CRAN Package Check Results for Package spinner

Last updated on 2025-07-30 07:50:21 CEST.

Flavor Version Tinstall Tcheck Ttotal Status Flags
r-devel-linux-x86_64-debian-clang 1.1.0 15.14 1186.99 1202.13 OK
r-devel-linux-x86_64-debian-gcc 1.1.0 10.18 1411.27 1421.45 OK
r-devel-linux-x86_64-fedora-clang 1.1.0 1249.96 OK
r-devel-linux-x86_64-fedora-gcc 1.1.0 1317.03 OK
r-devel-windows-x86_64 1.1.0 17.00 364.00 381.00 OK
r-patched-linux-x86_64 1.1.0 13.95 1022.97 1036.92 OK
r-release-linux-x86_64 1.1.0 13.72 1049.42 1063.14 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 16.00 323.00 339.00 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 22.00 404.00 426.00 ERROR

Check Details

Version: 1.1.0
Check: tests
Result: ERROR Running 'testthat.R' [264s] 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 28 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: 0.7880893 Val loss: 0.7255653 epoch: 20 Train loss: 0.8137968 Val loss: 0.7753562 epoch: 30 Train loss: 0.8466399 Val loss: 0.8031964 early stop at epoch: 30 Train loss: 0.8466399 Val loss: 0.8031964 epoch: 10 Train loss: 0.8181331 Val loss: 0.7062073 epoch: 20 Train loss: 0.7099457 Val loss: 0.6848931 epoch: 30 Train loss: 0.8088268 Val loss: 0.6160501 epoch: 40 Train loss: 0.7901298 Val loss: 0.7961853 early stop at epoch: 44 Train loss: 0.746365 Val loss: 0.8309304 epoch: 10 Train loss: 0.6312017 Val loss: 0.8636278 epoch: 20 Train loss: 0.6782193 Val loss: 0.4614531 epoch: 30 Train loss: 0.6822445 Val loss: 0.7606828 epoch: 40 Train loss: 0.6555291 Val loss: 0.6954718 early stop at epoch: 46 Train loss: 0.6502698 Val loss: 0.9003091 epoch: 10 Train loss: 0.6378485 Val loss: 0.5317824 epoch: 20 Train loss: 0.6150324 Val loss: 0.3747623 epoch: 30 Train loss: 0.6623861 Val loss: 0.5424011 epoch: 40 Train loss: 0.6301767 Val loss: 0.4165615 epoch: 50 Train loss: 0.6125575 Val loss: 0.4503262 epoch: 60 Train loss: 0.6257965 Val loss: 0.4263246 early stop at epoch: 65 Train loss: 0.745899 Val loss: 0.53618 time: 64.89 sec elapsed epoch: 10 Train loss: 0.7739093 Val loss: 0.770283 epoch: 20 Train loss: 0.7055809 Val loss: 0.7759296 epoch: 30 Train loss: 0.7352791 Val loss: 0.7941313 early stop at epoch: 30 Train loss: 0.7352791 Val loss: 0.7941313 epoch: 10 Train loss: 0.7002555 Val loss: 0.694304 epoch: 20 Train loss: 0.706356 Val loss: 0.7246676 epoch: 30 Train loss: 0.7121664 Val loss: 0.7258093 early stop at epoch: 35 Train loss: 0.6510051 Val loss: 0.7416199 epoch: 10 Train loss: 0.7479615 Val loss: 0.7007744 epoch: 20 Train loss: 0.6650078 Val loss: 0.6358659 epoch: 30 Train loss: 0.6284428 Val loss: 0.6328874 early stop at epoch: 39 Train loss: 0.5988222 Val loss: 0.7966897 epoch: 10 Train loss: 0.8012241 Val loss: 0.7569225 epoch: 20 Train loss: 0.7572383 Val loss: 0.8048664 epoch: 30 Train loss: 0.7556403 Val loss: 0.7126857 early stop at epoch: 31 Train loss: 0.7280132 Val loss: 0.7868891 time: 45.17 sec elapsed epoch: 10 Train loss: 0.3415718 Val loss: 0.2547229 epoch: 20 Train loss: 0.3281042 Val loss: 0.2208681 epoch: 30 Train loss: 0.3274095 Val loss: 0.2383749 early stop at epoch: 33 Train loss: 0.2672565 Val loss: 0.3439875 epoch: 10 Train loss: 0.3655412 Val loss: 0.2983556 epoch: 20 Train loss: 0.3302536 Val loss: 0.3878253 epoch: 30 Train loss: 0.3235301 Val loss: 0.4651515 early stop at epoch: 30 Train loss: 0.3235301 Val loss: 0.4651515 epoch: 10 Train loss: 0.2556229 Val loss: 0.3100896 epoch: 20 Train loss: 0.2315873 Val loss: 0.3082713 