renaissance-movie-lens_0
[2024-11-14T07:53:46.807Z] Running test renaissance-movie-lens_0 ...
[2024-11-14T07:53:46.807Z] ===============================================
[2024-11-14T07:53:46.807Z] renaissance-movie-lens_0 Start Time: Thu Nov 14 07:53:46 2024 Epoch Time (ms): 1731570826264
[2024-11-14T07:53:46.807Z] variation: NoOptions
[2024-11-14T07:53:46.807Z] JVM_OPTIONS:
[2024-11-14T07:53:46.807Z] { \
[2024-11-14T07:53:46.807Z] echo ""; echo "TEST SETUP:"; \
[2024-11-14T07:53:46.807Z] echo "Nothing to be done for setup."; \
[2024-11-14T07:53:46.807Z] mkdir -p "/home/jenkins/workspace/Test_openjdk11_hs_extended.perf_ppc64_aix/aqa-tests/TKG/../TKG/output_17315698171927/renaissance-movie-lens_0"; \
[2024-11-14T07:53:46.807Z] cd "/home/jenkins/workspace/Test_openjdk11_hs_extended.perf_ppc64_aix/aqa-tests/TKG/../TKG/output_17315698171927/renaissance-movie-lens_0"; \
[2024-11-14T07:53:46.807Z] echo ""; echo "TESTING:"; \
[2024-11-14T07:53:46.807Z] "/home/jenkins/workspace/Test_openjdk11_hs_extended.perf_ppc64_aix/jdkbinary/j2sdk-image/bin/java" --add-opens java.base/java.lang=ALL-UNNAMED --add-opens java.base/java.util=ALL-UNNAMED --add-opens java.base/java.util.concurrent=ALL-UNNAMED --add-opens java.base/java.nio=ALL-UNNAMED --add-opens java.base/sun.nio.ch=ALL-UNNAMED --add-opens java.base/java.lang.invoke=ALL-UNNAMED -jar "/home/jenkins/workspace/Test_openjdk11_hs_extended.perf_ppc64_aix/aqa-tests/TKG/../../jvmtest/perf/renaissance/renaissance.jar" --json ""/home/jenkins/workspace/Test_openjdk11_hs_extended.perf_ppc64_aix/aqa-tests/TKG/../TKG/output_17315698171927/renaissance-movie-lens_0"/movie-lens.json" movie-lens; \
[2024-11-14T07:53:46.807Z] if [ $? -eq 0 ]; then echo "-----------------------------------"; echo "renaissance-movie-lens_0""_PASSED"; echo "-----------------------------------"; cd /home/jenkins/workspace/Test_openjdk11_hs_extended.perf_ppc64_aix/aqa-tests/TKG/..; rm -f -r "/home/jenkins/workspace/Test_openjdk11_hs_extended.perf_ppc64_aix/aqa-tests/TKG/../TKG/output_17315698171927/renaissance-movie-lens_0"; else echo "-----------------------------------"; echo "renaissance-movie-lens_0""_FAILED"; echo "-----------------------------------"; fi; \
[2024-11-14T07:53:46.807Z] echo ""; echo "TEST TEARDOWN:"; \
[2024-11-14T07:53:46.807Z] echo "Nothing to be done for teardown."; \
[2024-11-14T07:53:46.807Z] } 2>&1 | tee -a "/home/jenkins/workspace/Test_openjdk11_hs_extended.perf_ppc64_aix/aqa-tests/TKG/../TKG/output_17315698171927/TestTargetResult";
[2024-11-14T07:53:46.807Z]
[2024-11-14T07:53:46.807Z] TEST SETUP:
[2024-11-14T07:53:46.807Z] Nothing to be done for setup.
[2024-11-14T07:53:46.807Z]
[2024-11-14T07:53:46.807Z] TESTING:
[2024-11-14T07:53:51.234Z] Using Spark's default log4j profile: org/apache/spark/log4j-defaults.properties
[2024-11-14T07:53:54.631Z] NOTE: 'movie-lens' benchmark uses Spark local executor with 8 (out of 24) threads.
[2024-11-14T07:53:59.054Z] Got 100004 ratings from 671 users on 9066 movies.
[2024-11-14T07:53:59.054Z] Training: 60056, validation: 20285, test: 19854
[2024-11-14T07:53:59.054Z] ====== movie-lens (apache-spark) [default], iteration 0 started ======
[2024-11-14T07:53:59.054Z] GC before operation: completed in 61.042 ms, heap usage 145.352 MB -> 37.972 MB.
