Running 'testthat.R' [167s]
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 48 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:  0.8061404    Val loss:  0.5482487 
  epoch:  20    Train loss:  0.7380227    Val loss:  0.6465623 
  epoch:  30    Train loss:  0.7724982    Val loss:  0.788485 
  epoch:  40    Train loss:  0.6623524    Val loss:  0.6620206 
  epoch:  50    Train loss:  0.7462353    Val loss:  0.6011 
  early stop at epoch:  51    Train loss:  0.835241    Val loss:  0.6738663 
  epoch:  10    Train loss:  0.6806947    Val loss:  0.6325717 
  epoch:  20    Train loss:  0.7542855    Val loss:  0.602317 
  epoch:  30    Train loss:  0.7352697    Val loss:  0.5348321 
  epoch:  40    Train loss:  0.7501861    Val loss:  0.5957316 
  epoch:  50    Train loss:  0.6314989    Val loss:  0.5180935 
  epoch:  60    Train loss:  0.7282295    Val loss:  0.5609654 
  epoch:  70    Train loss:  0.7171292    Val loss:  0.5193985 
  early stop at epoch:  75    Train loss:  0.7400748    Val loss:  0.5977196 
  epoch:  10    Train loss:  0.5092819    Val loss:  0.7355657 
  epoch:  20    Train loss:  0.6814578    Val loss:  0.6071072 
  epoch:  30    Train loss:  0.6571016    Val loss:  0.6171868 
  early stop at epoch:  35    Train loss:  0.6551835    Val loss:  0.8852804 
  epoch:  10    Train loss:  0.7168008    Val loss:  0.8740277 
  epoch:  20    Train loss:  0.6815715    Val loss:  0.7993171 
  epoch:  30    Train loss:  0.5723138    Val loss:  0.8041168 
  early stop at epoch:  32    Train loss:  0.6762758    Val loss:  0.8796535 
  time: 38.83 sec elapsed
  epoch:  10    Train loss:  0.7695161    Val loss:  0.7693247 
  epoch:  20    Train loss:  0.7249582    Val loss:  0.7717 
  epoch:  30    Train loss:  0.7607822    Val loss:  0.7262363 
  early stop at epoch:  35    Train loss:  0.7250659    Val loss:  0.7797406 
  epoch:  10    Train loss:  0.7417306    Val loss:  0.6602627 
  epoch:  20    Train loss:  0.7570003    Val loss:  0.7115033 
  epoch:  30    Train loss:  0.7384138    Val loss:  0.7118347 
  early stop at epoch:  35    Train loss:  0.7163192    Val loss:  0.7481382 
  epoch:  10    Train loss:  0.7308993    Val loss:  0.7019094 
  epoch:  20    Train loss:  0.7430325    Val loss:  0.5989621 
  epoch:  30    Train loss:  0.7061505    Val loss:  0.636339 
  early stop at epoch:  36    Train loss:  0.6219019    Val loss:  0.7425168 
  epoch:  10    Train loss:  0.7531924    Val loss:  0.7621492 
  epoch:  20    Train loss:  0.7205262    Val loss:  0.6925997 
  epoch:  30    Train loss:  0.6789572    Val loss:  0.7350411 
  epoch:  40    Train loss:  0.6894916    Val loss:  0.712056 
  epoch:  50    Train loss:  0.6754813    Val loss:  0.5334786 
  epoch:  60    Train loss:  0.6726221    Val loss:  0.7878741 
  early stop at epoch:  60    Train loss:  0.6726221    Val loss:  0.7878741 
  time: 33.97 sec elapsed
  epoch:  10    Train loss:  0.3133188    Val loss:  0.2302221 
  epoch:  20    Train loss:  0.3415328    Val loss:  0.2720648 
  epoch:  30    Train loss:  0.3720775    Val loss:  0.3055868 
  early stop at epoch:  31    Train loss:  0.2878031    Val loss:  0.369683 
  epoch:  10    Train loss:  0.3827278    Val loss:  0.4059166 
  epoch:  20    Train loss:  0.3040415    Val loss:  0.2691119 
  epoch:  30    Train loss:  0.3338324    Val loss:  0.227812 
  epoch:  40    Train loss:  0.28597    Val loss:  0.3459767 
  early stop at epoch:  48    Train loss:  0.2705259    Val loss:  0.3718761 
  epoch:  10    Train loss:  0.3141214    Val loss:  0.201799 
  epoch:  20    Train loss:  0.2393231    Val loss:  0.1009228 
  epoch:  30    Train loss:  0.2281825    Val loss:  0.224231 
  early stop at epoch:  39    Train loss:  0.245666    Val loss:  0.3921757 
  epoch:  10    Train loss:  0.3467214    Val loss:  0.4189227 
