36. └─mlr3:::.__ResultData__initialize(...)
37. ├─data[, `:=`("task_hash", task[[1L]]$hash), by = "uhash"]
38. └─data.table:::`[.data.table`(...)
── Error ('test_mlr_graphs_bagging.R:49:3'): Bagging with replacement ──────────
Error in ``[.data.table`(data, , `:=`("task_hash", task[[1L]]$hash), by = "uhash")`: attempt access index 9/9 in VECTOR_ELT
Backtrace:
▆
1. └─mlr3::resample(tsk, GraphLearner$new(p), rsmp("holdout")) at test_mlr_graphs_bagging.R:49:3
2. └─ResultData$new(data, data_extra, store_backends = store_backends)
3. └─mlr3 (local) initialize(...)
4. └─mlr3:::.__ResultData__initialize(...)
5. ├─data[, `:=`("task_hash", task[[1L]]$hash), by = "uhash"]
6. └─data.table:::`[.data.table`(...)
── Error ('test_mlr_graphs_stacking.R:16:3'): Stacking Pipeline ────────────────
Error in ``[.data.table`(data, , `:=`("task_hash", task[[1L]]$hash), by = "uhash")`: attempt access index 9/9 in VECTOR_ELT
This happened in PipeOp base.rpart's $train()
Backtrace:
▆
1. ├─graph_learner$train(tsk("iris")) at test_mlr_graphs_stacking.R:16:3
2. │ └─mlr3:::.__Learner__train(...)
3. │ └─mlr3:::learner_train(...)
4. │ └─mlr3misc::encapsulate(...)
5. │ ├─mlr3misc::invoke(...)
6. │ │ └─base::eval.parent(expr, n = 1L)
7. │ │ └─base::eval(expr, p)
8. │ │ └─base::eval(expr, p)
9. │ └─mlr3 (local) .f(learner = `<GrphLrnr>`, task = `<TskClssf>`)
10. │ └─get_private(learner)$.train(task)
11. │ └─mlr3pipelines:::.__GraphLearner__.train(...)
12. │ └─self$graph$train(task)
13. │ └─mlr3pipelines:::.__Graph__train(...)
14. │ └─mlr3pipelines:::graph_reduce(self, input, "train", single_input)
15. │ └─op[[fun]](input)
16. │ └─mlr3pipelines:::.__PipeOp__train(...)
17. │ ├─base::withCallingHandlers(...)
18. │ └─private$.train(input)
19. │ └─mlr3pipelines:::.__PipeOpTaskPreproc__.train(...)
20. │ └─private$.train_task(intask)
21. │ └─mlr3pipelines:::.__PipeOpLearnerCV__.train_task(...)
22. │ └─mlr3::resample(...)
23. │ └─ResultData$new(data, data_extra, store_backends = store_backends)
24. │ └─mlr3 (local) initialize(...)
25. │ └─mlr3:::.__ResultData__initialize(...)
26. │ ├─data[, `:=`("task_hash", task[[1L]]$hash), by = "uhash"]
27. │ └─data.table:::`[.data.table`(...)
28. └─base::.handleSimpleError(...)
29. └─mlr3pipelines (local) h(simpleError(msg, call))
── Error ('test_pipeop_learnerpicvplus.R:35:3'): PipeOpLearnerPICVPlus - basic properties ──
Error in ``[.data.table`(data, , `:=`("task_hash", task[[1L]]$hash), by = "uhash")`: attempt access index 9/9 in VECTOR_ELT
This happened in PipeOp regr.featureless's $train()
Backtrace:
▆
1. ├─global train_pipeop(po, list(task)) at test_pipeop_learnerpicvplus.R:35:3
2. │ └─po$train(inputs)
3. │ └─mlr3pipelines:::.__PipeOp__train(...)
4. │ ├─base::withCallingHandlers(...)
5. │ └─private$.train(input)
6. │ └─mlr3pipelines:::.__PipeOpLearnerPICVPlus__.train(...)
7. │ └─mlr3::resample(task, private$.learner, rdesc, store_models = TRUE)
8. │ └─ResultData$new(data, data_extra, store_backends = store_backends)
9. │ └─mlr3 (local) initialize(...)
10. │ └─mlr3:::.__ResultData__initialize(...)
11. │ ├─data[, `:=`("task_hash", task[[1L]]$hash), by = "uhash"]
12. │ └─data.table:::`[.data.table`(...)
13. └─base::.handleSimpleError(...)
14. └─mlr3pipelines (local) h(simpleError(msg, call))
── Error ('test_pipeop_learnerpicvplus.R:91:3'): PipeOpLearnerPICVPlus - model active binding to state ──
Error in ``[.data.table`(data, , `:=`("task_hash", task[[1L]]$hash), by = "uhash")`: attempt access index 9/9 in VECTOR_ELT
This happened in PipeOp regr.featureless's $train()
Backtrace:
▆
1. ├─po$train(list(task)) at test_pipeop_learnerpicvplus.R:91:3
2. │ └─mlr3pipelines:::.__PipeOp__train(...)
3. │ ├─base::withCallingHandlers(...)
4. │ └─private$.train(input)
5. │ └─mlr3pipelines:::.__PipeOpLearnerPICVPlus__.train(...)
6. │ └─mlr3::resample(task, private$.learner, rdesc, store_models = TRUE)
7. │ └─ResultData$new(data, data_extra, store_backends = store_backends)
8. │ └─mlr3 (local) initialize(...)
9. │ └─mlr3:::.__ResultData__initialize(...)
10. │ ├─data[, `:=`("task_hash", task[[1L]]$hash), by = "uhash"]
11. │ └─data.table:::`[.data.table`(...)
12. └─base::.handleSimpleError(...)
13. └─mlr3pipelines (local) h(simpleError(msg, call))
── Error ('test_pipeop_learnerpicvplus.R:116:3'): PipeOpLearnerPICVPlus - integration with larger graph ──
Error in ``[.data.table`(data, , `:=`("task_hash", task[[1L]]$hash), by = "uhash")`: attempt access index 9/9 in VECTOR_ELT
This happened in PipeOp regr.rpart's $train()
Backtrace:
▆
1. ├─graph$train(task) at test_pipeop_learnerpicvplus.R:116:3
2. │ └─mlr3pipelines:::.__Graph__train(...)
3. │ └─mlr3pipelines:::graph_reduce(self, input, "train", single_input)
4. │ └─op[[fun]](input)
5. │ └─mlr3pipelines:::.__PipeOp__train(...)
6. │ ├─base::withCallingHandlers(...)
7. │ └─private$.train(input)
8. │ └─mlr3pipelines:::.__PipeOpLearnerPICVPlus__.train(...)
9. │ └─mlr3::resample(task, private$.learner, rdesc, store_models = TRUE)
10. │ └─ResultData$new(data, data_extra, store_backends = store_backends)
11. │ └─mlr3 (local) initialize(...)
12. │ └─mlr3:::.__ResultData__initialize(...)
13. │ ├─data[, `:=`("task_hash", task[[1L]]$hash), by = "uhash"]
14. │ └─data.table:::`[.data.table`(...)
15. └─base::.handleSimpleError(...)
16. └─mlr3pipelines (local) h(simpleError(msg, call))
── Error ('test_pipeop_learnerpicvplus.R:130:3'): marshal ──────────────────────
Error in ``[.data.table`(data, , `:=`("task_hash", task[[1L]]$hash), by = "uhash")`: attempt access index 9/9 in VECTOR_ELT
This happened in PipeOp regr.debug's $train()
Backtrace:
▆
1. ├─po$train(list(task)) at test_pipeop_learnerpicvplus.R:130:3
2. │ └─mlr3pipelines:::.__PipeOp__train(...)
3. │ ├─base::withCallingHandlers(...)
4. │ └─private$.train(input)
5. │ └─mlr3pipelines:::.__PipeOpLearnerPICVPlus__.train(...)
6. │ └─mlr3::resample(task, private$.learner, rdesc, store_models = TRUE)
7. │ └─ResultData$new(data, data_extra, store_backends = store_backends)
8. │ └─mlr3 (local) initialize(...)
9. │ └─mlr3:::.__ResultData__initialize(...)
10. │ ├─data[, `:=`("task_hash", task[[1L]]$hash), by = "uhash"]
11. │ └─data.table:::`[.data.table`(...)
12. └─base::.handleSimpleError(...)
13. └─mlr3pipelines (local) h(simpleError(msg, call))
── Error ('test_pipeop_learnerpicvplus.R:152:3'): marshal multiplicity ─────────
Error in ``[.data.table`(data, , `:=`("task_hash", task[[1L]]$hash), by = "uhash")`: attempt access index 9/9 in VECTOR_ELT
This happened in PipeOp regr.debug's $train()
Backtrace:
▆
1. ├─po$train(list(Multiplicity(task1, task2))) at test_pipeop_learnerpicvplus.R:152:3
2. │ └─mlr3pipelines:::.__PipeOp__train(...)
3. │ └─mlr3pipelines:::evaluate_multiplicities(...)
4. │ └─mlr3misc::imap(...)
5. │ ├─stats::setNames(mapply_list(.f, list(.x, .nn), list(...)), names(.x))
6. │ └─mlr3misc:::mapply_list(.f, list(.x, .nn), list(...))
7. │ └─base::.mapply(.f, .dots, .args)
8. │ └─mlr3pipelines (local) `<fn>`(dots[[1L]][[1L]], dots[[2L]][[1L]])
9. │ └─self[[evalcall]](input)
10. │ └─mlr3pipelines:::.__PipeOp__train(...)
11. │ ├─base::withCallingHandlers(...)
12. │ └─private$.train(input)
13. │ └─mlr3pipelines:::.__PipeOpLearnerPICVPlus__.train(...)
14. │ └─mlr3::resample(task, private$.learner, rdesc, store_models = TRUE)
15. │ └─ResultData$new(data, data_extra, store_backends = store_backends)
16. │ └─mlr3 (local) initialize(...)
17. │ └─mlr3:::.__ResultData__initialize(...)
18. │ ├─data[, `:=`("task_hash", task[[1L]]$hash), by = "uhash"]
19. │ └─data.table:::`[.data.table`(...)
20. └─base::.handleSimpleError(...)
21. └─mlr3pipelines (local) h(simpleError(msg, call))
── Error ('test_pipeop_learnercv.R:11:3'): PipeOpLearnerCV - basic properties ──
Error in ``[.data.table`(data, , `:=`("task_hash", task[[1L]]$hash), by = "uhash")`: attempt access index 9/9 in VECTOR_ELT
This happened in PipeOp classif.featureless's $train()
Backtrace:
▆
1. ├─global train_pipeop(po, list(task = task)) at test_pipeop_learnercv.R:11:3
2. │ └─po$train(inputs)
3. │ └─mlr3pipelines:::.__PipeOp__train(...)
4. │ ├─base::withCallingHandlers(...)
5. │ └─private$.train(input)
6. │ └─mlr3pipelines:::.__PipeOpTaskPreproc__.train(...)
7. │ └─private$.train_task(intask)
8. │ └─mlr3pipelines:::.__PipeOpLearnerCV__.train_task(...)
9. │ └─mlr3::resample(...)
10. │ └─ResultData$new(data, data_extra, store_backends = store_backends)
11. │ └─mlr3 (local) initialize(...)
12. │ └─mlr3:::.__ResultData__initialize(...)
13. │ ├─data[, `:=`("task_hash", task[[1L]]$hash), by = "uhash"]
14. │ └─data.table:::`[.data.table`(...)
15. └─base::.handleSimpleError(...)
16. └─mlr3pipelines (local) h(simpleError(msg, call))
── Error ('test_pipeop_learnercv.R:100:3'): PipeOpLearnerCV - cv ensemble averages fold learners ──
Error in ``[.data.table`(data, , `:=`("task_hash", task[[1L]]$hash), by = "uhash")`: attempt access index 9/9 in VECTOR_ELT
This happened in PipeOp classif.rpart's $train()
Backtrace:
▆
1. ├─po$train(list(task)) at test_pipeop_learnercv.R:100:3
2. │ └─mlr3pipelines:::.__PipeOp__train(...)
3. │ ├─base::withCallingHandlers(...)
4. │ └─private$.train(input)
5. │ └─mlr3pipelines:::.__PipeOpTaskPreproc__.train(...)
6. │ └─private$.train_task(intask)
7. │ └─mlr3pipelines:::.__PipeOpLearnerCV__.train_task(...)
8. │ └─mlr3::resample(...)
9. │ └─ResultData$new(data, data_extra, store_backends = store_backends)
10. │ └─mlr3 (local) initialize(...)
11. │ └─mlr3:::.__ResultData__initialize(...)
12. │ ├─data[, `:=`("task_hash", task[[1L]]$hash), by = "uhash"]
13. │ └─data.table:::`[.data.table`(...)
14. └─base::.handleSimpleError(...)
15. └─mlr3pipelines (local) h(simpleError(msg, call))
── Error ('test_pipeop_learnercv.R:139:3'): PipeOpLearnerCV - cv ensemble drops response when requested ──
Error in ``[.data.table`(data, , `:=`("task_hash", task[[1L]]$hash), by = "uhash")`: attempt access index 9/9 in VECTOR_ELT
This happened in PipeOp classif.rpart's $train()
Backtrace:
▆
1. ├─po$train(list(task)) at test_pipeop_learnercv.R:139:3
2. │ └─mlr3pipelines:::.__PipeOp__train(...)
3. │ ├─base::withCallingHandlers(...)
4. │ └─private$.train(input)
5. │ └─mlr3pipelines:::.__PipeOpTaskPreproc__.train(...)
6. │ └─private$.train_task(intask)
7. │ └─mlr3pipelines:::.__PipeOpLearnerCV__.train_task(...)
8. │ └─mlr3::resample(...)
9. │ └─ResultData$new(data, data_extra, store_backends = store_backends)
10. │ └─mlr3 (local) initialize(...)
11. │ └─mlr3:::.__ResultData__initialize(...)
12. │ ├─data[, `:=`("task_hash", task[[1L]]$hash), by = "uhash"]
13. │ └─data.table:::`[.data.table`(...)
14. └─base::.handleSimpleError(...)
15. └─mlr3pipelines (local) h(simpleError(msg, call))
── Error ('test_pipeop_learnercv.R:152:3'): PipeOpLearnerCV - cv ensemble averages classif responses ──
Error in ``[.data.table`(data, , `:=`("task_hash", task[[1L]]$hash), by = "uhash")`: attempt access index 9/9 in VECTOR_ELT
This happened in PipeOp classif.rpart's $train()
Backtrace:
▆
1. ├─po$train(list(task)) at test_pipeop_learnercv.R:152:3
2. │ └─mlr3pipelines:::.__PipeOp__train(...)
3. │ ├─base::withCallingHandlers(...)
4. │ └─private$.train(input)
5. │ └─mlr3pipelines:::.__PipeOpTaskPreproc__.train(...)
6. │ └─private$.train_task(intask)
7. │ └─mlr3pipelines:::.__PipeOpLearnerCV__.train_task(...)
8. │ └─mlr3::resample(...)
9. │ └─ResultData$new(data, data_extra, store_backends = store_backends)
10. │ └─mlr3 (local) initialize(...)
11. │ └─mlr3:::.__ResultData__initialize(...)
12. │ ├─data[, `:=`("task_hash", task[[1L]]$hash), by = "uhash"]
13. │ └─data.table:::`[.data.table`(...)
14. └─base::.handleSimpleError(...)
15. └─mlr3pipelines (local) h(simpleError(msg, call))
── Error ('test_pipeop_learnercv.R:203:3'): PipeOpLearnerCV - cv ensemble log prob aggregation ──
Error in ``[.data.table`(data, , `:=`("task_hash", task[[1L]]$hash), by = "uhash")`: attempt access index 9/9 in VECTOR_ELT
This happened in PipeOp classif.rpart's $train()
Backtrace:
▆
1. ├─po$train(list(task)) at test_pipeop_learnercv.R:203:3
2. │ └─mlr3pipelines:::.__PipeOp__train(...)
3. │ ├─base::withCallingHandlers(...)
4. │ └─private$.train(input)
5. │ └─mlr3pipelines:::.__PipeOpTaskPreproc__.train(...)
6. │ └─private$.train_task(intask)
7. │ └─mlr3pipelines:::.__PipeOpLearnerCV__.train_task(...)
8. │ └─mlr3::resample(...)
9. │ └─ResultData$new(data, data_extra, store_backends = store_backends)
10. │ └─mlr3 (local) initialize(...)
11. │ └─mlr3:::.__ResultData__initialize(...)
12. │ ├─data[, `:=`("task_hash", task[[1L]]$hash), by = "uhash"]
13. │ └─data.table:::`[.data.table`(...)
14. └─base::.handleSimpleError(...)
15. └─mlr3pipelines (local) h(simpleError(msg, call))
── Error ('test_pipeop_learnercv.R:250:3'): PipeOpLearnerCV - log aggregation with zeros uses epsilon ──
Error in ``[.data.table`(data, , `:=`("task_hash", task[[1L]]$hash), by = "uhash")`: attempt access index 9/9 in VECTOR_ELT
This happened in PipeOp classif.featureless's $train()
Backtrace:
▆
1. ├─po$train(list(task)) at test_pipeop_learnercv.R:250:3
2. │ └─mlr3pipelines:::.__PipeOp__train(...)
3. │ ├─base::withCallingHandlers(...)
4. │ └─private$.train(input)
5. │ └─mlr3pipelines:::.__PipeOpTaskPreproc__.train(...)
6. │ └─private$.train_task(intask)
7. │ └─mlr3pipelines:::.__PipeOpLearnerCV__.train_task(...)
8. │ └─mlr3::resample(...)
9. │ └─ResultData$new(data, data_extra, store_backends = store_backends)
10. │ └─mlr3 (local) initialize(...)
11. │ └─mlr3:::.__ResultData__initialize(...)
12. │ ├─data[, `:=`("task_hash", task[[1L]]$hash), by = "uhash"]
13. │ └─data.table:::`[.data.table`(...)
14. └─base::.handleSimpleError(...)
15. └─mlr3pipelines (local) h(simpleError(msg, call))
── Error ('test_pipeop_learnercv.R:278:3'): PipeOpLearnerCV - log aggregation epsilon controls shrinkage ──
Error in ``[.data.table`(data, , `:=`("task_hash", task[[1L]]$hash), by = "uhash")`: attempt access index 9/9 in VECTOR_ELT
This happened in PipeOp classif.featureless's $train()
Backtrace:
▆
1. ├─po$train(list(task)) at test_pipeop_learnercv.R:278:3
2. │ └─mlr3pipelines:::.__PipeOp__train(...)
3. │ ├─base::withCallingHandlers(...)
4. │ └─private$.train(input)
5. │ └─mlr3pipelines:::.__PipeOpTaskPreproc__.train(...)
6. │ └─private$.train_task(intask)
7. │ └─mlr3pipelines:::.__PipeOpLearnerCV__.train_task(...)
8. │ └─mlr3::resample(...)
9. │ └─ResultData$new(data, data_extra, store_backends = store_backends)
10. │ └─mlr3 (local) initialize(...)
11. │ └─mlr3:::.__ResultData__initialize(...)
12. │ ├─data[, `:=`("task_hash", task[[1L]]$hash), by = "uhash"]
13. │ └─data.table:::`[.data.table`(...)
14. └─base::.handleSimpleError(...)
15. └─mlr3pipelines (local) h(simpleError(msg, call))
── Error ('test_pipeop_learnercv.R:323:3'): PipeOpLearnerCV - cv ensemble averages regression predictions ──
Error in ``[.data.table`(data, , `:=`("task_hash", task[[1L]]$hash), by = "uhash")`: attempt access index 9/9 in VECTOR_ELT
This happened in PipeOp regr.rpart's $train()
Backtrace:
▆
1. ├─po$train(list(task)) at test_pipeop_learnercv.R:323:3
2. │ └─mlr3pipelines:::.__PipeOp__train(...)
3. │ ├─base::withCallingHandlers(...)
4. │ └─private$.train(input)
5. │ └─mlr3pipelines:::.__PipeOpTaskPreproc__.train(...)
6. │ └─private$.train_task(intask)
7. │ └─mlr3pipelines:::.__PipeOpLearnerCV__.train_task(...)
8. │ └─mlr3::resample(...)
9. │ └─ResultData$new(data, data_extra, store_backends = store_backends)
10. │ └─mlr3 (local) initialize(...)
11. │ └─mlr3:::.__ResultData__initialize(...)
12. │ ├─data[, `:=`("task_hash", task[[1L]]$hash), by = "uhash"]
13. │ └─data.table:::`[.data.table`(...)
14. └─base::.handleSimpleError(...)
15. └─mlr3pipelines (local) h(simpleError(msg, call))
── Error ('test_pipeop_learnercv.R:350:3'): PipeOpLearnerCV - cv ensemble handles multiplicity ──
Error in ``[.data.table`(data, , `:=`("task_hash", task[[1L]]$hash), by = "uhash")`: attempt access index 9/9 in VECTOR_ELT
This happened in PipeOp classif.rpart's $train()
Backtrace:
▆
1. ├─po$train(list(tasks)) at test_pipeop_learnercv.R:350:3
2. │ └─mlr3pipelines:::.__PipeOp__train(...)
3. │ └─mlr3pipelines:::evaluate_multiplicities(...)
4. │ └─mlr3misc::imap(...)
5. │ ├─stats::setNames(mapply_list(.f, list(.x, .nn), list(...)), names(.x))
6. │ └─mlr3misc:::mapply_list(.f, list(.x, .nn), list(...))
7. │ └─base::.mapply(.f, .dots, .args)
8. │ └─mlr3pipelines (local) `<fn>`(dots[[1L]][[1L]], dots[[2L]][[1L]])
9. │ └─self[[evalcall]](input)
10. │ └─mlr3pipelines:::.__PipeOp__train(...)
11. │ ├─base::withCallingHandlers(...)
12. │ └─private$.train(input)
13. │ └─mlr3pipelines:::.__PipeOpTaskPreproc__.train(...)
14. │ └─private$.train_task(intask)
15. │ └─mlr3pipelines:::.__PipeOpLearnerCV__.train_task(...)
16. │ └─mlr3::resample(...)
17. │ └─ResultData$new(data, data_extra, store_backends = store_backends)
18. │ └─mlr3 (local) initialize(...)
19. │ └─mlr3:::.__ResultData__initialize(...)
20. │ ├─data[, `:=`("task_hash", task[[1L]]$hash), by = "uhash"]
21. │ └─data.table:::`[.data.table`(...)
22. └─base::.handleSimpleError(...)
23. └─mlr3pipelines (local) h(simpleError(msg, call))
── Error ('test_pipeop_learnercv.R:387:3'): PipeOpLearnerCV - learner_model returns averaged ensemble ──
Error in ``[.data.table`(data, , `:=`("task_hash", task[[1L]]$hash), by = "uhash")`: attempt access index 9/9 in VECTOR_ELT
This happened in PipeOp classif.rpart's $train()
Backtrace:
▆
1. ├─po$train(list(task)) at test_pipeop_learnercv.R:387:3
2. │ └─mlr3pipelines:::.__PipeOp__train(...)
3. │ ├─base::withCallingHandlers(...)
4. │ └─private$.train(input)
5. │ └─mlr3pipelines:::.__PipeOpTaskPreproc__.train(...)
6. │ └─private$.train_task(intask)
7. │ └─mlr3pipelines:::.__PipeOpLearnerCV__.train_task(...)
8. │ └─mlr3::resample(...)
9. │ └─ResultData$new(data, data_extra, store_backends = store_backends)
10. │ └─mlr3 (local) initialize(...)
11. │ └─mlr3:::.__ResultData__initialize(...)
12. │ ├─data[, `:=`("task_hash", task[[1L]]$hash), by = "uhash"]
13. │ └─data.table:::`[.data.table`(...)
14. └─base::.handleSimpleError(...)
15. └─mlr3pipelines (local) h(simpleError(msg, call))
── Error ('test_pipeop_learnercv.R:419:3'): PipeOpLearnerCV - cv ensemble with predict_type = 'se' ──
Error in ``[.data.table`(data, , `:=`("task_hash", task[[1L]]$hash), by = "uhash")`: attempt access index 9/9 in VECTOR_ELT
This happened in PipeOp regr.lm's $train()
Backtrace:
▆
1. ├─po$train(list(task)) at test_pipeop_learnercv.R:419:3
2. │ └─mlr3pipelines:::.__PipeOp__train(...)
3. │ ├─base::withCallingHandlers(...)
4. │ └─private$.train(input)
5. │ └─mlr3pipelines:::.__PipeOpTaskPreproc__.train(...)
6. │ └─private$.train_task(intask)
7. │ └─mlr3pipelines:::.__PipeOpLearnerCV__.train_task(...)
8. │ └─mlr3::resample(...)
9. │ └─ResultData$new(data, data_extra, store_backends = store_backends)
10. │ └─mlr3 (local) initialize(...)
11. │ └─mlr3:::.__ResultData__initialize(...)
12. │ ├─data[, `:=`("task_hash", task[[1L]]$hash), by = "uhash"]
13. │ └─data.table:::`[.data.table`(...)
14. └─base::.handleSimpleError(...)
15. └─mlr3pipelines (local) h(simpleError(msg, call))
── Error ('test_pipeop_learnercv.R:455:3'): PipeOpLearnerCV - within resampling ──
Error in ``[.data.table`(data, , `:=`("task_hash", task[[1L]]$hash), by = "uhash")`: attempt access index 9/9 in VECTOR_ELT
This happened in PipeOp classif.rpart's $train()
Backtrace:
▆
1. └─mlr3::resample(tsk("iris"), gr, rsmp("holdout")) at test_pipeop_learnercv.R:455:3
2. └─mlr3:::future_map(...)
3. └─future.apply::future_mapply(...)
4. └─future.apply:::future_xapply(...)
5. └─base::tryCatch(...)
6. └─base (local) tryCatchList(expr, classes, parentenv, handlers)
7. └─base (local) tryCatchOne(...)
8. └─value[[3L]](cond)
9. └─future.apply:::onError(e, futures = fs, debug = debug)
── Error ('test_pipeop_learnercv.R:493:3'): PipeOpLearnerCV - model active binding to state ──
Error in ``[.data.table`(data, , `:=`("task_hash", task[[1L]]$hash), by = "uhash")`: attempt access index 9/9 in VECTOR_ELT
This happened in PipeOp classif.featureless's $train()
Backtrace:
▆
1. ├─po$train(list(task)) at test_pipeop_learnercv.R:493:3
2. │ └─mlr3pipelines:::.__PipeOp__train(...)
3. │ ├─base::withCallingHandlers(...)
4. │ └─private$.train(input)
5. │ └─mlr3pipelines:::.__PipeOpTaskPreproc__.train(...)
6. │ └─private$.train_task(intask)
7. │ └─mlr3pipelines:::.__PipeOpLearnerCV__.train_task(...)
8. │ └─mlr3::resample(...)
9. │ └─ResultData$new(data, data_extra, store_backends = store_backends)
10. │ └─mlr3 (local) initialize(...)
11. │ └─mlr3:::.__ResultData__initialize(...)
12. │ ├─data[, `:=`("task_hash", task[[1L]]$hash), by = "uhash"]
13. │ └─data.table:::`[.data.table`(...)
14. └─base::.handleSimpleError(...)
15. └─mlr3pipelines (local) h(simpleError(msg, call))
── Error ('test_pipeop_learnercv.R:516:3'): predict_type ───────────────────────
Error in ``[.data.table`(data, , `:=`("task_hash", task[[1L]]$hash), by = "uhash")`: attempt access index 9/9 in VECTOR_ELT
This happened in PipeOp classif.rpart's $train()
Backtrace:
▆
1. ├─global expect_equal(...) at test_pipeop_learnercv.R:516:3
2. │ ├─testthat::expect_true(...)
3. │ │ └─testthat::quasi_label(enquo(object), label)
4. │ │ └─rlang::eval_bare(expr, quo_get_env(quo))
5. │ └─base::all.equal(...)
6. ├─lcv$train(list(tsk("iris")))
7. │ └─mlr3pipelines:::.__PipeOp__train(...)
8. │ ├─base::withCallingHandlers(...)
9. │ └─private$.train(input)
10. │ └─mlr3pipelines:::.__PipeOpTaskPreproc__.train(...)
11. │ └─private$.train_task(intask)
12. │ └─mlr3pipelines:::.__PipeOpLearnerCV__.train_task(...)
13. │ └─mlr3::resample(...)
14. │ └─ResultData$new(data, data_extra, store_backends = store_backends)
15. │ └─mlr3 (local) initialize(...)
16. │ └─mlr3:::.__ResultData__initialize(...)
17. │ ├─data[, `:=`("task_hash", task[[1L]]$hash), by = "uhash"]
18. │ └─data.table:::`[.data.table`(...)
19. └─base::.handleSimpleError(...)
20. └─mlr3pipelines (local) h(simpleError(msg, call))
── Error ('test_pipeop_learnercv.R:531:3'): marshal ────────────────────────────
Error in ``[.data.table`(data, , `:=`("task_hash", task[[1L]]$hash), by = "uhash")`: attempt access index 9/9 in VECTOR_ELT
This happened in PipeOp classif.debug's $train()
Backtrace:
▆
1. ├─po_lrn$train(list(task)) at test_pipeop_learnercv.R:531:3
2. │ └─mlr3pipelines:::.__PipeOp__train(...)
3. │ ├─base::withCallingHandlers(...)
4. │ └─private$.train(input)
5. │ └─mlr3pipelines:::.__PipeOpTaskPreproc__.train(...)
6. │ └─private$.train_task(intask)
7. │ └─mlr3pipelines:::.__PipeOpLearnerCV__.train_task(...)
8. │ └─mlr3::resample(...)
9. │ └─ResultData$new(data, data_extra, store_backends = store_backends)
10. │ └─mlr3 (local) initialize(...)
11. │ └─mlr3:::.__ResultData__initialize(...)
12. │ ├─data[, `:=`("task_hash", task[[1L]]$hash), by = "uhash"]
13. │ └─data.table:::`[.data.table`(...)
14. └─base::.handleSimpleError(...)
15. └─mlr3pipelines (local) h(simpleError(msg, call))
── Error ('test_pipeop_learnercv.R:557:3'): marshal multiplicity ───────────────
Error in ``[.data.table`(data, , `:=`("task_hash", task[[1L]]$hash), by = "uhash")`: attempt access index 9/9 in VECTOR_ELT
This happened in PipeOp classif.debug's $train()
Backtrace:
▆
1. ├─po$train(list(Multiplicity(tsk("iris"), tsk("sonar")))) at test_pipeop_learnercv.R:557:3
2. │ └─mlr3pipelines:::.__PipeOp__train(...)
3. │ └─mlr3pipelines:::evaluate_multiplicities(...)
4. │ └─mlr3misc::imap(...)
5. │ ├─stats::setNames(mapply_list(.f, list(.x, .nn), list(...)), names(.x))
6. │ └─mlr3misc:::mapply_list(.f, list(.x, .nn), list(...))
7. │ └─base::.mapply(.f, .dots, .args)
8. │ └─mlr3pipelines (local) `<fn>`(dots[[1L]][[1L]], dots[[2L]][[1L]])
9. │ └─self[[evalcall]](input)
10. │ └─mlr3pipelines:::.__PipeOp__train(...)
11. │ ├─base::withCallingHandlers(...)
12. │ └─private$.train(input)
13. │ └─mlr3pipelines:::.__PipeOpTaskPreproc__.train(...)
14. │ └─private$.train_task(intask)
15. │ └─mlr3pipelines:::.__PipeOpLearnerCV__.train_task(...)
16. │ └─mlr3::resample(...)
17. │ └─ResultData$new(data, data_extra, store_backends = store_backends)
18. │ └─mlr3 (local) initialize(...)
19. │ └─mlr3:::.__ResultData__initialize(...)
20. │ ├─data[, `:=`("task_hash", task[[1L]]$hash), by = "uhash"]
21. │ └─data.table:::`[.data.table`(...)
22. └─base::.handleSimpleError(...)