[2024-11-14T07:54:05.863Z] RMSE (validation) = 3.6219689545487506 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-14T07:54:08.820Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-14T07:54:13.580Z] RMSE (validation) = 1.3105189674610935 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-14T07:54:16.972Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-14T07:54:18.550Z] RMSE (validation) = 1.2218330581874073 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-14T07:54:21.015Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-14T07:54:23.467Z] RMSE (validation) = 0.9275717391338142 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-14T07:54:25.039Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-14T07:54:25.801Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9073522634082535.
[2024-11-14T07:54:25.801Z] The best model improves the baseline by 14.43%.
[2024-11-14T07:54:25.801Z] Movies recommended for you:
[2024-11-14T07:54:25.801Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-14T07:54:25.801Z] There is no way to check that no silent failure occurred.
[2024-11-14T07:54:25.801Z] ====== movie-lens (apache-spark) [default], iteration 0 completed (26592.507 ms) ======
[2024-11-14T07:54:25.801Z] ====== movie-lens (apache-spark) [default], iteration 1 started ======
[2024-11-14T07:54:25.801Z] GC before operation: completed in 107.727 ms, heap usage 576.481 MB -> 55.185 MB.
[2024-11-14T07:54:29.200Z] RMSE (validation) = 3.621968954548751 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-14T07:54:32.596Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-14T07:54:35.990Z] RMSE (validation) = 1.3105189674610933 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-14T07:54:39.404Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-14T07:54:41.870Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-14T07:54:43.440Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-14T07:54:45.894Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-14T07:54:47.469Z] RMSE (validation) = 0.9001440981626694 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-14T07:54:48.232Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9073522634082535.
[2024-11-14T07:54:48.232Z] The best model improves the baseline by 14.43%.
[2024-11-14T07:54:48.232Z] Movies recommended for you:
[2024-11-14T07:54:48.232Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-14T07:54:48.232Z] There is no way to check that no silent failure occurred.
[2024-11-14T07:54:48.232Z] ====== movie-lens (apache-spark) [default], iteration 1 completed (22382.733 ms) ======
[2024-11-14T07:54:48.232Z] ====== movie-lens (apache-spark) [default], iteration 2 started ======
[2024-11-14T07:54:48.232Z] GC before operation: completed in 122.965 ms, heap usage 296.266 MB -> 55.084 MB.
[2024-11-14T07:54:51.625Z] RMSE (validation) = 3.621968954548751 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-14T07:54:55.016Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-14T07:54:58.408Z] RMSE (validation) = 1.3105189674610935 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-14T07:55:01.806Z] RMSE (validation) = 1.004540774800519 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-14T07:55:03.380Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-14T07:55:04.951Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-14T07:55:07.394Z] RMSE (validation) = 0.927571739133814 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-14T07:55:08.966Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-14T07:55:09.727Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9073522634082535.
[2024-11-14T07:55:09.727Z] The best model improves the baseline by 14.43%.
[2024-11-14T07:55:09.727Z] Movies recommended for you:
[2024-11-14T07:55:09.727Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-14T07:55:09.727Z] There is no way to check that no silent failure occurred.
[2024-11-14T07:55:09.727Z] ====== movie-lens (apache-spark) [default], iteration 2 completed (21332.922 ms) ======
[2024-11-14T07:55:09.727Z] ====== movie-lens (apache-spark) [default], iteration 3 started ======
[2024-11-14T07:55:09.727Z] GC before operation: completed in 122.044 ms, heap usage 257.981 MB -> 52.176 MB.
[2024-11-14T07:55:13.119Z] RMSE (validation) = 3.6219689545487506 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-14T07:55:16.509Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-14T07:55:19.917Z] RMSE (validation) = 1.3105189674610935 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-14T07:55:22.378Z] RMSE (validation) = 1.004540774800519 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-14T07:55:24.829Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-14T07:55:26.400Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-14T07:55:28.854Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-14T07:55:30.437Z] RMSE (validation) = 0.9001440981626694 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-14T07:55:30.437Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9073522634082535.
[2024-11-14T07:55:30.437Z] The best model improves the baseline by 14.43%.
[2024-11-14T07:55:31.198Z] Movies recommended for you:
[2024-11-14T07:55:31.198Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-14T07:55:31.198Z] There is no way to check that no silent failure occurred.
[2024-11-14T07:55:31.198Z] ====== movie-lens (apache-spark) [default], iteration 3 completed (21092.428 ms) ======
[2024-11-14T07:55:31.198Z] ====== movie-lens (apache-spark) [default], iteration 4 started ======
[2024-11-14T07:55:31.198Z] GC before operation: completed in 114.298 ms, heap usage 267.026 MB -> 52.407 MB.
