Running ‘testthat.R’ [115s/101s]
Running the tests in ‘tests/testthat.R’ failed.
Complete output:
  > if (requireNamespace("testthat", quietly = TRUE)) {
  +   library(testthat)
  +   library(mlr3learners)
  +   test_check("mlr3learners")
  + }
  Loading required package: mlr3
  [09:13:30] WARNING: amalgamation/../src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
  [09:13:31] WARNING: amalgamation/../src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
  ══ Skipped tests ═══════════════════════════════════════════════════════════════
  • On CRAN (2)
  • On Solaris (6)
  
  ══ Failed tests ════════════════════════════════════════════════════════════════
  ── Error (test_regr_cv_glmnet.R:15:3): selected_features ───────────────────────
  Error: 'NA' indices are not (yet?) supported for sparse Matrices
  Backtrace:
       █
    1. └─learner$train(task) test_regr_cv_glmnet.R:15:2
    2.   └─mlr3:::.__Learner__train(...)
    3.     └─mlr3:::learner_train(self, task, row_ids)
    4.       └─mlr3misc::encapsulate(...)
    5.         ├─mlr3misc::invoke(...)
    6.         │ └─base::eval.parent(expr, n = 1L)
    7.         │   └─base::eval(expr, p)
    8.         │     └─base::eval(expr, p)
    9.         └─mlr3:::.f(learner = <environment>, task = <environment>)
   10.           └─get_private(learner)$.train(task)
   11.             └─mlr3learners:::.__LearnerRegrCVGlmnet__.train(...)
   12.               └─mlr3learners:::glmnet_invoke(data, target, pv, cv = TRUE)
   13.                 ├─mlr3misc::invoke(...)
   14.                 │ └─base::eval.parent(expr, n = 1L)
   15.                 │   └─base::eval(expr, p)
   16.                 │     └─base::eval(expr, p)
   17.                 └─(if (cv) glmnet::cv.glmnet else glmnet::glmnet)(...)
   18.                   └─glmnet:::cv.glmnet.raw(...)
   19.                     ├─glmnet::buildPredmat(...)
   20.                     └─glmnet:::buildPredmat.default(...)
   21.                       ├─stats::predict(...)
   22.                       ├─glmnet:::predict.elnet(...)
   23.                       ├─base::NextMethod("predict")
   24.                       └─glmnet::predict.glmnet(...)
   25.                         ├─nbeta[, lamlist$left, drop = FALSE]
   26.                         └─nbeta[, lamlist$left, drop = FALSE]
   27.                           └─Matrix:::subCsp_cols(x, j, drop = drop)
   28.                             └─Matrix:::intI(j, n = x@Dim[2], dn[[2]], give.dn = FALSE)
  ── Error (test_regr_glmnet.R:17:3): selected_features ──────────────────────────
  Error: need at least two non-NA values to interpolate
  Backtrace:
       █
    1. ├─testthat::expect_equal(learner$selected_features(0), task$feature_names) test_regr_glmnet.R:17:2
    2. │ └─testthat::quasi_label(enquo(object), label, arg = "object")
    3. │   └─rlang::eval_bare(expr, quo_get_env(quo))
    4. └─learner$selected_features(0)
    5.   └─mlr3learners:::.__LearnerRegrGlmnet__selected_features(...)
    6.     └─mlr3learners:::glmnet_selected_features(self, lambda)
    7.       ├─stats::predict(self$model, type = "nonzero", s = lambda)
    8.       ├─glmnet:::predict.elnet(self$model, type = "nonzero", s = lambda)
    9.       ├─base::NextMethod("predict")
   10.       └─glmnet::predict.glmnet(self$model, type = "nonzero", s = lambda)
   11.         └─glmnet:::lambda.interp(lambda, s)
   12.           └─stats::approx(lambda, seq(lambda), sfrac)
  ── Failure (test_surv_cv_glmnet.R:17:3): selected_features ─────────────────────
  learner$selected_features(0) (`actual`) not equal to task$feature_names (`expected`).
  
   actual | expected        
          - "age"        [1]
          - "estrg_recp" [2]
          - "grade"      [3]
          - "hormone"    [4]
          - "menopause"  [5]
          - "nodes"      [6]
          - "prog_recp"  [7]
          - "size"       [8]
  ── Error (test_surv_glmnet.R:15:3): selected_features ──────────────────────────
  Error: from glmnet Fortran code (error code 10000); All penalty factors are <= 0
  Backtrace:
       █
    1. └─learner$train(task) test_surv_glmnet.R:15:2
    2.   └─mlr3:::.__Learner__train(...)
    3.     └─mlr3:::learner_train(self, task, row_ids)
    4.       └─mlr3misc::encapsulate(...)
    5.         ├─mlr3misc::invoke(...)
    6.         │ └─base::eval.parent(expr, n = 1L)
    7.         │   └─base::eval(expr, p)
    8.         │     └─base::eval(expr, p)
    9.         └─mlr3:::.f(learner = <environment>, task = <environment>)
   10.           └─get_private(learner)$.train(task)
   11.             └─mlr3learners:::.__LearnerSurvCVGlmnet__.train(...)
   12.               └─mlr3learners:::glmnet_invoke(data, target, pv, cv = TRUE)
   13.                 ├─mlr3misc::invoke(...)
   14.                 │ └─base::eval.parent(expr, n = 1L)
   15.                 │   └─base::eval(expr, p)
   16.                 │     └─base::eval(expr, p)
   17.                 └─(if (cv) glmnet::cv.glmnet else glmnet::glmnet)(...)
   18.                   └─glmnet:::cv.glmnet.raw(...)
   19.                     └─glmnet::glmnet(...)
   20.                       └─glmnet:::coxnet(...)
  
  [ FAIL 4 | WARN 2 | SKIP 8 | PASS 393 ]
  Error: Test failures
  Execution halted