Running 'testthat.R' [175s]
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:  0.7486569    Val loss:  0.7800032 
  epoch:  20    Train loss:  0.7147133    Val loss:  0.7812155 
  epoch:  30    Train loss:  0.5311723    Val loss:  0.8140318 
  early stop at epoch:  30    Train loss:  0.5311723    Val loss:  0.8140318 
  epoch:  10    Train loss:  0.8149799    Val loss:  0.6569888 
  epoch:  20    Train loss:  0.7742518    Val loss:  0.679642 
  epoch:  30    Train loss:  0.7390918    Val loss:  0.7518083 
  epoch:  40    Train loss:  0.7262532    Val loss:  0.7133583 
  epoch:  50    Train loss:  0.6861349    Val loss:  0.7356399 
  epoch:  60    Train loss:  0.741177    Val loss:  0.6589386 
  early stop at epoch:  62    Train loss:  0.8439299    Val loss:  0.7158753 
  epoch:  10    Train loss:  0.714011    Val loss:  0.6018636 
  epoch:  20    Train loss:  0.6375813    Val loss:  0.6132447 
  epoch:  30    Train loss:  0.6658431    Val loss:  0.6168148 
  epoch:  40    Train loss:  0.741826    Val loss:  0.7606002 
  early stop at epoch:  48    Train loss:  0.8146416    Val loss:  0.6643777 
  epoch:  10    Train loss:  0.6187043    Val loss:  0.6570629 
  epoch:  20    Train loss:  0.6403114    Val loss:  0.528401 
  epoch:  30    Train loss:  0.663863    Val loss:  0.7588031 
  early stop at epoch:  38    Train loss:  0.6762978    Val loss:  0.5857113 
  time: 37.01 sec elapsed
  epoch:  10    Train loss:  0.7436095    Val loss:  0.6927934 
  epoch:  20    Train loss:  0.800496    Val loss:  0.7240676 
  epoch:  30    Train loss:  0.7728008    Val loss:  0.7102702 
  epoch:  40    Train loss:  0.7943477    Val loss:  0.647045 
  early stop at epoch:  48    Train loss:  0.8220833    Val loss:  0.7192143 
  epoch:  10    Train loss:  0.7441427    Val loss:  0.7084176 
  epoch:  20    Train loss:  0.7173657    Val loss:  0.7212861 
  epoch:  30    Train loss:  0.7472219    Val loss:  0.6843075 
  epoch:  40    Train loss:  0.7721328    Val loss:  0.7066767 
  epoch:  50    Train loss:  0.7647601    Val loss:  0.7069074 
  epoch:  60    Train loss:  0.7533206    Val loss:  0.727624 
  epoch:  70    Train loss:  0.7161092    Val loss:  0.6867431 
  epoch:  80    Train loss:  0.7361518    Val loss:  0.7121109 
  early stop at epoch:  81    Train loss:  0.7610539    Val loss:  0.7137319 
  epoch:  10    Train loss:  0.5843707    Val loss:  0.7889569 
  epoch:  20    Train loss:  0.7383975    Val loss:  0.705764 
  epoch:  30    Train loss:  0.6087832    Val loss:  0.818958 
  early stop at epoch:  30    Train loss:  0.6087832    Val loss:  0.818958 
  epoch:  10    Train loss:  0.7361695    Val loss:  0.5001021 
  epoch:  20    Train loss:  0.6927315    Val loss:  0.4913872 
  epoch:  30    Train loss:  0.7349723    Val loss:  0.5151635 
  epoch:  40    Train loss:  0.72136    Val loss:  0.4980692 
  epoch:  50    Train loss:  0.7344683    Val loss:  0.5309991 
  epoch:  60    Train loss:  0.7187083    Val loss:  0.5345974 
  epoch:  70    Train loss:  0.7534055    Val loss:  0.6033567 
  epoch:  80    Train loss:  0.7158951    Val loss:  0.4875903 
  epoch:  90    Train loss:  0.7363089    Val loss:  0.4868031 
  epoch:  100    Train loss:  0.7365759    Val loss:  0.563059 
  time: 52.46 sec elapsed
  epoch:  10    Train loss:  0.3203349    Val loss:  0.2916249 
  epoch:  20    Train loss:  0.2584534    Val loss:  0.2832893 
  epoch:  30    Train loss:  0.3461084    Val loss:  0.4698916 
  early stop at epoch:  30    Train loss:  0.3461084    Val loss:  0.4698916 
  epoch:  10    Train loss:  0.2917067    Val loss:  0.340226 
  epoch:  20    Train loss:  0.2737002    Val loss:  0.4729629 
  epoch:  30    Train loss:  0.3829814    Val loss:  0.2404467 
  early stop at epoch:  39    Train loss:  0.282572    Val loss:  0.3722654 
  epoch:  10    Train loss:  0.2662156    Val loss:  0.4312515 
  epoch:  20    Train loss:  0.2852738    Val loss:  0.3266925 
  epoch:  30    Train loss:  0.2425076    Val loss:  0.1510627 