23. └─mlr3pipelines (local) h(simpleError(msg, call))
── Error ('test_pipeop_learnercv.R:612:3'): marshal with cv ensemble ───────────
Error in ``[.data.table`(data, , `:=`("task_hash", task[[1L]]$hash), by = "uhash")`: attempt access index 9/9 in VECTOR_ELT
This happened in PipeOp classif.rpart's $train()
Backtrace:
▆
1. ├─po$train(list(task)) at test_pipeop_learnercv.R:612:3
2. │ └─mlr3pipelines:::.__PipeOp__train(...)
3. │ ├─base::withCallingHandlers(...)
4. │ └─private$.train(input)
5. │ └─mlr3pipelines:::.__PipeOpTaskPreproc__.train(...)
6. │ └─private$.train_task(intask)
7. │ └─mlr3pipelines:::.__PipeOpLearnerCV__.train_task(...)
8. │ └─mlr3::resample(...)
9. │ └─ResultData$new(data, data_extra, store_backends = store_backends)
10. │ └─mlr3 (local) initialize(...)
11. │ └─mlr3:::.__ResultData__initialize(...)
12. │ ├─data[, `:=`("task_hash", task[[1L]]$hash), by = "uhash"]
13. │ └─data.table:::`[.data.table`(...)
14. └─base::.handleSimpleError(...)
15. └─mlr3pipelines (local) h(simpleError(msg, call))
── Error ('test_pipeop_learnercv.R:628:3'): state class and multiplicity ───────
Error in ``[.data.table`(data, , `:=`("task_hash", task[[1L]]$hash), by = "uhash")`: attempt access index 9/9 in VECTOR_ELT
This happened in PipeOp classif.debug's $train()
Backtrace:
▆
1. ├─po$train(list(Multiplicity(tsk("iris")))) at test_pipeop_learnercv.R:628:3
2. │ └─mlr3pipelines:::.__PipeOp__train(...)
3. │ └─mlr3pipelines:::evaluate_multiplicities(...)
4. │ └─mlr3misc::imap(...)
5. │ ├─stats::setNames(mapply_list(.f, list(.x, .nn), list(...)), names(.x))
6. │ └─mlr3misc:::mapply_list(.f, list(.x, .nn), list(...))
7. │ └─base::.mapply(.f, .dots, .args)
8. │ └─mlr3pipelines (local) `<fn>`(dots[[1L]][[1L]], dots[[2L]][[1L]])
9. │ └─self[[evalcall]](input)
10. │ └─mlr3pipelines:::.__PipeOp__train(...)
11. │ ├─base::withCallingHandlers(...)
12. │ └─private$.train(input)
13. │ └─mlr3pipelines:::.__PipeOpTaskPreproc__.train(...)
14. │ └─private$.train_task(intask)
15. │ └─mlr3pipelines:::.__PipeOpLearnerCV__.train_task(...)
16. │ └─mlr3::resample(...)
17. │ └─ResultData$new(data, data_extra, store_backends = store_backends)
18. │ └─mlr3 (local) initialize(...)
19. │ └─mlr3:::.__ResultData__initialize(...)
20. │ ├─data[, `:=`("task_hash", task[[1L]]$hash), by = "uhash"]
21. │ └─data.table:::`[.data.table`(...)
22. └─base::.handleSimpleError(...)
23. └─mlr3pipelines (local) h(simpleError(msg, call))
── Error ('test_pipeop_learnercv.R:671:5'): PipeOpLearnerCV cv ensemble aggregates SE like PipeOpRegrAvg ──
Error in ``[.data.table`(data, , `:=`("task_hash", task[[1L]]$hash), by = "uhash")`: attempt access index 9/9 in VECTOR_ELT
This happened in PipeOp regr.debug's $train()
Backtrace:
▆
1. ├─po$train(list(task)) at test_pipeop_learnercv.R:671:5
2. │ └─mlr3pipelines:::.__PipeOp__train(...)
3. │ ├─base::withCallingHandlers(...)
4. │ └─private$.train(input)
5. │ └─mlr3pipelines:::.__PipeOpTaskPreproc__.train(...)
6. │ └─private$.train_task(intask)
7. │ └─mlr3pipelines:::.__PipeOpLearnerCV__.train_task(...)
8. │ └─mlr3::resample(...)
9. │ └─ResultData$new(data, data_extra, store_backends = store_backends)
10. │ └─mlr3 (local) initialize(...)
11. │ └─mlr3:::.__ResultData__initialize(...)
12. │ ├─data[, `:=`("task_hash", task[[1L]]$hash), by = "uhash"]
13. │ └─data.table:::`[.data.table`(...)
14. └─base::.handleSimpleError(...)
15. └─mlr3pipelines (local) h(simpleError(msg, call))
── Error ('test_pipeop_tunethreshold.R:7:3'): threshold works for multiclass ───
Error in ``[.data.table`(data, , `:=`("task_hash", task[[1L]]$hash), by = "uhash")`: attempt access index 9/9 in VECTOR_ELT
This happened in PipeOp classif.rpart's $train()
Backtrace:
▆
1. ├─po_cv$train(list(t)) at test_pipeop_tunethreshold.R:7:3
2. │ └─mlr3pipelines:::.__PipeOp__train(...)
3. │ ├─base::withCallingHandlers(...)
4. │ └─private$.train(input)
5. │ └─mlr3pipelines:::.__PipeOpTaskPreproc__.train(...)
6. │ └─private$.train_task(intask)
7. │ └─mlr3pipelines:::.__PipeOpLearnerCV__.train_task(...)
8. │ └─mlr3::resample(...)
9. │ └─ResultData$new(data, data_extra, store_backends = store_backends)
10. │ └─mlr3 (local) initialize(...)
11. │ └─mlr3:::.__ResultData__initialize(...)
12. │ ├─data[, `:=`("task_hash", task[[1L]]$hash), by = "uhash"]
13. │ └─data.table:::`[.data.table`(...)
14. └─base::.handleSimpleError(...)
15. └─mlr3pipelines (local) h(simpleError(msg, call))
── Error ('test_pipeop_tunethreshold.R:38:3'): threshold works for binary ──────
Error in ``[.data.table`(data, , `:=`("task_hash", task[[1L]]$hash), by = "uhash")`: attempt access index 9/9 in VECTOR_ELT
This happened in PipeOp classif.rpart's $train()
Backtrace:
▆
1. ├─po_cv$train(list(t)) at test_pipeop_tunethreshold.R:38:3
2. │ └─mlr3pipelines:::.__PipeOp__train(...)
3. │ ├─base::withCallingHandlers(...)
4. │ └─private$.train(input)
5. │ └─mlr3pipelines:::.__PipeOpTaskPreproc__.train(...)
6. │ └─private$.train_task(intask)
7. │ └─mlr3pipelines:::.__PipeOpLearnerCV__.train_task(...)
8. │ └─mlr3::resample(...)
9. │ └─ResultData$new(data, data_extra, store_backends = store_backends)
10. │ └─mlr3 (local) initialize(...)
11. │ └─mlr3:::.__ResultData__initialize(...)
12. │ ├─data[, `:=`("task_hash", task[[1L]]$hash), by = "uhash"]
13. │ └─data.table:::`[.data.table`(...)
14. └─base::.handleSimpleError(...)
15. └─mlr3pipelines (local) h(simpleError(msg, call))
── Error ('test_pipeop_tunethreshold.R:73:3'): tunethreshold graph works ───────
Error in ``[.data.table`(data, , `:=`("task_hash", task[[1L]]$hash), by = "uhash")`: attempt access index 9/9 in VECTOR_ELT
This happened in PipeOp classif.rpart's $train()
Backtrace:
▆
1. ├─graph$train(tsk("pima")) at test_pipeop_tunethreshold.R:73:3
2. │ └─mlr3pipelines:::.__Graph__train(...)
3. │ └─mlr3pipelines:::graph_reduce(self, input, "train", single_input)
4. │ └─op[[fun]](input)
5. │ └─mlr3pipelines:::.__PipeOp__train(...)
6. │ ├─base::withCallingHandlers(...)
7. │ └─private$.train(input)
8. │ └─mlr3pipelines:::.__PipeOpTaskPreproc__.train(...)
9. │ └─private$.train_task(intask)
10. │ └─mlr3pipelines:::.__PipeOpLearnerCV__.train_task(...)
11. │ └─mlr3::resample(...)
12. │ └─ResultData$new(data, data_extra, store_backends = store_backends)
13. │ └─mlr3 (local) initialize(...)
14. │ └─mlr3:::.__ResultData__initialize(...)
15. │ ├─data[, `:=`("task_hash", task[[1L]]$hash), by = "uhash"]
16. │ └─data.table:::`[.data.table`(...)
17. └─base::.handleSimpleError(...)
18. └─mlr3pipelines (local) h(simpleError(msg, call))
── Error ('test_pipeop_tunethreshold.R:101:3'): threshold works for classes that are not valid R names ──
Error in ``[.data.table`(data, , `:=`("task_hash", task[[1L]]$hash), by = "uhash")`: attempt access index 9/9 in VECTOR_ELT
This happened in PipeOp classif.rpart's $train()
Backtrace:
▆
1. ├─ppl$train(testtask) at test_pipeop_tunethreshold.R:101:3
2. │ └─mlr3pipelines:::.__Graph__train(...)
3. │ └─mlr3pipelines:::graph_reduce(self, input, "train", single_input)
4. │ └─op[[fun]](input)
5. │ └─mlr3pipelines:::.__PipeOp__train(...)
6. │ ├─base::withCallingHandlers(...)
7. │ └─private$.train(input)
8. │ └─mlr3pipelines:::.__PipeOpTaskPreproc__.train(...)
9. │ └─private$.train_task(intask)
10. │ └─mlr3pipelines:::.__PipeOpLearnerCV__.train_task(...)
11. │ └─mlr3::resample(...)
12. │ └─ResultData$new(data, data_extra, store_backends = store_backends)
13. │ └─mlr3 (local) initialize(...)
14. │ └─mlr3:::.__ResultData__initialize(...)
15. │ ├─data[, `:=`("task_hash", task[[1L]]$hash), by = "uhash"]
16. │ └─data.table:::`[.data.table`(...)
17. └─base::.handleSimpleError(...)
18. └─mlr3pipelines (local) h(simpleError(msg, call))
── Error ('test_pipeop_tunethreshold.R:260:3'): threshold graph transparency ───
Error in ``[.data.table`(data, , `:=`("task_hash", task[[1L]]$hash), by = "uhash")`: attempt access index 9/9 in VECTOR_ELT
This happened in PipeOp classif.rpart's $train()
Backtrace:
▆
1. ├─lrn_prob$train(t) at test_pipeop_tunethreshold.R:260:3
2. │ └─mlr3:::.__Learner__train(...)
3. │ └─mlr3:::learner_train(...)
4. │ └─mlr3misc::encapsulate(...)
5. │ ├─mlr3misc::invoke(...)
6. │ │ └─base::eval.parent(expr, n = 1L)
7. │ │ └─base::eval(expr, p)
8. │ │ └─base::eval(expr, p)
9. │ └─mlr3 (local) .f(learner = `<GrphLrnr>`, task = `<TskClssf>`)
10. │ └─get_private(learner)$.train(task)
11. │ └─mlr3pipelines:::.__GraphLearner__.train(...)
12. │ └─self$graph$train(task)
13. │ └─mlr3pipelines:::.__Graph__train(...)
14. │ └─mlr3pipelines:::graph_reduce(self, input, "train", single_input)
15. │ └─op[[fun]](input)
16. │ └─mlr3pipelines:::.__PipeOp__train(...)
17. │ ├─base::withCallingHandlers(...)
18. │ └─private$.train(input)
19. │ └─mlr3pipelines:::.__PipeOpTaskPreproc__.train(...)
20. │ └─private$.train_task(intask)
21. │ └─mlr3pipelines:::.__PipeOpLearnerCV__.train_task(...)
22. │ └─mlr3::resample(...)
23. │ └─ResultData$new(data, data_extra, store_backends = store_backends)
24. │ └─mlr3 (local) initialize(...)
25. │ └─mlr3:::.__ResultData__initialize(...)
26. │ ├─data[, `:=`("task_hash", task[[1L]]$hash), by = "uhash"]
27. │ └─data.table:::`[.data.table`(...)
28. └─base::.handleSimpleError(...)
29. └─mlr3pipelines (local) h(simpleError(msg, call))
── Error ('test_resample.R:13:3'): PipeOp - Resample ───────────────────────────
Error in ``[.data.table`(data, , `:=`("task_hash", task[[1L]]$hash), by = "uhash")`: attempt access index 9/9 in VECTOR_ELT
Backtrace:
▆
1. └─mlr3::resample(task, pp, resa) at test_resample.R:13:3
2. └─ResultData$new(data, data_extra, store_backends = store_backends)
3. └─mlr3 (local) initialize(...)
4. └─mlr3:::.__ResultData__initialize(...)
5. ├─data[, `:=`("task_hash", task[[1L]]$hash), by = "uhash"]
6. └─data.table:::`[.data.table`(...)
── Error ('test_ppl.R:73:3'): mlr3book authors don't sleepwalk through life ────
Error in ``[.data.table`(data, , `:=`("task_hash", task[[1L]]$hash), by = "uhash")`: attempt access index 9/9 in VECTOR_ELT
This happened in PipeOp classif.rpart.classif.rpart's $train()
Backtrace:
▆
1. └─mlr3::benchmark(benchmark_grid(tasks, learners, rsmp("cv", folds = 2))) at test_ppl.R:73:3
2. └─mlr3:::future_map(...)
3. └─future.apply::future_mapply(...)
4. └─future.apply:::future_xapply(...)
5. └─base::tryCatch(...)
6. └─base (local) tryCatchList(expr, classes, parentenv, handlers)
7. └─base (local) tryCatchOne(...)
8. └─value[[3L]](cond)
9. └─future.apply:::onError(e, futures = fs, debug = debug)
── Error ('test_usecases.R:153:3'): stacking ───────────────────────────────────
Error in ``[.data.table`(data, , `:=`("task_hash", task[[1L]]$hash), by = "uhash")`: attempt access index 9/9 in VECTOR_ELT
This happened in PipeOp classif.rpart's $train()
Backtrace:
▆
1. ├─pipe$train(task) at test_usecases.R:153:3
2. │ └─mlr3pipelines:::.__Graph__train(...)
3. │ └─mlr3pipelines:::graph_reduce(self, input, "train", single_input)
4. │ └─op[[fun]](input)
5. │ └─mlr3pipelines:::.__PipeOp__train(...)
6. │ ├─base::withCallingHandlers(...)
7. │ └─private$.train(input)
8. │ └─mlr3pipelines:::.__PipeOpTaskPreproc__.train(...)
9. │ └─private$.train_task(intask)
10. │ └─mlr3pipelines:::.__PipeOpLearnerCV__.train_task(...)
11. │ └─mlr3::resample(...)
12. │ └─ResultData$new(data, data_extra, store_backends = store_backends)
13. │ └─mlr3 (local) initialize(...)
14. │ └─mlr3:::.__ResultData__initialize(...)
15. │ ├─data[, `:=`("task_hash", task[[1L]]$hash), by = "uhash"]
16. │ └─data.table:::`[.data.table`(...)
17. └─base::.handleSimpleError(...)
18. └─mlr3pipelines (local) h(simpleError(msg, call))
[ FAIL 38 | WARN 2 | SKIP 98 | PASS 12316 ]
Error:
! Test failures.
Execution halted
Flavor: r-devel-linux-x86_64-debian-gcc
Version: 0.10.0
Check: examples
Result: ERROR
Running examples in ‘mlr3pipelines-Ex.R’ failed
The error most likely occurred in:
> ### Name: mlr_graphs_stacking
> ### Title: Create A Graph to Perform Stacking.
> ### Aliases: mlr_graphs_stacking pipeline_stacking
>
> ### ** Examples
>
> ## Don't show:
> if (mlr3misc::require_namespaces("rpart", quietly = TRUE)) withAutoprint({ # examplesIf
+ ## End(Don't show)
+ library(mlr3)
+ library(mlr3learners)
+
+ base_learners = list(
+ lrn("classif.rpart", predict_type = "prob"),
+ lrn("classif.nnet", predict_type = "prob")
+ )
+ super_learner = lrn("classif.log_reg")
+
+ graph_stack = pipeline_stacking(base_learners, super_learner)
+ graph_learner = as_learner(graph_stack)
+ graph_learner$train(tsk("german_credit"))
+ ## Don't show:
+ }) # examplesIf
> library(mlr3)
> library(mlr3learners)
> base_learners = list(lrn("classif.rpart", predict_type = "prob"), lrn("classif.nnet",
+ predict_type = "prob"))
> super_learner = lrn("classif.log_reg")
> graph_stack = pipeline_stacking(base_learners, super_learner)
> graph_learner = as_learner(graph_stack)
> graph_learner$train(tsk("german_credit"))
INFO [17:55:07.334] [mlr3] Resampling 'cv' is being instantiated on task 'german_credit'
INFO [17:55:07.897] [mlr3] Applying learner 'classif.rpart' on task 'german_credit' (iter 1/3)
INFO [17:55:08.022] [mlr3] Applying learner 'classif.rpart' on task 'german_credit' (iter 2/3)
INFO [17:55:08.141] [mlr3] Applying learner 'classif.rpart' on task 'german_credit' (iter 3/3)
Error in `[.data.table`(data, , `:=`("task_hash", task[[1L]]$hash), by = "uhash") :
attempt access index 9/9 in VECTOR_ELT
This happened in PipeOp classif.rpart's $train()
Calls: withAutoprint ... .__ResultData__initialize -> [ -> [.data.table -> .handleSimpleError -> h
Execution halted
Flavor: r-devel-linux-x86_64-fedora-clang
Version: 0.10.0
Check: tests
Result: ERROR
Running ‘testthat.R’ [648s/590s]
Running the tests in ‘tests/testthat.R’ failed.
Complete output:
> if (requireNamespace("testthat", quietly = TRUE)) {
+ library("checkmate")
+ library("testthat")
+ library("mlr3")
+ library("paradox")
+ library("mlr3pipelines")
+ test_check("mlr3pipelines")
+ }
Starting 2 test processes.
> test_Graph.R: Training debug.multi with input list(input_1 = 1, input_2 = 1)
> test_PipeOp.R: Training test_autotrain
> test_PipeOp.R: Predicting test_autotrain
> test_PipeOp.R: Training test_autotrain
> test_PipeOp.R: Training test_autotrain
> test_PipeOp.R: Predicting test_autotrain
> test_PipeOp.R: Predicting test_autotrain
> test_PipeOp.R: Training test_autotrain
> test_PipeOp.R: Training test_autotrain
> test_PipeOp.R: Training test_autotrain
> test_PipeOp.R: Predicting test_autotrain
> test_PipeOp.R: Predicting test_autotrain
> test_PipeOp.R: Predicting test_autotrain
> test_PipeOp.R: Training test_autotrain
> test_PipeOp.R:
> test_PipeOp.R: Predicting test_autotrain
> test_PipeOp.R: Training test_autotrain
> test_PipeOp.R: Training test_autotrain
> test_PipeOp.R: Predicting test_autotrain
> test_PipeOp.R: Predicting test_autotrain
> test_PipeOp.R: Training test_autotrain
> test_PipeOp.R: Training test_autotrain
> test_PipeOp.R: Training test_autotrain
> test_PipeOp.R: Predicting test_autotrain
> test_PipeOp.R: Predicting test_autotrain
> test_PipeOp.R:
> test_PipeOp.R: Predicting test_autotrain
> test_PipeOp.R: Training test_autotrain
> test_PipeOp.R: Predicting test_autotrain
Saving _problems/test_conversion-143.R
Saving _problems/test_conversion-165.R
> test_filter_ensemble.R: OptimInstanceSingleCrit is deprecated. Use OptimInstanceBatchSingleCrit instead.
Saving _problems/test_filter_ensemble-291.R
Saving _problems/test_filter_ensemble-447.R
Saving _problems/test_mlr_graphs_bagging-49.R
Saving _problems/test_mlr_graphs_stacking-16.R
> test_mlr_graphs_robustify.R: 'as(<dgCMatrix>, "dgTMatrix")' is deprecated.
> test_mlr_graphs_robustify.R: Use 'as(., "TsparseMatrix")' instead.
> test_mlr_graphs_robustify.R: See help("Deprecated") and help("Matrix-deprecated").
> test_multiplicities.R:
> test_multiplicities.R: [[1]]
> test_multiplicities.R: [1] 0
> test_multiplicities.R:
> test_multiplicities.R:
> test_pipeop_blsmote.R: [1] "Borderline-SMOTE done"
> test_pipeop_blsmote.R: [1] "Borderline-SMOTE done"
> test_pipeop_blsmote.R: [1] "Borderline-SMOTE done"
> test_pipeop_blsmote.R: [1] "Borderline-SMOTE done"
> test_pipeop_isomap.R: 2025-12-19 17:59:36.782777: Isomap START
> test_pipeop_isomap.R: 2025-12-19 17:59:36.788131: constructing knn graph
> test_pipeop_isomap.R: 2025-12-19 17:59:36.823643: calculating geodesic distances
> test_pipeop_isomap.R: 2025-12-19 17:59:36.905406: Classical Scaling
> test_pipeop_isomap.R: 2025-12-19 17:59:37.148005: Isomap START
> test_pipeop_isomap.R: 2025-12-19 17:59:37.154927: constructing knn graph
> test_pipeop_isomap.R: 2025-12-19 17:59:37.217492: calculating geodesic distances
> test_pipeop_isomap.R: 2025-12-19 17:59:37.293734: Classical Scaling
> test_pipeop_isomap.R: 2025-12-19 17:59:37.427053: L-Isomap embed START
> test_pipeop_isomap.R: 2025-12-19 17:59:37.428331: constructing knn graph
> test_pipeop_isomap.R: 2025-12-19 17:59:37.492675: calculating geodesic distances
> test_pipeop_isomap.R: 2025-12-19 17:59:37.638962: embedding
> test_pipeop_isomap.R: 2025-12-19 17:59:37.647458: DONE
> test_pipeop_isomap.R: 2025-12-19 17:59:37.738513: L-Isomap embed START
> test_pipeop_isomap.R: 2025-12-19 17:59:37.739327: constructing knn graph
> test_pipeop_isomap.R: 2025-12-19 17:59:37.948353: calculating geodesic distances
> test_pipeop_isomap.R: 2025-12-19 17:59:38.085035: embedding
> test_pipeop_isomap.R: 2025-12-19 17:59:38.094977: DONE
> test_pipeop_isomap.R: 2025-12-19 17:59:38.420522: Isomap START
> test_pipeop_isomap.R: 2025-12-19 17:59:38.423569: constructing knn graph
> test_pipeop_isomap.R: 2025-12-19 17:59:38.473072: calculating geodesic distances
> test_pipeop_isomap.R: 2025-12-19 17:59:38.788484: Classical Scaling
> test_pipeop_isomap.R: 2025-12-19 17:59:38.914818: L-Isomap embed START
> test_pipeop_isomap.R: 2025-12-19 17:59:38.915874: constructing knn graph
> test_pipeop_isomap.R: 2025-12-19 17:59:39.272942: calculating geodesic distances
> test_pipeop_isomap.R: 2025-12-19 17:59:40.080209: embedding
> test_pipeop_isomap.R: 2025-12-19 17:59:40.095217: DONE
> test_pipeop_isomap.R: 2025-12-19 17:59:40.516095: Isomap START
> test_pipeop_isomap.R: 2025-12-19 17:59:40.51697: constructing knn graph
> test_pipeop_isomap.R: 2025-12-19 17:59:40.53553: calculating geodesic distances
> test_pipeop_isomap.R: 2025-12-19 17:59:40.587082: Classical Scaling
> test_pipeop_isomap.R: 2025-12-19 17:59:40.759665: L-Isomap embed START
> test_pipeop_isomap.R: 2025-12-19 17:59:40.766864: constructing knn graph
> test_pipeop_isomap.R: 2025-12-19 17:59:40.827477: calculating geodesic distances
> test_pipeop_isomap.R: 2025-12-19 17:59:40.971903: embedding
> test_pipeop_isomap.R: 2025-12-19 17:59:40.980267: DONE
> test_pipeop_isomap.R: 2025-12-19 17:59:41.518087: Isomap START
> test_pipeop_isomap.R: 2025-12-19 17:59:41.52384: constructing knn graph
> test_pipeop_isomap.R: 2025-12-19 17:59:41.606861: calculating geodesic distances
> test_pipeop_isomap.R: 2025-12-19 17:59:41.673903: Classical Scaling
> test_pipeop_isomap.R: 2025-12-19 17:59:41.888372: L-Isomap embed START
> test_pipeop_isomap.R: 2025-12-19 17:59:41.889426: constructing knn graph
> test_pipeop_isomap.R: 2025-12-19 17:59:41.942357: calculating geodesic distances
> test_pipeop_isomap.R: 2025-12-19 17:59:42.084522: embedding
> test_pipeop_isomap.R: 2025-12-19 17:59:42.086255: DONE
> test_pipeop_isomap.R: 2025-12-19 17:59:42.363597: Isomap START
> test_pipeop_isomap.R: 2025-12-19 17:59:42.364286: constructing knn graph
> test_pipeop_isomap.R: 2025-12-19 17:59:42.388848: calculating geodesic distances
> test_pipeop_isomap.R: 2025-12-19 17:59:42.453632: Classical Scaling
> test_pipeop_isomap.R: 2025-12-19 17:59:42.587881: L-Isomap embed START
> test_pipeop_isomap.R: 2025-12-19 17:59:42.591163: constructing knn graph
> test_pipeop_isomap.R: 2025-12-19 17:59:42.640784: calculating geodesic distances
> test_pipeop_isomap.R: 2025-12-19 17:59:42.733569: embedding
> test_pipeop_isomap.R: 2025-12-19 17:59:42.737515: DONE
> test_pipeop_isomap.R: 2025-12-19 17:59:42.919924: Isomap START
> test_pipeop_isomap.R: 2025-12-19 17:59:42.920698: constructing knn graph
> test_pipeop_isomap.R: 2025-12-19 17:59:42.95613: calculating geodesic distances
> test_pipeop_isomap.R: 2025-12-19 17:59:43.015573: Classical Scaling
> test_pipeop_isomap.R: 2025-12-19 17:59:43.186374: L-Isomap embed START
> test_pipeop_isomap.R: 2025-12-19 17:59:43.191629: constructing knn graph
> test_pipeop_isomap.R: 2025-12-19 17:59:43.241857: calculating geodesic distances
> test_pipeop_isomap.R: 2025-12-19 17:59:43.377422: embedding
> test_pipeop_isomap.R: 2025-12-19 17:59:43.381857: DONE
> test_pipeop_isomap.R: 2025-12-19 17:59:43.668476: Isomap START
> test_pipeop_isomap.R: 2025-12-19 17:59:43.669277: constructing knn graph
> test_pipeop_isomap.R: 2025-12-19 17:59:43.694776: calculating geodesic distances
> test_pipeop_isomap.R: 2025-12-19 17:59:43.794544: Classical Scaling
> test_pipeop_isomap.R: 2025-12-19 17:59:43.992826: L-Isomap embed START
> test_pipeop_isomap.R: 2025-12-19 17:59:43.993972: constructing knn graph
> test_pipeop_isomap.R: 2025-12-19 17:59:44.044239: calculating geodesic distances
> test_pipeop_isomap.R: 2025-12-19 17:59:44.185145: embedding
> test_pipeop_isomap.R: 2025-12-19 17:59:44.187031: DONE
> test_pipeop_isomap.R: 2025-12-19 17:59:44.574771: Isomap START
> test_pipeop_isomap.R: 2025-12-19 17:59:44.581328: constructing knn graph
> test_pipeop_isomap.R: 2025-12-19 17:59:44.604874: calculating geodesic distances
> test_pipeop_isomap.R: 2025-12-19 17:59:44.672687: Classical Scaling
> test_pipeop_isomap.R: 2025-12-19 17:59:45.045445: Isomap START
> test_pipeop_isomap.R: 2025-12-19 17:59:45.050264: constructing knn graph
> test_pipeop_isomap.R: 2025-12-19 17:59:45.077913: calculating geodesic distances
> test_pipeop_isomap.R: 2025-12-19 17:59:45.202453: Classical Scaling
> test_pipeop_isomap.R: 2025-12-19 17:59:45.332021: Isomap START
> test_pipeop_isomap.R: 2025-12-19 17:59:45.332778: constructing knn graph
> test_pipeop_isomap.R: 2025-12-19 17:59:45.365373: calculating geodesic distances
> test_pipeop_isomap.R: 2025-12-19 17:59:45.429788: Classical Scaling
Saving _problems/test_pipeop_learnerpicvplus-35.R
Saving _problems/test_pipeop_learnerpicvplus-91.R
Saving _problems/test_pipeop_learnerpicvplus-116.R
Saving _problems/test_pipeop_learnerpicvplus-130.R
Saving _problems/test_pipeop_learnerpicvplus-152.R
Saving _problems/test_pipeop_learnercv-11.R
Saving _problems/test_pipeop_learnercv-100.R
Saving _problems/test_pipeop_learnercv-139.R
Saving _problems/test_pipeop_learnercv-152.R
Saving _problems/test_pipeop_learnercv-203.R
Saving _problems/test_pipeop_learnercv-250.R
Saving _problems/test_pipeop_learnercv-278.R
Saving _problems/test_pipeop_learnercv-323.R
Saving _problems/test_pipeop_learnercv-350.R
Saving _problems/test_pipeop_learnercv-387.R
Saving _problems/test_pipeop_learnercv-419.R
Saving _problems/test_pipeop_learnercv-455.R
Saving _problems/test_pipeop_learnercv-493.R
Saving _problems/test_pipeop_learnercv-516.R
Saving _problems/test_pipeop_learnercv-531.R
Saving _problems/test_pipeop_learnercv-557.R
Saving _problems/test_pipeop_learnercv-612.R
Saving _problems/test_pipeop_learnercv-628.R
Saving _problems/test_pipeop_learnercv-671.R
> test_pipeop_nmf.R: [PipeOpNMFstate]
> test_pipeop_nmf.R: [PipeOpNMFstate]
> test_pipeop_task_preproc.R: Training debug_affectcols
> test_pipeop_task_preproc.R: Training debug_affectcols
> test_pipeop_task_preproc.R: Training debug_affectcols
> test_pipeop_task_preproc.R:
> test_pipeop_task_preproc.R: Training debug_affectcols
> test_pipeop_task_preproc.R: Training debug_affectcols
> test_pipeop_task_preproc.R: Training debug_affectcols
> test_pipeop_task_preproc.R: Training debug_affectcols
> test_pipeop_task_preproc.R:
> test_pipeop_task_preproc.R: Training debug_affectcols
> test_pipeop_task_preproc.R: Training debug_affectcols
> test_pipeop_task_preproc.R: Training debug_affectcols
> test_pipeop_task_preproc.R: Training debug_affectcols
> test_pipeop_task_preproc.R: Training debug_affectcols
> test_pipeop_task_preproc.R:
> test_pipeop_task_preproc.R: Training debug_affectcols
> test_pipeop_task_preproc.R: Training debug_affectcols
> test_pipeop_task_preproc.R: Training debug_affectcols
> test_pipeop_task_preproc.R: Training debug_affectcols
> test_pipeop_task_preproc.R: Training debug_affectcols
> test_pipeop_task_preproc.R: Training debug_affectcols
> test_pipeop_textvectorizer.R: 'as(<dgCMatrix>, "dgTMatrix")' is deprecated.
> test_pipeop_textvectorizer.R: Use 'as(., "TsparseMatrix")' instead.
> test_pipeop_textvectorizer.R: See help("Deprecated") and help("Matrix-deprecated").
