CRAN Package Check Results for Package spinner

Last updated on 2025-04-15 09:52:34 CEST.

Flavor Version Tinstall Tcheck Ttotal Status Flags
r-devel-linux-x86_64-debian-clang 1.1.0 13.25 1300.88 1314.13 OK
r-devel-linux-x86_64-debian-gcc 1.1.0 10.16 1282.21 1292.37 OK
r-devel-linux-x86_64-fedora-clang 1.1.0 1168.92 OK
r-devel-linux-x86_64-fedora-gcc 1.1.0 1076.06 OK
r-devel-windows-x86_64 1.1.0 14.00 273.00 287.00 ERROR
r-patched-linux-x86_64 1.1.0 13.84 1218.94 1232.78 OK
r-release-linux-x86_64 1.1.0 13.63 1170.85 1184.48 OK
r-release-macos-arm64 1.1.0 280.00 OK
r-release-macos-x86_64 1.1.0 70.00 OK
r-release-windows-x86_64 1.1.0 16.00 348.00 364.00 OK
r-oldrel-macos-arm64 1.1.0 44.00 OK
r-oldrel-macos-x86_64 1.1.0 73.00 OK
r-oldrel-windows-x86_64 1.1.0 21.00 478.00 499.00 OK

Check Details

Version: 1.1.0
Check: tests
Result: ERROR 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 Flavor: r-devel-windows-x86_64