[2024-11-14T07:55:34.602Z] RMSE (validation) = 3.6219689545487506 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-14T07:55:37.046Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-14T07:55:40.451Z] RMSE (validation) = 1.3105189674610935 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-14T07:55:43.848Z] RMSE (validation) = 1.004540774800519 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-14T07:55:45.422Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-14T07:55:46.996Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-14T07:55:49.459Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-14T07:55:51.031Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-14T07:55:51.031Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9073522634082535.
[2024-11-14T07:55:51.031Z] The best model improves the baseline by 14.43%.
[2024-11-14T07:55:51.792Z] Movies recommended for you:
[2024-11-14T07:55:51.792Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-14T07:55:51.792Z] There is no way to check that no silent failure occurred.
[2024-11-14T07:55:51.792Z] ====== movie-lens (apache-spark) [default], iteration 4 completed (20538.988 ms) ======
[2024-11-14T07:55:51.792Z] ====== movie-lens (apache-spark) [default], iteration 5 started ======
[2024-11-14T07:55:51.792Z] GC before operation: completed in 121.670 ms, heap usage 518.943 MB -> 56.078 MB.
[2024-11-14T07:55:55.190Z] RMSE (validation) = 3.6219689545487506 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-14T07:55:57.649Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-14T07:56:01.041Z] RMSE (validation) = 1.3105189674610935 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-14T07:56:04.439Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-14T07:56:06.018Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-14T07:56:08.465Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-14T07:56:10.049Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-14T07:56:11.647Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-14T07:56:12.407Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9073522634082535.
[2024-11-14T07:56:12.407Z] The best model improves the baseline by 14.43%.
[2024-11-14T07:56:12.407Z] Movies recommended for you:
[2024-11-14T07:56:12.407Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-14T07:56:12.407Z] There is no way to check that no silent failure occurred.
[2024-11-14T07:56:12.407Z] ====== movie-lens (apache-spark) [default], iteration 5 completed (20801.123 ms) ======
[2024-11-14T07:56:12.407Z] ====== movie-lens (apache-spark) [default], iteration 6 started ======
[2024-11-14T07:56:12.407Z] GC before operation: completed in 113.285 ms, heap usage 408.351 MB -> 52.699 MB.
[2024-11-14T07:56:15.797Z] RMSE (validation) = 3.621968954548751 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-14T07:56:19.211Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-14T07:56:22.616Z] RMSE (validation) = 1.3105189674610935 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-14T07:56:25.058Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-14T07:56:27.516Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-14T07:56:29.106Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-14T07:56:31.555Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-14T07:56:33.135Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-14T07:56:33.135Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9073522634082535.
[2024-11-14T07:56:33.135Z] The best model improves the baseline by 14.43%.
[2024-11-14T07:56:33.895Z] Movies recommended for you:
[2024-11-14T07:56:33.895Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-14T07:56:33.895Z] There is no way to check that no silent failure occurred.
[2024-11-14T07:56:33.895Z] ====== movie-lens (apache-spark) [default], iteration 6 completed (20962.939 ms) ======
[2024-11-14T07:56:33.895Z] ====== movie-lens (apache-spark) [default], iteration 7 started ======
[2024-11-14T07:56:33.895Z] GC before operation: completed in 119.568 ms, heap usage 493.628 MB -> 56.181 MB.
[2024-11-14T07:56:37.299Z] RMSE (validation) = 3.6219689545487506 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-14T07:56:39.753Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-14T07:56:43.140Z] RMSE (validation) = 1.3105189674610933 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-14T07:56:46.545Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-14T07:56:48.116Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-14T07:56:49.693Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-14T07:56:52.149Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-14T07:56:53.723Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-14T07:56:54.487Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9073522634082535.
[2024-11-14T07:56:54.487Z] The best model improves the baseline by 14.43%.
[2024-11-14T07:56:54.487Z] Movies recommended for you:
[2024-11-14T07:56:54.487Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-14T07:56:54.487Z] There is no way to check that no silent failure occurred.
[2024-11-14T07:56:54.487Z] ====== movie-lens (apache-spark) [default], iteration 7 completed (20798.201 ms) ======
[2024-11-14T07:56:54.487Z] ====== movie-lens (apache-spark) [default], iteration 8 started ======
[2024-11-14T07:56:54.487Z] GC before operation: completed in 119.465 ms, heap usage 277.113 MB -> 53.034 MB.