> test_pipeop_task_preproc.R: Training debug_affectcols
> test_pipeop_task_preproc.R: Training debug_affectcols
> test_pipeop_task_preproc.R:
> test_pipeop_task_preproc.R: Training debug_affectcols
> test_pipeop_task_preproc.R:
> test_pipeop_task_preproc.R: Training debug_affectcols
Saving _problems/test_pipeop_tunethreshold-7.R
Saving _problems/test_pipeop_tunethreshold-38.R
Saving _problems/test_pipeop_tunethreshold-73.R
Saving _problems/test_pipeop_tunethreshold-101.R
Saving _problems/test_pipeop_tunethreshold-260.R
Saving _problems/test_resample-13.R
Saving _problems/test_ppl-73.R
Saving _problems/test_usecases-153.R
[ FAIL 38 | WARN 2 | SKIP 98 | PASS 12316 ]
══ Skipped tests (98) ══════════════════════════════════════════════════════════
• On CRAN (95): 'test_CnfFormula_simplify.R:6:3', 'test_CnfFormula.R:591:3',
'test_Graph.R:283:3', 'test_PipeOp.R:32:1', 'test_GraphLearner.R:5:3',
'test_GraphLearner.R:221:3', 'test_GraphLearner.R:343:3',
'test_GraphLearner.R:408:3', 'test_GraphLearner.R:571:3',
'test_dictionary.R:7:3', 'test_learner_weightedaverage.R:5:3',
'test_learner_weightedaverage.R:57:3',
'test_learner_weightedaverage.R:105:3',
'test_learner_weightedaverage.R:152:3', 'test_meta.R:39:3',
'test_mlr_graphs_bagging.R:6:3', 'test_mlr_graphs_branching.R:26:3',
'test_mlr_graphs_robustify.R:5:3', 'test_pipeop_adas.R:8:3',
'test_pipeop_blsmote.R:8:3', 'test_pipeop_branch.R:4:3',
'test_pipeop_chunk.R:4:3', 'test_pipeop_classbalancing.R:7:3',
'test_pipeop_classweights.R:10:3', 'test_pipeop_boxcox.R:7:3',
'test_pipeop_collapsefactors.R:6:3', 'test_pipeop_colapply.R:9:3',
'test_pipeop_copy.R:5:3', 'test_pipeop_colroles.R:6:3',
'test_pipeop_decode.R:14:3', 'test_pipeop_encode.R:21:3',
'test_pipeop_datefeatures.R:10:3', 'test_pipeop_encodeimpact.R:11:3',
'test_pipeop_encodepl.R:5:3', 'test_pipeop_encodepl.R:72:3',
'test_pipeop_encodelmer.R:15:3', 'test_pipeop_encodelmer.R:37:3',
'test_pipeop_encodelmer.R:80:3', 'test_pipeop_featureunion.R:9:3',
'test_pipeop_featureunion.R:134:3', 'test_pipeop_filter.R:7:3',
'test_pipeop_fixfactors.R:9:3', 'test_pipeop_histbin.R:7:3',
'test_pipeop_ica.R:7:3', 'test_pipeop_ensemble.R:6:3',
'test_pipeop_impute.R:4:3', 'test_pipeop_imputelearner.R:43:3',
'test_pipeop_isomap.R:10:3', 'test_pipeop_kernelpca.R:9:3',
'test_pipeop_learner.R:17:3', 'test_pipeop_info.R:6:3',
'test_pipeop_learnerpicvplus.R:163:3', 'test_pipeop_missind.R:6:3',
'test_pipeop_modelmatrix.R:7:3', 'test_pipeop_multiplicityexply.R:9:3',
'test_pipeop_mutate.R:9:3', 'test_pipeop_nearmiss.R:7:3',
'test_pipeop_multiplicityimply.R:9:3', 'test_pipeop_ovr.R:9:3',
'test_pipeop_ovr.R:48:3', 'test_pipeop_pca.R:8:3',
'test_pipeop_proxy.R:14:3', 'test_pipeop_quantilebin.R:5:3',
'test_pipeop_randomprojection.R:6:3', 'test_pipeop_randomresponse.R:5:3',
'test_pipeop_removeconstants.R:6:3', 'test_pipeop_renamecolumns.R:6:3',
'test_pipeop_replicate.R:9:3', 'test_pipeop_rowapply.R:6:3',
'test_pipeop_scale.R:6:3', 'test_pipeop_scale.R:10:3',
'test_pipeop_scalemaxabs.R:6:3', 'test_pipeop_scalerange.R:7:3',
'test_pipeop_select.R:9:3', 'test_pipeop_smote.R:10:3',
'test_pipeop_smotenc.R:8:3', 'test_pipeop_spatialsign.R:6:3',
'test_pipeop_nmf.R:6:3', 'test_pipeop_targetinvert.R:4:3',
'test_pipeop_targetmutate.R:5:3', 'test_pipeop_targettrafo.R:4:3',
'test_pipeop_targettrafoscalerange.R:5:3', 'test_pipeop_subsample.R:6:3',
'test_pipeop_task_preproc.R:4:3', 'test_pipeop_task_preproc.R:14:3',
'test_pipeop_tomek.R:7:3', 'test_pipeop_tunethreshold.R:111:3',
'test_pipeop_tunethreshold.R:191:3', 'test_pipeop_textvectorizer.R:37:3',
'test_pipeop_textvectorizer.R:186:3', 'test_pipeop_unbranch.R:10:3',
'test_pipeop_updatetarget.R:89:3', 'test_pipeop_vtreat.R:9:3',
'test_pipeop_yeojohnson.R:7:3', 'test_typecheck.R:188:3'
• Skipping (1): 'test_GraphLearner.R:1278:3'
• empty test (2): 'test_pipeop_isomap.R:111:1', 'test_pipeop_missind.R:101:1'
══ Failed tests ════════════════════════════════════════════════════════════════
── Error ('test_conversion.R:143:3'): Graph to GraphLearner ────────────────────
Error in ``[.data.table`(data, , `:=`("task_hash", task[[1L]]$hash), by = "uhash")`: attempt access index 9/9 in VECTOR_ELT
Backtrace:
▆
1. └─mlr3::resample(task, glrn1, cv) at test_conversion.R:143:3
2. └─ResultData$new(data, data_extra, store_backends = store_backends)
3. └─mlr3 (local) initialize(...)
4. └─mlr3:::.__ResultData__initialize(...)
5. ├─data[, `:=`("task_hash", task[[1L]]$hash), by = "uhash"]
6. └─data.table:::`[.data.table`(...)
── Error ('test_conversion.R:165:3'): PipeOp to GraphLearner ───────────────────
Error in ``[.data.table`(data, , `:=`("task_hash", task[[1L]]$hash), by = "uhash")`: attempt access index 9/9 in VECTOR_ELT
Backtrace:
▆
1. └─mlr3::resample(task, glrn1, cv) at test_conversion.R:165:3
2. └─ResultData$new(data, data_extra, store_backends = store_backends)
3. └─mlr3 (local) initialize(...)
4. └─mlr3:::.__ResultData__initialize(...)
5. ├─data[, `:=`("task_hash", task[[1L]]$hash), by = "uhash"]
6. └─data.table:::`[.data.table`(...)
── Error ('test_filter_ensemble.R:291:3'): FilterEnsemble ignores NA scores from wrapped filters ──
Error in ``[.data.table`(data, , `:=`("task_hash", task[[1L]]$hash), by = "uhash")`: attempt access index 9/9 in VECTOR_ELT
Backtrace:
▆
1. └─permutation_filter$calculate(task) at test_filter_ensemble.R:291:3
2. └─mlr3filters:::.__Filter__calculate(...)
3. └─private$.calculate(task, nfeat)
4. └─mlr3filters:::.__FilterPermutation__.calculate(...)
5. └─mlr3::resample(task, self$learner, self$resampling)
6. └─ResultData$new(data, data_extra, store_backends = store_backends)
7. └─mlr3 (local) initialize(...)
8. └─mlr3:::.__ResultData__initialize(...)
9. ├─data[, `:=`("task_hash", task[[1L]]$hash), by = "uhash"]
10. └─data.table:::`[.data.table`(...)
── Error ('test_filter_ensemble.R:447:7'): FilterEnsemble weight search space works with bbotk ──
Error in ``[.data.table`(data, , `:=`("task_hash", task[[1L]]$hash), by = "uhash")`: attempt access index 9/9 in VECTOR_ELT
Backtrace:
▆
1. └─optimizer$optimize(instance) at test_filter_ensemble.R:463:3
2. └─bbotk:::.__OptimizerBatch__optimize(...)
3. └─bbotk::optimize_batch_default(inst, self)
4. ├─base::tryCatch(...)
5. │ └─base (local) tryCatchList(expr, classes, parentenv, handlers)
6. │ └─base (local) tryCatchOne(expr, names, parentenv, handlers[[1L]])
7. │ └─base (local) doTryCatch(return(expr), name, parentenv, handler)
8. └─get_private(optimizer)$.optimize(instance)
9. └─bbotk:::.__OptimizerBatchRandomSearch__.optimize(...)
10. └─inst$eval_batch(design$data)
11. └─bbotk:::.__OptimInstanceBatch__eval_batch(...)
12. └─self$objective$eval_many(xss_trafoed)
13. └─bbotk:::.__Objective__eval_many(...)
14. ├─mlr3misc::invoke(private$.eval_many, xss = xss, .args = self$constants$values)
15. │ └─base::eval.parent(expr, n = 1L)
16. │ └─base::eval(expr, p)
17. │ └─base::eval(expr, p)
18. └─private$.eval_many(xss = xss)
19. └─bbotk:::.__Objective__.eval_many(...)
20. └─mlr3misc::map_dtr(...)
21. ├─data.table::rbindlist(...)
22. ├─base::unname(map(.x, .f, ...))
23. └─mlr3misc::map(.x, .f, ...)
24. └─base::lapply(.x, .f, ...)
25. └─bbotk (local) FUN(X[[i]], ...)
26. └─self$eval(xs)
27. └─bbotk:::.__ObjectiveRFun__eval(...)
28. ├─mlr3misc::invoke(private$.fun, xs, .args = self$constants$values)
29. │ └─base::eval.parent(expr, n = 1L)
30. │ └─base::eval(expr, p)
31. │ └─base::eval(expr, p)
32. └─private$.fun(xs)
33. └─mlr3::resample(task, learner, resampling) at test_filter_ensemble.R:447:7
34. └─ResultData$new(data, data_extra, store_backends = store_backends)
35. └─mlr3 (local) initialize(...)
36. └─mlr3:::.__ResultData__initialize(...)
37. ├─data[, `:=`("task_hash", task[[1L]]$hash), by = "uhash"]
38. └─data.table:::`[.data.table`(...)
── Error ('test_mlr_graphs_bagging.R:49:3'): Bagging with replacement ──────────
Error in ``[.data.table`(data, , `:=`("task_hash", task[[1L]]$hash), by = "uhash")`: attempt access index 9/9 in VECTOR_ELT
Backtrace:
▆
1. └─mlr3::resample(tsk, GraphLearner$new(p), rsmp("holdout")) at test_mlr_graphs_bagging.R:49:3
2. └─ResultData$new(data, data_extra, store_backends = store_backends)
3. └─mlr3 (local) initialize(...)
4. └─mlr3:::.__ResultData__initialize(...)
5. ├─data[, `:=`("task_hash", task[[1L]]$hash), by = "uhash"]
6. └─data.table:::`[.data.table`(...)
── Error ('test_mlr_graphs_stacking.R:16:3'): Stacking Pipeline ────────────────
Error in ``[.data.table`(data, , `:=`("task_hash", task[[1L]]$hash), by = "uhash")`: attempt access index 9/9 in VECTOR_ELT
This happened in PipeOp base.rpart's $train()
Backtrace:
▆
1. ├─graph_learner$train(tsk("iris")) at test_mlr_graphs_stacking.R:16:3
2. │ └─mlr3:::.__Learner__train(...)
3. │ └─mlr3:::learner_train(...)
4. │ └─mlr3misc::encapsulate(...)
5. │ ├─mlr3misc::invoke(...)
6. │ │ └─base::eval.parent(expr, n = 1L)
7. │ │ └─base::eval(expr, p)
8. │ │ └─base::eval(expr, p)
9. │ └─mlr3 (local) .f(learner = `<GrphLrnr>`, task = `<TskClssf>`)
10. │ └─get_private(learner)$.train(task)
11. │ └─mlr3pipelines:::.__GraphLearner__.train(...)
12. │ └─self$graph$train(task)
13. │ └─mlr3pipelines:::.__Graph__train(...)
14. │ └─mlr3pipelines:::graph_reduce(self, input, "train", single_input)
15. │ └─op[[fun]](input)
16. │ └─mlr3pipelines:::.__PipeOp__train(...)
17. │ ├─base::withCallingHandlers(...)
18. │ └─private$.train(input)
19. │ └─mlr3pipelines:::.__PipeOpTaskPreproc__.train(...)
20. │ └─private$.train_task(intask)
21. │ └─mlr3pipelines:::.__PipeOpLearnerCV__.train_task(...)
22. │ └─mlr3::resample(...)
23. │ └─ResultData$new(data, data_extra, store_backends = store_backends)
24. │ └─mlr3 (local) initialize(...)
25. │ └─mlr3:::.__ResultData__initialize(...)
26. │ ├─data[, `:=`("task_hash", task[[1L]]$hash), by = "uhash"]
27. │ └─data.table:::`[.data.table`(...)
28. └─base::.handleSimpleError(...)
29. └─mlr3pipelines (local) h(simpleError(msg, call))
── Error ('test_pipeop_learnerpicvplus.R:35:3'): PipeOpLearnerPICVPlus - basic properties ──
Error in ``[.data.table`(data, , `:=`("task_hash", task[[1L]]$hash), by = "uhash")`: attempt access index 9/9 in VECTOR_ELT
This happened in PipeOp regr.featureless's $train()
Backtrace:
▆
1. ├─global train_pipeop(po, list(task)) at test_pipeop_learnerpicvplus.R:35:3
2. │ └─po$train(inputs)
3. │ └─mlr3pipelines:::.__PipeOp__train(...)
4. │ ├─base::withCallingHandlers(...)
5. │ └─private$.train(input)
6. │ └─mlr3pipelines:::.__PipeOpLearnerPICVPlus__.train(...)
7. │ └─mlr3::resample(task, private$.learner, rdesc, store_models = TRUE)
8. │ └─ResultData$new(data, data_extra, store_backends = store_backends)
9. │ └─mlr3 (local) initialize(...)
10. │ └─mlr3:::.__ResultData__initialize(...)
11. │ ├─data[, `:=`("task_hash", task[[1L]]$hash), by = "uhash"]
12. │ └─data.table:::`[.data.table`(...)
13. └─base::.handleSimpleError(...)
14. └─mlr3pipelines (local) h(simpleError(msg, call))
── Error ('test_pipeop_learnerpicvplus.R:91:3'): PipeOpLearnerPICVPlus - model active binding to state ──
Error in ``[.data.table`(data, , `:=`("task_hash", task[[1L]]$hash), by = "uhash")`: attempt access index 9/9 in VECTOR_ELT
This happened in PipeOp regr.featureless's $train()
Backtrace:
▆
1. ├─po$train(list(task)) at test_pipeop_learnerpicvplus.R:91:3
2. │ └─mlr3pipelines:::.__PipeOp__train(...)
3. │ ├─base::withCallingHandlers(...)
4. │ └─private$.train(input)
5. │ └─mlr3pipelines:::.__PipeOpLearnerPICVPlus__.train(...)
6. │ └─mlr3::resample(task, private$.learner, rdesc, store_models = TRUE)
7. │ └─ResultData$new(data, data_extra, store_backends = store_backends)
8. │ └─mlr3 (local) initialize(...)
9. │ └─mlr3:::.__ResultData__initialize(...)
10. │ ├─data[, `:=`("task_hash", task[[1L]]$hash), by = "uhash"]
11. │ └─data.table:::`[.data.table`(...)
12. └─base::.handleSimpleError(...)
13. └─mlr3pipelines (local) h(simpleError(msg, call))
── Error ('test_pipeop_learnerpicvplus.R:116:3'): PipeOpLearnerPICVPlus - integration with larger graph ──
Error in ``[.data.table`(data, , `:=`("task_hash", task[[1L]]$hash), by = "uhash")`: attempt access index 9/9 in VECTOR_ELT
This happened in PipeOp regr.rpart's $train()
Backtrace:
▆
1. ├─graph$train(task) at test_pipeop_learnerpicvplus.R:116:3
2. │ └─mlr3pipelines:::.__Graph__train(...)
3. │ └─mlr3pipelines:::graph_reduce(self, input, "train", single_input)
4. │ └─op[[fun]](input)
5. │ └─mlr3pipelines:::.__PipeOp__train(...)
6. │ ├─base::withCallingHandlers(...)
7. │ └─private$.train(input)
8. │ └─mlr3pipelines:::.__PipeOpLearnerPICVPlus__.train(...)
9. │ └─mlr3::resample(task, private$.learner, rdesc, store_models = TRUE)
10. │ └─ResultData$new(data, data_extra, store_backends = store_backends)
11. │ └─mlr3 (local) initialize(...)
12. │ └─mlr3:::.__ResultData__initialize(...)
13. │ ├─data[, `:=`("task_hash", task[[1L]]$hash), by = "uhash"]
14. │ └─data.table:::`[.data.table`(...)
15. └─base::.handleSimpleError(...)
16. └─mlr3pipelines (local) h(simpleError(msg, call))
── Error ('test_pipeop_learnerpicvplus.R:130:3'): marshal ──────────────────────
Error in ``[.data.table`(data, , `:=`("task_hash", task[[1L]]$hash), by = "uhash")`: attempt access index 9/9 in VECTOR_ELT
This happened in PipeOp regr.debug's $train()
Backtrace:
▆
1. ├─po$train(list(task)) at test_pipeop_learnerpicvplus.R:130:3
2. │ └─mlr3pipelines:::.__PipeOp__train(...)
3. │ ├─base::withCallingHandlers(...)
4. │ └─private$.train(input)
5. │ └─mlr3pipelines:::.__PipeOpLearnerPICVPlus__.train(...)
6. │ └─mlr3::resample(task, private$.learner, rdesc, store_models = TRUE)
7. │ └─ResultData$new(data, data_extra, store_backends = store_backends)
8. │ └─mlr3 (local) initialize(...)
9. │ └─mlr3:::.__ResultData__initialize(...)
10. │ ├─data[, `:=`("task_hash", task[[1L]]$hash), by = "uhash"]
11. │ └─data.table:::`[.data.table`(...)
12. └─base::.handleSimpleError(...)
13. └─mlr3pipelines (local) h(simpleError(msg, call))
── Error ('test_pipeop_learnerpicvplus.R:152:3'): marshal multiplicity ─────────
Error in ``[.data.table`(data, , `:=`("task_hash", task[[1L]]$hash), by = "uhash")`: attempt access index 9/9 in VECTOR_ELT
This happened in PipeOp regr.debug's $train()
Backtrace:
▆
1. ├─po$train(list(Multiplicity(task1, task2))) at test_pipeop_learnerpicvplus.R:152:3
2. │ └─mlr3pipelines:::.__PipeOp__train(...)
3. │ └─mlr3pipelines:::evaluate_multiplicities(...)
4. │ └─mlr3misc::imap(...)
5. │ ├─stats::setNames(mapply_list(.f, list(.x, .nn), list(...)), names(.x))
6. │ └─mlr3misc:::mapply_list(.f, list(.x, .nn), list(...))
7. │ └─base::.mapply(.f, .dots, .args)
8. │ └─mlr3pipelines (local) `<fn>`(dots[[1L]][[1L]], dots[[2L]][[1L]])
9. │ └─self[[evalcall]](input)
10. │ └─mlr3pipelines:::.__PipeOp__train(...)
11. │ ├─base::withCallingHandlers(...)
12. │ └─private$.train(input)
13. │ └─mlr3pipelines:::.__PipeOpLearnerPICVPlus__.train(...)
14. │ └─mlr3::resample(task, private$.learner, rdesc, store_models = TRUE)
15. │ └─ResultData$new(data, data_extra, store_backends = store_backends)
16. │ └─mlr3 (local) initialize(...)
17. │ └─mlr3:::.__ResultData__initialize(...)
18. │ ├─data[, `:=`("task_hash", task[[1L]]$hash), by = "uhash"]
19. │ └─data.table:::`[.data.table`(...)
20. └─base::.handleSimpleError(...)
21. └─mlr3pipelines (local) h(simpleError(msg, call))
── Error ('test_pipeop_learnercv.R:11:3'): PipeOpLearnerCV - basic properties ──
Error in ``[.data.table`(data, , `:=`("task_hash", task[[1L]]$hash), by = "uhash")`: attempt access index 9/9 in VECTOR_ELT
This happened in PipeOp classif.featureless's $train()
Backtrace:
▆
1. ├─global train_pipeop(po, list(task = task)) at test_pipeop_learnercv.R:11:3
2. │ └─po$train(inputs)
3. │ └─mlr3pipelines:::.__PipeOp__train(...)
4. │ ├─base::withCallingHandlers(...)
5. │ └─private$.train(input)
6. │ └─mlr3pipelines:::.__PipeOpTaskPreproc__.train(...)
7. │ └─private$.train_task(intask)
8. │ └─mlr3pipelines:::.__PipeOpLearnerCV__.train_task(...)
9. │ └─mlr3::resample(...)
10. │ └─ResultData$new(data, data_extra, store_backends = store_backends)
11. │ └─mlr3 (local) initialize(...)
12. │ └─mlr3:::.__ResultData__initialize(...)
13. │ ├─data[, `:=`("task_hash", task[[1L]]$hash), by = "uhash"]
14. │ └─data.table:::`[.data.table`(...)
15. └─base::.handleSimpleError(...)
16. └─mlr3pipelines (local) h(simpleError(msg, call))
── Error ('test_pipeop_learnercv.R:100:3'): PipeOpLearnerCV - cv ensemble averages fold learners ──
Error in ``[.data.table`(data, , `:=`("task_hash", task[[1L]]$hash), by = "uhash")`: attempt access index 9/9 in VECTOR_ELT
This happened in PipeOp classif.rpart's $train()
Backtrace:
▆
1. ├─po$train(list(task)) at test_pipeop_learnercv.R:100:3
2. │ └─mlr3pipelines:::.__PipeOp__train(...)
3. │ ├─base::withCallingHandlers(...)
4. │ └─private$.train(input)
5. │ └─mlr3pipelines:::.__PipeOpTaskPreproc__.train(...)
6. │ └─private$.train_task(intask)
7. │ └─mlr3pipelines:::.__PipeOpLearnerCV__.train_task(...)
8. │ └─mlr3::resample(...)
9. │ └─ResultData$new(data, data_extra, store_backends = store_backends)
10. │ └─mlr3 (local) initialize(...)
11. │ └─mlr3:::.__ResultData__initialize(...)
12. │ ├─data[, `:=`("task_hash", task[[1L]]$hash), by = "uhash"]
13. │ └─data.table:::`[.data.table`(...)
14. └─base::.handleSimpleError(...)
15. └─mlr3pipelines (local) h(simpleError(msg, call))
── Error ('test_pipeop_learnercv.R:139:3'): PipeOpLearnerCV - cv ensemble drops response when requested ──
Error in ``[.data.table`(data, , `:=`("task_hash", task[[1L]]$hash), by = "uhash")`: attempt access index 9/9 in VECTOR_ELT
This happened in PipeOp classif.rpart's $train()
Backtrace:
▆
1. ├─po$train(list(task)) at test_pipeop_learnercv.R:139:3
2. │ └─mlr3pipelines:::.__PipeOp__train(...)
3. │ ├─base::withCallingHandlers(...)
4. │ └─private$.train(input)
5. │ └─mlr3pipelines:::.__PipeOpTaskPreproc__.train(...)
6. │ └─private$.train_task(intask)
7. │ └─mlr3pipelines:::.__PipeOpLearnerCV__.train_task(...)
8. │ └─mlr3::resample(...)
9. │ └─ResultData$new(data, data_extra, store_backends = store_backends)
10. │ └─mlr3 (local) initialize(...)
11. │ └─mlr3:::.__ResultData__initialize(...)
12. │ ├─data[, `:=`("task_hash", task[[1L]]$hash), by = "uhash"]
13. │ └─data.table:::`[.data.table`(...)
14. └─base::.handleSimpleError(...)
15. └─mlr3pipelines (local) h(simpleError(msg, call))
── Error ('test_pipeop_learnercv.R:152:3'): PipeOpLearnerCV - cv ensemble averages classif responses ──
Error in ``[.data.table`(data, , `:=`("task_hash", task[[1L]]$hash), by = "uhash")`: attempt access index 9/9 in VECTOR_ELT
This happened in PipeOp classif.rpart's $train()
Backtrace:
▆
1. ├─po$train(list(task)) at test_pipeop_learnercv.R:152:3
2. │ └─mlr3pipelines:::.__PipeOp__train(...)
3. │ ├─base::withCallingHandlers(...)
4. │ └─private$.train(input)
5. │ └─mlr3pipelines:::.__PipeOpTaskPreproc__.train(...)
6. │ └─private$.train_task(intask)
7. │ └─mlr3pipelines:::.__PipeOpLearnerCV__.train_task(...)
8. │ └─mlr3::resample(...)
9. │ └─ResultData$new(data, data_extra, store_backends = store_backends)
10. │ └─mlr3 (local) initialize(...)
11. │ └─mlr3:::.__ResultData__initialize(...)
12. │ ├─data[, `:=`("task_hash", task[[1L]]$hash), by = "uhash"]
13. │ └─data.table:::`[.data.table`(...)
14. └─base::.handleSimpleError(...)
15. └─mlr3pipelines (local) h(simpleError(msg, call))
── Error ('test_pipeop_learnercv.R:203:3'): PipeOpLearnerCV - cv ensemble log prob aggregation ──
Error in ``[.data.table`(data, , `:=`("task_hash", task[[1L]]$hash), by = "uhash")`: attempt access index 9/9 in VECTOR_ELT
This happened in PipeOp classif.rpart's $train()
Backtrace:
▆
1. ├─po$train(list(task)) at test_pipeop_learnercv.R:203:3
2. │ └─mlr3pipelines:::.__PipeOp__train(...)
3. │ ├─base::withCallingHandlers(...)
4. │ └─private$.train(input)
5. │ └─mlr3pipelines:::.__PipeOpTaskPreproc__.train(...)
6. │ └─private$.train_task(intask)
7. │ └─mlr3pipelines:::.__PipeOpLearnerCV__.train_task(...)
8. │ └─mlr3::resample(...)
9. │ └─ResultData$new(data, data_extra, store_backends = store_backends)
10. │ └─mlr3 (local) initialize(...)
11. │ └─mlr3:::.__ResultData__initialize(...)
12. │ ├─data[, `:=`("task_hash", task[[1L]]$hash), by = "uhash"]
13. │ └─data.table:::`[.data.table`(...)
14. └─base::.handleSimpleError(...)
15. └─mlr3pipelines (local) h(simpleError(msg, call))
── Error ('test_pipeop_learnercv.R:250:3'): PipeOpLearnerCV - log aggregation with zeros uses epsilon ──
Error in ``[.data.table`(data, , `:=`("task_hash", task[[1L]]$hash), by = "uhash")`: attempt access index 9/9 in VECTOR_ELT
This happened in PipeOp classif.featureless's $train()
Backtrace:
▆
1. ├─po$train(list(task)) at test_pipeop_learnercv.R:250:3
2. │ └─mlr3pipelines:::.__PipeOp__train(...)
3. │ ├─base::withCallingHandlers(...)
4. │ └─private$.train(input)
5. │ └─mlr3pipelines:::.__PipeOpTaskPreproc__.train(...)
6. │ └─private$.train_task(intask)
7. │ └─mlr3pipelines:::.__PipeOpLearnerCV__.train_task(...)
8. │ └─mlr3::resample(...)
9. │ └─ResultData$new(data, data_extra, store_backends = store_backends)
10. │ └─mlr3 (local) initialize(...)
11. │ └─mlr3:::.__ResultData__initialize(...)
12. │ ├─data[, `:=`("task_hash", task[[1L]]$hash), by = "uhash"]
13. │ └─data.table:::`[.data.table`(...)
14. └─base::.handleSimpleError(...)
15. └─mlr3pipelines (local) h(simpleError(msg, call))
── Error ('test_pipeop_learnercv.R:278:3'): PipeOpLearnerCV - log aggregation epsilon controls shrinkage ──
Error in ``[.data.table`(data, , `:=`("task_hash", task[[1L]]$hash), by = "uhash")`: attempt access index 9/9 in VECTOR_ELT
This happened in PipeOp classif.featureless's $train()
Backtrace:
▆
1. ├─po$train(list(task)) at test_pipeop_learnercv.R:278:3
2. │ └─mlr3pipelines:::.__PipeOp__train(...)
3. │ ├─base::withCallingHandlers(...)
4. │ └─private$.train(input)
5. │ └─mlr3pipelines:::.__PipeOpTaskPreproc__.train(...)
6. │ └─private$.train_task(intask)
7. │ └─mlr3pipelines:::.__PipeOpLearnerCV__.train_task(...)
8. │ └─mlr3::resample(...)
9. │ └─ResultData$new(data, data_extra, store_backends = store_backends)
10. │ └─mlr3 (local) initialize(...)
11. │ └─mlr3:::.__ResultData__initialize(...)
12. │ ├─data[, `:=`("task_hash", task[[1L]]$hash), by = "uhash"]
13. │ └─data.table:::`[.data.table`(...)
14. └─base::.handleSimpleError(...)
15. └─mlr3pipelines (local) h(simpleError(msg, call))
── Error ('test_pipeop_learnercv.R:323:3'): PipeOpLearnerCV - cv ensemble averages regression predictions ──
Error in ``[.data.table`(data, , `:=`("task_hash", task[[1L]]$hash), by = "uhash")`: attempt access index 9/9 in VECTOR_ELT
This happened in PipeOp regr.rpart's $train()
Backtrace:
▆
1. ├─po$train(list(task)) at test_pipeop_learnercv.R:323:3
2. │ └─mlr3pipelines:::.__PipeOp__train(...)
3. │ ├─base::withCallingHandlers(...)
4. │ └─private$.train(input)
5. │ └─mlr3pipelines:::.__PipeOpTaskPreproc__.train(...)
6. │ └─private$.train_task(intask)
7. │ └─mlr3pipelines:::.__PipeOpLearnerCV__.train_task(...)
8. │ └─mlr3::resample(...)
9. │ └─ResultData$new(data, data_extra, store_backends = store_backends)
10. │ └─mlr3 (local) initialize(...)
11. │ └─mlr3:::.__ResultData__initialize(...)
12. │ ├─data[, `:=`("task_hash", task[[1L]]$hash), by = "uhash"]
13. │ └─data.table:::`[.data.table`(...)
14. └─base::.handleSimpleError(...)
15. └─mlr3pipelines (local) h(simpleError(msg, call))
── Error ('test_pipeop_learnercv.R:350:3'): PipeOpLearnerCV - cv ensemble handles multiplicity ──
Error in ``[.data.table`(data, , `:=`("task_hash", task[[1L]]$hash), by = "uhash")`: attempt access index 9/9 in VECTOR_ELT
This happened in PipeOp classif.rpart's $train()
Backtrace:
▆
1. ├─po$train(list(tasks)) at test_pipeop_learnercv.R:350:3
2. │ └─mlr3pipelines:::.__PipeOp__train(...)
3. │ └─mlr3pipelines:::evaluate_multiplicities(...)
4. │ └─mlr3misc::imap(...)
5. │ ├─stats::setNames(mapply_list(.f, list(.x, .nn), list(...)), names(.x))
6. │ └─mlr3misc:::mapply_list(.f, list(.x, .nn), list(...))
7. │ └─base::.mapply(.f, .dots, .args)
8. │ └─mlr3pipelines (local) `<fn>`(dots[[1L]][[1L]], dots[[2L]][[1L]])
9. │ └─self[[evalcall]](input)
10. │ └─mlr3pipelines:::.__PipeOp__train(...)
11. │ ├─base::withCallingHandlers(...)
12. │ └─private$.train(input)
13. │ └─mlr3pipelines:::.__PipeOpTaskPreproc__.train(...)
14. │ └─private$.train_task(intask)
15. │ └─mlr3pipelines:::.__PipeOpLearnerCV__.train_task(...)
16. │ └─mlr3::resample(...)
17. │ └─ResultData$new(data, data_extra, store_backends = store_backends)
18. │ └─mlr3 (local) initialize(...)
19. │ └─mlr3:::.__ResultData__initialize(...)
20. │ ├─data[, `:=`("task_hash", task[[1L]]$hash), by = "uhash"]
21. │ └─data.table:::`[.data.table`(...)
22. └─base::.handleSimpleError(...)