  early stop at epoch:  31    Train loss:  0.2641311    Val loss:  0.4720969 
  epoch:  10    Train loss:  0.3119422    Val loss:  0.2394703 
  epoch:  20    Train loss:  0.305493    Val loss:  0.2802586 
  epoch:  30    Train loss:  0.3377549    Val loss:  0.3785875 
  epoch:  40    Train loss:  0.2927964    Val loss:  0.1815028 
  early stop at epoch:  46    Train loss:  0.2517458    Val loss:  0.5039786 
  time: 27.59 sec elapsed
  epoch:  10    Train loss:  0.321931    Val loss:  0.5873973 
  epoch:  20    Train loss:  0.321931    Val loss:  0.5888883 
  epoch:  30    Train loss:  0.321931    Val loss:  0.5888883 
  early stop at epoch:  33    Train loss:  0.321931    Val loss:  0.6174102 
  epoch:  10    Train loss:  0.7876375    Val loss:  0.6463987 
  epoch:  20    Train loss:  0.7876375    Val loss:  0.6463987 
  epoch:  30    Train loss:  0.7876375    Val loss:  0.6463987 
  epoch:  40    Train loss:  0.7876375    Val loss:  0.6788496 
  epoch:  50    Train loss:  0.7876375    Val loss:  0.6463987 
  epoch:  60    Train loss:  0.7876375    Val loss:  0.6477246 
  epoch:  70    Train loss:  0.7942315    Val loss:  0.6798368 
  epoch:  80    Train loss:  0.7876375    Val loss:  0.6722983 
  epoch:  90    Train loss:  0.7876375    Val loss:  0.644978 
  epoch:  100    Train loss:  0.7876375    Val loss:  0.6463987 
  epoch:  10    Train loss:  0.6406012    Val loss:  0.5059289 
  epoch:  20    Train loss:  0.6406012    Val loss:  0.5059289 
  epoch:  30    Train loss:  0.6406012    Val loss:  0.5059289 
  epoch:  40    Train loss:  0.6583429    Val loss:  0.5059289 
  epoch:  50    Train loss:  0.6406012    Val loss:  0.5059289 
  epoch:  60    Train loss:  0.6406012    Val loss:  0.5696048 
  epoch:  70    Train loss:  0.6374811    Val loss:  0.5059289 
  epoch:  80    Train loss:  0.6659683    Val loss:  0.5059289 
  epoch:  90    Train loss:  0.6406012    Val loss:  0.5059289 
  epoch:  100    Train loss:  0.6406012    Val loss:  0.5059289 
  time: 20.01 sec elapsed
  epoch:  10    Train loss:  0.6911417    Val loss:  0.5956179 
  epoch:  20    Train loss:  0.6911417    Val loss:  0.5845087 
  epoch:  30    Train loss:  0.6911417    Val loss:  0.587561 
  early stop at epoch:  33    Train loss:  0.7172304    Val loss:  0.587561 
  epoch:  10    Train loss:  0.7261904    Val loss:  0.7383378 
  epoch:  20    Train loss:  0.7196836    Val loss:  0.6981781 
  epoch:  30    Train loss:  0.7512888    Val loss:  0.7383378 
  epoch:  40    Train loss:  0.7196836    Val loss:  0.6981781 
  epoch:  50    Train loss:  0.7196836    Val loss:  0.6981781 
  epoch:  60    Train loss:  0.7196836    Val loss:  0.6981781 
  early stop at epoch:  67    Train loss:  0.6642933    Val loss:  0.7383378 
  epoch:  10    Train loss:  0.2583119    Val loss:  0.3011031 
  epoch:  20    Train loss:  0.4441832    Val loss:  0.2575635 
  epoch:  30    Train loss:  0.261421    Val loss:  0.254054 
  early stop at epoch:  31    Train loss:  0.4870647    Val loss:  0.2995192 
  time: 12.99 sec elapsed
  epoch:  10    Train loss:  0.5787235    Val loss:  0.6820362 
  epoch:  20    Train loss:  0.6734827    Val loss:  0.7054596 
  epoch:  30    Train loss:  0.6681605    Val loss:  0.6837615 
  early stop at epoch:  31    Train loss:  0.7518322    Val loss:  0.7065848 
  epoch:  10    Train loss:  0.496736    Val loss:  0.5855419 
  epoch:  20    Train loss:  0.4847084    Val loss:  0.2743525 
  epoch:  30    Train loss:  0.4644409    Val loss:  0.6274837 
  early stop at epoch:  30    Train loss:  0.4644409    Val loss:  0.6274837 
  epoch:  10    Train loss:  0.5467301    Val loss:  0.5948564 
  epoch:  20    Train loss:  0.5125578    Val loss:  0.5383613 
  epoch:  30    Train loss:  0.5572807    Val loss:  0.6518743 
  early stop at epoch:  30    Train loss:  0.5572807    Val loss:  0.6518743 
  time: 17.66 sec elapsed
  random search: 50.67 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