CRAN Package Check Results for Package mlr3viz

Last updated on 2020-11-27 03:50:14 CET.

Flavor Version Tinstall Tcheck Ttotal Status Flags
r-devel-linux-x86_64-debian-clang 0.5.0 6.15 149.46 155.61 OK
r-devel-linux-x86_64-debian-gcc 0.5.0 4.96 110.62 115.58 OK
r-devel-linux-x86_64-fedora-clang 0.5.0 175.83 OK
r-devel-linux-x86_64-fedora-gcc 0.5.0 161.31 OK
r-devel-windows-ix86+x86_64 0.5.0 14.00 149.00 163.00 OK
r-patched-linux-x86_64 0.5.0 6.62 135.22 141.84 OK
r-patched-solaris-x86 0.5.0 206.90 ERROR
r-release-linux-x86_64 0.5.0 5.81 133.76 139.57 OK
r-release-macos-x86_64 0.5.0 OK
r-release-windows-ix86+x86_64 0.5.0 14.00 167.00 181.00 OK
r-oldrel-macos-x86_64 0.5.0 OK
r-oldrel-windows-ix86+x86_64 0.5.0 7.00 136.00 143.00 OK

Check Details

Version: 0.5.0
Check: tests
Result: ERROR
     Running ‘testthat.R’ [51s/51s]
    Running the tests in ‘tests/testthat.R’ failed.
    Complete output:
     > library(testthat)
     > library(mlr3viz)
     >
     > test_check("mlr3viz")
     ── ERROR (test_LearnerClassifCVGlmnet.R:9:3): autoplot.LearnerClassifCVGlmnet ──
     Error: 'NA' indices are not (yet?) supported for sparse Matrices
     Backtrace:
     █
     1. └─mlr3::lrn("classif.cv_glmnet")$train(mlr3::tsk("wine")) test_LearnerClassifCVGlmnet.R:9:2
     2. └─mlr3:::.__Learner__train(...)
     3. └─mlr3:::learner_train(self, task, row_ids)
     4. └─mlr3misc::encapsulate(...)
     5. ├─mlr3misc::invoke(...)
     6. │ └─base::eval.parent(expr, n = 1L)
     7. │ └─base::eval(expr, p)
     8. │ └─base::eval(expr, p)
     9. └─mlr3:::.f(learner = <environment>, task = <environment>)
     10. └─get_private(learner)$.train(task)
     11. └─mlr3learners:::.__LearnerClassifCVGlmnet__.train(...)
     12. ├─mlr3misc::invoke(glmnet::cv.glmnet, x = data, y = target, .args = pars)
     13. │ └─base::eval.parent(expr, n = 1L)
     14. │ └─base::eval(expr, p)
     15. │ └─base::eval(expr, p)
     16. └─glmnet::cv.glmnet(x = data, y = target, family = "multinomial")
     17. └─glmnet:::cv.glmnet.raw(...)
     18. ├─glmnet::buildPredmat(...)
     19. └─glmnet:::buildPredmat.multnetlist(...)
     20. └─glmnet:::buildPredmat.array(...)
     21. ├─stats::predict(...)
     22. └─glmnet:::predict.multnet(...)
     23. ├─kbeta[, lamlist$left, drop = FALSE]
     24. └─kbeta[, lamlist$left, drop = FALSE]
     25. └─Matrix:::subCsp_cols(x, j, drop = drop)
     26. └─Matrix:::intI(j, n = x@Dim[2], dn[[2]], give.dn = FALSE)
    
     ── ERROR (test_LearnerRegrCVGlmnet.R:9:3): autoplot.LearnerRegrGlmnet ──────────
     Error: missing value where TRUE/FALSE needed
     Backtrace:
     █
     1. └─mlr3::lrn("regr.cv_glmnet")$train(mlr3::tsk("mtcars")) test_LearnerRegrCVGlmnet.R:9:2
     2. └─mlr3:::.__Learner__train(...)
     3. └─mlr3:::learner_train(self, task, row_ids)
     4. └─mlr3misc::encapsulate(...)
     5. ├─mlr3misc::invoke(...)
     6. │ └─base::eval.parent(expr, n = 1L)
     7. │ └─base::eval(expr, p)
     8. │ └─base::eval(expr, p)
     9. └─mlr3:::.f(learner = <environment>, task = <environment>)
     10. └─get_private(learner)$.train(task)
     11. └─mlr3learners:::.__LearnerRegrCVGlmnet__.train(...)
     12. ├─mlr3misc::invoke(glmnet::cv.glmnet, x = data, y = target, .args = pars)
     13. │ └─base::eval.parent(expr, n = 1L)
     14. │ └─base::eval(expr, p)
     15. │ └─base::eval(expr, p)
     16. └─glmnet::cv.glmnet(x = data, y = target, family = "gaussian")
     17. └─glmnet:::cv.glmnet.raw(...)
     18. └─glmnet::glmnet(...)
     19. └─glmnet:::elnet(...)
    
     ── ERROR (test_LearnerRegrGlmnet.R:9:3): autoplot.LearnerRegrGlmnet ────────────
     Error: missing value where TRUE/FALSE needed
     Backtrace:
     █
     1. └─mlr3::lrn("regr.glmnet")$train(mlr3::tsk("mtcars")) test_LearnerRegrGlmnet.R:9:2
     2. └─mlr3:::.__Learner__train(...)
     3. └─mlr3:::learner_train(self, task, row_ids)
     4. └─mlr3misc::encapsulate(...)
     5. ├─mlr3misc::invoke(...)
     6. │ └─base::eval.parent(expr, n = 1L)
     7. │ └─base::eval(expr, p)
     8. │ └─base::eval(expr, p)
     9. └─mlr3:::.f(learner = <environment>, task = <environment>)
     10. └─get_private(learner)$.train(task)
     11. └─mlr3learners:::.__LearnerRegrGlmnet__.train(...)
     12. ├─mlr3misc::invoke(glmnet::glmnet, x = data, y = target, .args = pars)
     13. │ └─base::eval.parent(expr, n = 1L)
     14. │ └─base::eval(expr, p)
     15. │ └─base::eval(expr, p)
     16. └─glmnet::glmnet(x = data, y = target, family = "gaussian")
     17. └─glmnet:::elnet(...)
    
     ── Warning (test_PredictionClust.R:18:3): autoplot.PredictionClust ─────────────
     `select_()` is deprecated as of dplyr 0.7.0.
     Please use `select()` instead.
     This warning is displayed once every 8 hours.
     Call `lifecycle::last_warnings()` to see where this warning was generated.
    
     ══ testthat results ═══════════════════════════════════════════════════════════
     ERROR (test_LearnerClassifCVGlmnet.R:9:3): autoplot.LearnerClassifCVGlmnet
     ERROR (test_LearnerRegrCVGlmnet.R:9:3): autoplot.LearnerRegrGlmnet
     ERROR (test_LearnerRegrGlmnet.R:9:3): autoplot.LearnerRegrGlmnet
     Warning (test_PredictionClust.R:18:3): autoplot.PredictionClust
    
     [ FAIL 3 | WARN 1 | SKIP 0 | PASS 103 ]
     Error: Test failures
     Execution halted
Flavor: r-patched-solaris-x86