23. └─mlr3pipelines (local) h(simpleError(msg, call))
── Error ('test_pipeop_learnercv.R:387:3'): PipeOpLearnerCV - learner_model returns averaged ensemble ──
Error in ``[.data.table`(data, , `:=`("task_hash", task[[1L]]$hash), by = "uhash")`: attempt access index 9/9 in VECTOR_ELT
This happened in PipeOp classif.rpart's $train()
Backtrace:
▆
1. ├─po$train(list(task)) at test_pipeop_learnercv.R:387:3
2. │ └─mlr3pipelines:::.__PipeOp__train(...)
3. │ ├─base::withCallingHandlers(...)
4. │ └─private$.train(input)
5. │ └─mlr3pipelines:::.__PipeOpTaskPreproc__.train(...)
6. │ └─private$.train_task(intask)
7. │ └─mlr3pipelines:::.__PipeOpLearnerCV__.train_task(...)
8. │ └─mlr3::resample(...)
9. │ └─ResultData$new(data, data_extra, store_backends = store_backends)
10. │ └─mlr3 (local) initialize(...)
11. │ └─mlr3:::.__ResultData__initialize(...)
12. │ ├─data[, `:=`("task_hash", task[[1L]]$hash), by = "uhash"]
13. │ └─data.table:::`[.data.table`(...)
14. └─base::.handleSimpleError(...)
15. └─mlr3pipelines (local) h(simpleError(msg, call))
── Error ('test_pipeop_learnercv.R:419:3'): PipeOpLearnerCV - cv ensemble with predict_type = 'se' ──
Error in ``[.data.table`(data, , `:=`("task_hash", task[[1L]]$hash), by = "uhash")`: attempt access index 9/9 in VECTOR_ELT
This happened in PipeOp regr.lm's $train()
Backtrace:
▆
1. ├─po$train(list(task)) at test_pipeop_learnercv.R:419:3
2. │ └─mlr3pipelines:::.__PipeOp__train(...)
3. │ ├─base::withCallingHandlers(...)
4. │ └─private$.train(input)
5. │ └─mlr3pipelines:::.__PipeOpTaskPreproc__.train(...)
6. │ └─private$.train_task(intask)
7. │ └─mlr3pipelines:::.__PipeOpLearnerCV__.train_task(...)
8. │ └─mlr3::resample(...)
9. │ └─ResultData$new(data, data_extra, store_backends = store_backends)
10. │ └─mlr3 (local) initialize(...)
11. │ └─mlr3:::.__ResultData__initialize(...)
12. │ ├─data[, `:=`("task_hash", task[[1L]]$hash), by = "uhash"]
13. │ └─data.table:::`[.data.table`(...)
14. └─base::.handleSimpleError(...)
15. └─mlr3pipelines (local) h(simpleError(msg, call))
── Error ('test_pipeop_learnercv.R:455:3'): PipeOpLearnerCV - within resampling ──
Error in ``[.data.table`(data, , `:=`("task_hash", task[[1L]]$hash), by = "uhash")`: attempt access index 9/9 in VECTOR_ELT
This happened in PipeOp classif.rpart's $train()
Backtrace:
▆
1. └─mlr3::resample(tsk("iris"), gr, rsmp("holdout")) at test_pipeop_learnercv.R:455:3
2. └─mlr3:::future_map(...)
3. └─future.apply::future_mapply(...)
4. └─future.apply:::future_xapply(...)
5. └─base::tryCatch(...)
6. └─base (local) tryCatchList(expr, classes, parentenv, handlers)
7. └─base (local) tryCatchOne(...)
8. └─value[[3L]](cond)
9. └─future.apply:::onError(e, futures = fs, debug = debug)
── Error ('test_pipeop_learnercv.R:493:3'): PipeOpLearnerCV - model active binding to state ──
Error in ``[.data.table`(data, , `:=`("task_hash", task[[1L]]$hash), by = "uhash")`: attempt access index 9/9 in VECTOR_ELT
This happened in PipeOp classif.featureless's $train()
Backtrace:
▆
1. ├─po$train(list(task)) at test_pipeop_learnercv.R:493:3
2. │ └─mlr3pipelines:::.__PipeOp__train(...)
3. │ ├─base::withCallingHandlers(...)
4. │ └─private$.train(input)
5. │ └─mlr3pipelines:::.__PipeOpTaskPreproc__.train(...)
6. │ └─private$.train_task(intask)
7. │ └─mlr3pipelines:::.__PipeOpLearnerCV__.train_task(...)
8. │ └─mlr3::resample(...)
9. │ └─ResultData$new(data, data_extra, store_backends = store_backends)
10. │ └─mlr3 (local) initialize(...)
11. │ └─mlr3:::.__ResultData__initialize(...)
12. │ ├─data[, `:=`("task_hash", task[[1L]]$hash), by = "uhash"]
13. │ └─data.table:::`[.data.table`(...)
14. └─base::.handleSimpleError(...)
15. └─mlr3pipelines (local) h(simpleError(msg, call))
── Error ('test_pipeop_learnercv.R:516:3'): predict_type ───────────────────────
Error in ``[.data.table`(data, , `:=`("task_hash", task[[1L]]$hash), by = "uhash")`: attempt access index 9/9 in VECTOR_ELT
This happened in PipeOp classif.rpart's $train()
Backtrace:
▆
1. ├─global expect_equal(...) at test_pipeop_learnercv.R:516:3
2. │ ├─testthat::expect_true(...)
3. │ │ └─testthat::quasi_label(enquo(object), label)
4. │ │ └─rlang::eval_bare(expr, quo_get_env(quo))
5. │ └─base::all.equal(...)
6. ├─lcv$train(list(tsk("iris")))
7. │ └─mlr3pipelines:::.__PipeOp__train(...)
8. │ ├─base::withCallingHandlers(...)
9. │ └─private$.train(input)
10. │ └─mlr3pipelines:::.__PipeOpTaskPreproc__.train(...)
11. │ └─private$.train_task(intask)
12. │ └─mlr3pipelines:::.__PipeOpLearnerCV__.train_task(...)
13. │ └─mlr3::resample(...)
14. │ └─ResultData$new(data, data_extra, store_backends = store_backends)
15. │ └─mlr3 (local) initialize(...)
16. │ └─mlr3:::.__ResultData__initialize(...)
17. │ ├─data[, `:=`("task_hash", task[[1L]]$hash), by = "uhash"]
18. │ └─data.table:::`[.data.table`(...)
19. └─base::.handleSimpleError(...)
20. └─mlr3pipelines (local) h(simpleError(msg, call))
── Error ('test_pipeop_learnercv.R:531:3'): marshal ────────────────────────────
Error in ``[.data.table`(data, , `:=`("task_hash", task[[1L]]$hash), by = "uhash")`: attempt access index 9/9 in VECTOR_ELT
This happened in PipeOp classif.debug's $train()
Backtrace:
▆
1. ├─po_lrn$train(list(task)) at test_pipeop_learnercv.R:531:3
2. │ └─mlr3pipelines:::.__PipeOp__train(...)
3. │ ├─base::withCallingHandlers(...)
4. │ └─private$.train(input)
5. │ └─mlr3pipelines:::.__PipeOpTaskPreproc__.train(...)
6. │ └─private$.train_task(intask)
7. │ └─mlr3pipelines:::.__PipeOpLearnerCV__.train_task(...)
8. │ └─mlr3::resample(...)
9. │ └─ResultData$new(data, data_extra, store_backends = store_backends)
10. │ └─mlr3 (local) initialize(...)
11. │ └─mlr3:::.__ResultData__initialize(...)
12. │ ├─data[, `:=`("task_hash", task[[1L]]$hash), by = "uhash"]
13. │ └─data.table:::`[.data.table`(...)
14. └─base::.handleSimpleError(...)
15. └─mlr3pipelines (local) h(simpleError(msg, call))
── Error ('test_pipeop_learnercv.R:557:3'): marshal multiplicity ───────────────
Error in ``[.data.table`(data, , `:=`("task_hash", task[[1L]]$hash), by = "uhash")`: attempt access index 9/9 in VECTOR_ELT
This happened in PipeOp classif.debug's $train()
Backtrace:
▆
1. ├─po$train(list(Multiplicity(tsk("iris"), tsk("sonar")))) at test_pipeop_learnercv.R:557:3
2. │ └─mlr3pipelines:::.__PipeOp__train(...)
3. │ └─mlr3pipelines:::evaluate_multiplicities(...)
4. │ └─mlr3misc::imap(...)
5. │ ├─stats::setNames(mapply_list(.f, list(.x, .nn), list(...)), names(.x))
6. │ └─mlr3misc:::mapply_list(.f, list(.x, .nn), list(...))
7. │ └─base::.mapply(.f, .dots, .args)
8. │ └─mlr3pipelines (local) `<fn>`(dots[[1L]][[1L]], dots[[2L]][[1L]])
9. │ └─self[[evalcall]](input)
10. │ └─mlr3pipelines:::.__PipeOp__train(...)
11. │ ├─base::withCallingHandlers(...)
12. │ └─private$.train(input)
13. │ └─mlr3pipelines:::.__PipeOpTaskPreproc__.train(...)
14. │ └─private$.train_task(intask)
15. │ └─mlr3pipelines:::.__PipeOpLearnerCV__.train_task(...)
16. │ └─mlr3::resample(...)
17. │ └─ResultData$new(data, data_extra, store_backends = store_backends)
18. │ └─mlr3 (local) initialize(...)
19. │ └─mlr3:::.__ResultData__initialize(...)
20. │ ├─data[, `:=`("task_hash", task[[1L]]$hash), by = "uhash"]
21. │ └─data.table:::`[.data.table`(...)
22. └─base::.handleSimpleError(...)
23. └─mlr3pipelines (local) h(simpleError(msg, call))
── Error ('test_pipeop_learnercv.R:612:3'): marshal with cv ensemble ───────────
Error in ``[.data.table`(data, , `:=`("task_hash", task[[1L]]$hash), by = "uhash")`: attempt access index 9/9 in VECTOR_ELT
This happened in PipeOp classif.rpart's $train()
Backtrace:
▆
1. ├─po$train(list(task)) at test_pipeop_learnercv.R:612:3
2. │ └─mlr3pipelines:::.__PipeOp__train(...)
3. │ ├─base::withCallingHandlers(...)
4. │ └─private$.train(input)
5. │ └─mlr3pipelines:::.__PipeOpTaskPreproc__.train(...)
6. │ └─private$.train_task(intask)
7. │ └─mlr3pipelines:::.__PipeOpLearnerCV__.train_task(...)
8. │ └─mlr3::resample(...)
9. │ └─ResultData$new(data, data_extra, store_backends = store_backends)
10. │ └─mlr3 (local) initialize(...)
11. │ └─mlr3:::.__ResultData__initialize(...)
12. │ ├─data[, `:=`("task_hash", task[[1L]]$hash), by = "uhash"]
13. │ └─data.table:::`[.data.table`(...)
14. └─base::.handleSimpleError(...)
15. └─mlr3pipelines (local) h(simpleError(msg, call))
── Error ('test_pipeop_learnercv.R:628:3'): state class and multiplicity ───────
Error in ``[.data.table`(data, , `:=`("task_hash", task[[1L]]$hash), by = "uhash")`: attempt access index 9/9 in VECTOR_ELT
This happened in PipeOp classif.debug's $train()
Backtrace:
▆
1. ├─po$train(list(Multiplicity(tsk("iris")))) at test_pipeop_learnercv.R:628:3
2. │ └─mlr3pipelines:::.__PipeOp__train(...)
3. │ └─mlr3pipelines:::evaluate_multiplicities(...)
4. │ └─mlr3misc::imap(...)
5. │ ├─stats::setNames(mapply_list(.f, list(.x, .nn), list(...)), names(.x))
6. │ └─mlr3misc:::mapply_list(.f, list(.x, .nn), list(...))
7. │ └─base::.mapply(.f, .dots, .args)
8. │ └─mlr3pipelines (local) `<fn>`(dots[[1L]][[1L]], dots[[2L]][[1L]])
9. │ └─self[[evalcall]](input)
10. │ └─mlr3pipelines:::.__PipeOp__train(...)
11. │ ├─base::withCallingHandlers(...)
12. │ └─private$.train(input)
13. │ └─mlr3pipelines:::.__PipeOpTaskPreproc__.train(...)
14. │ └─private$.train_task(intask)
15. │ └─mlr3pipelines:::.__PipeOpLearnerCV__.train_task(...)
16. │ └─mlr3::resample(...)
17. │ └─ResultData$new(data, data_extra, store_backends = store_backends)
18. │ └─mlr3 (local) initialize(...)
19. │ └─mlr3:::.__ResultData__initialize(...)
20. │ ├─data[, `:=`("task_hash", task[[1L]]$hash), by = "uhash"]
21. │ └─data.table:::`[.data.table`(...)
22. └─base::.handleSimpleError(...)
23. └─mlr3pipelines (local) h(simpleError(msg, call))
── Error ('test_pipeop_learnercv.R:671:5'): PipeOpLearnerCV cv ensemble aggregates SE like PipeOpRegrAvg ──
Error in ``[.data.table`(data, , `:=`("task_hash", task[[1L]]$hash), by = "uhash")`: attempt access index 9/9 in VECTOR_ELT
This happened in PipeOp regr.debug's $train()
Backtrace:
▆
1. ├─po$train(list(task)) at test_pipeop_learnercv.R:671:5
2. │ └─mlr3pipelines:::.__PipeOp__train(...)
3. │ ├─base::withCallingHandlers(...)
4. │ └─private$.train(input)
5. │ └─mlr3pipelines:::.__PipeOpTaskPreproc__.train(...)
6. │ └─private$.train_task(intask)
7. │ └─mlr3pipelines:::.__PipeOpLearnerCV__.train_task(...)
8. │ └─mlr3::resample(...)
9. │ └─ResultData$new(data, data_extra, store_backends = store_backends)
10. │ └─mlr3 (local) initialize(...)
11. │ └─mlr3:::.__ResultData__initialize(...)
12. │ ├─data[, `:=`("task_hash", task[[1L]]$hash), by = "uhash"]
13. │ └─data.table:::`[.data.table`(...)
14. └─base::.handleSimpleError(...)
15. └─mlr3pipelines (local) h(simpleError(msg, call))
── Error ('test_pipeop_tunethreshold.R:7:3'): threshold works for multiclass ───
Error in ``[.data.table`(data, , `:=`("task_hash", task[[1L]]$hash), by = "uhash")`: attempt access index 9/9 in VECTOR_ELT
This happened in PipeOp classif.rpart's $train()
Backtrace:
▆
1. ├─po_cv$train(list(t)) at test_pipeop_tunethreshold.R:7:3
2. │ └─mlr3pipelines:::.__PipeOp__train(...)
3. │ ├─base::withCallingHandlers(...)
4. │ └─private$.train(input)
5. │ └─mlr3pipelines:::.__PipeOpTaskPreproc__.train(...)
6. │ └─private$.train_task(intask)
7. │ └─mlr3pipelines:::.__PipeOpLearnerCV__.train_task(...)
8. │ └─mlr3::resample(...)
9. │ └─ResultData$new(data, data_extra, store_backends = store_backends)
10. │ └─mlr3 (local) initialize(...)
11. │ └─mlr3:::.__ResultData__initialize(...)
12. │ ├─data[, `:=`("task_hash", task[[1L]]$hash), by = "uhash"]
13. │ └─data.table:::`[.data.table`(...)
14. └─base::.handleSimpleError(...)
15. └─mlr3pipelines (local) h(simpleError(msg, call))
── Error ('test_pipeop_tunethreshold.R:38:3'): threshold works for binary ──────
Error in ``[.data.table`(data, , `:=`("task_hash", task[[1L]]$hash), by = "uhash")`: attempt access index 9/9 in VECTOR_ELT
This happened in PipeOp classif.rpart's $train()
Backtrace:
▆
1. ├─po_cv$train(list(t)) at test_pipeop_tunethreshold.R:38:3
2. │ └─mlr3pipelines:::.__PipeOp__train(...)
3. │ ├─base::withCallingHandlers(...)
4. │ └─private$.train(input)
5. │ └─mlr3pipelines:::.__PipeOpTaskPreproc__.train(...)
6. │ └─private$.train_task(intask)
7. │ └─mlr3pipelines:::.__PipeOpLearnerCV__.train_task(...)
8. │ └─mlr3::resample(...)
9. │ └─ResultData$new(data, data_extra, store_backends = store_backends)
10. │ └─mlr3 (local) initialize(...)
11. │ └─mlr3:::.__ResultData__initialize(...)
12. │ ├─data[, `:=`("task_hash", task[[1L]]$hash), by = "uhash"]
13. │ └─data.table:::`[.data.table`(...)
14. └─base::.handleSimpleError(...)
15. └─mlr3pipelines (local) h(simpleError(msg, call))
── Error ('test_pipeop_tunethreshold.R:73:3'): tunethreshold graph works ───────
Error in ``[.data.table`(data, , `:=`("task_hash", task[[1L]]$hash), by = "uhash")`: attempt access index 9/9 in VECTOR_ELT
This happened in PipeOp classif.rpart's $train()
Backtrace:
▆
1. ├─graph$train(tsk("pima")) at test_pipeop_tunethreshold.R:73:3
2. │ └─mlr3pipelines:::.__Graph__train(...)
3. │ └─mlr3pipelines:::graph_reduce(self, input, "train", single_input)
4. │ └─op[[fun]](input)
5. │ └─mlr3pipelines:::.__PipeOp__train(...)
6. │ ├─base::withCallingHandlers(...)
7. │ └─private$.train(input)
8. │ └─mlr3pipelines:::.__PipeOpTaskPreproc__.train(...)
9. │ └─private$.train_task(intask)
10. │ └─mlr3pipelines:::.__PipeOpLearnerCV__.train_task(...)
11. │ └─mlr3::resample(...)
12. │ └─ResultData$new(data, data_extra, store_backends = store_backends)
13. │ └─mlr3 (local) initialize(...)
14. │ └─mlr3:::.__ResultData__initialize(...)
15. │ ├─data[, `:=`("task_hash", task[[1L]]$hash), by = "uhash"]
16. │ └─data.table:::`[.data.table`(...)
17. └─base::.handleSimpleError(...)
18. └─mlr3pipelines (local) h(simpleError(msg, call))
── Error ('test_pipeop_tunethreshold.R:101:3'): threshold works for classes that are not valid R names ──
Error in ``[.data.table`(data, , `:=`("task_hash", task[[1L]]$hash), by = "uhash")`: attempt access index 9/9 in VECTOR_ELT
This happened in PipeOp classif.rpart's $train()
Backtrace:
▆
1. ├─ppl$train(testtask) at test_pipeop_tunethreshold.R:101:3
2. │ └─mlr3pipelines:::.__Graph__train(...)
3. │ └─mlr3pipelines:::graph_reduce(self, input, "train", single_input)
4. │ └─op[[fun]](input)
5. │ └─mlr3pipelines:::.__PipeOp__train(...)
6. │ ├─base::withCallingHandlers(...)
7. │ └─private$.train(input)
8. │ └─mlr3pipelines:::.__PipeOpTaskPreproc__.train(...)
9. │ └─private$.train_task(intask)
10. │ └─mlr3pipelines:::.__PipeOpLearnerCV__.train_task(...)
11. │ └─mlr3::resample(...)
12. │ └─ResultData$new(data, data_extra, store_backends = store_backends)
13. │ └─mlr3 (local) initialize(...)
14. │ └─mlr3:::.__ResultData__initialize(...)
15. │ ├─data[, `:=`("task_hash", task[[1L]]$hash), by = "uhash"]
16. │ └─data.table:::`[.data.table`(...)
17. └─base::.handleSimpleError(...)
18. └─mlr3pipelines (local) h(simpleError(msg, call))
── Error ('test_pipeop_tunethreshold.R:260:3'): threshold graph transparency ───
Error in ``[.data.table`(data, , `:=`("task_hash", task[[1L]]$hash), by = "uhash")`: attempt access index 9/9 in VECTOR_ELT
This happened in PipeOp classif.rpart's $train()
Backtrace:
▆
1. ├─lrn_prob$train(t) at test_pipeop_tunethreshold.R:260:3
2. │ └─mlr3:::.__Learner__train(...)
3. │ └─mlr3:::learner_train(...)
4. │ └─mlr3misc::encapsulate(...)
5. │ ├─mlr3misc::invoke(...)
6. │ │ └─base::eval.parent(expr, n = 1L)
7. │ │ └─base::eval(expr, p)
8. │ │ └─base::eval(expr, p)
9. │ └─mlr3 (local) .f(learner = `<GrphLrnr>`, task = `<TskClssf>`)
10. │ └─get_private(learner)$.train(task)
11. │ └─mlr3pipelines:::.__GraphLearner__.train(...)
12. │ └─self$graph$train(task)
13. │ └─mlr3pipelines:::.__Graph__train(...)
14. │ └─mlr3pipelines:::graph_reduce(self, input, "train", single_input)
15. │ └─op[[fun]](input)
16. │ └─mlr3pipelines:::.__PipeOp__train(...)
17. │ ├─base::withCallingHandlers(...)
18. │ └─private$.train(input)
19. │ └─mlr3pipelines:::.__PipeOpTaskPreproc__.train(...)
20. │ └─private$.train_task(intask)
21. │ └─mlr3pipelines:::.__PipeOpLearnerCV__.train_task(...)
22. │ └─mlr3::resample(...)
23. │ └─ResultData$new(data, data_extra, store_backends = store_backends)
24. │ └─mlr3 (local) initialize(...)
25. │ └─mlr3:::.__ResultData__initialize(...)
26. │ ├─data[, `:=`("task_hash", task[[1L]]$hash), by = "uhash"]
27. │ └─data.table:::`[.data.table`(...)
28. └─base::.handleSimpleError(...)
29. └─mlr3pipelines (local) h(simpleError(msg, call))
── Error ('test_resample.R:13:3'): PipeOp - Resample ───────────────────────────
Error in ``[.data.table`(data, , `:=`("task_hash", task[[1L]]$hash), by = "uhash")`: attempt access index 9/9 in VECTOR_ELT
Backtrace:
▆
1. └─mlr3::resample(task, pp, resa) at test_resample.R:13:3
2. └─ResultData$new(data, data_extra, store_backends = store_backends)
3. └─mlr3 (local) initialize(...)
4. └─mlr3:::.__ResultData__initialize(...)
5. ├─data[, `:=`("task_hash", task[[1L]]$hash), by = "uhash"]
6. └─data.table:::`[.data.table`(...)
── Error ('test_ppl.R:73:3'): mlr3book authors don't sleepwalk through life ────
Error in ``[.data.table`(data, , `:=`("task_hash", task[[1L]]$hash), by = "uhash")`: attempt access index 9/9 in VECTOR_ELT
This happened in PipeOp classif.rpart.classif.rpart's $train()
Backtrace:
▆
1. └─mlr3::benchmark(benchmark_grid(tasks, learners, rsmp("cv", folds = 2))) at test_ppl.R:73:3
2. └─mlr3:::future_map(...)
3. └─future.apply::future_mapply(...)
4. └─future.apply:::future_xapply(...)
5. └─base::tryCatch(...)
6. └─base (local) tryCatchList(expr, classes, parentenv, handlers)
7. └─base (local) tryCatchOne(...)
8. └─value[[3L]](cond)
9. └─future.apply:::onError(e, futures = fs, debug = debug)
── Error ('test_usecases.R:153:3'): stacking ───────────────────────────────────
Error in ``[.data.table`(data, , `:=`("task_hash", task[[1L]]$hash), by = "uhash")`: attempt access index 9/9 in VECTOR_ELT
This happened in PipeOp classif.rpart's $train()
Backtrace:
▆
1. ├─pipe$train(task) at test_usecases.R:153:3
2. │ └─mlr3pipelines:::.__Graph__train(...)
3. │ └─mlr3pipelines:::graph_reduce(self, input, "train", single_input)
4. │ └─op[[fun]](input)
5. │ └─mlr3pipelines:::.__PipeOp__train(...)
6. │ ├─base::withCallingHandlers(...)
7. │ └─private$.train(input)
8. │ └─mlr3pipelines:::.__PipeOpTaskPreproc__.train(...)
9. │ └─private$.train_task(intask)
10. │ └─mlr3pipelines:::.__PipeOpLearnerCV__.train_task(...)
11. │ └─mlr3::resample(...)
12. │ └─ResultData$new(data, data_extra, store_backends = store_backends)
13. │ └─mlr3 (local) initialize(...)
14. │ └─mlr3:::.__ResultData__initialize(...)
15. │ ├─data[, `:=`("task_hash", task[[1L]]$hash), by = "uhash"]
16. │ └─data.table:::`[.data.table`(...)
17. └─base::.handleSimpleError(...)
18. └─mlr3pipelines (local) h(simpleError(msg, call))
[ FAIL 38 | WARN 2 | SKIP 98 | PASS 12316 ]
Error:
! Test failures.
Execution halted
Flavor: r-devel-linux-x86_64-fedora-clang
Version: 0.10.0
Check: examples
Result: ERROR
Running examples in ‘mlr3pipelines-Ex.R’ failed
The error most likely occurred in:
> ### Name: mlr_graphs_stacking
> ### Title: Create A Graph to Perform Stacking.
> ### Aliases: mlr_graphs_stacking pipeline_stacking
>
> ### ** Examples
>
> ## Don't show:
> if (mlr3misc::require_namespaces("rpart", quietly = TRUE)) withAutoprint({ # examplesIf
+ ## End(Don't show)
+ library(mlr3)
+ library(mlr3learners)
+
+ base_learners = list(
+ lrn("classif.rpart", predict_type = "prob"),
+ lrn("classif.nnet", predict_type = "prob")
+ )
+ super_learner = lrn("classif.log_reg")
+
+ graph_stack = pipeline_stacking(base_learners, super_learner)
+ graph_learner = as_learner(graph_stack)
+ graph_learner$train(tsk("german_credit"))
+ ## Don't show:
+ }) # examplesIf
> library(mlr3)
> library(mlr3learners)
> base_learners = list(lrn("classif.rpart", predict_type = "prob"), lrn("classif.nnet",
+ predict_type = "prob"))
> super_learner = lrn("classif.log_reg")
> graph_stack = pipeline_stacking(base_learners, super_learner)
> graph_learner = as_learner(graph_stack)
> graph_learner$train(tsk("german_credit"))
INFO [00:12:04.066] [mlr3] Resampling 'cv' is being instantiated on task 'german_credit'
INFO [00:12:04.465] [mlr3] Applying learner 'classif.rpart' on task 'german_credit' (iter 1/3)
INFO [00:12:04.696] [mlr3] Applying learner 'classif.rpart' on task 'german_credit' (iter 2/3)
INFO [00:12:04.814] [mlr3] Applying learner 'classif.rpart' on task 'german_credit' (iter 3/3)
Error in `[.data.table`(data, , `:=`("task_hash", task[[1L]]$hash), by = "uhash") :
attempt access index 9/9 in VECTOR_ELT
This happened in PipeOp classif.rpart's $train()
Calls: withAutoprint ... .__ResultData__initialize -> [ -> [.data.table -> .handleSimpleError -> h
Execution halted
Flavor: r-devel-linux-x86_64-fedora-gcc
Version: 0.10.0
Check: tests
Result: ERROR
Running ‘testthat.R’ [619s/355s]
Running the tests in ‘tests/testthat.R’ failed.
Complete output:
> if (requireNamespace("testthat", quietly = TRUE)) {
+ library("checkmate")
+ library("testthat")
+ library("mlr3")
+ library("paradox")
+ library("mlr3pipelines")
+ test_check("mlr3pipelines")
+ }
Starting 2 test processes.
> test_Graph.R: Training debug.multi with input list(input_1 = 1, input_2 = 1)
> test_PipeOp.R: Training test_autotrain
> test_PipeOp.R: Predicting test_autotrain
> test_PipeOp.R: Training test_autotrain
> test_PipeOp.R: Training test_autotrain
> test_PipeOp.R: Predicting test_autotrain
> test_PipeOp.R: Predicting test_autotrain
> test_PipeOp.R: Training test_autotrain
> test_PipeOp.R:
> test_PipeOp.R: Training test_autotrain
> test_PipeOp.R: Training test_autotrain
> test_PipeOp.R: Predicting test_autotrain
> test_PipeOp.R:
> test_PipeOp.R: Predicting test_autotrain
> test_PipeOp.R: Predicting test_autotrain
> test_PipeOp.R: Training test_autotrain
> test_PipeOp.R:
> test_PipeOp.R: Predicting test_autotrain
> test_PipeOp.R: Training test_autotrain
> test_PipeOp.R: Training test_autotrain
> test_PipeOp.R: Predicting test_autotrain
> test_PipeOp.R: Predicting test_autotrain
> test_PipeOp.R:
> test_PipeOp.R: Training test_autotrain
> test_PipeOp.R: Training test_autotrain
> test_PipeOp.R:
> test_PipeOp.R: Training test_autotrain
> test_PipeOp.R: Predicting test_autotrain
> test_PipeOp.R:
> test_PipeOp.R: Predicting test_autotrain
> test_PipeOp.R: Predicting test_autotrain
> test_PipeOp.R: Training test_autotrain
> test_PipeOp.R: Predicting test_autotrain
Saving _problems/test_conversion-143.R
Saving _problems/test_conversion-165.R
> test_filter_ensemble.R: OptimInstanceSingleCrit is deprecated. Use OptimInstanceBatchSingleCrit instead.
Saving _problems/test_filter_ensemble-291.R
Saving _problems/test_filter_ensemble-447.R
Saving _problems/test_mlr_graphs_bagging-49.R
Saving _problems/test_mlr_graphs_stacking-16.R
> test_mlr_graphs_robustify.R: 'as(<dgCMatrix>, "dgTMatrix")' is deprecated.
> test_mlr_graphs_robustify.R: Use 'as(., "TsparseMatrix")' instead.
> test_mlr_graphs_robustify.R: See help("Deprecated") and help("Matrix-depr
> test_mlr_graphs_robustify.R: ecated").
