CRAN Package Check Results for Package spinner

Last updated on 2025-04-12 18:52:09 CEST.

Flavor	Version	Tinstall	Tcheck	Ttotal	Status	Flags
r-devel-linux-x86_64-debian-clang	1.1.0	14.67	1095.44	1110.11	OK
r-devel-linux-x86_64-debian-gcc	1.1.0	9.48	1262.76	1272.24	OK
r-devel-linux-x86_64-fedora-clang	1.1.0			1248.46	OK
r-devel-linux-x86_64-fedora-gcc	1.1.0			1076.06	OK
r-devel-macos-arm64	1.1.0			280.00	OK
r-devel-macos-x86_64	1.1.0			70.00	OK
r-devel-windows-x86_64	1.1.0	16.00	260.00	276.00	ERROR
r-patched-linux-x86_64	1.1.0	13.84	1218.94	1232.78	OK
r-release-linux-x86_64	1.1.0				OK
r-release-macos-arm64	1.1.0			44.00	OK
r-release-macos-x86_64	1.1.0			73.00	OK
r-release-windows-x86_64	1.1.0	20.00	455.00	475.00	OK
r-oldrel-macos-arm64	1.1.0				OK
r-oldrel-macos-x86_64	1.1.0			60.00	OK
r-oldrel-windows-x86_64	1.1.0	20.00	527.00	547.00	OK

Check Details

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