Running ‘testthat.R’ [69s/99s]
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
  WARN  [02:31:03.991] [mlr3] train: one multinomial or binomial class has fewer than 8  observations; dangerous ground
  WARN  [02:31:04.755] [mlr3] train: one multinomial or binomial class has fewer than 8  observations; dangerous ground
  WARN  [02:31:06.427] [mlr3] train: one multinomial or binomial class has fewer than 8  observations; dangerous ground
  WARN  [02:31:07.017] [mlr3] train: one multinomial or binomial class has fewer than 8  observations; dangerous ground
  WARN  [02:31:07.019] [mlr3] train: one multinomial or binomial class has fewer than 8  observations; dangerous ground
  WARN  [02:31:07.025] [mlr3] train: one multinomial or binomial class has fewer than 8  observations; dangerous ground
  WARN  [02:31:07.491] [mlr3] train: one multinomial or binomial class has fewer than 8  observations; dangerous ground
  WARN  [02:31:07.493] [mlr3] train: one multinomial or binomial class has fewer than 8  observations; dangerous ground
  WARN  [02:31:38.060] [mlr3] train: glm.fit: algorithm did not converge
  WARN  [02:31:38.066] [mlr3] train: glm.fit: fitted probabilities numerically 0 or 1 occurred
  WARN  [02:31:38.182] [mlr3] train: glm.fit: algorithm did not converge
  WARN  [02:31:38.184] [mlr3] train: glm.fit: fitted probabilities numerically 0 or 1 occurred
  WARN  [02:31:38.266] [mlr3] train: glm.fit: algorithm did not converge
  WARN  [02:31:38.268] [mlr3] train: glm.fit: fitted probabilities numerically 0 or 1 occurred
  WARN  [02:31:38.520] [mlr3] train: glm.fit: algorithm did not converge
  WARN  [02:31:38.526] [mlr3] train: glm.fit: fitted probabilities numerically 0 or 1 occurred
  WARN  [02:31:38.757] [mlr3] train: glm.fit: algorithm did not converge
  WARN  [02:31:38.759] [mlr3] train: glm.fit: fitted probabilities numerically 0 or 1 occurred
  WARN  [02:31:38.840] [mlr3] train: glm.fit: algorithm did not converge
  WARN  [02:31:38.842] [mlr3] train: glm.fit: fitted probabilities numerically 0 or 1 occurred
  # weights:  18 (10 variable)
  initial  value 164.791843 
  iter  10 value 16.177348
  iter  20 value 7.111438
  iter  30 value 6.182999
  iter  40 value 5.984028
  iter  50 value 5.961278
  iter  60 value 5.954900
  iter  70 value 5.951851
  iter  80 value 5.950343
  iter  90 value 5.949904
  iter 100 value 5.949867
  final  value 5.949867 
  stopped after 100 iterations
  [ FAIL 1 | WARN 0 | SKIP 3 | PASS 549 ]
  
  ══ Skipped tests (3) ═══════════════════════════════════════════════════════════
  • On CRAN (3): 'test_classif_nnet.R:2:1', 'test_classif_xgboost.R:2:1',
    'test_regr_xgboost.R:2:1'
  
  ══ Failed tests ════════════════════════════════════════════════════════════════
  ── Failure ('test_regr_nnet.R:7:3'): autotest ──────────────────────────────────
  `result` is not TRUE
  
  `actual` is a list
  `expected` is a logical vector (TRUE)
  [score()] learner 'regr.nnet:response' on task 'sanity_reordered' failed: sanity check failed
  
  [ FAIL 1 | WARN 0 | SKIP 3 | PASS 549 ]
  Error: Test failures
  Execution halted