Running 'testthat.R' [264s]
Running the tests in 'tests/testthat.R' failed.
Complete output:
  > # This file is part of the standard setup for testthat.
  > # It is recommended that you do not modify it.
  > #
  > # Where should you do additional test configuration?
  > # Learn more about the roles of various files in:
  > # * https://r-pkgs.org/tests.html
  > # * https://testthat.r-lib.org/reference/test_package.html#special-files
  > 
  > library(testthat)
  > library(spinner)
  > 
  > test_check("spinner")
  OMP: Warning #96: Cannot form a team with 28 threads, using 2 instead.
  OMP: Hint Consider unsetting KMP_DEVICE_THREAD_LIMIT (KMP_ALL_THREADS), KMP_TEAMS_THREAD_LIMIT, and OMP_THREAD_LIMIT (if any are set).
  epoch:  10    Train loss:  0.7880893    Val loss:  0.7255653 
  epoch:  20    Train loss:  0.8137968    Val loss:  0.7753562 
  epoch:  30    Train loss:  0.8466399    Val loss:  0.8031964 
  early stop at epoch:  30    Train loss:  0.8466399    Val loss:  0.8031964 
  epoch:  10    Train loss:  0.8181331    Val loss:  0.7062073 
  epoch:  20    Train loss:  0.7099457    Val loss:  0.6848931 
  epoch:  30    Train loss:  0.8088268    Val loss:  0.6160501 
  epoch:  40    Train loss:  0.7901298    Val loss:  0.7961853 
  early stop at epoch:  44    Train loss:  0.746365    Val loss:  0.8309304 
  epoch:  10    Train loss:  0.6312017    Val loss:  0.8636278 
  epoch:  20    Train loss:  0.6782193    Val loss:  0.4614531 
  epoch:  30    Train loss:  0.6822445    Val loss:  0.7606828 
  epoch:  40    Train loss:  0.6555291    Val loss:  0.6954718 
  early stop at epoch:  46    Train loss:  0.6502698    Val loss:  0.9003091 
  epoch:  10    Train loss:  0.6378485    Val loss:  0.5317824 
  epoch:  20    Train loss:  0.6150324    Val loss:  0.3747623 
  epoch:  30    Train loss:  0.6623861    Val loss:  0.5424011 
  epoch:  40    Train loss:  0.6301767    Val loss:  0.4165615 
  epoch:  50    Train loss:  0.6125575    Val loss:  0.4503262 
  epoch:  60    Train loss:  0.6257965    Val loss:  0.4263246 
  early stop at epoch:  65    Train loss:  0.745899    Val loss:  0.53618 
  time: 64.89 sec elapsed
  epoch:  10    Train loss:  0.7739093    Val loss:  0.770283 
  epoch:  20    Train loss:  0.7055809    Val loss:  0.7759296 
  epoch:  30    Train loss:  0.7352791    Val loss:  0.7941313 
  early stop at epoch:  30    Train loss:  0.7352791    Val loss:  0.7941313 
  epoch:  10    Train loss:  0.7002555    Val loss:  0.694304 
  epoch:  20    Train loss:  0.706356    Val loss:  0.7246676 
  epoch:  30    Train loss:  0.7121664    Val loss:  0.7258093 
  early stop at epoch:  35    Train loss:  0.6510051    Val loss:  0.7416199 
  epoch:  10    Train loss:  0.7479615    Val loss:  0.7007744 
  epoch:  20    Train loss:  0.6650078    Val loss:  0.6358659 
  epoch:  30    Train loss:  0.6284428    Val loss:  0.6328874 
  early stop at epoch:  39    Train loss:  0.5988222    Val loss:  0.7966897 
  epoch:  10    Train loss:  0.8012241    Val loss:  0.7569225 
  epoch:  20    Train loss:  0.7572383    Val loss:  0.8048664 
  epoch:  30    Train loss:  0.7556403    Val loss:  0.7126857 
  early stop at epoch:  31    Train loss:  0.7280132    Val loss:  0.7868891 
  time: 45.17 sec elapsed
  epoch:  10    Train loss:  0.3415718    Val loss:  0.2547229 
  epoch:  20    Train loss:  0.3281042    Val loss:  0.2208681 
  epoch:  30    Train loss:  0.3274095    Val loss:  0.2383749 
  early stop at epoch:  33    Train loss:  0.2672565    Val loss:  0.3439875 
  epoch:  10    Train loss:  0.3655412    Val loss:  0.2983556 
  epoch:  20    Train loss:  0.3302536    Val loss:  0.3878253 
  epoch:  30    Train loss:  0.3235301    Val loss:  0.4651515 
  early stop at epoch:  30    Train loss:  0.3235301    Val loss:  0.4651515 
  epoch:  10    Train loss:  0.2556229    Val loss:  0.3100896 
  epoch:  20    Train loss:  0.2315873    Val loss:  0.3082713 
  epoch:  30    Train loss:  0.2328274    Val loss:  0.2574663 
  early stop at epoch:  34    Train loss:  0.2209101    Val loss:  0.306387 
  epoch:  10    Train loss:  0.3312292    Val loss:  0.4367475 
  epoch:  20    Train loss:  0.3224618    Val loss:  0.2174875 
  epoch:  30    Train loss:  0.2501336    Val loss:  0.2137407 
  early stop at epoch:  32    Train loss:  0.2638713    Val loss:  0.4487271 
  time: 42.06 sec elapsed
  epoch:  10    Train loss:  0.4114213    Val loss:  0.525591 
  epoch:  20    Train loss:  0.4114213    Val loss:  0.5224094 
  epoch:  30    Train loss:  0.4114213    Val loss:  0.5224094 