> test_multiplicities.R:
> test_multiplicities.R: [[1]]
> test_multiplicities.R: [1] 0
> test_multiplicities.R:
> test_multiplicities.R:
> test_pipeop_blsmote.R: [1]
> test_pipeop_blsmote.R: "Borderline-SMOTE done"
> test_pipeop_blsmote.R: [1] "Borderline-SMOTE done"
> test_pipeop_blsmote.R: [1] "Borderline-SMOTE done"
> test_pipeop_blsmote.R: [1]
> test_pipeop_blsmote.R: "Borderline-SMOTE done"
> test_pipeop_isomap.R: 2025-12-22 00:14:46.194992: Isomap START
> test_pipeop_isomap.R: 2025-12-22 00:14:46.196115: constructing knn graph
> test_pipeop_isomap.R: 2025-12-22 00:14:46.223586: calculating geodesic distances
> test_pipeop_isomap.R: 2025-12-22 00:14:46.255276: Classical Scaling
> test_pipeop_isomap.R: 2025-12-22 00:14:46.338729: Isomap START
> test_pipeop_isomap.R: 2025-12-22 00:14:46.339449: constructing knn graph
> test_pipeop_isomap.R: 2025-12-22 00:14:46.355589: calculating geodesic distances
> test_pipeop_isomap.R: 2025-12-22 00:14:46.391414: Classical Scaling
> test_pipeop_isomap.R: 2025-12-22 00:14:46.44009: L-Isomap embed START
> test_pipeop_isomap.R: 2025-12-22 00:14:46.4432: constructing knn graph
> test_pipeop_isomap.R: 2025-12-22 00:14:46.467103: calculating geodesic distances
> test_pipeop_isomap.R: 2025-12-22 00:14:46.531911: embedding
> test_pipeop_isomap.R: 2025-12-22 00:14:46.535963: DONE
> test_pipeop_isomap.R: 2025-12-22 00:14:46.572513: L-Isomap embed START
> test_pipeop_isomap.R: 2025-12-22 00:14:46.575602: constructing knn graph
> test_pipeop_isomap.R: 2025-12-22 00:14:46.629767: calculating geodesic distances
> test_pipeop_isomap.R: 2025-12-22 00:14:46.696064: embedding
> test_pipeop_isomap.R: 2025-12-22 00:14:46.70023: DONE
> test_pipeop_isomap.R: 2025-12-22 00:14:46.868734: Isomap START
> test_pipeop_isomap.R: 2025-12-22 00:14:46.869844: constructing knn graph
> test_pipeop_isomap.R: 2025-12-22 00:14:46.893008: calculating geodesic distances
> test_pipeop_isomap.R: 2025-12-22 00:14:47.044152: Classical Scaling
> test_pipeop_isomap.R: 2025-12-22 00:14:47.098104: L-Isomap embed START
> test_pipeop_isomap.R: 2025-12-22 00:14:47.10175: constructing knn graph
> test_pipeop_isomap.R: 2025-12-22 00:14:47.172462: calculating geodesic distances
> test_pipeop_isomap.R: 2025-12-22 00:14:47.496292: embedding
> test_pipeop_isomap.R: 2025-12-22 00:14:47.502979: DONE
> test_pipeop_isomap.R: 2025-12-22 00:14:47.736426: Isomap START
> test_pipeop_isomap.R: 2025-12-22 00:14:47.737119: constructing knn graph
> test_pipeop_isomap.R: 2025-12-22 00:14:47.750529: calculating geodesic distances
> test_pipeop_isomap.R: 2025-12-22 00:14:47.781252: Classical Scaling
> test_pipeop_isomap.R: 2025-12-22 00:14:47.828796: L-Isomap embed START
> test_pipeop_isomap.R: 2025-12-22 00:14:47.832161: constructing knn graph
> test_pipeop_isomap.R: 2025-12-22 00:14:47.858837: calculating geodesic distances
> test_pipeop_isomap.R: 2025-12-22 00:14:47.923917: embedding
> test_pipeop_isomap.R: 2025-12-22 00:14:47.928197: DONE
> test_pipeop_isomap.R: 2025-12-22 00:14:48.17312: Isomap START
> test_pipeop_isomap.R: 2025-12-22 00:14:48.175875: constructing knn graph
> test_pipeop_isomap.R: 2025-12-22 00:14:48.190323: calculating geodesic distances
> test_pipeop_isomap.R: 2025-12-22 00:14:48.221833: Classical Scaling
> test_pipeop_isomap.R: 2025-12-22 00:14:48.301948: L-Isomap embed START
> test_pipeop_isomap.R: 2025-12-22 00:14:48.305237: constructing knn graph
> test_pipeop_isomap.R: 2025-12-22 00:14:48.331094: calculating geodesic distances
> test_pipeop_isomap.R: 2025-12-22 00:14:48.39641: embedding
> test_pipeop_isomap.R: 2025-12-22 00:14:48.402595: DONE
> test_pipeop_isomap.R: 2025-12-22 00:14:48.536938: Isomap START
> test_pipeop_isomap.R: 2025-12-22 00:14:48.537648: constructing knn graph
> test_pipeop_isomap.R: 2025-12-22 00:14:48.551397: calculating geodesic distances
> test_pipeop_isomap.R: 2025-12-22 00:14:48.590179: Classical Scaling
> test_pipeop_isomap.R: 2025-12-22 00:14:48.838233: L-Isomap embed START
> test_pipeop_isomap.R: 2025-12-22 00:14:48.839369: constructing knn graph
> test_pipeop_isomap.R: 2025-12-22 00:14:48.867209: calculating geodesic distances
> test_pipeop_isomap.R: 2025-12-22 00:14:48.94989: embedding
> test_pipeop_isomap.R: 2025-12-22 00:14:48.955322: DONE
> test_pipeop_isomap.R: 2025-12-22 00:14:49.104003: Isomap START
> test_pipeop_isomap.R: 2025-12-22 00:14:49.104746: constructing knn graph
> test_pipeop_isomap.R: 2025-12-22 00:14:49.119502: calculating geodesic distances
> test_pipeop_isomap.R: 2025-12-22 00:14:49.15744: Classical Scaling
> test_pipeop_isomap.R: 2025-12-22 00:14:49.231536: L-Isomap embed START
> test_pipeop_isomap.R: 2025-12-22 00:14:49.232549: constructing knn graph
> test_pipeop_isomap.R: 2025-12-22 00:14:49.260679: calculating geodesic distances
> test_pipeop_isomap.R: 2025-12-22 00:14:49.325991: embedding
> test_pipeop_isomap.R: 2025-12-22 00:14:49.330358: DONE
> test_pipeop_isomap.R: 2025-12-22 00:14:49.460304: Isomap START
> test_pipeop_isomap.R: 2025-12-22 00:14:49.461009: constructing knn graph
> test_pipeop_isomap.R: 2025-12-22 00:14:49.479462: calculating geodesic distances
> test_pipeop_isomap.R: 2025-12-22 00:14:49.519416: Classical Scaling
> test_pipeop_isomap.R: 2025-12-22 00:14:49.635107: L-Isomap embed START
> test_pipeop_isomap.R: 2025-12-22 00:14:49.636754: constructing knn graph
> test_pipeop_isomap.R: 2025-12-22 00:14:49.668386: calculating geodesic distances
> test_pipeop_isomap.R: 2025-12-22 00:14:49.745751: embedding
> test_pipeop_isomap.R: 2025-12-22 00:14:49.750515: DONE
> test_pipeop_isomap.R: 2025-12-22 00:14:49.897416: Isomap START
> test_pipeop_isomap.R: 2025-12-22 00:14:49.900436: constructing knn graph
> test_pipeop_isomap.R: 2025-12-22 00:14:49.917072: calculating geodesic distances
> test_pipeop_isomap.R: 2025-12-22 00:14:49.950853: Classical Scaling
> test_pipeop_isomap.R: 2025-12-22 00:14:50.091607: Isomap START
> test_pipeop_isomap.R: 2025-12-22 00:14:50.093216: constructing knn graph
> test_pipeop_isomap.R: 2025-12-22 00:14:50.108793: calculating geodesic distances
> test_pipeop_isomap.R: 2025-12-22 00:14:50.149088: Classical Scaling
> test_pipeop_isomap.R: 2025-12-22 00:14:50.188545: Isomap START
> test_pipeop_isomap.R: 2025-12-22 00:14:50.189249: constructing knn graph
> test_pipeop_isomap.R: 2025-12-22 00:14:50.202983: calculating geodesic distances
> test_pipeop_isomap.R: 2025-12-22 00:14:50.233355: Classical Scaling
Saving _problems/test_pipeop_learnerpicvplus-35.R
Saving _problems/test_pipeop_learnerpicvplus-91.R
Saving _problems/test_pipeop_learnerpicvplus-116.R
Saving _problems/test_pipeop_learnerpicvplus-130.R
Saving _problems/test_pipeop_learnerpicvplus-152.R
Saving _problems/test_pipeop_learnercv-11.R
Saving _problems/test_pipeop_learnercv-100.R
Saving _problems/test_pipeop_learnercv-139.R
Saving _problems/test_pipeop_learnercv-152.R
Saving _problems/test_pipeop_learnercv-203.R
Saving _problems/test_pipeop_learnercv-250.R
Saving _problems/test_pipeop_learnercv-278.R
Saving _problems/test_pipeop_learnercv-323.R
Saving _problems/test_pipeop_learnercv-350.R
Saving _problems/test_pipeop_learnercv-387.R
Saving _problems/test_pipeop_learnercv-419.R
Saving _problems/test_pipeop_learnercv-455.R
Saving _problems/test_pipeop_learnercv-493.R
Saving _problems/test_pipeop_learnercv-516.R
Saving _problems/test_pipeop_learnercv-531.R
Saving _problems/test_pipeop_learnercv-557.R
Saving _problems/test_pipeop_learnercv-612.R
Saving _problems/test_pipeop_learnercv-628.R
Saving _problems/test_pipeop_learnercv-671.R
> test_pipeop_nmf.R: [PipeOpNMFstate]
> test_pipeop_nmf.R: [PipeOpNMFstate]
> test_pipeop_task_preproc.R: Training debug_affectcols
> test_pipeop_task_preproc.R:
> test_pipeop_task_preproc.R: Training debug_affectcols
> test_pipeop_task_preproc.R: Training debug_affectcols
> test_pipeop_task_preproc.R:
> test_pipeop_task_preproc.R: Training debug_affectcols
> test_pipeop_task_preproc.R: Training debug_affectcols
> test_pipeop_task_preproc.R: Training debug_affectcols
> test_pipeop_task_preproc.R: Training debug_affectcols
> test_pipeop_task_preproc.R:
> test_pipeop_task_preproc.R: Training debug_affectcols
> test_pipeop_task_preproc.R: Training debug_affectcols
> test_pipeop_task_preproc.R: Training debug_affectcols
> test_pipeop_task_preproc.R: Training debug_affectcols
> test_pipeop_task_preproc.R:
> test_pipeop_task_preproc.R: Training debug_affectcols
> test_pipeop_task_preproc.R:
> test_pipeop_task_preproc.R: Training debug_affectcols
> test_pipeop_task_preproc.R:
> test_pipeop_task_preproc.R: Training debug_affectcols
> test_pipeop_task_preproc.R: Training debug_affectcols
> test_pipeop_task_preproc.R: Training debug_affectcols
> test_pipeop_task_preproc.R: Training debug_affectcols
> test_pipeop_task_preproc.R: Training debug_affectcols
> test_pipeop_task_preproc.R: Training debug_affectcols
> test_pipeop_task_preproc.R: Training debug_affectcols
> test_pipeop_task_preproc.R:
> test_pipeop_task_preproc.R: Training debug_affectcols
> test_pipeop_task_preproc.R: Training debug_affectcols
> test_pipeop_textvectorizer.R: 'as(<dgCMatrix>, "dgTMatrix")' is deprecated.
> test_pipeop_textvectorizer.R: Use 'as(., "TsparseMatrix")' instead.
> test_pipeop_textvectorizer.R: See help("Deprecated") and help("Matrix-deprecated").
Saving _problems/test_pipeop_tunethreshold-7.R
Saving _problems/test_pipeop_tunethreshold-38.R
Saving _problems/test_pipeop_tunethreshold-73.R
Saving _problems/test_pipeop_tunethreshold-101.R
Saving _problems/test_pipeop_tunethreshold-260.R
Saving _problems/test_resample-13.R
Saving _problems/test_usecases-153.R
Saving _problems/test_ppl-73.R
[ FAIL 38 | WARN 2 | SKIP 98 | PASS 12316 ]
══ Skipped tests (98) ══════════════════════════════════════════════════════════
• On CRAN (95): 'test_CnfFormula_simplify.R:6:3', 'test_CnfFormula.R:591:3',
'test_GraphLearner.R:5:3', 'test_GraphLearner.R:221:3',
'test_GraphLearner.R:343:3', 'test_GraphLearner.R:408:3',
'test_GraphLearner.R:571:3', 'test_Graph.R:283:3', 'test_PipeOp.R:32:1',
'test_dictionary.R:7:3', 'test_learner_weightedaverage.R:5:3',
'test_learner_weightedaverage.R:57:3',
'test_learner_weightedaverage.R:105:3',
'test_learner_weightedaverage.R:152:3', 'test_meta.R:39:3',
'test_mlr_graphs_bagging.R:6:3', 'test_mlr_graphs_branching.R:26:3',
'test_mlr_graphs_robustify.R:5:3', 'test_pipeop_adas.R:8:3',
'test_pipeop_blsmote.R:8:3', 'test_pipeop_branch.R:4:3',
'test_pipeop_chunk.R:4:3', 'test_pipeop_classbalancing.R:7:3',
'test_pipeop_classweights.R:10:3', 'test_pipeop_boxcox.R:7:3',
'test_pipeop_collapsefactors.R:6:3', 'test_pipeop_colapply.R:9:3',
'test_pipeop_copy.R:5:3', 'test_pipeop_colroles.R:6:3',
'test_pipeop_decode.R:14:3', 'test_pipeop_encode.R:21:3',
'test_pipeop_encodeimpact.R:11:3', 'test_pipeop_datefeatures.R:10:3',
'test_pipeop_encodepl.R:5:3', 'test_pipeop_encodepl.R:72:3',
'test_pipeop_encodelmer.R:15:3', 'test_pipeop_encodelmer.R:37:3',
'test_pipeop_encodelmer.R:80:3', 'test_pipeop_featureunion.R:9:3',
'test_pipeop_featureunion.R:134:3', 'test_pipeop_filter.R:7:3',
'test_pipeop_fixfactors.R:9:3', 'test_pipeop_histbin.R:7:3',
'test_pipeop_ica.R:7:3', 'test_pipeop_ensemble.R:6:3',
'test_pipeop_impute.R:4:3', 'test_pipeop_imputelearner.R:43:3',
'test_pipeop_isomap.R:10:3', 'test_pipeop_kernelpca.R:9:3',
'test_pipeop_learner.R:17:3', 'test_pipeop_info.R:6:3',
'test_pipeop_learnerpicvplus.R:163:3', 'test_pipeop_missind.R:6:3',
'test_pipeop_modelmatrix.R:7:3', 'test_pipeop_multiplicityexply.R:9:3',
'test_pipeop_multiplicityimply.R:9:3', 'test_pipeop_mutate.R:9:3',
'test_pipeop_nearmiss.R:7:3', 'test_pipeop_ovr.R:9:3',
'test_pipeop_ovr.R:48:3', 'test_pipeop_pca.R:8:3',
'test_pipeop_proxy.R:14:3', 'test_pipeop_quantilebin.R:5:3',
'test_pipeop_randomprojection.R:6:3', 'test_pipeop_randomresponse.R:5:3',
'test_pipeop_removeconstants.R:6:3', 'test_pipeop_renamecolumns.R:6:3',
'test_pipeop_replicate.R:9:3', 'test_pipeop_rowapply.R:6:3',
'test_pipeop_scale.R:6:3', 'test_pipeop_scale.R:10:3',
'test_pipeop_scalemaxabs.R:6:3', 'test_pipeop_scalerange.R:7:3',
'test_pipeop_select.R:9:3', 'test_pipeop_smote.R:10:3',
'test_pipeop_smotenc.R:8:3', 'test_pipeop_spatialsign.R:6:3',
'test_pipeop_nmf.R:6:3', 'test_pipeop_targetinvert.R:4:3',
'test_pipeop_targetmutate.R:5:3', 'test_pipeop_targettrafo.R:4:3',
'test_pipeop_targettrafoscalerange.R:5:3', 'test_pipeop_task_preproc.R:4:3',
'test_pipeop_task_preproc.R:14:3', 'test_pipeop_subsample.R:6:3',
'test_pipeop_tomek.R:7:3', 'test_pipeop_tunethreshold.R:111:3',
'test_pipeop_tunethreshold.R:191:3', 'test_pipeop_unbranch.R:10:3',
'test_pipeop_textvectorizer.R:37:3', 'test_pipeop_textvectorizer.R:186:3',
'test_pipeop_vtreat.R:9:3', 'test_pipeop_updatetarget.R:89:3',
'test_pipeop_yeojohnson.R:7:3', 'test_typecheck.R:188:3'
• Skipping (1): 'test_GraphLearner.R:1278:3'
• empty test (2): 'test_pipeop_isomap.R:111:1', 'test_pipeop_missind.R:101:1'
══ Failed tests ════════════════════════════════════════════════════════════════
── Error ('test_conversion.R:143:3'): Graph to GraphLearner ────────────────────
Error in ``[.data.table`(data, , `:=`("task_hash", task[[1L]]$hash), by = "uhash")`: attempt access index 9/9 in VECTOR_ELT
Backtrace:
▆
1. └─mlr3::resample(task, glrn1, cv) at test_conversion.R:143:3
2. └─ResultData$new(data, data_extra, store_backends = store_backends)
3. └─mlr3 (local) initialize(...)
4. └─mlr3:::.__ResultData__initialize(...)
5. ├─data[, `:=`("task_hash", task[[1L]]$hash), by = "uhash"]
6. └─data.table:::`[.data.table`(...)
── Error ('test_conversion.R:165:3'): PipeOp to GraphLearner ───────────────────
Error in ``[.data.table`(data, , `:=`("task_hash", task[[1L]]$hash), by = "uhash")`: attempt access index 9/9 in VECTOR_ELT
Backtrace:
▆
1. └─mlr3::resample(task, glrn1, cv) at test_conversion.R:165:3
2. └─ResultData$new(data, data_extra, store_backends = store_backends)
3. └─mlr3 (local) initialize(...)
4. └─mlr3:::.__ResultData__initialize(...)
5. ├─data[, `:=`("task_hash", task[[1L]]$hash), by = "uhash"]
6. └─data.table:::`[.data.table`(...)
── Error ('test_filter_ensemble.R:291:3'): FilterEnsemble ignores NA scores from wrapped filters ──
Error in ``[.data.table`(data, , `:=`("task_hash", task[[1L]]$hash), by = "uhash")`: attempt access index 9/9 in VECTOR_ELT
Backtrace:
▆
1. └─permutation_filter$calculate(task) at test_filter_ensemble.R:291:3
2. └─mlr3filters:::.__Filter__calculate(...)
3. └─private$.calculate(task, nfeat)
4. └─mlr3filters:::.__FilterPermutation__.calculate(...)
5. └─mlr3::resample(task, self$learner, self$resampling)
6. └─ResultData$new(data, data_extra, store_backends = store_backends)
7. └─mlr3 (local) initialize(...)
8. └─mlr3:::.__ResultData__initialize(...)
9. ├─data[, `:=`("task_hash", task[[1L]]$hash), by = "uhash"]
10. └─data.table:::`[.data.table`(...)
── Error ('test_filter_ensemble.R:447:7'): FilterEnsemble weight search space works with bbotk ──
Error in ``[.data.table`(data, , `:=`("task_hash", task[[1L]]$hash), by = "uhash")`: attempt access index 9/9 in VECTOR_ELT
Backtrace:
▆
1. └─optimizer$optimize(instance) at test_filter_ensemble.R:463:3
2. └─bbotk:::.__OptimizerBatch__optimize(...)
3. └─bbotk::optimize_batch_default(inst, self)
4. ├─base::tryCatch(...)
5. │ └─base (local) tryCatchList(expr, classes, parentenv, handlers)
6. │ └─base (local) tryCatchOne(expr, names, parentenv, handlers[[1L]])
7. │ └─base (local) doTryCatch(return(expr), name, parentenv, handler)
8. └─get_private(optimizer)$.optimize(instance)
9. └─bbotk:::.__OptimizerBatchRandomSearch__.optimize(...)
10. └─inst$eval_batch(design$data)
11. └─bbotk:::.__OptimInstanceBatch__eval_batch(...)
12. └─self$objective$eval_many(xss_trafoed)
13. └─bbotk:::.__Objective__eval_many(...)
14. ├─mlr3misc::invoke(private$.eval_many, xss = xss, .args = self$constants$values)
15. │ └─base::eval.parent(expr, n = 1L)
16. │ └─base::eval(expr, p)
17. │ └─base::eval(expr, p)
18. └─private$.eval_many(xss = xss)
19. └─bbotk:::.__Objective__.eval_many(...)
20. └─mlr3misc::map_dtr(...)
21. ├─data.table::rbindlist(...)
22. ├─base::unname(map(.x, .f, ...))
23. └─mlr3misc::map(.x, .f, ...)
24. └─base::lapply(.x, .f, ...)
25. └─bbotk (local) FUN(X[[i]], ...)
26. └─self$eval(xs)
27. └─bbotk:::.__ObjectiveRFun__eval(...)
28. ├─mlr3misc::invoke(private$.fun, xs, .args = self$constants$values)
29. │ └─base::eval.parent(expr, n = 1L)
30. │ └─base::eval(expr, p)
31. │ └─base::eval(expr, p)
32. └─private$.fun(xs)
33. └─mlr3::resample(task, learner, resampling) at test_filter_ensemble.R:447:7
34. └─ResultData$new(data, data_extra, store_backends = store_backends)
35. └─mlr3 (local) initialize(...)
36. └─mlr3:::.__ResultData__initialize(...)
37. ├─data[, `:=`("task_hash", task[[1L]]$hash), by = "uhash"]
38. └─data.table:::`[.data.table`(...)
── Error ('test_mlr_graphs_bagging.R:49:3'): Bagging with replacement ──────────
Error in ``[.data.table`(data, , `:=`("task_hash", task[[1L]]$hash), by = "uhash")`: attempt access index 9/9 in VECTOR_ELT
Backtrace:
▆
1. └─mlr3::resample(tsk, GraphLearner$new(p), rsmp("holdout")) at test_mlr_graphs_bagging.R:49:3
2. └─ResultData$new(data, data_extra, store_backends = store_backends)
3. └─mlr3 (local) initialize(...)
4. └─mlr3:::.__ResultData__initialize(...)
5. ├─data[, `:=`("task_hash", task[[1L]]$hash), by = "uhash"]
6. └─data.table:::`[.data.table`(...)
── Error ('test_mlr_graphs_stacking.R:16:3'): Stacking Pipeline ────────────────
Error in ``[.data.table`(data, , `:=`("task_hash", task[[1L]]$hash), by = "uhash")`: attempt access index 9/9 in VECTOR_ELT
This happened in PipeOp base.rpart's $train()
Backtrace:
▆
1. ├─graph_learner$train(tsk("iris")) at test_mlr_graphs_stacking.R:16:3
2. │ └─mlr3:::.__Learner__train(...)
3. │ └─mlr3:::learner_train(...)
4. │ └─mlr3misc::encapsulate(...)
5. │ ├─mlr3misc::invoke(...)
6. │ │ └─base::eval.parent(expr, n = 1L)
7. │ │ └─base::eval(expr, p)
8. │ │ └─base::eval(expr, p)
9. │ └─mlr3 (local) .f(learner = `<GrphLrnr>`, task = `<TskClssf>`)
10. │ └─get_private(learner)$.train(task)
11. │ └─mlr3pipelines:::.__GraphLearner__.train(...)
12. │ └─self$graph$train(task)
13. │ └─mlr3pipelines:::.__Graph__train(...)
14. │ └─mlr3pipelines:::graph_reduce(self, input, "train", single_input)
15. │ └─op[[fun]](input)
16. │ └─mlr3pipelines:::.__PipeOp__train(...)
17. │ ├─base::withCallingHandlers(...)
18. │ └─private$.train(input)
19. │ └─mlr3pipelines:::.__PipeOpTaskPreproc__.train(...)
20. │ └─private$.train_task(intask)
21. │ └─mlr3pipelines:::.__PipeOpLearnerCV__.train_task(...)
22. │ └─mlr3::resample(...)
23. │ └─ResultData$new(data, data_extra, store_backends = store_backends)
24. │ └─mlr3 (local) initialize(...)
25. │ └─mlr3:::.__ResultData__initialize(...)
26. │ ├─data[, `:=`("task_hash", task[[1L]]$hash), by = "uhash"]
27. │ └─data.table:::`[.data.table`(...)
28. └─base::.handleSimpleError(...)
29. └─mlr3pipelines (local) h(simpleError(msg, call))
── Error ('test_pipeop_learnerpicvplus.R:35:3'): PipeOpLearnerPICVPlus - basic properties ──
Error in ``[.data.table`(data, , `:=`("task_hash", task[[1L]]$hash), by = "uhash")`: attempt access index 9/9 in VECTOR_ELT
This happened in PipeOp regr.featureless's $train()
Backtrace:
▆
1. ├─global train_pipeop(po, list(task)) at test_pipeop_learnerpicvplus.R:35:3
2. │ └─po$train(inputs)
3. │ └─mlr3pipelines:::.__PipeOp__train(...)
4. │ ├─base::withCallingHandlers(...)
5. │ └─private$.train(input)
6. │ └─mlr3pipelines:::.__PipeOpLearnerPICVPlus__.train(...)
7. │ └─mlr3::resample(task, private$.learner, rdesc, store_models = TRUE)
8. │ └─ResultData$new(data, data_extra, store_backends = store_backends)
9. │ └─mlr3 (local) initialize(...)
10. │ └─mlr3:::.__ResultData__initialize(...)
11. │ ├─data[, `:=`("task_hash", task[[1L]]$hash), by = "uhash"]
12. │ └─data.table:::`[.data.table`(...)
13. └─base::.handleSimpleError(...)
14. └─mlr3pipelines (local) h(simpleError(msg, call))
── Error ('test_pipeop_learnerpicvplus.R:91:3'): PipeOpLearnerPICVPlus - model active binding to state ──
Error in ``[.data.table`(data, , `:=`("task_hash", task[[1L]]$hash), by = "uhash")`: attempt access index 9/9 in VECTOR_ELT
This happened in PipeOp regr.featureless's $train()
Backtrace:
▆
1. ├─po$train(list(task)) at test_pipeop_learnerpicvplus.R:91:3
2. │ └─mlr3pipelines:::.__PipeOp__train(...)
3. │ ├─base::withCallingHandlers(...)
4. │ └─private$.train(input)
5. │ └─mlr3pipelines:::.__PipeOpLearnerPICVPlus__.train(...)
6. │ └─mlr3::resample(task, private$.learner, rdesc, store_models = TRUE)
7. │ └─ResultData$new(data, data_extra, store_backends = store_backends)
8. │ └─mlr3 (local) initialize(...)
9. │ └─mlr3:::.__ResultData__initialize(...)
10. │ ├─data[, `:=`("task_hash", task[[1L]]$hash), by = "uhash"]
11. │ └─data.table:::`[.data.table`(...)
12. └─base::.handleSimpleError(...)
13. └─mlr3pipelines (local) h(simpleError(msg, call))
── Error ('test_pipeop_learnerpicvplus.R:116:3'): PipeOpLearnerPICVPlus - integration with larger graph ──
Error in ``[.data.table`(data, , `:=`("task_hash", task[[1L]]$hash), by = "uhash")`: attempt access index 9/9 in VECTOR_ELT
This happened in PipeOp regr.rpart's $train()
Backtrace:
▆
1. ├─graph$train(task) at test_pipeop_learnerpicvplus.R:116:3
2. │ └─mlr3pipelines:::.__Graph__train(...)
3. │ └─mlr3pipelines:::graph_reduce(self, input, "train", single_input)
4. │ └─op[[fun]](input)
5. │ └─mlr3pipelines:::.__PipeOp__train(...)
6. │ ├─base::withCallingHandlers(...)
7. │ └─private$.train(input)
8. │ └─mlr3pipelines:::.__PipeOpLearnerPICVPlus__.train(...)
9. │ └─mlr3::resample(task, private$.learner, rdesc, store_models = TRUE)
10. │ └─ResultData$new(data, data_extra, store_backends = store_backends)
11. │ └─mlr3 (local) initialize(...)
12. │ └─mlr3:::.__ResultData__initialize(...)
13. │ ├─data[, `:=`("task_hash", task[[1L]]$hash), by = "uhash"]
14. │ └─data.table:::`[.data.table`(...)
15. └─base::.handleSimpleError(...)
16. └─mlr3pipelines (local) h(simpleError(msg, call))
── Error ('test_pipeop_learnerpicvplus.R:130:3'): marshal ──────────────────────
Error in ``[.data.table`(data, , `:=`("task_hash", task[[1L]]$hash), by = "uhash")`: attempt access index 9/9 in VECTOR_ELT
This happened in PipeOp regr.debug's $train()
Backtrace:
▆
1. ├─po$train(list(task)) at test_pipeop_learnerpicvplus.R:130:3
2. │ └─mlr3pipelines:::.__PipeOp__train(...)
3. │ ├─base::withCallingHandlers(...)
4. │ └─private$.train(input)
5. │ └─mlr3pipelines:::.__PipeOpLearnerPICVPlus__.train(...)
6. │ └─mlr3::resample(task, private$.learner, rdesc, store_models = TRUE)
7. │ └─ResultData$new(data, data_extra, store_backends = store_backends)
8. │ └─mlr3 (local) initialize(...)
9. │ └─mlr3:::.__ResultData__initialize(...)
10. │ ├─data[, `:=`("task_hash", task[[1L]]$hash), by = "uhash"]
11. │ └─data.table:::`[.data.table`(...)
12. └─base::.handleSimpleError(...)
13. └─mlr3pipelines (local) h(simpleError(msg, call))
── Error ('test_pipeop_learnerpicvplus.R:152:3'): marshal multiplicity ─────────
Error in ``[.data.table`(data, , `:=`("task_hash", task[[1L]]$hash), by = "uhash")`: attempt access index 9/9 in VECTOR_ELT
This happened in PipeOp regr.debug's $train()
Backtrace:
▆
1. ├─po$train(list(Multiplicity(task1, task2))) at test_pipeop_learnerpicvplus.R:152:3
2. │ └─mlr3pipelines:::.__PipeOp__train(...)
3. │ └─mlr3pipelines:::evaluate_multiplicities(...)
4. │ └─mlr3misc::imap(...)
5. │ ├─stats::setNames(mapply_list(.f, list(.x, .nn), list(...)), names(.x))
6. │ └─mlr3misc:::mapply_list(.f, list(.x, .nn), list(...))
7. │ └─base::.mapply(.f, .dots, .args)
8. │ └─mlr3pipelines (local) `<fn>`(dots[[1L]][[1L]], dots[[2L]][[1L]])
9. │ └─self[[evalcall]](input)
10. │ └─mlr3pipelines:::.__PipeOp__train(...)
11. │ ├─base::withCallingHandlers(...)
12. │ └─private$.train(input)
13. │ └─mlr3pipelines:::.__PipeOpLearnerPICVPlus__.train(...)
14. │ └─mlr3::resample(task, private$.learner, rdesc, store_models = TRUE)
15. │ └─ResultData$new(data, data_extra, store_backends = store_backends)
16. │ └─mlr3 (local) initialize(...)
17. │ └─mlr3:::.__ResultData__initialize(...)
18. │ ├─data[, `:=`("task_hash", task[[1L]]$hash), by = "uhash"]
19. │ └─data.table:::`[.data.table`(...)
20. └─base::.handleSimpleError(...)
21. └─mlr3pipelines (local) h(simpleError(msg, call))
── Error ('test_pipeop_learnercv.R:11:3'): PipeOpLearnerCV - basic properties ──
Error in ``[.data.table`(data, , `:=`("task_hash", task[[1L]]$hash), by = "uhash")`: attempt access index 9/9 in VECTOR_ELT
This happened in PipeOp classif.featureless's $train()
Backtrace:
▆
1. ├─global train_pipeop(po, list(task = task)) at test_pipeop_learnercv.R:11:3
2. │ └─po$train(inputs)
3. │ └─mlr3pipelines:::.__PipeOp__train(...)
4. │ ├─base::withCallingHandlers(...)
5. │ └─private$.train(input)
6. │ └─mlr3pipelines:::.__PipeOpTaskPreproc__.train(...)
7. │ └─private$.train_task(intask)
8. │ └─mlr3pipelines:::.__PipeOpLearnerCV__.train_task(...)
9. │ └─mlr3::resample(...)
10. │ └─ResultData$new(data, data_extra, store_backends = store_backends)
11. │ └─mlr3 (local) initialize(...)
12. │ └─mlr3:::.__ResultData__initialize(...)
13. │ ├─data[, `:=`("task_hash", task[[1L]]$hash), by = "uhash"]
14. │ └─data.table:::`[.data.table`(...)
15. └─base::.handleSimpleError(...)
16. └─mlr3pipelines (local) h(simpleError(msg, call))
── Error ('test_pipeop_learnercv.R:100:3'): PipeOpLearnerCV - cv ensemble averages fold learners ──
Error in ``[.data.table`(data, , `:=`("task_hash", task[[1L]]$hash), by = "uhash")`: attempt access index 9/9 in VECTOR_ELT
This happened in PipeOp classif.rpart's $train()
Backtrace:
▆
1. ├─po$train(list(task)) at test_pipeop_learnercv.R:100:3
2. │ └─mlr3pipelines:::.__PipeOp__train(...)
3. │ ├─base::withCallingHandlers(...)
4. │ └─private$.train(input)
5. │ └─mlr3pipelines:::.__PipeOpTaskPreproc__.train(...)
6. │ └─private$.train_task(intask)
7. │ └─mlr3pipelines:::.__PipeOpLearnerCV__.train_task(...)
8. │ └─mlr3::resample(...)
9. │ └─ResultData$new(data, data_extra, store_backends = store_backends)
10. │ └─mlr3 (local) initialize(...)
11. │ └─mlr3:::.__ResultData__initialize(...)
12. │ ├─data[, `:=`("task_hash", task[[1L]]$hash), by = "uhash"]
13. │ └─data.table:::`[.data.table`(...)
14. └─base::.handleSimpleError(...)