  epoch:  20    Train loss:  0.3316925    Val loss:  0.3388704 
  epoch:  30    Train loss:  0.3088655    Val loss:  0.1898563 
  epoch:  40    Train loss:  0.3151669    Val loss:  0.1364199 
  early stop at epoch:  46    Train loss:  0.3444043    Val loss:  0.5244277 
  time: 32.61 sec elapsed
  epoch:  10    Train loss:  0.4536715    Val loss:  0.5207518 
  epoch:  20    Train loss:  0.4536715    Val loss:  0.518501 
  epoch:  30    Train loss:  0.4536715    Val loss:  0.518501 
  epoch:  40    Train loss:  0.4536715    Val loss:  0.518501 
  early stop at epoch:  42    Train loss:  0.4536715    Val loss:  0.5217085 
  epoch:  10    Train loss:  0.6923867    Val loss:  0.6368757 
  epoch:  20    Train loss:  0.6923867    Val loss:  0.6368757 
  epoch:  30    Train loss:  0.6923867    Val loss:  0.6368757 
  epoch:  40    Train loss:  0.6923867    Val loss:  0.6368757 
  epoch:  50    Train loss:  0.6923867    Val loss:  0.6368757 
  epoch:  60    Train loss:  0.6923867    Val loss:  0.6395952 
  epoch:  70    Train loss:  0.6923867    Val loss:  0.6809872 
  epoch:  80    Train loss:  0.6923867    Val loss:  0.6368757 
  epoch:  90    Train loss:  0.6923867    Val loss:  0.661891 
  epoch:  100    Train loss:  0.6923867    Val loss:  0.6368757 
  epoch:  10    Train loss:  0.4730558    Val loss:  0.4758229 
  epoch:  20    Train loss:  0.5874595    Val loss:  0.4761094 
  epoch:  30    Train loss:  0.4675779    Val loss:  0.4761094 
  early stop at epoch:  38    Train loss:  0.4706497    Val loss:  0.5288506 
  time: 15.33 sec elapsed
  epoch:  10    Train loss:  0.2494065    Val loss:  0.5425652 
  epoch:  20    Train loss:  0.2494065    Val loss:  0.5568731 
  epoch:  30    Train loss:  0.2494065    Val loss:  0.5302667 
  early stop at epoch:  37    Train loss:  0.2494065    Val loss:  0.5602714 
  epoch:  10    Train loss:  0.7066783    Val loss:  0.5368235 
  epoch:  20    Train loss:  0.7066783    Val loss:  0.5380082 
  epoch:  30    Train loss:  0.7066783    Val loss:  0.5368235 
  epoch:  40    Train loss:  0.7252192    Val loss:  0.5368235 
  epoch:  50    Train loss:  0.7066783    Val loss:  0.5368235 
  epoch:  60    Train loss:  0.7066783    Val loss:  0.575885 
  epoch:  70    Train loss:  0.7210456    Val loss:  0.5360056 
  epoch:  80    Train loss:  0.7355306    Val loss:  0.5771982 
  epoch:  90    Train loss:  0.7066783    Val loss:  0.6939172 
  epoch:  100    Train loss:  0.7355306    Val loss:  0.5560763 
  epoch:  10    Train loss:  0.2198303    Val loss:  0.2083823 
  epoch:  20    Train loss:  0.2198303    Val loss:  0.6496652 
  epoch:  30    Train loss:  0.2198303    Val loss:  0.1604959 
  early stop at epoch:  33    Train loss:  0.2453717    Val loss:  0.6488711 
  time: 16.19 sec elapsed
  epoch:  10    Train loss:  0.487081    Val loss:  0.5569782 
  epoch:  20    Train loss:  0.5526847    Val loss:  0.5392978 
  epoch:  30    Train loss:  0.5995318    Val loss:  0.5479288 
  epoch:  40    Train loss:  0.6042935    Val loss:  0.5140972 
  early stop at epoch:  41    Train loss:  0.473222    Val loss:  0.6157176 
  epoch:  10    Train loss:  0.5139912    Val loss:  0.2434046 
  epoch:  20    Train loss:  0.4673555    Val loss:  0.3429999 
  epoch:  30    Train loss:  0.5324169    Val loss:  0.5671535 
  epoch:  40    Train loss:  0.4656374    Val loss:  0.2181005 
  early stop at epoch:  45    Train loss:  0.1817597    Val loss:  0.5981514 
  epoch:  10    Train loss:  0.6495675    Val loss:  0.7903188 
  epoch:  20    Train loss:  0.7311969    Val loss:  0.7372216 
  epoch:  30    Train loss:  0.5976614    Val loss:  0.7434065 
  early stop at epoch:  32    Train loss:  0.6955    Val loss:  0.7626142 
  time: 22.9 sec elapsed
  random search: 54.42 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