Running 'testthat.R' [139s]
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:  0.7317824    Val loss:  0.7621946 
  epoch:  20    Train loss:  0.6322789    Val loss:  0.7653031 
  epoch:  30    Train loss:  0.7221151    Val loss:  0.8039527 
  early stop at epoch:  34    Train loss:  0.6300904    Val loss:  0.756388 
  epoch:  10    Train loss:  0.7175471    Val loss:  0.5811712 
  epoch:  20    Train loss:  0.6909837    Val loss:  0.5899688 
  epoch:  30    Train loss:  0.6618134    Val loss:  0.6638206 
  epoch:  40    Train loss:  0.6234404    Val loss:  0.6321722 
  epoch:  50    Train loss:  0.6612909    Val loss:  0.5771581 
  early stop at epoch:  54    Train loss:  0.7050608    Val loss:  0.6041846 
  epoch:  10    Train loss:  0.654511    Val loss:  0.3184479 
  epoch:  20    Train loss:  0.7061145    Val loss:  0.4157068 
  epoch:  30    Train loss:  0.7505819    Val loss:  0.5504788 
  epoch:  40    Train loss:  0.5440403    Val loss:  0.5311794 
  epoch:  50    Train loss:  0.6966549    Val loss:  0.6133323 
  early stop at epoch:  55    Train loss:  0.803764    Val loss:  0.5741208 
  epoch:  10    Train loss:  0.5628225    Val loss:  0.3788545 
  epoch:  20    Train loss:  0.66157    Val loss:  0.797702 
  epoch:  30    Train loss:  0.5826113    Val loss:  0.7785869 
  early stop at epoch:  30    Train loss:  0.5826113    Val loss:  0.7785869 
  time: 35.16 sec elapsed
  epoch:  10    Train loss:  0.7409889    Val loss:  0.7343982 
  epoch:  20    Train loss:  0.6867293    Val loss:  0.7120999 
  epoch:  30    Train loss:  0.6315681    Val loss:  0.7207493 
  early stop at epoch:  31    Train loss:  0.5204234    Val loss:  0.777499 
  epoch:  10    Train loss:  0.6564538    Val loss:  0.7517146 
  epoch:  20    Train loss:  0.5716132    Val loss:  0.7110354 
  epoch:  30    Train loss:  0.6047138    Val loss:  0.7200903 
  early stop at epoch:  30    Train loss:  0.6047138    Val loss:  0.7200903 
  epoch:  10    Train loss:  0.7298493    Val loss:  0.5944011 
  epoch:  20    Train loss:  0.701436    Val loss:  0.7832389 
  epoch:  30    Train loss:  0.6641642    Val loss:  0.6373111 
  early stop at epoch:  34    Train loss:  0.5590758    Val loss:  0.7329341 
  epoch:  10    Train loss:  0.6905536    Val loss:  0.6138876 
  epoch:  20    Train loss:  0.6715042    Val loss:  0.722386 
  epoch:  30    Train loss:  0.694303    Val loss:  0.7603433 
  early stop at epoch:  33    Train loss:  0.7030374    Val loss:  0.7828748 
  time: 25.46 sec elapsed
  epoch:  10    Train loss:  0.3249352    Val loss:  0.3522149 
  epoch:  20    Train loss:  0.3366883    Val loss:  0.2461161 
  epoch:  30    Train loss:  0.3517227    Val loss:  0.258471 
  early stop at epoch:  31    Train loss:  0.2865163    Val loss:  0.5111788 
  epoch:  10    Train loss:  0.335785    Val loss:  0.4863305 
  epoch:  20    Train loss:  0.3319254    Val loss:  0.3984242 
  epoch:  30    Train loss:  0.338305    Val loss:  0.2768511 
  early stop at epoch:  32    Train loss:  0.3111433    Val loss:  0.4069659 
  epoch:  10    Train loss:  0.2867069    Val loss:  0.4243255 
  epoch:  20    Train loss:  0.2477084    Val loss:  0.4297781 
  epoch:  30    Train loss:  0.25424    Val loss:  0.3658085 
  early stop at epoch:  30    Train loss:  0.25424    Val loss:  0.3658085 