15. └─mlr3pipelines (local) h(simpleError(msg, call))
── Error ('test_pipeop_learnercv.R:139:3'): PipeOpLearnerCV - cv ensemble drops response when requested ──
Error in ``[.data.table`(data, , `:=`("task_hash", task[[1L]]$hash), by = "uhash")`: attempt access index 9/9 in VECTOR_ELT
This happened in PipeOp classif.rpart's $train()
Backtrace:
▆
1. ├─po$train(list(task)) at test_pipeop_learnercv.R:139:3
2. │ └─mlr3pipelines:::.__PipeOp__train(...)
3. │ ├─base::withCallingHandlers(...)
4. │ └─private$.train(input)
5. │ └─mlr3pipelines:::.__PipeOpTaskPreproc__.train(...)
6. │ └─private$.train_task(intask)
7. │ └─mlr3pipelines:::.__PipeOpLearnerCV__.train_task(...)
8. │ └─mlr3::resample(...)
9. │ └─ResultData$new(data, data_extra, store_backends = store_backends)
10. │ └─mlr3 (local) initialize(...)
11. │ └─mlr3:::.__ResultData__initialize(...)
12. │ ├─data[, `:=`("task_hash", task[[1L]]$hash), by = "uhash"]
13. │ └─data.table:::`[.data.table`(...)
14. └─base::.handleSimpleError(...)
15. └─mlr3pipelines (local) h(simpleError(msg, call))
── Error ('test_pipeop_learnercv.R:152:3'): PipeOpLearnerCV - cv ensemble averages classif responses ──
Error in ``[.data.table`(data, , `:=`("task_hash", task[[1L]]$hash), by = "uhash")`: attempt access index 9/9 in VECTOR_ELT
This happened in PipeOp classif.rpart's $train()
Backtrace:
▆
1. ├─po$train(list(task)) at test_pipeop_learnercv.R:152:3
2. │ └─mlr3pipelines:::.__PipeOp__train(...)
3. │ ├─base::withCallingHandlers(...)
4. │ └─private$.train(input)
5. │ └─mlr3pipelines:::.__PipeOpTaskPreproc__.train(...)
6. │ └─private$.train_task(intask)
7. │ └─mlr3pipelines:::.__PipeOpLearnerCV__.train_task(...)
8. │ └─mlr3::resample(...)
9. │ └─ResultData$new(data, data_extra, store_backends = store_backends)
10. │ └─mlr3 (local) initialize(...)
11. │ └─mlr3:::.__ResultData__initialize(...)
12. │ ├─data[, `:=`("task_hash", task[[1L]]$hash), by = "uhash"]
13. │ └─data.table:::`[.data.table`(...)
14. └─base::.handleSimpleError(...)
15. └─mlr3pipelines (local) h(simpleError(msg, call))
── Error ('test_pipeop_learnercv.R:203:3'): PipeOpLearnerCV - cv ensemble log prob aggregation ──
Error in ``[.data.table`(data, , `:=`("task_hash", task[[1L]]$hash), by = "uhash")`: attempt access index 9/9 in VECTOR_ELT
This happened in PipeOp classif.rpart's $train()
Backtrace:
▆
1. ├─po$train(list(task)) at test_pipeop_learnercv.R:203:3
2. │ └─mlr3pipelines:::.__PipeOp__train(...)
3. │ ├─base::withCallingHandlers(...)
4. │ └─private$.train(input)
5. │ └─mlr3pipelines:::.__PipeOpTaskPreproc__.train(...)
6. │ └─private$.train_task(intask)
7. │ └─mlr3pipelines:::.__PipeOpLearnerCV__.train_task(...)
8. │ └─mlr3::resample(...)
9. │ └─ResultData$new(data, data_extra, store_backends = store_backends)
10. │ └─mlr3 (local) initialize(...)
11. │ └─mlr3:::.__ResultData__initialize(...)
12. │ ├─data[, `:=`("task_hash", task[[1L]]$hash), by = "uhash"]
13. │ └─data.table:::`[.data.table`(...)
14. └─base::.handleSimpleError(...)
15. └─mlr3pipelines (local) h(simpleError(msg, call))
── Error ('test_pipeop_learnercv.R:250:3'): PipeOpLearnerCV - log aggregation with zeros uses epsilon ──
Error in ``[.data.table`(data, , `:=`("task_hash", task[[1L]]$hash), by = "uhash")`: attempt access index 9/9 in VECTOR_ELT
This happened in PipeOp classif.featureless's $train()
Backtrace:
▆
1. ├─po$train(list(task)) at test_pipeop_learnercv.R:250:3
2. │ └─mlr3pipelines:::.__PipeOp__train(...)
3. │ ├─base::withCallingHandlers(...)
4. │ └─private$.train(input)
5. │ └─mlr3pipelines:::.__PipeOpTaskPreproc__.train(...)
6. │ └─private$.train_task(intask)
7. │ └─mlr3pipelines:::.__PipeOpLearnerCV__.train_task(...)
8. │ └─mlr3::resample(...)
9. │ └─ResultData$new(data, data_extra, store_backends = store_backends)
10. │ └─mlr3 (local) initialize(...)
11. │ └─mlr3:::.__ResultData__initialize(...)
12. │ ├─data[, `:=`("task_hash", task[[1L]]$hash), by = "uhash"]
13. │ └─data.table:::`[.data.table`(...)
14. └─base::.handleSimpleError(...)
15. └─mlr3pipelines (local) h(simpleError(msg, call))
── Error ('test_pipeop_learnercv.R:278:3'): PipeOpLearnerCV - log aggregation epsilon controls shrinkage ──
Error in ``[.data.table`(data, , `:=`("task_hash", task[[1L]]$hash), by = "uhash")`: attempt access index 9/9 in VECTOR_ELT
This happened in PipeOp classif.featureless's $train()
Backtrace:
▆
1. ├─po$train(list(task)) at test_pipeop_learnercv.R:278:3
2. │ └─mlr3pipelines:::.__PipeOp__train(...)
3. │ ├─base::withCallingHandlers(...)
4. │ └─private$.train(input)
5. │ └─mlr3pipelines:::.__PipeOpTaskPreproc__.train(...)
6. │ └─private$.train_task(intask)
7. │ └─mlr3pipelines:::.__PipeOpLearnerCV__.train_task(...)
8. │ └─mlr3::resample(...)
9. │ └─ResultData$new(data, data_extra, store_backends = store_backends)
10. │ └─mlr3 (local) initialize(...)
11. │ └─mlr3:::.__ResultData__initialize(...)
12. │ ├─data[, `:=`("task_hash", task[[1L]]$hash), by = "uhash"]
13. │ └─data.table:::`[.data.table`(...)
14. └─base::.handleSimpleError(...)
15. └─mlr3pipelines (local) h(simpleError(msg, call))
── Error ('test_pipeop_learnercv.R:323:3'): PipeOpLearnerCV - cv ensemble averages regression predictions ──
Error in ``[.data.table`(data, , `:=`("task_hash", task[[1L]]$hash), by = "uhash")`: attempt access index 9/9 in VECTOR_ELT
This happened in PipeOp regr.rpart's $train()
Backtrace:
▆
1. ├─po$train(list(task)) at test_pipeop_learnercv.R:323:3
2. │ └─mlr3pipelines:::.__PipeOp__train(...)
3. │ ├─base::withCallingHandlers(...)
4. │ └─private$.train(input)
5. │ └─mlr3pipelines:::.__PipeOpTaskPreproc__.train(...)
6. │ └─private$.train_task(intask)
7. │ └─mlr3pipelines:::.__PipeOpLearnerCV__.train_task(...)
8. │ └─mlr3::resample(...)
9. │ └─ResultData$new(data, data_extra, store_backends = store_backends)
10. │ └─mlr3 (local) initialize(...)
11. │ └─mlr3:::.__ResultData__initialize(...)
12. │ ├─data[, `:=`("task_hash", task[[1L]]$hash), by = "uhash"]
13. │ └─data.table:::`[.data.table`(...)
14. └─base::.handleSimpleError(...)
15. └─mlr3pipelines (local) h(simpleError(msg, call))
── Error ('test_pipeop_learnercv.R:350:3'): PipeOpLearnerCV - cv ensemble handles multiplicity ──
Error in ``[.data.table`(data, , `:=`("task_hash", task[[1L]]$hash), by = "uhash")`: attempt access index 9/9 in VECTOR_ELT
This happened in PipeOp classif.rpart's $train()
Backtrace:
▆
1. ├─po$train(list(tasks)) at test_pipeop_learnercv.R:350:3
2. │ └─mlr3pipelines:::.__PipeOp__train(...)
3. │ └─mlr3pipelines:::evaluate_multiplicities(...)
4. │ └─mlr3misc::imap(...)
5. │ ├─stats::setNames(mapply_list(.f, list(.x, .nn), list(...)), names(.x))
6. │ └─mlr3misc:::mapply_list(.f, list(.x, .nn), list(...))
7. │ └─base::.mapply(.f, .dots, .args)
8. │ └─mlr3pipelines (local) `<fn>`(dots[[1L]][[1L]], dots[[2L]][[1L]])
9. │ └─self[[evalcall]](input)
10. │ └─mlr3pipelines:::.__PipeOp__train(...)
11. │ ├─base::withCallingHandlers(...)
12. │ └─private$.train(input)
13. │ └─mlr3pipelines:::.__PipeOpTaskPreproc__.train(...)
14. │ └─private$.train_task(intask)
15. │ └─mlr3pipelines:::.__PipeOpLearnerCV__.train_task(...)
16. │ └─mlr3::resample(...)
17. │ └─ResultData$new(data, data_extra, store_backends = store_backends)
18. │ └─mlr3 (local) initialize(...)
19. │ └─mlr3:::.__ResultData__initialize(...)
20. │ ├─data[, `:=`("task_hash", task[[1L]]$hash), by = "uhash"]
21. │ └─data.table:::`[.data.table`(...)
22. └─base::.handleSimpleError(...)
23. └─mlr3pipelines (local) h(simpleError(msg, call))
── Error ('test_pipeop_learnercv.R:387:3'): PipeOpLearnerCV - learner_model returns averaged ensemble ──
Error in ``[.data.table`(data, , `:=`("task_hash", task[[1L]]$hash), by = "uhash")`: attempt access index 9/9 in VECTOR_ELT
This happened in PipeOp classif.rpart's $train()
Backtrace:
▆
1. ├─po$train(list(task)) at test_pipeop_learnercv.R:387:3
2. │ └─mlr3pipelines:::.__PipeOp__train(...)
3. │ ├─base::withCallingHandlers(...)
4. │ └─private$.train(input)
5. │ └─mlr3pipelines:::.__PipeOpTaskPreproc__.train(...)
6. │ └─private$.train_task(intask)
7. │ └─mlr3pipelines:::.__PipeOpLearnerCV__.train_task(...)
8. │ └─mlr3::resample(...)
9. │ └─ResultData$new(data, data_extra, store_backends = store_backends)
10. │ └─mlr3 (local) initialize(...)
11. │ └─mlr3:::.__ResultData__initialize(...)
12. │ ├─data[, `:=`("task_hash", task[[1L]]$hash), by = "uhash"]
13. │ └─data.table:::`[.data.table`(...)
14. └─base::.handleSimpleError(...)
15. └─mlr3pipelines (local) h(simpleError(msg, call))
── Error ('test_pipeop_learnercv.R:419:3'): PipeOpLearnerCV - cv ensemble with predict_type = 'se' ──
Error in ``[.data.table`(data, , `:=`("task_hash", task[[1L]]$hash), by = "uhash")`: attempt access index 9/9 in VECTOR_ELT
This happened in PipeOp regr.lm's $train()
Backtrace:
▆
1. ├─po$train(list(task)) at test_pipeop_learnercv.R:419:3
2. │ └─mlr3pipelines:::.__PipeOp__train(...)
3. │ ├─base::withCallingHandlers(...)
4. │ └─private$.train(input)
5. │ └─mlr3pipelines:::.__PipeOpTaskPreproc__.train(...)
6. │ └─private$.train_task(intask)
7. │ └─mlr3pipelines:::.__PipeOpLearnerCV__.train_task(...)
8. │ └─mlr3::resample(...)
9. │ └─ResultData$new(data, data_extra, store_backends = store_backends)
10. │ └─mlr3 (local) initialize(...)
11. │ └─mlr3:::.__ResultData__initialize(...)
12. │ ├─data[, `:=`("task_hash", task[[1L]]$hash), by = "uhash"]
13. │ └─data.table:::`[.data.table`(...)
14. └─base::.handleSimpleError(...)
15. └─mlr3pipelines (local) h(simpleError(msg, call))
── Error ('test_pipeop_learnercv.R:455:3'): PipeOpLearnerCV - within resampling ──
Error in ``[.data.table`(data, , `:=`("task_hash", task[[1L]]$hash), by = "uhash")`: attempt access index 9/9 in VECTOR_ELT
This happened in PipeOp classif.rpart's $train()
Backtrace:
▆
1. └─mlr3::resample(tsk("iris"), gr, rsmp("holdout")) at test_pipeop_learnercv.R:455:3
2. └─mlr3:::future_map(...)
3. └─future.apply::future_mapply(...)
4. └─future.apply:::future_xapply(...)
5. └─base::tryCatch(...)
6. └─base (local) tryCatchList(expr, classes, parentenv, handlers)
7. └─base (local) tryCatchOne(...)
8. └─value[[3L]](cond)
9. └─future.apply:::onError(e, futures = fs, debug = debug)
── Error ('test_pipeop_learnercv.R:493:3'): PipeOpLearnerCV - model active binding to state ──
Error in ``[.data.table`(data, , `:=`("task_hash", task[[1L]]$hash), by = "uhash")`: attempt access index 9/9 in VECTOR_ELT
This happened in PipeOp classif.featureless's $train()
Backtrace:
▆
1. ├─po$train(list(task)) at test_pipeop_learnercv.R:493:3
2. │ └─mlr3pipelines:::.__PipeOp__train(...)
3. │ ├─base::withCallingHandlers(...)
4. │ └─private$.train(input)
5. │ └─mlr3pipelines:::.__PipeOpTaskPreproc__.train(...)
6. │ └─private$.train_task(intask)
7. │ └─mlr3pipelines:::.__PipeOpLearnerCV__.train_task(...)
8. │ └─mlr3::resample(...)
9. │ └─ResultData$new(data, data_extra, store_backends = store_backends)
10. │ └─mlr3 (local) initialize(...)
11. │ └─mlr3:::.__ResultData__initialize(...)
12. │ ├─data[, `:=`("task_hash", task[[1L]]$hash), by = "uhash"]
13. │ └─data.table:::`[.data.table`(...)
14. └─base::.handleSimpleError(...)
15. └─mlr3pipelines (local) h(simpleError(msg, call))
── Error ('test_pipeop_learnercv.R:516:3'): predict_type ───────────────────────
Error in ``[.data.table`(data, , `:=`("task_hash", task[[1L]]$hash), by = "uhash")`: attempt access index 9/9 in VECTOR_ELT
This happened in PipeOp classif.rpart's $train()
Backtrace:
▆
1. ├─global expect_equal(...) at test_pipeop_learnercv.R:516:3
2. │ ├─testthat::expect_true(...)
3. │ │ └─testthat::quasi_label(enquo(object), label)
4. │ │ └─rlang::eval_bare(expr, quo_get_env(quo))
5. │ └─base::all.equal(...)
6. ├─lcv$train(list(tsk("iris")))
7. │ └─mlr3pipelines:::.__PipeOp__train(...)
8. │ ├─base::withCallingHandlers(...)
9. │ └─private$.train(input)
10. │ └─mlr3pipelines:::.__PipeOpTaskPreproc__.train(...)
11. │ └─private$.train_task(intask)
12. │ └─mlr3pipelines:::.__PipeOpLearnerCV__.train_task(...)
13. │ └─mlr3::resample(...)
14. │ └─ResultData$new(data, data_extra, store_backends = store_backends)
15. │ └─mlr3 (local) initialize(...)
16. │ └─mlr3:::.__ResultData__initialize(...)
17. │ ├─data[, `:=`("task_hash", task[[1L]]$hash), by = "uhash"]
18. │ └─data.table:::`[.data.table`(...)
19. └─base::.handleSimpleError(...)
20. └─mlr3pipelines (local) h(simpleError(msg, call))
── Error ('test_pipeop_learnercv.R:531:3'): marshal ────────────────────────────
Error in ``[.data.table`(data, , `:=`("task_hash", task[[1L]]$hash), by = "uhash")`: attempt access index 9/9 in VECTOR_ELT
This happened in PipeOp classif.debug's $train()
Backtrace:
▆
1. ├─po_lrn$train(list(task)) at test_pipeop_learnercv.R:531:3
2. │ └─mlr3pipelines:::.__PipeOp__train(...)
3. │ ├─base::withCallingHandlers(...)
4. │ └─private$.train(input)
5. │ └─mlr3pipelines:::.__PipeOpTaskPreproc__.train(...)
6. │ └─private$.train_task(intask)
7. │ └─mlr3pipelines:::.__PipeOpLearnerCV__.train_task(...)
8. │ └─mlr3::resample(...)
9. │ └─ResultData$new(data, data_extra, store_backends = store_backends)
10. │ └─mlr3 (local) initialize(...)
11. │ └─mlr3:::.__ResultData__initialize(...)
12. │ ├─data[, `:=`("task_hash", task[[1L]]$hash), by = "uhash"]
13. │ └─data.table:::`[.data.table`(...)
14. └─base::.handleSimpleError(...)
15. └─mlr3pipelines (local) h(simpleError(msg, call))
── Error ('test_pipeop_learnercv.R:557:3'): marshal multiplicity ───────────────
Error in ``[.data.table`(data, , `:=`("task_hash", task[[1L]]$hash), by = "uhash")`: attempt access index 9/9 in VECTOR_ELT
This happened in PipeOp classif.debug's $train()
Backtrace:
▆
1. ├─po$train(list(Multiplicity(tsk("iris"), tsk("sonar")))) at test_pipeop_learnercv.R:557:3
2. │ └─mlr3pipelines:::.__PipeOp__train(...)
3. │ └─mlr3pipelines:::evaluate_multiplicities(...)
4. │ └─mlr3misc::imap(...)
5. │ ├─stats::setNames(mapply_list(.f, list(.x, .nn), list(...)), names(.x))
6. │ └─mlr3misc:::mapply_list(.f, list(.x, .nn), list(...))
7. │ └─base::.mapply(.f, .dots, .args)
8. │ └─mlr3pipelines (local) `<fn>`(dots[[1L]][[1L]], dots[[2L]][[1L]])
9. │ └─self[[evalcall]](input)
10. │ └─mlr3pipelines:::.__PipeOp__train(...)
11. │ ├─base::withCallingHandlers(...)
12. │ └─private$.train(input)
13. │ └─mlr3pipelines:::.__PipeOpTaskPreproc__.train(...)
14. │ └─private$.train_task(intask)
15. │ └─mlr3pipelines:::.__PipeOpLearnerCV__.train_task(...)
16. │ └─mlr3::resample(...)
17. │ └─ResultData$new(data, data_extra, store_backends = store_backends)
18. │ └─mlr3 (local) initialize(...)
19. │ └─mlr3:::.__ResultData__initialize(...)
20. │ ├─data[, `:=`("task_hash", task[[1L]]$hash), by = "uhash"]
21. │ └─data.table:::`[.data.table`(...)
22. └─base::.handleSimpleError(...)
23. └─mlr3pipelines (local) h(simpleError(msg, call))
── Error ('test_pipeop_learnercv.R:612:3'): marshal with cv ensemble ───────────
Error in ``[.data.table`(data, , `:=`("task_hash", task[[1L]]$hash), by = "uhash")`: attempt access index 9/9 in VECTOR_ELT
This happened in PipeOp classif.rpart's $train()
Backtrace:
▆
1. ├─po$train(list(task)) at test_pipeop_learnercv.R:612:3
2. │ └─mlr3pipelines:::.__PipeOp__train(...)
3. │ ├─base::withCallingHandlers(...)
4. │ └─private$.train(input)
5. │ └─mlr3pipelines:::.__PipeOpTaskPreproc__.train(...)
6. │ └─private$.train_task(intask)
7. │ └─mlr3pipelines:::.__PipeOpLearnerCV__.train_task(...)
8. │ └─mlr3::resample(...)
9. │ └─ResultData$new(data, data_extra, store_backends = store_backends)
10. │ └─mlr3 (local) initialize(...)
11. │ └─mlr3:::.__ResultData__initialize(...)
12. │ ├─data[, `:=`("task_hash", task[[1L]]$hash), by = "uhash"]
13. │ └─data.table:::`[.data.table`(...)
14. └─base::.handleSimpleError(...)
15. └─mlr3pipelines (local) h(simpleError(msg, call))
── Error ('test_pipeop_learnercv.R:628:3'): state class and multiplicity ───────
Error in ``[.data.table`(data, , `:=`("task_hash", task[[1L]]$hash), by = "uhash")`: attempt access index 9/9 in VECTOR_ELT
This happened in PipeOp classif.debug's $train()
Backtrace:
▆
1. ├─po$train(list(Multiplicity(tsk("iris")))) at test_pipeop_learnercv.R:628:3
2. │ └─mlr3pipelines:::.__PipeOp__train(...)
3. │ └─mlr3pipelines:::evaluate_multiplicities(...)
4. │ └─mlr3misc::imap(...)
5. │ ├─stats::setNames(mapply_list(.f, list(.x, .nn), list(...)), names(.x))
6. │ └─mlr3misc:::mapply_list(.f, list(.x, .nn), list(...))
7. │ └─base::.mapply(.f, .dots, .args)
8. │ └─mlr3pipelines (local) `<fn>`(dots[[1L]][[1L]], dots[[2L]][[1L]])
9. │ └─self[[evalcall]](input)
10. │ └─mlr3pipelines:::.__PipeOp__train(...)
11. │ ├─base::withCallingHandlers(...)
12. │ └─private$.train(input)
13. │ └─mlr3pipelines:::.__PipeOpTaskPreproc__.train(...)
14. │ └─private$.train_task(intask)
15. │ └─mlr3pipelines:::.__PipeOpLearnerCV__.train_task(...)
16. │ └─mlr3::resample(...)
17. │ └─ResultData$new(data, data_extra, store_backends = store_backends)
18. │ └─mlr3 (local) initialize(...)
19. │ └─mlr3:::.__ResultData__initialize(...)
20. │ ├─data[, `:=`("task_hash", task[[1L]]$hash), by = "uhash"]
21. │ └─data.table:::`[.data.table`(...)
22. └─base::.handleSimpleError(...)
23. └─mlr3pipelines (local) h(simpleError(msg, call))
── Error ('test_pipeop_learnercv.R:671:5'): PipeOpLearnerCV cv ensemble aggregates SE like PipeOpRegrAvg ──
Error in ``[.data.table`(data, , `:=`("task_hash", task[[1L]]$hash), by = "uhash")`: attempt access index 9/9 in VECTOR_ELT
This happened in PipeOp regr.debug's $train()
Backtrace:
▆
1. ├─po$train(list(task)) at test_pipeop_learnercv.R:671:5
2. │ └─mlr3pipelines:::.__PipeOp__train(...)
3. │ ├─base::withCallingHandlers(...)
4. │ └─private$.train(input)
5. │ └─mlr3pipelines:::.__PipeOpTaskPreproc__.train(...)
6. │ └─private$.train_task(intask)
7. │ └─mlr3pipelines:::.__PipeOpLearnerCV__.train_task(...)
8. │ └─mlr3::resample(...)
9. │ └─ResultData$new(data, data_extra, store_backends = store_backends)
10. │ └─mlr3 (local) initialize(...)
11. │ └─mlr3:::.__ResultData__initialize(...)
12. │ ├─data[, `:=`("task_hash", task[[1L]]$hash), by = "uhash"]
13. │ └─data.table:::`[.data.table`(...)
14. └─base::.handleSimpleError(...)
15. └─mlr3pipelines (local) h(simpleError(msg, call))
── Error ('test_pipeop_tunethreshold.R:7:3'): threshold works for multiclass ───
Error in ``[.data.table`(data, , `:=`("task_hash", task[[1L]]$hash), by = "uhash")`: attempt access index 9/9 in VECTOR_ELT
This happened in PipeOp classif.rpart's $train()
Backtrace:
▆
1. ├─po_cv$train(list(t)) at test_pipeop_tunethreshold.R:7:3
2. │ └─mlr3pipelines:::.__PipeOp__train(...)
3. │ ├─base::withCallingHandlers(...)
4. │ └─private$.train(input)
5. │ └─mlr3pipelines:::.__PipeOpTaskPreproc__.train(...)
6. │ └─private$.train_task(intask)
7. │ └─mlr3pipelines:::.__PipeOpLearnerCV__.train_task(...)
8. │ └─mlr3::resample(...)
9. │ └─ResultData$new(data, data_extra, store_backends = store_backends)
10. │ └─mlr3 (local) initialize(...)
11. │ └─mlr3:::.__ResultData__initialize(...)
12. │ ├─data[, `:=`("task_hash", task[[1L]]$hash), by = "uhash"]
13. │ └─data.table:::`[.data.table`(...)
14. └─base::.handleSimpleError(...)
15. └─mlr3pipelines (local) h(simpleError(msg, call))
── Error ('test_pipeop_tunethreshold.R:38:3'): threshold works for binary ──────
Error in ``[.data.table`(data, , `:=`("task_hash", task[[1L]]$hash), by = "uhash")`: attempt access index 9/9 in VECTOR_ELT
This happened in PipeOp classif.rpart's $train()
Backtrace:
▆
1. ├─po_cv$train(list(t)) at test_pipeop_tunethreshold.R:38:3
2. │ └─mlr3pipelines:::.__PipeOp__train(...)
3. │ ├─base::withCallingHandlers(...)
4. │ └─private$.train(input)
5. │ └─mlr3pipelines:::.__PipeOpTaskPreproc__.train(...)
6. │ └─private$.train_task(intask)
7. │ └─mlr3pipelines:::.__PipeOpLearnerCV__.train_task(...)
8. │ └─mlr3::resample(...)
9. │ └─ResultData$new(data, data_extra, store_backends = store_backends)
10. │ └─mlr3 (local) initialize(...)
11. │ └─mlr3:::.__ResultData__initialize(...)
12. │ ├─data[, `:=`("task_hash", task[[1L]]$hash), by = "uhash"]
13. │ └─data.table:::`[.data.table`(...)
14. └─base::.handleSimpleError(...)
15. └─mlr3pipelines (local) h(simpleError(msg, call))
── Error ('test_pipeop_tunethreshold.R:73:3'): tunethreshold graph works ───────
Error in ``[.data.table`(data, , `:=`("task_hash", task[[1L]]$hash), by = "uhash")`: attempt access index 9/9 in VECTOR_ELT
This happened in PipeOp classif.rpart's $train()
Backtrace:
▆
1. ├─graph$train(tsk("pima")) at test_pipeop_tunethreshold.R:73:3
2. │ └─mlr3pipelines:::.__Graph__train(...)
3. │ └─mlr3pipelines:::graph_reduce(self, input, "train", single_input)
4. │ └─op[[fun]](input)
5. │ └─mlr3pipelines:::.__PipeOp__train(...)
6. │ ├─base::withCallingHandlers(...)
7. │ └─private$.train(input)
8. │ └─mlr3pipelines:::.__PipeOpTaskPreproc__.train(...)
9. │ └─private$.train_task(intask)
10. │ └─mlr3pipelines:::.__PipeOpLearnerCV__.train_task(...)
11. │ └─mlr3::resample(...)
12. │ └─ResultData$new(data, data_extra, store_backends = store_backends)
13. │ └─mlr3 (local) initialize(...)
14. │ └─mlr3:::.__ResultData__initialize(...)
15. │ ├─data[, `:=`("task_hash", task[[1L]]$hash), by = "uhash"]
16. │ └─data.table:::`[.data.table`(...)
17. └─base::.handleSimpleError(...)
18. └─mlr3pipelines (local) h(simpleError(msg, call))
── Error ('test_pipeop_tunethreshold.R:101:3'): threshold works for classes that are not valid R names ──
Error in ``[.data.table`(data, , `:=`("task_hash", task[[1L]]$hash), by = "uhash")`: attempt access index 9/9 in VECTOR_ELT
This happened in PipeOp classif.rpart's $train()
Backtrace:
▆
1. ├─ppl$train(testtask) at test_pipeop_tunethreshold.R:101:3
2. │ └─mlr3pipelines:::.__Graph__train(...)
3. │ └─mlr3pipelines:::graph_reduce(self, input, "train", single_input)
4. │ └─op[[fun]](input)
5. │ └─mlr3pipelines:::.__PipeOp__train(...)
6. │ ├─base::withCallingHandlers(...)
7. │ └─private$.train(input)
8. │ └─mlr3pipelines:::.__PipeOpTaskPreproc__.train(...)
9. │ └─private$.train_task(intask)
10. │ └─mlr3pipelines:::.__PipeOpLearnerCV__.train_task(...)
11. │ └─mlr3::resample(...)
12. │ └─ResultData$new(data, data_extra, store_backends = store_backends)
13. │ └─mlr3 (local) initialize(...)
14. │ └─mlr3:::.__ResultData__initialize(...)
15. │ ├─data[, `:=`("task_hash", task[[1L]]$hash), by = "uhash"]
16. │ └─data.table:::`[.data.table`(...)
17. └─base::.handleSimpleError(...)
18. └─mlr3pipelines (local) h(simpleError(msg, call))
── Error ('test_pipeop_tunethreshold.R:260:3'): threshold graph transparency ───
Error in ``[.data.table`(data, , `:=`("task_hash", task[[1L]]$hash), by = "uhash")`: attempt access index 9/9 in VECTOR_ELT
This happened in PipeOp classif.rpart's $train()
Backtrace:
▆
1. ├─lrn_prob$train(t) at test_pipeop_tunethreshold.R:260:3
2. │ └─mlr3:::.__Learner__train(...)
3. │ └─mlr3:::learner_train(...)
4. │ └─mlr3misc::encapsulate(...)
5. │ ├─mlr3misc::invoke(...)
6. │ │ └─base::eval.parent(expr, n = 1L)
7. │ │ └─base::eval(expr, p)
8. │ │ └─base::eval(expr, p)
9. │ └─mlr3 (local) .f(learner = `<GrphLrnr>`, task = `<TskClssf>`)
10. │ └─get_private(learner)$.train(task)
11. │ └─mlr3pipelines:::.__GraphLearner__.train(...)
12. │ └─self$graph$train(task)
13. │ └─mlr3pipelines:::.__Graph__train(...)
14. │ └─mlr3pipelines:::graph_reduce(self, input, "train", single_input)
15. │ └─op[[fun]](input)
16. │ └─mlr3pipelines:::.__PipeOp__train(...)
17. │ ├─base::withCallingHandlers(...)
18. │ └─private$.train(input)
19. │ └─mlr3pipelines:::.__PipeOpTaskPreproc__.train(...)
20. │ └─private$.train_task(intask)
21. │ └─mlr3pipelines:::.__PipeOpLearnerCV__.train_task(...)
22. │ └─mlr3::resample(...)
23. │ └─ResultData$new(data, data_extra, store_backends = store_backends)
24. │ └─mlr3 (local) initialize(...)
25. │ └─mlr3:::.__ResultData__initialize(...)
26. │ ├─data[, `:=`("task_hash", task[[1L]]$hash), by = "uhash"]
27. │ └─data.table:::`[.data.table`(...)
28. └─base::.handleSimpleError(...)
29. └─mlr3pipelines (local) h(simpleError(msg, call))
── Error ('test_resample.R:13:3'): PipeOp - Resample ───────────────────────────
Error in ``[.data.table`(data, , `:=`("task_hash", task[[1L]]$hash), by = "uhash")`: attempt access index 9/9 in VECTOR_ELT
Backtrace:
▆
1. └─mlr3::resample(task, pp, resa) at test_resample.R:13:3
2. └─ResultData$new(data, data_extra, store_backends = store_backends)
3. └─mlr3 (local) initialize(...)
4. └─mlr3:::.__ResultData__initialize(...)
5. ├─data[, `:=`("task_hash", task[[1L]]$hash), by = "uhash"]
6. └─data.table:::`[.data.table`(...)
