Running 'testthat.R' [156s]
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:  0.7582316    Val loss:  0.7391504 
  epoch:  20    Train loss:  0.7072309    Val loss:  0.837458 
  epoch:  30    Train loss:  0.7379733    Val loss:  0.8203105 
  early stop at epoch:  30    Train loss:  0.7379733    Val loss:  0.8203105 
  epoch:  10    Train loss:  0.7947251    Val loss:  0.8099958 
  epoch:  20    Train loss:  0.7160762    Val loss:  0.768764 
  epoch:  30    Train loss:  0.7567238    Val loss:  0.8039885 
  early stop at epoch:  30    Train loss:  0.7567238    Val loss:  0.8039885 
  epoch:  10    Train loss:  0.7418396    Val loss:  0.5419666 
  epoch:  20    Train loss:  0.8346889    Val loss:  0.7731465 
  epoch:  30    Train loss:  0.7708114    Val loss:  0.8349199 
  epoch:  40    Train loss:  0.7779557    Val loss:  0.5689498 
  early stop at epoch:  48    Train loss:  0.5047165    Val loss:  0.7209942 
  epoch:  10    Train loss:  0.7208713    Val loss:  0.7897916 
  epoch:  20    Train loss:  0.7392786    Val loss:  0.7631863 
  epoch:  30    Train loss:  0.702674    Val loss:  0.611358 
  epoch:  40    Train loss:  0.7129558    Val loss:  0.4897506 
  epoch:  50    Train loss:  0.7146271    Val loss:  0.2373372 
  epoch:  60    Train loss:  0.7795256    Val loss:  0.5987026 
  epoch:  70    Train loss:  0.7179822    Val loss:  0.7206399 
  epoch:  80    Train loss:  0.7707198    Val loss:  0.4507432 
  epoch:  90    Train loss:  0.7182872    Val loss:  0.5520208 
  epoch:  100    Train loss:  0.735483    Val loss:  0.794884 
  time: 43.58 sec elapsed
  epoch:  10    Train loss:  0.737593    Val loss:  0.7964824 
  epoch:  20    Train loss:  0.7628953    Val loss:  0.7709303 
  epoch:  30    Train loss:  0.7256792    Val loss:  0.8058355 
  early stop at epoch:  30    Train loss:  0.7256792    Val loss:  0.8058355 
  epoch:  10    Train loss:  0.7123053    Val loss:  0.7207983 
  epoch:  20    Train loss:  0.7652806    Val loss:  0.7287926 
  epoch:  30    Train loss:  0.6607275    Val loss:  0.7177234 
  early stop at epoch:  34    Train loss:  0.7106651    Val loss:  0.7581643 
  epoch:  10    Train loss:  0.7217296    Val loss:  0.6899732 
  epoch:  20    Train loss:  0.6493021    Val loss:  0.6095485 
  epoch:  30    Train loss:  0.6911808    Val loss:  0.6813338 
  early stop at epoch:  38    Train loss:  0.6191614    Val loss:  0.7227661 
  epoch:  10    Train loss:  0.6781733    Val loss:  0.6063893 
  epoch:  20    Train loss:  0.6293673    Val loss:  0.6771112 
  epoch:  30    Train loss:  0.6510519    Val loss:  0.6451957 
  early stop at epoch:  37    Train loss:  0.6193765    Val loss:  0.7445753 
  time: 26.72 sec elapsed
  epoch:  10    Train loss:  0.3323455    Val loss:  0.2057697 
  epoch:  20    Train loss:  0.3128992    Val loss:  0.2961397 
  epoch:  30    Train loss:  0.3213869    Val loss:  0.3488863 
  early stop at epoch:  31    Train loss:  0.2884755    Val loss:  0.3758954 
  epoch:  10    Train loss:  0.3286971    Val loss:  0.4907381 
  epoch:  20    Train loss:  0.2883044    Val loss:  0.3324824 
  epoch:  30    Train loss:  0.3368618    Val loss:  0.2853904 
  early stop at epoch:  32    Train loss:  0.3554772    Val loss:  0.4938169 
  epoch:  10    Train loss:  0.2695414    Val loss:  0.2147921 
  epoch:  20    Train loss:  0.2195314    Val loss:  0.1746842 
  epoch:  30    Train loss:  0.2348154    Val loss:  0.3264962 
  early stop at epoch:  30    Train loss:  0.2348154    Val loss:  0.3264962 
  epoch:  10    Train loss:  0.2818162    Val loss:  0.3142842 
  epoch:  20    Train loss:  0.2333577    Val loss:  0.3371244 
  epoch:  30    Train loss:  0.2111709    Val loss:  0.1535637 