  epoch:  10    Train loss:  0.2299772    Val loss:  0.3072349 
  epoch:  20    Train loss:  0.2680575    Val loss:  0.169904 
  epoch:  30    Train loss:  0.2679525    Val loss:  0.1344198 
  early stop at epoch:  38    Train loss:  0.2458126    Val loss:  0.3700171 
  time: 26.53 sec elapsed
  epoch:  10    Train loss:  0.5702409    Val loss:  0.4774213 
  epoch:  20    Train loss:  0.5702409    Val loss:  0.4765813 
  epoch:  30    Train loss:  0.5702409    Val loss:  0.4765813 
  epoch:  40    Train loss:  0.5702409    Val loss:  0.4765813 
  epoch:  50    Train loss:  0.5702409    Val loss:  0.4765813 
  epoch:  60    Train loss:  0.5702409    Val loss:  0.4765813 
  epoch:  70    Train loss:  0.5702409    Val loss:  0.4765813 
  epoch:  80    Train loss:  0.5702409    Val loss:  0.4765813 
  epoch:  90    Train loss:  0.5702409    Val loss:  0.4765813 
  epoch:  100    Train loss:  0.5702409    Val loss:  0.4765813 
  epoch:  10    Train loss:  0.4554161    Val loss:  0.6724439 
  epoch:  20    Train loss:  0.4554161    Val loss:  0.6724439 
  epoch:  30    Train loss:  0.4554161    Val loss:  0.6724439 
  early stop at epoch:  31    Train loss:  0.4554161    Val loss:  0.7056807 
  epoch:  10    Train loss:  0.5752563    Val loss:  0.6791061 
  epoch:  20    Train loss:  0.5752563    Val loss:  0.6130908 
  epoch:  30    Train loss:  0.5752563    Val loss:  0.6069332 
  early stop at epoch:  37    Train loss:  0.5752563    Val loss:  0.6567641 
  time: 13.96 sec elapsed
  epoch:  10    Train loss:  0.5652817    Val loss:  0.5945494 
  epoch:  20    Train loss:  0.5766739    Val loss:  0.6136201 
  epoch:  30    Train loss:  0.5766739    Val loss:  0.6142852 
  early stop at epoch:  32    Train loss:  0.5766739    Val loss:  0.6047716 
  epoch:  10    Train loss:  0.7769034    Val loss:  0.6501483 
  epoch:  20    Train loss:  0.7769034    Val loss:  0.6713287 
  epoch:  30    Train loss:  0.7769034    Val loss:  0.6501483 
  epoch:  40    Train loss:  0.8044059    Val loss:  0.6763625 
  early stop at epoch:  40    Train loss:  0.8044059    Val loss:  0.6763625 
  epoch:  10    Train loss:  0.6855377    Val loss:  0.6947392 
  epoch:  20    Train loss:  0.6770796    Val loss:  0.6942887 
  epoch:  30    Train loss:  0.6759621    Val loss:  0.7478956 
  early stop at epoch:  30    Train loss:  0.6759621    Val loss:  0.7478956 
  time: 10.37 sec elapsed
  epoch:  10    Train loss:  0.5610423    Val loss:  0.6719236 
  epoch:  20    Train loss:  0.603376    Val loss:  0.7066973 
  epoch:  30    Train loss:  0.585977    Val loss:  0.6936437 
  early stop at epoch:  34    Train loss:  0.5812439    Val loss:  0.7304418 
  epoch:  10    Train loss:  0.4583791    Val loss:  0.4016303 
  epoch:  20    Train loss:  0.1186387    Val loss:  0.5465474 
  epoch:  30    Train loss:  0.4981215    Val loss:  0.5338627 
  epoch:  40    Train loss:  0.4663077    Val loss:  0.364223 
  early stop at epoch:  43    Train loss:  0.1277899    Val loss:  0.5148605 
  epoch:  10    Train loss:  0.3682945    Val loss:  0.4483344 
  epoch:  20    Train loss:  0.4255118    Val loss:  0.3754213 
  epoch:  30    Train loss:  0.4197976    Val loss:  0.4472946 
  early stop at epoch:  33    Train loss:  0.4434004    Val loss:  0.5070518 
  time: 20.74 sec elapsed
  random search: 45.09 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