── Error ('test_usecases.R:153:3'): stacking ───────────────────────────────────
Error in ``[.data.table`(data, , `:=`("task_hash", task[[1L]]$hash), by = "uhash")`: attempt access index 9/9 in VECTOR_ELT
This happened in PipeOp classif.rpart's $train()
Backtrace:
▆
1. ├─pipe$train(task) at test_usecases.R:153:3
2. │ └─mlr3pipelines:::.__Graph__train(...)
3. │ └─mlr3pipelines:::graph_reduce(self, input, "train", single_input)
4. │ └─op[[fun]](input)
5. │ └─mlr3pipelines:::.__PipeOp__train(...)
6. │ ├─base::withCallingHandlers(...)
7. │ └─private$.train(input)
8. │ └─mlr3pipelines:::.__PipeOpTaskPreproc__.train(...)
9. │ └─private$.train_task(intask)
10. │ └─mlr3pipelines:::.__PipeOpLearnerCV__.train_task(...)
11. │ └─mlr3::resample(...)
12. │ └─ResultData$new(data, data_extra, store_backends = store_backends)
13. │ └─mlr3 (local) initialize(...)
14. │ └─mlr3:::.__ResultData__initialize(...)
15. │ ├─data[, `:=`("task_hash", task[[1L]]$hash), by = "uhash"]
16. │ └─data.table:::`[.data.table`(...)
17. └─base::.handleSimpleError(...)
18. └─mlr3pipelines (local) h(simpleError(msg, call))
── Error ('test_ppl.R:73:3'): mlr3book authors don't sleepwalk through life ────
Error in ``[.data.table`(data, , `:=`("task_hash", task[[1L]]$hash), by = "uhash")`: attempt access index 9/9 in VECTOR_ELT
This happened in PipeOp classif.rpart.classif.rpart's $train()
Backtrace:
▆
1. └─mlr3::benchmark(benchmark_grid(tasks, learners, rsmp("cv", folds = 2))) at test_ppl.R:73:3
2. └─mlr3:::future_map(...)
3. └─future.apply::future_mapply(...)
4. └─future.apply:::future_xapply(...)
5. └─base::tryCatch(...)
6. └─base (local) tryCatchList(expr, classes, parentenv, handlers)
7. └─base (local) tryCatchOne(...)
8. └─value[[3L]](cond)
9. └─future.apply:::onError(e, futures = fs, debug = debug)
[ FAIL 38 | WARN 2 | SKIP 98 | PASS 12316 ]
Error:
! Test failures.
Execution halted
Flavor: r-devel-linux-x86_64-fedora-gcc
Version: 0.10.0
Check: examples
Result: ERROR
Running examples in ‘mlr3pipelines-Ex.R’ failed
The error most likely occurred in:
> ### Name: mlr_pipeops_nmf
> ### Title: Non-negative Matrix Factorization
> ### Aliases: mlr_pipeops_nmf PipeOpNMF
>
> ### ** Examples
>
> ## Don't show:
> if (mlr3misc::require_namespaces(c("NMF", "MASS"), quietly = TRUE)) withAutoprint({ # examplesIf
+ ## End(Don't show)
+ ## Don't show:
+ # NMF attaches these packages to search path on load, #929
+ lapply(c("package:Biobase", "package:BiocGenerics", "package:generics"), detach, character.only = TRUE)
+ ## End(Don't show)
+ library("mlr3")
+
+ task = tsk("iris")
+ pop = po("nmf")
+
+ task$data()
+ pop$train(list(task))[[1]]$data()
+
+ pop$state
+ ## Don't show:
+ # BiocGenerics overwrites printer for our tables mlr-org/mlr3#1112
+ # Necessary as detaching packages does not remove registered S3 methods
+ suppressWarnings(try(rm("format.list", envir = .BaseNamespaceEnv$.__S3MethodsTable__.), silent = TRUE))
+ ## End(Don't show)
+ ## Don't show:
+ }) # examplesIf
> lapply(c("package:Biobase", "package:BiocGenerics", "package:generics"),
+ detach, character.only = TRUE)
Error in FUN(X[[i]], ...) : invalid 'name' argument
Calls: withAutoprint ... withVisible -> eval -> eval -> lapply -> lapply -> FUN
Execution halted
Flavors: r-oldrel-macos-arm64, r-oldrel-macos-x86_64, r-oldrel-windows-x86_64
Version: 0.10.0
Check: tests
Result: ERROR
Running ‘testthat.R’ [102s/49s]
Running the tests in ‘tests/testthat.R’ failed.
Complete output:
> if (requireNamespace("testthat", quietly = TRUE)) {
+ library("checkmate")
+ library("testthat")
+ library("mlr3")
+ library("paradox")
+ library("mlr3pipelines")
+ test_check("mlr3pipelines")
+ }
Starting 2 test processes.
> test_Graph.R: Training debug.multi with input list(input_1 = 1, input_2 = 1)
> test_PipeOp.R: Training test_autotrain
> test_PipeOp.R: Predicting test_autotrain
> test_PipeOp.R: Training test_autotrain
> test_PipeOp.R: Training test_autotrain
> test_PipeOp.R: Predicting test_autotrain
> test_PipeOp.R: Predicting test_autotrain
> test_PipeOp.R: Training test_autotrain
> test_PipeOp.R: Training test_autotrain
> test_PipeOp.R: Training test_autotrain
> test_PipeOp.R: Predicting test_autotrain
> test_PipeOp.R: Predicting test_autotrain
> test_PipeOp.R: Predicting test_autotrain
> test_PipeOp.R: Training test_autotrain
> test_PipeOp.R: Predicting test_autotrain
> test_PipeOp.R: Training test_autotrain
> test_PipeOp.R: Training test_autotrain
> test_PipeOp.R: Predicting test_autotrain
> test_PipeOp.R: Predicting test_autotrain
> test_PipeOp.R: Training test_autotrain
> test_PipeOp.R: Training test_autotrain
> test_PipeOp.R: Training test_autotrain
> test_PipeOp.R: Predicting test_autotrain
> test_PipeOp.R: Predicting test_autotrain
> test_PipeOp.R: Predicting test_autotrain
> test_PipeOp.R: Training test_autotrain
> test_PipeOp.R: Predicting test_autotrain
> test_filter_ensemble.R: OptimInstanceSingleCrit is deprecated. Use OptimInstanceBatchSingleCrit instead.
> test_mlr_graphs_robustify.R: 'as(<dgCMatrix>, "dgTMatrix")' is deprecated.
> test_mlr_graphs_robustify.R: Use 'as(., "TsparseMatrix")' instead.
> test_mlr_graphs_robustify.R: See help("Deprecated") and help("Matrix-deprecated").
> test_multiplicities.R:
> test_multiplicities.R: [[1]]
> test_multiplicities.R: [1] 0
> test_multiplicities.R:
> test_multiplicities.R:
> test_pipeop_blsmote.R: [1] "Borderline-SMOTE done"
> test_pipeop_blsmote.R: [1] "Borderline-SMOTE done"
> test_pipeop_blsmote.R: [1] "Borderline-SMOTE done"
> test_pipeop_blsmote.R: [1] "Borderline-SMOTE done"
Saving _problems/test_pipeop_datefeatures-7.R
Saving _problems/test_pipeop_datefeatures-17.R
> test_pipeop_isomap.R: 2025-12-24 09:01:48.333816: Isomap START
> test_pipeop_isomap.R: 2025-12-24 09:01:48.334083: constructing knn graph
> test_pipeop_isomap.R: 2025-12-24 09:01:48.337071: calculating geodesic distances
> test_pipeop_isomap.R: 2025-12-24 09:01:48.343453: Classical Scaling
> test_pipeop_isomap.R: 2025-12-24 09:01:48.354515: Isomap START
> test_pipeop_isomap.R: 2025-12-24 09:01:48.35465: constructing knn graph
> test_pipeop_isomap.R: 2025-12-24 09:01:48.357063: calculating geodesic distances
> test_pipeop_isomap.R: 2025-12-24 09:01:48.363026: Classical Scaling
> test_pipeop_isomap.R: 2025-12-24 09:01:48.370728: L-Isomap embed START
> test_pipeop_isomap.R: 2025-12-24 09:01:48.370924: constructing knn graph
> test_pipeop_isomap.R: 2025-12-24 09:01:48.375427: calculating geodesic distances
> test_pipeop_isomap.R: 2025-12-24 09:01:48.390162: embedding
> test_pipeop_isomap.R: 2025-12-24 09:01:48.390605: DONE
> test_pipeop_isomap.R: 2025-12-24 09:01:48.395739: L-Isomap embed START
> test_pipeop_isomap.R: 2025-12-24 09:01:48.395883: constructing knn graph
> test_pipeop_isomap.R: 2025-12-24 09:01:48.400526: calculating geodesic distances
> test_pipeop_isomap.R: 2025-12-24 09:01:48.415655: embedding
> test_pipeop_isomap.R: 2025-12-24 09:01:48.416053: DONE
> test_pipeop_isomap.R: 2025-12-24 09:01:48.435307: Isomap START
> test_pipeop_isomap.R: 2025-12-24 09:01:48.435436: constructing knn graph
> test_pipeop_isomap.R: 2025-12-24 09:01:48.447824: calculating geodesic distances
> test_pipeop_isomap.R: 2025-12-24 09:01:48.482027: Classical Scaling
> test_pipeop_isomap.R: 2025-12-24 09:01:48.490798: L-Isomap embed START
> test_pipeop_isomap.R: 2025-12-24 09:01:48.490995: constructing knn graph
> test_pipeop_isomap.R: 2025-12-24 09:01:48.499449: calculating geodesic distances
> test_pipeop_isomap.R: 2025-12-24 09:01:48.580005: embedding
> test_pipeop_isomap.R: 2025-12-24 09:01:48.581295: DONE
> test_pipeop_isomap.R: 2025-12-24 09:01:48.61511: Isomap START
> test_pipeop_isomap.R: 2025-12-24 09:01:48.615251: constructing knn graph
> test_pipeop_isomap.R: 2025-12-24 09:01:48.617665: calculating geodesic distances
> test_pipeop_isomap.R: 2025-12-24 09:01:48.624337: Classical Scaling
> test_pipeop_isomap.R: 2025-12-24 09:01:48.631663: L-Isomap embed START
> test_pipeop_isomap.R: 2025-12-24 09:01:48.631827: constructing knn graph
> test_pipeop_isomap.R: 2025-12-24 09:01:48.636484: calculating geodesic distances
> test_pipeop_isomap.R: 2025-12-24 09:01:48.650506: embedding
> test_pipeop_isomap.R: 2025-12-24 09:01:48.650892: DONE
> test_pipeop_isomap.R: 2025-12-24 09:01:48.683062: Isomap START
> test_pipeop_isomap.R: 2025-12-24 09:01:48.683252: constructing knn graph
> test_pipeop_isomap.R: 2025-12-24 09:01:48.685861: calculating geodesic distances
> test_pipeop_isomap.R: 2025-12-24 09:01:48.69223: Classical Scaling
> test_pipeop_isomap.R: 2025-12-24 09:01:48.704331: L-Isomap embed START
> test_pipeop_isomap.R: 2025-12-24 09:01:48.704559: constructing knn graph
> test_pipeop_isomap.R: 2025-12-24 09:01:48.74746: calculating geodesic distances
> test_pipeop_isomap.R: 2025-12-24 09:01:48.763289: embedding
> test_pipeop_isomap.R: 2025-12-24 09:01:48.763902: DONE
> test_pipeop_isomap.R: 2025-12-24 09:01:48.785178: Isomap START
> test_pipeop_isomap.R: 2025-12-24 09:01:48.785366: constructing knn graph
> test_pipeop_isomap.R: 2025-12-24 09:01:48.787821: calculating geodesic distances
> test_pipeop_isomap.R: 2025-12-24 09:01:48.794024: Classical Scaling
> test_pipeop_isomap.R: 2025-12-24 09:01:48.80571: L-Isomap embed START
> test_pipeop_isomap.R: 2025-12-24 09:01:48.805908: constructing knn graph
> test_pipeop_isomap.R: 2025-12-24 09:01:48.810339: calculating geodesic distances
> test_pipeop_isomap.R: 2025-12-24 09:01:48.825771: embedding
> test_pipeop_isomap.R: 2025-12-24 09:01:48.826257: DONE
> test_pipeop_isomap.R: 2025-12-24 09:01:48.844154: Isomap START
> test_pipeop_isomap.R: 2025-12-24 09:01:48.844308: constructing knn graph
> test_pipeop_isomap.R: 2025-12-24 09:01:48.846644: calculating geodesic distances
> test_pipeop_isomap.R: 2025-12-24 09:01:48.853179: Classical Scaling
> test_pipeop_isomap.R: 2025-12-24 09:01:48.864593: L-Isomap embed START
> test_pipeop_isomap.R: 2025-12-24 09:01:48.864778: constructing knn graph
> test_pipeop_isomap.R: 2025-12-24 09:01:48.869459: calculating geodesic distances
> test_pipeop_isomap.R: 2025-12-24 09:01:48.883244: embedding
> test_pipeop_isomap.R: 2025-12-24 09:01:48.883672: DONE
> test_pipeop_isomap.R: 2025-12-24 09:01:48.898912: Isomap START
> test_pipeop_isomap.R: 2025-12-24 09:01:48.899061: constructing knn graph
> test_pipeop_isomap.R: 2025-12-24 09:01:48.901568: calculating geodesic distances
> test_pipeop_isomap.R: 2025-12-24 09:01:48.907702: Classical Scaling
> test_pipeop_isomap.R: 2025-12-24 09:01:48.919909: L-Isomap embed START
> test_pipeop_isomap.R: 2025-12-24 09:01:48.920169: constructing knn graph
> test_pipeop_isomap.R: 2025-12-24 09:01:48.925258: calculating geodesic distances
> test_pipeop_isomap.R: 2025-12-24 09:01:48.94124: embedding
> test_pipeop_isomap.R: 2025-12-24 09:01:48.941701: DONE
> test_pipeop_isomap.R: 2025-12-24 09:01:48.961909: Isomap START
> test_pipeop_isomap.R: 2025-12-24 09:01:48.962048: constructing knn graph
> test_pipeop_isomap.R: 2025-12-24 09:01:48.97101: calculating geodesic distances
> test_pipeop_isomap.R: 2025-12-24 09:01:48.977989: Classical Scaling
> test_pipeop_isomap.R: 2025-12-24 09:01:48.999403: Isomap START
> test_pipeop_isomap.R: 2025-12-24 09:01:48.999583: constructing knn graph
> test_pipeop_isomap.R: 2025-12-24 09:01:49.002064: calculating geodesic distances
> test_pipeop_isomap.R: 2025-12-24 09:01:49.008641: Classical Scaling
> test_pipeop_isomap.R: 2025-12-24 09:01:49.014081: Isomap START
> test_pipeop_isomap.R: 2025-12-24 09:01:49.014207: constructing knn graph
> test_pipeop_isomap.R: 2025-12-24 09:01:49.016373: calculating geodesic distances
> test_pipeop_isomap.R: 2025-12-24 09:01:49.022475: Classical Scaling
Saving _problems/test_pipeop_nmf-45.R
Saving _problems/test_pipeop_nmf-73.R
Saving _problems/test_pipeop_nmf-93.R
Saving _problems/test_pipeop_nmf-98.R
> test_pipeop_task_preproc.R: Training debug_affectcols
> test_pipeop_task_preproc.R: Training debug_affectcols
> test_pipeop_task_preproc.R: Training debug_affectcols
> test_pipeop_task_preproc.R: Training debug_affectcols
> test_pipeop_task_preproc.R: Training debug_affectcols
> test_pipeop_task_preproc.R: Training debug_affectcols
> test_pipeop_task_preproc.R: Training debug_affectcols
> test_pipeop_task_preproc.R: Training debug_affectcols
> test_pipeop_task_preproc.R: Training debug_affectcols
> test_pipeop_task_preproc.R: Training debug_affectcols
> test_pipeop_task_preproc.R: Training debug_affectcols
> test_pipeop_task_preproc.R: Training debug_affectcols
> test_pipeop_task_preproc.R: Training debug_affectcols
> test_pipeop_task_preproc.R: Training debug_affectcols
> test_pipeop_task_preproc.R: Training debug_affectcols
> test_pipeop_task_preproc.R: Training debug_affectcols
> test_pipeop_task_preproc.R: Training debug_affectcols
> test_pipeop_task_preproc.R: Training debug_affectcols
> test_pipeop_task_preproc.R: Training debug_affectcols
> test_pipeop_task_preproc.R: Training debug_affectcols
> test_pipeop_task_preproc.R: Training debug_affectcols
> test_pipeop_task_preproc.R: Training debug_affectcols
> test_pipeop_textvectorizer.R: 'as(<dgCMatrix>, "dgTMatrix")' is deprecated.
> test_pipeop_textvectorizer.R: Use 'as(., "TsparseMatrix")' instead.
> test_pipeop_textvectorizer.R: See help("Deprecated") and help("Matrix-deprecated").
> test_pipeop_tunethreshold.R: OptimInstanceSingleCrit is deprecated. Use OptimInstanceBatchSingleCrit instead.
> test_pipeop_tunethreshold.R: OptimInstanceSingleCrit is deprecated. Use OptimInstanceBatchSingleCrit instead.
> test_pipeop_tunethreshold.R: OptimInstanceSingleCrit is deprecated. Use OptimInstanceBatchSingleCrit instead.
> test_pipeop_tunethreshold.R: OptimInstanceSingleCrit is deprecated. Use OptimInstanceBatchSingleCrit instead.
> test_pipeop_tunethreshold.R: OptimInstanceSingleCrit is deprecated. Use OptimInstanceBatchSingleCrit instead.
> test_pipeop_tunethreshold.R: OptimInstanceSingleCrit is deprecated. Use OptimInstanceBatchSingleCrit instead.
> test_pipeop_tunethreshold.R: OptimInstanceSingleCrit is deprecated. Use OptimInstanceBatchSingleCrit instead.
[ FAIL 6 | WARN 0 | SKIP 99 | PASS 13006 ]
══ Skipped tests (99) ══════════════════════════════════════════════════════════
• On CRAN (95): 'test_CnfFormula_simplify.R:6:3', 'test_CnfFormula.R:591:3',
'test_Graph.R:283:3', 'test_PipeOp.R:32:1', 'test_GraphLearner.R:5:3',
'test_GraphLearner.R:221:3', 'test_GraphLearner.R:343:3',
'test_GraphLearner.R:408:3', 'test_GraphLearner.R:571:3',
'test_dictionary.R:7:3', 'test_learner_weightedaverage.R:5:3',
'test_learner_weightedaverage.R:57:3',
'test_learner_weightedaverage.R:105:3',
'test_learner_weightedaverage.R:152:3', 'test_meta.R:39:3',
'test_mlr_graphs_branching.R:26:3', 'test_mlr_graphs_bagging.R:6:3',
'test_mlr_graphs_robustify.R:5:3', 'test_pipeop_adas.R:8:3',
'test_pipeop_blsmote.R:8:3', 'test_pipeop_branch.R:4:3',
'test_pipeop_chunk.R:4:3', 'test_pipeop_classbalancing.R:7:3',
'test_pipeop_boxcox.R:7:3', 'test_pipeop_classweights.R:10:3',
'test_pipeop_collapsefactors.R:6:3', 'test_pipeop_colapply.R:9:3',
'test_pipeop_copy.R:5:3', 'test_pipeop_colroles.R:6:3',
'test_pipeop_decode.R:14:3', 'test_pipeop_encode.R:21:3',
'test_pipeop_encodeimpact.R:11:3', 'test_pipeop_encodepl.R:5:3',
'test_pipeop_encodepl.R:72:3', 'test_pipeop_encodelmer.R:15:3',
'test_pipeop_encodelmer.R:37:3', 'test_pipeop_encodelmer.R:80:3',
'test_pipeop_featureunion.R:9:3', 'test_pipeop_featureunion.R:134:3',
'test_pipeop_filter.R:7:3', 'test_pipeop_fixfactors.R:9:3',
'test_pipeop_ensemble.R:6:3', 'test_pipeop_histbin.R:7:3',
'test_pipeop_ica.R:7:3', 'test_pipeop_imputelearner.R:43:3',
'test_pipeop_impute.R:4:3', 'test_pipeop_isomap.R:10:3',
'test_pipeop_kernelpca.R:9:3', 'test_pipeop_learner.R:17:3',
'test_pipeop_info.R:6:3', 'test_pipeop_learnerpicvplus.R:163:3',
'test_pipeop_missind.R:6:3', 'test_pipeop_modelmatrix.R:7:3',
'test_pipeop_multiplicityexply.R:9:3', 'test_pipeop_multiplicityimply.R:9:3',
'test_pipeop_mutate.R:9:3', 'test_pipeop_nearmiss.R:7:3',
'test_pipeop_learnercv.R:31:3', 'test_pipeop_ovr.R:9:3',
'test_pipeop_ovr.R:48:3', 'test_pipeop_pca.R:8:3',
'test_pipeop_proxy.R:14:3', 'test_pipeop_quantilebin.R:5:3',
'test_pipeop_randomprojection.R:6:3', 'test_pipeop_randomresponse.R:5:3',
'test_pipeop_removeconstants.R:6:3', 'test_pipeop_renamecolumns.R:6:3',
'test_pipeop_nmf.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_rowapply.R:6:3',
'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-macos-arm64
Version: 0.10.0
Check: tests
Result: ERROR
Running ‘testthat.R’ [6m/11m]
Running the tests in ‘tests/testthat.R’ failed.
Complete output:
> if (requireNamespace("testthat", quietly = TRUE)) {
+ library("checkmate")
+ library("testthat")
+ library("mlr3")
+ library("paradox")
+ library("mlr3pipelines")
+ test_check("mlr3pipelines")
+ }
Starting 2 test processes.
> test_Graph.R: Training debug.multi with input list(input_1 = 1, input_2 = 1)
> test_PipeOp.R: Training test_autotrain
> test_PipeOp.R: Predicting test_autotrain
> test_PipeOp.R: Training test_autotrain
> test_PipeOp.R: Training test_autotrain
> test_PipeOp.R: Predicting test_autotrain
> test_PipeOp.R: Predicting test_autotrain
> test_PipeOp.R:
> test_PipeOp.R: Training test_autotrain
> test_PipeOp.R: Training test_autotrain
> test_PipeOp.R: Training test_autotrain
> test_PipeOp.R: Predicting test_autotrain
> test_PipeOp.R: Predicting test_autotrain
> test_PipeOp.R:
> test_PipeOp.R: Predicting test_autotrain
> test_PipeOp.R: Training 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: Predicting test_autotrain
> test_PipeOp.R: Predicting test_autotrain
> test_PipeOp.R: Training test_autotrain
> test_PipeOp.R: Training test_autotrain
> test_PipeOp.R: Training test_autotrain
> test_PipeOp.R: Predicting test_autotrain
> test_PipeOp.R: Predicting test_autotrain
> test_PipeOp.R: Predicting test_autotrain
> test_PipeOp.R: Training test_autotrain
> test_PipeOp.R: Predicting test_autotrain
> test_filter_ensemble.R: OptimInstanceSingleCrit is deprecated. Use OptimInstanceBatchSingleCrit instead.
> test_mlr_graphs_robustify.R: 'as(<dgCMatrix>, "dgTMatrix")' is deprecated.
> test_mlr_graphs_robustify.R: Use 'as(., "TsparseMatrix")' instead.
> test_mlr_graphs_robustify.R: See help("Deprecated") and help("Matrix-deprecated").
> test_multiplicities.R:
> test_multiplicities.R: [[1]]
> test_multiplicities.R: [1] 0
> test_multiplicities.R:
> test_multiplicities.R:
> test_pipeop_blsmote.R: [1] "Borderline-SMOTE done"
> test_pipeop_blsmote.R: [1] "Borderline-SMOTE done"
> test_pipeop_blsmote.R: [1] "Borderline-SMOTE done"
> test_pipeop_blsmote.R: [1] "Borderline-SMOTE done"
Saving _problems/test_pipeop_datefeatures-7.R
Saving _problems/test_pipeop_datefeatures-17.R
> test_pipeop_isomap.R: 2025-12-23 12:12:50.78634: Isomap START
> test_pipeop_isomap.R: 2025-12-23 12:12:50.788606: constructing knn graph
> test_pipeop_isomap.R: 2025-12-23 12:12:50.811182: calculating geodesic distances
> test_pipeop_isomap.R: 2025-12-23 12:12:50.924102: Classical Scaling
> test_pipeop_isomap.R: 2025-12-23 12:12:51.143548: Isomap START
> test_pipeop_isomap.R: 2025-12-23 12:12:51.143892: constructing knn graph
> test_pipeop_isomap.R: 2025-12-23 12:12:51.160629: calculating geodesic distances
> test_pipeop_isomap.R: 2025-12-23 12:12:51.219015: Classical Scaling
> test_pipeop_isomap.R: 2025-12-23 12:12:51.331973: L-Isomap embed START
> test_pipeop_isomap.R: 2025-12-23 12:12:51.332633: constructing knn graph
> test_pipeop_isomap.R: 2025-12-23 12:12:51.392578: calculating geodesic distances
> test_pipeop_isomap.R: 2025-12-23 12:12:51.5367: embedding
> test_pipeop_isomap.R: 2025-12-23 12:12:51.539805: DONE
> test_pipeop_isomap.R: 2025-12-23 12:12:51.576409: L-Isomap embed START
> test_pipeop_isomap.R: 2025-12-23 12:12:51.576744: constructing knn graph
> test_pipeop_isomap.R: 2025-12-23 12:12:51.655909: calculating geodesic distances
> test_pipeop_isomap.R: 2025-12-23 12:12:51.788205: embedding
> test_pipeop_isomap.R: 2025-12-23 12:12:51.78968: DONE
> test_pipeop_isomap.R: 2025-12-23 12:12:52.261972: Isomap START
> test_pipeop_isomap.R: 2025-12-23 12:12:52.262297: constructing knn graph
> test_pipeop_isomap.R: 2025-12-23 12:12:52.334614: calculating geodesic distances
> test_pipeop_isomap.R: 2025-12-23 12:12:52.736891: Classical Scaling
> test_pipeop_isomap.R: 2025-12-23 12:12:52.835434: L-Isomap embed START
> test_pipeop_isomap.R: 2025-12-23 12:12:52.835951: constructing knn graph
> test_pipeop_isomap.R: 2025-12-23 12:12:52.951139: calculating geodesic distances
> test_pipeop_isomap.R: 2025-12-23 12:12:53.714783: embedding
> test_pipeop_isomap.R: 2025-12-23 12:12:53.736684: DONE
> test_pipeop_isomap.R: 2025-12-23 12:12:54.161135: Isomap START
> test_pipeop_isomap.R: 2025-12-23 12:12:54.161691: constructing knn graph
> test_pipeop_isomap.R: 2025-12-23 12:12:54.239762: calculating geodesic distances
> test_pipeop_isomap.R: 2025-12-23 12:12:54.266414: Classical Scaling
> test_pipeop_isomap.R: 2025-12-23 12:12:54.366376: L-Isomap embed START
> test_pipeop_isomap.R: 2025-12-23 12:12:54.366826: constructing knn graph
> test_pipeop_isomap.R: 2025-12-23 12:12:54.408542: calculating geodesic distances
> test_pipeop_isomap.R: 2025-12-23 12:12:54.535974: embedding
> test_pipeop_isomap.R: 2025-12-23 12:12:54.537838: DONE
> test_pipeop_isomap.R: 2025-12-23 12:12:55.039269: Isomap START
> test_pipeop_isomap.R: 2025-12-23 12:12:55.039573: constructing knn graph
> test_pipeop_isomap.R: 2025-12-23 12:12:55.06092: calculating geodesic distances
> test_pipeop_isomap.R: 2025-12-23 12:12:55.189169: Classical Scaling
> test_pipeop_isomap.R: 2025-12-23 12:12:55.267526: L-Isomap embed START
> test_pipeop_isomap.R: 2025-12-23 12:12:55.267979: constructing knn graph
> test_pipeop_isomap.R: 2025-12-23 12:12:55.309034: calculating geodesic distances
> test_pipeop_isomap.R: 2025-12-23 12:12:55.525154: embedding
> test_pipeop_isomap.R: 2025-12-23 12:12:55.526418: DONE
> test_pipeop_isomap.R: 2025-12-23 12:12:55.812851: Isomap START
> test_pipeop_isomap.R: 2025-12-23 12:12:55.813208: constructing knn graph
> test_pipeop_isomap.R: 2025-12-23 12:12:55.874545: calculating geodesic distances
> test_pipeop_isomap.R: 2025-12-23 12:12:55.921168: Classical Scaling
> test_pipeop_isomap.R: 2025-12-23 12:12:56.13092: L-Isomap embed START
> test_pipeop_isomap.R: 2025-12-23 12:12:56.132584: constructing knn graph
> test_pipeop_isomap.R: 2025-12-23 12:12:56.179188: calculating geodesic distances
> test_pipeop_isomap.R: 2025-12-23 12:12:56.330511: embedding
> test_pipeop_isomap.R: 2025-12-23 12:12:56.331864: DONE
> test_pipeop_isomap.R: 2025-12-23 12:12:56.567124: Isomap START
> test_pipeop_isomap.R: 2025-12-23 12:12:56.62172: constructing knn graph
> test_pipeop_isomap.R: 2025-12-23 12:12:56.630811: calculating geodesic distances
> test_pipeop_isomap.R: 2025-12-23 12:12:56.735511: Classical Scaling
> test_pipeop_isomap.R: 2025-12-23 12:12:56.877753: L-Isomap embed START
> test_pipeop_isomap.R: 2025-12-23 12:12:56.878436: constructing knn graph
> test_pipeop_isomap.R: 2025-12-23 12:12:56.977285: calculating geodesic distances
> test_pipeop_isomap.R: 2025-12-23 12:12:57.120255: embedding
> test_pipeop_isomap.R: 2025-12-23 12:12:57.121632: DONE
> test_pipeop_isomap.R: 2025-12-23 12:12:57.496038: Isomap START
> test_pipeop_isomap.R: 2025-12-23 12:12:57.496386: constructing knn graph
> test_pipeop_isomap.R: 2025-12-23 12:12:57.588286: calculating geodesic distances
> test_pipeop_isomap.R: 2025-12-23 12:12:57.642409: Classical Scaling
> test_pipeop_isomap.R: 2025-12-23 12:12:57.805813: L-Isomap embed START
> test_pipeop_isomap.R: 2025-12-23 12:12:57.806394: constructing knn graph
> test_pipeop_isomap.R: 2025-12-23 12:12:57.862115: calculating geodesic distances
> test_pipeop_isomap.R: 2025-12-23 12:12:57.99654: embedding
> test_pipeop_isomap.R: 2025-12-23 12:12:57.998839: DONE
> test_pipeop_isomap.R: 2025-12-23 12:12:58.216389: Isomap START
> test_pipeop_isomap.R: 2025-12-23 12:12:58.216702: constructing knn graph
> test_pipeop_isomap.R: 2025-12-23 12:12:58.250304: calculating geodesic distances
> test_pipeop_isomap.R: 2025-12-23 12:12:58.362189: Classical Scaling
> test_pipeop_isomap.R: 2025-12-23 12:12:58.548636: Isomap START
> test_pipeop_isomap.R: 2025-12-23 12:12:58.549099: constructing knn graph
> test_pipeop_isomap.R: 2025-12-23 12:12:58.601918: calculating geodesic distances
> test_pipeop_isomap.R: 2025-12-23 12:12:58.65823: Classical Scaling
> test_pipeop_isomap.R: 2025-12-23 12:12:58.717314: Isomap START
> test_pipeop_isomap.R: 2025-12-23 12:12:58.721104: constructing knn graph
> test_pipeop_isomap.R: 2025-12-23 12:12:58.751829: calculating geodesic distances
> test_pipeop_isomap.R: 2025-12-23 12:12:58.822928: Classical Scaling
Saving _problems/test_pipeop_nmf-45.R
Saving _problems/test_pipeop_nmf-73.R
Saving _problems/test_pipeop_nmf-93.R
Saving _problems/test_pipeop_nmf-98.R
> test_pipeop_task_preproc.R: Training debug_affectcols
> test_pipeop_task_preproc.R: Training debug_affectcols
> test_pipeop_task_preproc.R: Training debug_affectcols
> test_pipeop_task_preproc.R: Training debug_affectcols
> test_pipeop_task_preproc.R: Training debug_affectcols
> test_pipeop_task_preproc.R: Training debug_affectcols
> test_pipeop_task_preproc.R: Training debug_affectcols
> test_pipeop_task_preproc.R: Training debug_affectcols
> test_pipeop_task_preproc.R: Training debug_affectcols
> test_pipeop_task_preproc.R: Training debug_affectcols
> test_pipeop_task_preproc.R: Training debug_affectcols
> test_pipeop_task_preproc.R: Training debug_affectcols
> test_pipeop_task_preproc.R: Training debug_affectcols
> test_pipeop_task_preproc.R: Training debug_affectcols
> test_pipeop_task_preproc.R: Training debug_affectcols
> test_pipeop_task_preproc.R: Training debug_affectcols
> test_pipeop_task_preproc.R: Training debug_affectcols
> test_pipeop_task_preproc.R: Training debug_affectcols
> test_pipeop_task_preproc.R: Training debug_affectcols
> test_pipeop_task_preproc.R: Training debug_affectcols
> test_pipeop_task_preproc.R: Training debug_affectcols
> test_pipeop_task_preproc.R: Training debug_affectcols
> test_pipeop_textvectorizer.R: 'as(<dgCMatrix>, "dgTMatrix")' is deprecated.