  epoch:  40    Train loss:  0.4114213    Val loss:  0.5224094 
  early stop at epoch:  49    Train loss:  0.4114213    Val loss:  0.54846 
  epoch:  10    Train loss:  0.7400234    Val loss:  0.6073773 
  epoch:  20    Train loss:  0.7400234    Val loss:  0.6315222 
  epoch:  30    Train loss:  0.7400234    Val loss:  0.6073773 
  epoch:  40    Train loss:  0.7400234    Val loss:  0.6073773 
  epoch:  50    Train loss:  0.7400234    Val loss:  0.619832 
  epoch:  60    Train loss:  0.7400234    Val loss:  0.6073773 
  epoch:  70    Train loss:  0.7400234    Val loss:  0.6351814 
  epoch:  80    Train loss:  0.7400234    Val loss:  0.6105238 
  epoch:  90    Train loss:  0.7436123    Val loss:  0.6320261 
  epoch:  100    Train loss:  0.7604493    Val loss:  0.6365073 
  epoch:  10    Train loss:  0.6387671    Val loss:  0.6326262 
  epoch:  20    Train loss:  0.6387671    Val loss:  0.6326262 
  epoch:  30    Train loss:  0.6387671    Val loss:  0.6326262 
  early stop at epoch:  36    Train loss:  0.6387671    Val loss:  0.6757869 
  time: 24.17 sec elapsed
  epoch:  10    Train loss:  0.6167455    Val loss:  0.5800428 
  epoch:  20    Train loss:  0.5978186    Val loss:  0.6064745 
  epoch:  30    Train loss:  0.5978186    Val loss:  0.5752624 
  early stop at epoch:  33    Train loss:  0.5784105    Val loss:  0.6041983 
  epoch:  10    Train loss:  0.7262605    Val loss:  0.6754378 
  epoch:  20    Train loss:  0.7212899    Val loss:  0.6759892 
  epoch:  30    Train loss:  0.7568199    Val loss:  0.6754378 
  epoch:  40    Train loss:  0.7212899    Val loss:  0.6759892 
  epoch:  50    Train loss:  0.7212899    Val loss:  0.6759892 
  epoch:  60    Train loss:  0.7212899    Val loss:  0.6759892 
  epoch:  70    Train loss:  0.7212899    Val loss:  0.6759892 
  epoch:  80    Train loss:  0.7212899    Val loss:  0.7039152 
  epoch:  90    Train loss:  0.7212899    Val loss:  0.6635614 
  epoch:  100    Train loss:  0.7212899    Val loss:  0.6754378 
  epoch:  10    Train loss:  0.5362894    Val loss:  0.4748831 
  epoch:  20    Train loss:  0.5311361    Val loss:  0.4533411 
  epoch:  30    Train loss:  0.4691363    Val loss:  0.4594963 
  early stop at epoch:  34    Train loss:  0.4733386    Val loss:  0.6216606 
  time: 25.61 sec elapsed
  epoch:  10    Train loss:  0.7356187    Val loss:  0.6312646 
  epoch:  20    Train loss:  0.7813594    Val loss:  0.6261184 
  epoch:  30    Train loss:  0.748969    Val loss:  0.6655114 
  early stop at epoch:  32    Train loss:  0.7748536    Val loss:  0.6572163 
  epoch:  10    Train loss:  0.5083613    Val loss:  0.5274738 
  epoch:  20    Train loss:  0.5270655    Val loss:  0.3788466 
  epoch:  30    Train loss:  0.4570093    Val loss:  0.5561817 
  early stop at epoch:  30    Train loss:  0.4570093    Val loss:  0.5561817 
  epoch:  10    Train loss:  0.6286951    Val loss:  0.6322638 
  epoch:  20    Train loss:  0.6143529    Val loss:  0.5438521 
  epoch:  30    Train loss:  0.6301204    Val loss:  0.5574672 
  epoch:  40    Train loss:  0.6114512    Val loss:  0.5973297 
  epoch:  50    Train loss:  0.6404253    Val loss:  0.5391501 
  epoch:  60    Train loss:  0.6294305    Val loss:  0.5511015 
  epoch:  70    Train loss:  0.6345275    Val loss:  0.583086 
  epoch:  80    Train loss:  0.6088005    Val loss:  0.5950584 
  epoch:  90    Train loss:  0.6323931    Val loss:  0.5589393 
  early stop at epoch:  96    Train loss:  0.6434103    Val loss:  0.5927544 
  time: 52.6 sec elapsed
  random search: 102.4 sec elapsed
  [ FAIL 1 | WARN 66 | SKIP 0 | PASS 46 ]
  
  ══ Failed tests ════════════════════════════════════════════════════════════════
  ── Error ('test.R:89:13'): Correct outcome format and size for base outcome3 ───
  <purrr_error_indexed/rlang_error/error/condition>
  Error in `purrr::pmap(hyper_params, ~spinner(graph, target, node_labels, 
      edge_labels, context_labels, direction = ..1, sampling = NA, 
      threshold = 0.01, method = ..2, node_embedding_size = ..13, 
      edge_embedding_size = ..14, context_embedding_size = ..15, 
      update_order = ..3, n_layers = ..4, skip_shortcut = ..5, 
      forward_layer = ..6, forward_activation = ..7, forward_drop = ..8, 
      mode = ..9, optimization = ..10, epochs, lr = ..11, patience, 
      weight_decay = ..12, reps, folds, holdout, verbose, seed))`: i In index: 1.
  Caused by error in `pmap()`:
  i In index: 1.
  Caused by error in `training_function()`:
  ! not enough data for training
  
  [ FAIL 1 | WARN 66 | SKIP 0 | PASS 46 ]
  Error: Test failures
  Execution halted