  early stop at epoch:  33    Train loss:  0.2429444    Val loss:  0.5181284 
  time: 24.42 sec elapsed
  epoch:  10    Train loss:  1.062477    Val loss:  0.4094163 
  epoch:  20    Train loss:  1.062477    Val loss:  0.4131407 
  epoch:  30    Train loss:  1.062477    Val loss:  0.4131407 
  epoch:  40    Train loss:  1.062477    Val loss:  0.4131407 
  epoch:  50    Train loss:  1.062477    Val loss:  0.4131407 
  epoch:  60    Train loss:  1.062477    Val loss:  0.4131407 
  epoch:  70    Train loss:  1.062477    Val loss:  0.4131407 
  epoch:  80    Train loss:  1.062477    Val loss:  0.4131407 
  epoch:  90    Train loss:  1.062477    Val loss:  0.4131407 
  epoch:  100    Train loss:  1.062477    Val loss:  0.4131407 
  epoch:  10    Train loss:  0.4641428    Val loss:  0.6449512 
  epoch:  20    Train loss:  0.4641428    Val loss:  0.6449512 
  epoch:  30    Train loss:  0.4641428    Val loss:  0.6449512 
  early stop at epoch:  32    Train loss:  0.4641428    Val loss:  0.6756557 
  epoch:  10    Train loss:  0.3412416    Val loss:  0.3526876 
  epoch:  20    Train loss:  0.3412416    Val loss:  0.3527545 
  epoch:  30    Train loss:  0.375169    Val loss:  0.3526876 
  early stop at epoch:  30    Train loss:  0.375169    Val loss:  0.3526876 
  time: 14.17 sec elapsed
  epoch:  10    Train loss:  0.5028319    Val loss:  0.5703739 
  epoch:  20    Train loss:  0.5028319    Val loss:  0.5852911 
  epoch:  30    Train loss:  0.5028319    Val loss:  0.5871185 
  early stop at epoch:  30    Train loss:  0.5028319    Val loss:  0.5871185 
  epoch:  10    Train loss:  0.712052    Val loss:  0.7242787 
  epoch:  20    Train loss:  0.712052    Val loss:  0.6541753 
  epoch:  30    Train loss:  0.712052    Val loss:  0.7090073 
  epoch:  40    Train loss:  0.712052    Val loss:  0.6541753 
  epoch:  50    Train loss:  0.712052    Val loss:  0.6840382 
  epoch:  60    Train loss:  0.712052    Val loss:  0.6578554 
  epoch:  70    Train loss:  0.7296538    Val loss:  0.6514337 
  epoch:  80    Train loss:  0.712052    Val loss:  0.7169247 
  epoch:  90    Train loss:  0.712052    Val loss:  0.65622 
  early stop at epoch:  95    Train loss:  0.7360142    Val loss:  0.671432 
  epoch:  10    Train loss:  0.6344905    Val loss:  0.6964411 
  epoch:  20    Train loss:  0.6629397    Val loss:  0.6997733 
  epoch:  30    Train loss:  0.6344905    Val loss:  0.7371905 
  early stop at epoch:  30    Train loss:  0.6344905    Val loss:  0.7371905 
  time: 15.05 sec elapsed
  epoch:  10    Train loss:  0.6652319    Val loss:  0.638321 
  epoch:  20    Train loss:  0.6470566    Val loss:  0.5407597 
  epoch:  30    Train loss:  0.6489133    Val loss:  0.5958034 
  epoch:  40    Train loss:  0.6663947    Val loss:  0.5789232 
  epoch:  50    Train loss:  0.6749999    Val loss:  0.5949006 
  epoch:  60    Train loss:  0.6133571    Val loss:  0.6109828 
  early stop at epoch:  62    Train loss:  0.7054131    Val loss:  0.6310146 
  epoch:  10    Train loss:  0.4299256    Val loss:  0.566372 
  epoch:  20    Train loss:  0.4163472    Val loss:  0.5448638 
  epoch:  30    Train loss:  0.4592139    Val loss:  0.2928243 
  epoch:  40    Train loss:  0.3731425    Val loss:  0.5574184 
  early stop at epoch:  40    Train loss:  0.3731425    Val loss:  0.5574184 
  epoch:  10    Train loss:  0.6346897    Val loss:  0.4070109 
  epoch:  20    Train loss:  0.6061462    Val loss:  0.5735349 
  epoch:  30    Train loss:  0.5904976    Val loss:  0.4818239 
  early stop at epoch:  31    Train loss:  0.5914701    Val loss:  0.7047771 
  time: 25.3 sec elapsed
  random search: 54.52 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