epoch: 30 Train loss: 0.2328274 Val loss: 0.2574663 early stop at epoch: 34 Train loss: 0.2209101 Val loss: 0.306387 epoch: 10 Train loss: 0.3312292 Val loss: 0.4367475 epoch: 20 Train loss: 0.3224618 Val loss: 0.2174875 epoch: 30 Train loss: 0.2501336 Val loss: 0.2137407 early stop at epoch: 32 Train loss: 0.2638713 Val loss: 0.4487271 time: 42.06 sec elapsed epoch: 10 Train loss: 0.4114213 Val loss: 0.525591 epoch: 20 Train loss: 0.4114213 Val loss: 0.5224094 epoch: 30 Train loss: 0.4114213 Val loss: 0.5224094 epoch: 40 Train loss: 0.4114213 Val loss: 0.5224094 early stop at epoch: 49 Train loss: 0.4114213 Val loss: 0.54846 epoch: 10 Train loss: 0.7400234 Val loss: 0.6073773 epoch: 20 Train loss: 0.7400234 Val loss: 0.6315222 epoch: 30 Train loss: 0.7400234 Val loss: 0.6073773 epoch: 40 Train loss: 0.7400234 Val loss: 0.6073773 epoch: 50 Train loss: 0.7400234 Val loss: 0.619832 epoch: 60 Train loss: 0.7400234 Val loss: 0.6073773 epoch: 70 Train loss: 0.7400234 Val loss: 0.6351814 epoch: 80 Train loss: 0.7400234 Val loss: 0.6105238 epoch: 90 Train loss: 0.7436123 Val loss: 0.6320261 epoch: 100 Train loss: 0.7604493 Val loss: 0.6365073 epoch: 10 Train loss: 0.6387671 Val loss: 0.6326262 epoch: 20 Train loss: 0.6387671 Val loss: 0.6326262 epoch: 30 Train loss: 0.6387671 Val loss: 0.6326262 early stop at epoch: 36 Train loss: 0.6387671 Val loss: 0.6757869 time: 24.17 sec elapsed epoch: 10 Train loss: 0.6167455 Val loss: 0.5800428 epoch: 20 Train loss: 0.5978186 Val loss: 0.6064745 epoch: 30 Train loss: 0.5978186 Val loss: 0.5752624 early stop at epoch: 33 Train loss: 0.5784105 Val loss: 0.6041983 epoch: 10 Train loss: 0.7262605 Val loss: 0.6754378 epoch: 20 Train loss: 0.7212899 Val loss: 0.6759892 epoch: 30 Train loss: 0.7568199 Val loss: 0.6754378 epoch: 40 Train loss: 0.7212899 Val loss: 0.6759892 epoch: 50 Train loss: 0.7212899 Val loss: 0.6759892 epoch: 60 Train loss: 0.7212899 Val loss: 0.6759892 epoch: 70 Train loss: 0.7212899 Val loss: 0.6759892 epoch: 80 Train loss: 0.7212899 Val loss: 0.7039152 epoch: 90 Train loss: 0.7212899 Val loss: 0.6635614 epoch: 100 Train loss: 0.7212899 Val loss: 0.6754378 epoch: 10 Train loss: 0.5362894 Val loss: 0.4748831 epoch: 20 Train loss: 0.5311361 Val loss: 0.4533411 epoch: 30 Train loss: 0.4691363 Val loss: 0.4594963 early stop at epoch: 34 Train loss: 0.4733386 Val loss: 0.6216606 time: 25.61 sec elapsed epoch: 10 Train loss: 0.7356187 Val loss: 0.6312646 epoch: 20 Train loss: 0.7813594 Val loss: 0.6261184 epoch: 30 Train loss: 0.748969 Val loss: 0.6655114 early stop at epoch: 32 Train loss: 0.7748536 Val loss: 0.6572163 epoch: 10 Train loss: 0.5083613 Val loss: 0.5274738 epoch: 20 Train loss: 0.5270655 Val loss: 0.3788466 epoch: 30 Train loss: 0.4570093 Val loss: 0.5561817 early stop at epoch: 30 Train loss: 0.4570093 Val loss: 0.5561817 epoch: 10 Train loss: 0.6286951 Val loss: 0.6322638 epoch: 20 Train loss: 0.6143529 Val loss: 0.5438521 epoch: 30 Train loss: 0.6301204 Val loss: 0.5574672 epoch: 40 Train loss: 0.6114512 Val loss: 0.5973297 epoch: 50 Train loss: 0.6404253 Val loss: 0.5391501 epoch: 60 Train loss: 0.6294305 Val loss: 0.5511015 epoch: 70 Train loss: 0.6345275 Val loss: 0.583086 epoch: 80 Train loss: 0.6088005 Val loss: 0.5950584 epoch: 90 Train loss: 0.6323931 Val loss: 0.5589393 early stop at epoch: 96 Train loss: 0.6434103 Val loss: 0.5927544 time: 52.6 sec elapsed random search: 102.4 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-oldrel-windows-x86_64