> test_pipeop_textvectorizer.R: Use 'as(., "TsparseMatrix")' instead.
> test_pipeop_textvectorizer.R: See help("Deprecated") and help("Matrix-deprecated").
> test_pipeop_tunethreshold.R: OptimInstanceSingleCrit is deprecated. Use OptimInstanceBatchSingleCrit instead.
> test_pipeop_tunethreshold.R: OptimInstanceSingleCrit is deprecated. Use OptimInstanceBatchSingleCrit instead.
> test_pipeop_tunethreshold.R: OptimInstanceSingleCrit is deprecated. Use OptimInstanceBatchSingleCrit instead.
> test_pipeop_tunethreshold.R: OptimInstanceSingleCrit is deprecated. Use OptimInstanceBatchSingleCrit instead.
> test_pipeop_tunethreshold.R: OptimInstanceSingleCrit is deprecated. Use OptimInstanceBatchSingleCrit instead.
> test_pipeop_tunethreshold.R: OptimInstanceSingleCrit is deprecated. Use OptimInstanceBatchSingleCrit instead.
> test_pipeop_tunethreshold.R: OptimInstanceSingleCrit is deprecated. Use OptimInstanceBatchSingleCrit instead.
[ FAIL 6 | WARN 0 | SKIP 99 | PASS 13006 ]
══ Skipped tests (99) ══════════════════════════════════════════════════════════
• On CRAN (95): 'test_CnfFormula_simplify.R:6:3', 'test_CnfFormula.R:591:3',
'test_Graph.R:283:3', 'test_PipeOp.R:32:1', 'test_GraphLearner.R:5:3',
'test_GraphLearner.R:221:3', 'test_GraphLearner.R:343:3',
'test_GraphLearner.R:408:3', 'test_GraphLearner.R:571:3',
'test_dictionary.R:7:3', 'test_learner_weightedaverage.R:5:3',
'test_learner_weightedaverage.R:57:3',
'test_learner_weightedaverage.R:105:3',
'test_learner_weightedaverage.R:152:3', 'test_meta.R:39:3',
'test_mlr_graphs_branching.R:26:3', 'test_mlr_graphs_bagging.R:6:3',
'test_mlr_graphs_robustify.R:5:3', 'test_pipeop_adas.R:8:3',
'test_pipeop_blsmote.R:8:3', 'test_pipeop_branch.R:4:3',
'test_pipeop_chunk.R:4:3', 'test_pipeop_classbalancing.R:7:3',
'test_pipeop_boxcox.R:7:3', 'test_pipeop_classweights.R:10:3',
'test_pipeop_collapsefactors.R:6:3', 'test_pipeop_colapply.R:9:3',
'test_pipeop_copy.R:5:3', 'test_pipeop_colroles.R:6:3',
'test_pipeop_decode.R:14:3', 'test_pipeop_encode.R:21:3',
'test_pipeop_encodeimpact.R:11:3', 'test_pipeop_encodepl.R:5:3',
'test_pipeop_encodepl.R:72:3', 'test_pipeop_encodelmer.R:15:3',
'test_pipeop_encodelmer.R:37:3', 'test_pipeop_encodelmer.R:80:3',
'test_pipeop_featureunion.R:9:3', 'test_pipeop_featureunion.R:134:3',
'test_pipeop_filter.R:7:3', 'test_pipeop_fixfactors.R:9:3',
'test_pipeop_histbin.R:7:3', 'test_pipeop_ica.R:7:3',
'test_pipeop_ensemble.R:6:3', 'test_pipeop_impute.R:4:3',
'test_pipeop_imputelearner.R:43:3', 'test_pipeop_isomap.R:10:3',
'test_pipeop_kernelpca.R:9:3', 'test_pipeop_learner.R:17:3',
'test_pipeop_info.R:6:3', 'test_pipeop_learnerpicvplus.R:163:3',
'test_pipeop_missind.R:6:3', 'test_pipeop_modelmatrix.R:7:3',
'test_pipeop_multiplicityexply.R:9:3', 'test_pipeop_multiplicityimply.R:9:3',
'test_pipeop_mutate.R:9:3', 'test_pipeop_nearmiss.R:7:3',
'test_pipeop_learnercv.R:31:3', 'test_pipeop_ovr.R:9:3',
'test_pipeop_ovr.R:48:3', 'test_pipeop_pca.R:8:3',
'test_pipeop_proxy.R:14:3', 'test_pipeop_quantilebin.R:5:3',
'test_pipeop_randomprojection.R:6:3', 'test_pipeop_nmf.R:6:3',
'test_pipeop_randomresponse.R:5:3', 'test_pipeop_removeconstants.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_rowapply.R:6:3', 'test_pipeop_smotenc.R:8:3',
'test_pipeop_subsample.R:6:3', 'test_pipeop_targetinvert.R:4:3',
'test_pipeop_targetmutate.R:5:3', 'test_pipeop_targettrafo.R:4:3',
'test_pipeop_targettrafoscalerange.R:5:3', 'test_pipeop_task_preproc.R:4:3',
'test_pipeop_task_preproc.R:14:3', 'test_pipeop_spatialsign.R:6:3',
'test_pipeop_tomek.R:7:3', 'test_pipeop_textvectorizer.R:37:3',
'test_pipeop_textvectorizer.R:186:3', 'test_pipeop_unbranch.R:10:3',
'test_pipeop_updatetarget.R:89:3', 'test_pipeop_vtreat.R:9:3',
'test_pipeop_yeojohnson.R:7:3', 'test_pipeop_tunethreshold.R:111:3',
'test_pipeop_tunethreshold.R:191:3', 'test_typecheck.R:188:3'
• Skipping (1): 'test_GraphLearner.R:1278:3'
• empty test (3): 'test_pipeop_isomap.R:111:1', 'test_pipeop_missind.R:101:1',
'test_ppl.R:61:1'
══ Failed tests ════════════════════════════════════════════════════════════════
── Error ('test_pipeop_datefeatures.R:7:3'): PipeOpDateFeatures - basic properties ──
Error in `seq.Date(as.Date("2020-01-31"), length.out = 150L)`: exactly two of 'to', 'by' and 'length.out' / 'along.with' must be specified
Backtrace:
▆
1. ├─base::seq(as.Date("2020-01-31"), length.out = 150L) at test_pipeop_datefeatures.R:7:3
2. └─base::seq.Date(as.Date("2020-01-31"), length.out = 150L)
── Error ('test_pipeop_datefeatures.R:17:3'): PipeOpDateFeatures - finds POSIXct column ──
Error in `seq.Date(as.Date("2020-01-31"), length.out = 150L)`: exactly two of 'to', 'by' and 'length.out' / 'along.with' must be specified
Backtrace:
▆
1. ├─base::seq(as.Date("2020-01-31"), length.out = 150L) at test_pipeop_datefeatures.R:17:3
2. └─base::seq.Date(as.Date("2020-01-31"), length.out = 150L)
── Error ('test_pipeop_nmf.R:45:3'): PipeOpNMF - does not modify search path when NMF is not loaded, fix for #929 ──
Error in `detach(package:generics)`: invalid 'name' argument
Backtrace:
▆
1. └─base::detach(package:generics) at test_pipeop_nmf.R:45:3
── Failure ('test_pipeop_nmf.R:73:3'): PipeOpNMF - does not modify search path when NMF is loaded, fix for #929 ──
Expected `all(...)` to be TRUE.
Differences:
`actual`: FALSE
`expected`: TRUE
── Failure ('test_pipeop_nmf.R:93:3'): PipeOpNMF - does not modify search path when some of NMF's dependencies are loaded, fix for #929 ──
Expected `all(paste0("package:", c("BiocGenerics", "generics")) %in% search())` to be TRUE.
Differences:
`actual`: FALSE
`expected`: TRUE
── Error ('test_pipeop_nmf.R:98:3'): PipeOpNMF - does not modify search path when some of NMF's dependencies are loaded, fix for #929 ──
Error in `FUN(X[[i]], ...)`: invalid 'name' argument
This happened in PipeOp nmf's $train()
Backtrace:
▆
1. ├─op$train(list(tsk("iris"))) at test_pipeop_nmf.R:98:3
2. │ └─mlr3pipelines:::.__PipeOp__train(...)
3. │ ├─base::withCallingHandlers(...)
4. │ └─private$.train(input)
5. │ └─mlr3pipelines:::.__PipeOpTaskPreproc__.train(...)
6. │ └─private$.train_task(intask)
7. │ └─mlr3pipelines:::.__PipeOpTaskPreproc__.train_task(...)
8. │ ├─data.table::as.data.table(...)
9. │ └─private$.train_dt(dt, task$levels(cols), task$truth())
10. │ └─mlr3pipelines:::.__PipeOpNMF__.train_dt(...)
11. │ └─mlr3misc::map(to_be_detached, detach, character.only = TRUE)
12. │ └─base::lapply(.x, .f, ...)
13. │ └─base (local) FUN(X[[i]], ...)
14. │ └─base::stop("invalid 'name' argument")
15. └─base::.handleSimpleError(...)
16. └─mlr3pipelines (local) h(simpleError(msg, call))
[ FAIL 6 | WARN 0 | SKIP 99 | PASS 13006 ]
Error:
! Test failures.
Execution halted
Flavor: r-oldrel-macos-x86_64
Version: 0.10.0
Check: tests
Result: ERROR
Running 'testthat.R' [283s]
Running the tests in 'tests/testthat.R' failed.
Complete output:
> if (requireNamespace("testthat", quietly = TRUE)) {
+ library("checkmate")
+ library("testthat")
+ library("mlr3")
+ library("paradox")
+ library("mlr3pipelines")
+ test_check("mlr3pipelines")
+ }
Starting 2 test processes.
> test_Graph.R: Training debug.multi with input list(input_1 = 1, input_2 = 1)
> test_PipeOp.R: Training test_autotrain
> test_PipeOp.R: Predicting test_autotrain
> test_PipeOp.R: Training test_autotrain
> test_PipeOp.R: Training test_autotrain
> test_PipeOp.R: Predicting test_autotrain
> test_PipeOp.R: Predicting test_autotrain
> test_PipeOp.R: Training test_autotrain
> test_PipeOp.R: Training test_autotrain
> test_PipeOp.R: Training test_autotrain
> test_PipeOp.R: Predicting test_autotrain
> test_PipeOp.R: Predicting test_autotrain
> test_PipeOp.R: Predicting test_autotrain
> test_PipeOp.R: Training test_autotrain
> test_PipeOp.R: Predicting test_autotrain
> test_PipeOp.R: Training test_autotrain
> test_PipeOp.R: Training test_autotrain
> test_PipeOp.R: Predicting test_autotrain
> test_PipeOp.R: Predicting test_autotrain
> test_PipeOp.R: Training test_autotrain
> test_PipeOp.R: Training test_autotrain
> test_PipeOp.R: Training test_autotrain
> test_PipeOp.R: Predicting test_autotrain
> test_PipeOp.R: Predicting test_autotrain
> test_PipeOp.R: Predicting test_autotrain
> test_PipeOp.R: Training test_autotrain
> test_PipeOp.R: Predicting test_autotrain
> test_filter_ensemble.R: OptimInstanceSingleCrit is deprecated. Use OptimInstanceBatchSingleCrit instead.
> test_mlr_graphs_robustify.R: 'as(<dgCMatrix>, "dgTMatrix")' is deprecated.
> test_mlr_graphs_robustify.R: Use 'as(., "TsparseMatrix")' instead.
> test_mlr_graphs_robustify.R: See help("Deprecated") and help("Matrix-deprecated").
> test_multiplicities.R:
> test_multiplicities.R: [[1]]
> test_multiplicities.R:
> test_multiplicities.R: [1] 0
> test_multiplicities.R:
> test_pipeop_blsmote.R: [1] "Borderline-SMOTE done"
> test_pipeop_blsmote.R: [1] "Borderline-SMOTE done"
> test_pipeop_blsmote.R: [1] "Borderline-SMOTE done"
> test_pipeop_blsmote.R: [1] "Borderline-SMOTE done"
Saving _problems/test_pipeop_datefeatures-7.R
Saving _problems/test_pipeop_datefeatures-17.R
> test_pipeop_isomap.R: 2025-12-22 15:20:13.299257: Isomap START
> test_pipeop_isomap.R: 2025-12-22 15:20:13.300242: constructing knn graph
> test_pipeop_isomap.R: 2025-12-22 15:20:13.313809: calculating geodesic distances
> test_pipeop_isomap.R: 2025-12-22 15:20:13.336109: Classical Scaling
> test_pipeop_isomap.R: 2025-12-22 15:20:13.416001: Isomap START
> test_pipeop_isomap.R: 2025-12-22 15:20:13.41666: constructing knn graph
> test_pipeop_isomap.R: 2025-12-22 15:20:13.427963: calculating geodesic distances
> test_pipeop_isomap.R: 2025-12-22 15:20:13.450985: Classical Scaling
> test_pipeop_isomap.R: 2025-12-22 15:20:13.493861: L-Isomap embed START
> test_pipeop_isomap.R: 2025-12-22 15:20:13.494781: constructing knn graph
> test_pipeop_isomap.R: 2025-12-22 15:20:13.516055: calculating geodesic distances
> test_pipeop_isomap.R: 2025-12-22 15:20:13.564643: embedding
> test_pipeop_isomap.R: 2025-12-22 15:20:13.56627: DONE
> test_pipeop_isomap.R: 2025-12-22 15:20:13.606333: L-Isomap embed START
> test_pipeop_isomap.R: 2025-12-22 15:20:13.607032: constructing knn graph
> test_pipeop_isomap.R: 2025-12-22 15:20:13.62741: calculating geodesic distances
> test_pipeop_isomap.R: 2025-12-22 15:20:13.677365: embedding
> test_pipeop_isomap.R: 2025-12-22 15:20:13.678945: DONE
> test_pipeop_isomap.R: 2025-12-22 15:20:13.803843: Isomap START
> test_pipeop_isomap.R: 2025-12-22 15:20:13.804476: constructing knn graph
> test_pipeop_isomap.R: 2025-12-22 15:20:13.829882: calculating geodesic distances
> test_pipeop_isomap.R: 2025-12-22 15:20:13.942266: Classical Scaling
> test_pipeop_isomap.R: 2025-12-22 15:20:13.994147: L-Isomap embed START
> test_pipeop_isomap.R: 2025-12-22 15:20:13.995056: constructing knn graph
> test_pipeop_isomap.R: 2025-12-22 15:20:14.057107: calculating geodesic distances
> test_pipeop_isomap.R: 2025-12-22 15:20:14.278284: embedding
> test_pipeop_isomap.R: 2025-12-22 15:20:14.283452: DONE
> test_pipeop_isomap.R: 2025-12-22 15:20:14.512786: Isomap START
> test_pipeop_isomap.R: 2025-12-22 15:20:14.513557: constructing knn graph
> test_pipeop_isomap.R: 2025-12-22 15:20:14.524763: calculating geodesic distances
> test_pipeop_isomap.R: 2025-12-22 15:20:14.547382: Classical Scaling
> test_pipeop_isomap.R: 2025-12-22 15:20:14.599349: L-Isomap embed START
> test_pipeop_isomap.R: 2025-12-22 15:20:14.600375: constructing knn graph
> test_pipeop_isomap.R: 2025-12-22 15:20:14.62077: calculating geodesic distances
> test_pipeop_isomap.R: 2025-12-22 15:20:14.661497: embedding
> test_pipeop_isomap.R: 2025-12-22 15:20:14.662764: DONE
> test_pipeop_isomap.R: 2025-12-22 15:20:14.845617: Isomap START
> test_pipeop_isomap.R: 2025-12-22 15:20:14.846285: constructing knn graph
> test_pipeop_isomap.R: 2025-12-22 15:20:14.858026: calculating geodesic distances
> test_pipeop_isomap.R: 2025-12-22 15:20:14.880703: Classical Scaling
> test_pipeop_isomap.R: 2025-12-22 15:20:14.960588: L-Isomap embed START
> test_pipeop_isomap.R: 2025-12-22 15:20:14.961684: constructing knn graph
> test_pipeop_isomap.R: 2025-12-22 15:20:14.9852: calculating geodesic distances
> test_pipeop_isomap.R: 2025-12-22 15:20:15.03418: embedding
> test_pipeop_isomap.R: 2025-12-22 15:20:15.057644: DONE
> test_pipeop_isomap.R: 2025-12-22 15:20:15.184788: Isomap START
> test_pipeop_isomap.R: 2025-12-22 15:20:15.185558: constructing knn graph
> test_pipeop_isomap.R: 2025-12-22 15:20:15.196751: calculating geodesic distances
> test_pipeop_isomap.R: 2025-12-22 15:20:15.219105: Classical Scaling
> test_pipeop_isomap.R: 2025-12-22 15:20:15.2964: L-Isomap embed START
> test_pipeop_isomap.R: 2025-12-22 15:20:15.297343: constructing knn graph
> test_pipeop_isomap.R: 2025-12-22 15:20:15.316794: calculating geodesic distances
> test_pipeop_isomap.R: 2025-12-22 15:20:15.366068: embedding
> test_pipeop_isomap.R: 2025-12-22 15:20:15.367332: DONE
> test_pipeop_isomap.R: 2025-12-22 15:20:15.474467: Isomap START
> test_pipeop_isomap.R: 2025-12-22 15:20:15.475143: constructing knn graph
> test_pipeop_isomap.R: 2025-12-22 15:20:15.485318: calculating geodesic distances
> test_pipeop_isomap.R: 2025-12-22 15:20:15.507591: Classical Scaling
> test_pipeop_isomap.R: 2025-12-22 15:20:15.572011: L-Isomap embed START
> test_pipeop_isomap.R: 2025-12-22 15:20:15.572655: constructing knn graph
> test_pipeop_isomap.R: 2025-12-22 15:20:15.592848: calculating geodesic distances
> test_pipeop_isomap.R: 2025-12-22 15:20:15.640865: embedding
> test_pipeop_isomap.R: 2025-12-22 15:20:15.642868: DONE
> test_pipeop_isomap.R: 2025-12-22 15:20:15.754993: Isomap START
> test_pipeop_isomap.R: 2025-12-22 15:20:15.755698: constructing knn graph
> test_pipeop_isomap.R: 2025-12-22 15:20:15.766362: calculating geodesic distances
> test_pipeop_isomap.R: 2025-12-22 15:20:15.788108: Classical Scaling
> test_pipeop_isomap.R: 2025-12-22 15:20:15.881901: L-Isomap embed START
> test_pipeop_isomap.R: 2025-12-22 15:20:15.883002: constructing knn graph
> test_pipeop_isomap.R: 2025-12-22 15:20:15.904368: calculating geodesic distances
> test_pipeop_isomap.R: 2025-12-22 15:20:15.953444: embedding
> test_pipeop_isomap.R: 2025-12-22 15:20:15.955361: DONE
> test_pipeop_isomap.R: 2025-12-22 15:20:16.0885: Isomap START
> test_pipeop_isomap.R: 2025-12-22 15:20:16.089165: constructing knn graph
> test_pipeop_isomap.R: 2025-12-22 15:20:16.101325: calculating geodesic distances
> test_pipeop_isomap.R: 2025-12-22 15:20:16.123926: Classical Scaling
> test_pipeop_isomap.R: 2025-12-22 15:20:16.242435: Isomap START
> test_pipeop_isomap.R: 2025-12-22 15:20:16.243082: constructing knn graph
> test_pipeop_isomap.R: 2025-12-22 15:20:16.255289: calculating geodesic distances
> test_pipeop_isomap.R: 2025-12-22 15:20:16.277987: Classical Scaling
> test_pipeop_isomap.R: 2025-12-22 15:20:16.312319: Isomap START
> test_pipeop_isomap.R: 2025-12-22 15:20:16.313095: constructing knn graph
> test_pipeop_isomap.R: 2025-12-22 15:20:16.326189: calculating geodesic distances
> test_pipeop_isomap.R: 2025-12-22 15:20:16.348884: Classical Scaling
Saving _problems/test_pipeop_nmf-45.R
Saving _problems/test_pipeop_nmf-73.R
Saving _problems/test_pipeop_nmf-93.R
Saving _problems/test_pipeop_nmf-98.R
> test_pipeop_task_preproc.R: Training debug_affectcols
> test_pipeop_task_preproc.R: Training debug_affectcols
> test_pipeop_task_preproc.R: Training debug_affectcols
> test_pipeop_task_preproc.R: Training debug_affectcols
> test_pipeop_task_preproc.R: Training debug_affectcols
> test_pipeop_task_preproc.R: Training debug_affectcols
> test_pipeop_task_preproc.R: Training debug_affectcols
> test_pipeop_task_preproc.R: Training debug_affectcols
> test_pipeop_task_preproc.R: Training debug_affectcols
> test_pipeop_task_preproc.R: Training debug_affectcols
> test_pipeop_task_preproc.R: Training debug_affectcols
> test_pipeop_task_preproc.R: Training debug_affectcols
> test_pipeop_task_preproc.R: Training debug_affectcols
> test_pipeop_task_preproc.R: Training debug_affectcols
> test_pipeop_task_preproc.R: Training debug_affectcols
> test_pipeop_task_preproc.R: Training debug_affectcols
> test_pipeop_task_preproc.R: Training debug_affectcols
> test_pipeop_task_preproc.R: Training debug_affectcols
> test_pipeop_task_preproc.R: Training debug_affectcols
> test_pipeop_task_preproc.R: Training debug_affectcols
> test_pipeop_task_preproc.R: Training debug_affectcols
> test_pipeop_task_preproc.R: Training debug_affectcols
> test_pipeop_tunethreshold.R: OptimInstanceSingleCrit is deprecated. Use OptimInstanceBatchSingleCrit instead.
> test_pipeop_tunethreshold.R: OptimInstanceSingleCrit is deprecated. Use OptimInstanceBatchSingleCrit instead.
> test_pipeop_tunethreshold.R: OptimInstanceSingleCrit is deprecated. Use OptimInstanceBatchSingleCrit instead.
> test_pipeop_tunethreshold.R: OptimInstanceSingleCrit is deprecated. Use OptimInstanceBatchSingleCrit instead.
> test_pipeop_tunethreshold.R: OptimInstanceSingleCrit is deprecated. Use OptimInstanceBatchSingleCrit instead.
> test_pipeop_tunethreshold.R: OptimInstanceSingleCrit is deprecated. Use OptimInstanceBatchSingleCrit instead.
> test_pipeop_tunethreshold.R: OptimInstanceSingleCrit is deprecated. Use OptimInstanceBatchSingleCrit instead.
[ FAIL 6 | WARN 0 | SKIP 99 | PASS 13006 ]
══ Skipped tests (99) ══════════════════════════════════════════════════════════
• On CRAN (95): 'test_CnfFormula_simplify.R:6:3', 'test_CnfFormula.R:591:3',
'test_Graph.R:283:3', 'test_PipeOp.R:32:1', 'test_GraphLearner.R:5:3',
'test_GraphLearner.R:221:3', 'test_GraphLearner.R:343:3',
'test_GraphLearner.R:408:3', 'test_GraphLearner.R:571:3',
'test_dictionary.R:7:3', 'test_learner_weightedaverage.R:5:3',
'test_learner_weightedaverage.R:57:3',
'test_learner_weightedaverage.R:105:3',
'test_learner_weightedaverage.R:152:3', 'test_meta.R:39:3',
'test_mlr_graphs_branching.R:26:3', 'test_mlr_graphs_bagging.R:6:3',
'test_mlr_graphs_robustify.R:5:3', 'test_pipeop_adas.R:8:3',
'test_pipeop_blsmote.R:8:3', 'test_pipeop_branch.R:4:3',
'test_pipeop_chunk.R:4:3', 'test_pipeop_classbalancing.R:7:3',
'test_pipeop_boxcox.R:7:3', 'test_pipeop_classweights.R:10:3',
'test_pipeop_collapsefactors.R:6:3', 'test_pipeop_colapply.R:9:3',
'test_pipeop_copy.R:5:3', 'test_pipeop_colroles.R:6:3',
'test_pipeop_decode.R:14:3', 'test_pipeop_encode.R:21:3',
'test_pipeop_encodeimpact.R:11:3', 'test_pipeop_encodepl.R:5:3',
'test_pipeop_encodepl.R:72:3', 'test_pipeop_encodelmer.R:15:3',
'test_pipeop_encodelmer.R:37:3', 'test_pipeop_encodelmer.R:80:3',
'test_pipeop_featureunion.R:9:3', 'test_pipeop_featureunion.R:134:3',
'test_pipeop_filter.R:7:3', 'test_pipeop_fixfactors.R:9:3',
'test_pipeop_histbin.R:7:3', 'test_pipeop_ica.R:7:3',
'test_pipeop_ensemble.R:6:3', 'test_pipeop_impute.R:4:3',
'test_pipeop_imputelearner.R:43:3', 'test_pipeop_isomap.R:10:3',
'test_pipeop_kernelpca.R:9:3', 'test_pipeop_learner.R:17:3',
'test_pipeop_info.R:6:3', 'test_pipeop_learnerpicvplus.R:163:3',
'test_pipeop_missind.R:6:3', 'test_pipeop_modelmatrix.R:7:3',
'test_pipeop_multiplicityexply.R:9:3', 'test_pipeop_learnercv.R:31:3',
'test_pipeop_mutate.R:9:3', 'test_pipeop_multiplicityimply.R:9:3',
'test_pipeop_nearmiss.R:7:3', 'test_pipeop_ovr.R:9:3',
'test_pipeop_ovr.R:48:3', 'test_pipeop_pca.R:8:3',
'test_pipeop_proxy.R:14:3', 'test_pipeop_quantilebin.R:5:3',
'test_pipeop_randomprojection.R:6:3', 'test_pipeop_randomresponse.R:5:3',
'test_pipeop_removeconstants.R:6:3', 'test_pipeop_renamecolumns.R:6:3',
'test_pipeop_replicate.R:9:3', 'test_pipeop_nmf.R:6:3',
'test_pipeop_rowapply.R:6:3', 'test_pipeop_scale.R:6:3',
'test_pipeop_scale.R:10:3', 'test_pipeop_scalemaxabs.R:6:3',
'test_pipeop_scalerange.R:7:3', 'test_pipeop_select.R:9:3',
'test_pipeop_smotenc.R:8:3', 'test_pipeop_smote.R:10:3',
'test_pipeop_subsample.R:6:3', 'test_pipeop_targetinvert.R:4:3',
'test_pipeop_targetmutate.R:5:3', 'test_pipeop_targettrafo.R:4:3',
'test_pipeop_targettrafoscalerange.R:5:3', 'test_pipeop_task_preproc.R:4:3',
'test_pipeop_task_preproc.R:14:3', 'test_pipeop_spatialsign.R:6:3',
'test_pipeop_tomek.R:7:3', 'test_pipeop_textvectorizer.R:37:3',
'test_pipeop_textvectorizer.R:186:3', 'test_pipeop_unbranch.R:10:3',
'test_pipeop_updatetarget.R:89:3', 'test_pipeop_vtreat.R:9:3',
'test_pipeop_yeojohnson.R:7:3', 'test_pipeop_tunethreshold.R:111:3',
'test_pipeop_tunethreshold.R:191:3', 'test_typecheck.R:188:3'
• Skipping (1): 'test_GraphLearner.R:1278:3'
• empty test (3): 'test_pipeop_isomap.R:111:1', 'test_pipeop_missind.R:101:1',
'test_ppl.R:61:1'
══ Failed tests ════════════════════════════════════════════════════════════════
── Error ('test_pipeop_datefeatures.R:7:3'): PipeOpDateFeatures - basic properties ──
Error in `seq.Date(as.Date("2020-01-31"), length.out = 150L)`: exactly two of 'to', 'by' and 'length.out' / 'along.with' must be specified
Backtrace:
▆
1. ├─base::seq(as.Date("2020-01-31"), length.out = 150L) at test_pipeop_datefeatures.R:7:3
2. └─base::seq.Date(as.Date("2020-01-31"), length.out = 150L)
── Error ('test_pipeop_datefeatures.R:17:3'): PipeOpDateFeatures - finds POSIXct column ──
Error in `seq.Date(as.Date("2020-01-31"), length.out = 150L)`: exactly two of 'to', 'by' and 'length.out' / 'along.with' must be specified
Backtrace:
▆
1. ├─base::seq(as.Date("2020-01-31"), length.out = 150L) at test_pipeop_datefeatures.R:17:3
2. └─base::seq.Date(as.Date("2020-01-31"), length.out = 150L)
── Error ('test_pipeop_nmf.R:45:3'): PipeOpNMF - does not modify search path when NMF is not loaded, fix for #929 ──
Error in `detach(package:generics)`: invalid 'name' argument
Backtrace:
▆
1. └─base::detach(package:generics) at test_pipeop_nmf.R:45:3
── Failure ('test_pipeop_nmf.R:73:3'): PipeOpNMF - does not modify search path when NMF is loaded, fix for #929 ──
Expected `all(...)` to be TRUE.
Differences:
`actual`: FALSE
`expected`: TRUE
── Failure ('test_pipeop_nmf.R:93:3'): PipeOpNMF - does not modify search path when some of NMF's dependencies are loaded, fix for #929 ──
Expected `all(paste0("package:", c("BiocGenerics", "generics")) %in% search())` to be TRUE.
Differences:
`actual`: FALSE
`expected`: TRUE
── Error ('test_pipeop_nmf.R:98:3'): PipeOpNMF - does not modify search path when some of NMF's dependencies are loaded, fix for #929 ──
Error in `FUN(X[[i]], ...)`: invalid 'name' argument
This happened in PipeOp nmf's $train()
Backtrace:
▆
1. ├─op$train(list(tsk("iris"))) at test_pipeop_nmf.R:98:3
2. │ └─mlr3pipelines:::.__PipeOp__train(...)
3. │ ├─base::withCallingHandlers(...)
4. │ └─private$.train(input)
5. │ └─mlr3pipelines:::.__PipeOpTaskPreproc__.train(...)
6. │ └─private$.train_task(intask)
7. │ └─mlr3pipelines:::.__PipeOpTaskPreproc__.train_task(...)
8. │ ├─data.table::as.data.table(...)
9. │ └─private$.train_dt(dt, task$levels(cols), task$truth())
10. │ └─mlr3pipelines:::.__PipeOpNMF__.train_dt(...)
11. │ └─mlr3misc::map(to_be_detached, detach, character.only = TRUE)
12. │ └─base::lapply(.x, .f, ...)
13. │ └─base (local) FUN(X[[i]], ...)
14. │ └─base::stop("invalid 'name' argument")
15. └─base::.handleSimpleError(...)
16. └─mlr3pipelines (local) h(simpleError(msg, call))
[ FAIL 6 | WARN 0 | SKIP 99 | PASS 13006 ]
Error:
! Test failures.
Execution halted
Flavor: r-oldrel-windows-x86_64
Current CRAN status: OK: 13
Current CRAN status: OK: 13