CRAN Package Check Results for Package spinner

Last updated on 2025-08-18 05:49:51 CEST.

Flavor Version Tinstall Tcheck Ttotal Status Flags
r-devel-linux-x86_64-debian-clang 1.1.0 14.40 1248.99 1263.39 OK
r-devel-linux-x86_64-debian-gcc 1.1.0 10.88 1082.59 1093.47 OK
r-devel-linux-x86_64-fedora-clang 1.1.0 1321.94 OK
r-devel-linux-x86_64-fedora-gcc 1.1.0 1134.47 OK
r-devel-windows-x86_64 1.1.0 16.00 361.00 377.00 ERROR
r-patched-linux-x86_64 1.1.0 14.62 1111.71 1126.33 OK
r-release-linux-x86_64 1.1.0 13.54 1150.39 1163.93 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 17.00 328.00 345.00 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 21.00 463.00 484.00 OK

Check Details

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