[2024-11-14T07:56:57.881Z] RMSE (validation) = 3.6219689545487506 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-14T07:57:00.497Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-14T07:57:03.896Z] RMSE (validation) = 1.3105189674610933 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-14T07:57:07.297Z] RMSE (validation) = 1.004540774800519 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-14T07:57:08.871Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-14T07:57:11.325Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-14T07:57:12.907Z] RMSE (validation) = 0.9275717391338142 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-14T07:57:14.489Z] RMSE (validation) = 0.9001440981626694 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-14T07:57:15.250Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9073522634082535.
[2024-11-14T07:57:15.250Z] The best model improves the baseline by 14.43%.
[2024-11-14T07:57:15.250Z] Movies recommended for you:
[2024-11-14T07:57:15.250Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-14T07:57:15.250Z] There is no way to check that no silent failure occurred.
[2024-11-14T07:57:15.250Z] ====== movie-lens (apache-spark) [default], iteration 8 completed (20740.332 ms) ======
[2024-11-14T07:57:15.250Z] ====== movie-lens (apache-spark) [default], iteration 9 started ======
[2024-11-14T07:57:15.250Z] GC before operation: completed in 119.986 ms, heap usage 295.861 MB -> 52.904 MB.
[2024-11-14T07:57:18.639Z] RMSE (validation) = 3.621968954548751 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-14T07:57:22.060Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-14T07:57:24.508Z] RMSE (validation) = 1.3105189674610933 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-14T07:57:27.921Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-14T07:57:29.498Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-14T07:57:31.946Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-14T07:57:33.520Z] RMSE (validation) = 0.9275717391338142 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-14T07:57:35.986Z] RMSE (validation) = 0.9001440981626694 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-14T07:57:35.986Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9073522634082535.
[2024-11-14T07:57:35.986Z] The best model improves the baseline by 14.43%.
[2024-11-14T07:57:35.986Z] Movies recommended for you:
[2024-11-14T07:57:35.986Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-14T07:57:35.986Z] There is no way to check that no silent failure occurred.
[2024-11-14T07:57:35.986Z] ====== movie-lens (apache-spark) [default], iteration 9 completed (20609.442 ms) ======
[2024-11-14T07:57:35.986Z] ====== movie-lens (apache-spark) [default], iteration 10 started ======
[2024-11-14T07:57:35.986Z] GC before operation: completed in 131.174 ms, heap usage 230.135 MB -> 52.974 MB.
[2024-11-14T07:57:39.378Z] RMSE (validation) = 3.621968954548751 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-14T07:57:42.773Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-14T07:57:46.167Z] RMSE (validation) = 1.3105189674610933 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-14T07:57:48.620Z] RMSE (validation) = 1.004540774800519 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-14T07:57:51.085Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-14T07:57:52.683Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-14T07:57:55.146Z] RMSE (validation) = 0.927571739133814 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-14T07:57:56.734Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-14T07:57:56.734Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9073522634082535.
[2024-11-14T07:57:56.734Z] The best model improves the baseline by 14.43%.
[2024-11-14T07:57:56.734Z] Movies recommended for you:
[2024-11-14T07:57:56.734Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-14T07:57:56.734Z] There is no way to check that no silent failure occurred.
[2024-11-14T07:57:56.734Z] ====== movie-lens (apache-spark) [default], iteration 10 completed (20897.961 ms) ======
[2024-11-14T07:57:56.734Z] ====== movie-lens (apache-spark) [default], iteration 11 started ======
[2024-11-14T07:57:57.499Z] GC before operation: completed in 119.975 ms, heap usage 394.308 MB -> 52.806 MB.
[2024-11-14T07:57:59.969Z] RMSE (validation) = 3.621968954548751 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-14T07:58:03.365Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-14T07:58:06.757Z] RMSE (validation) = 1.3105189674610935 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-14T07:58:10.157Z] RMSE (validation) = 1.004540774800519 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-14T07:58:11.732Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-14T07:58:13.307Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-14T07:58:15.765Z] RMSE (validation) = 0.9275717391338142 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-14T07:58:17.374Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-14T07:58:17.374Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9073522634082535.
[2024-11-14T07:58:17.374Z] The best model improves the baseline by 14.43%.
[2024-11-14T07:58:18.134Z] Movies recommended for you:
[2024-11-14T07:58:18.134Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-14T07:58:18.134Z] There is no way to check that no silent failure occurred.
[2024-11-14T07:58:18.134Z] ====== movie-lens (apache-spark) [default], iteration 11 completed (20646.034 ms) ======
[2024-11-14T07:58:18.134Z] ====== movie-lens (apache-spark) [default], iteration 12 started ======
[2024-11-14T07:58:18.134Z] GC before operation: completed in 123.619 ms, heap usage 299.799 MB -> 52.954 MB.
[2024-11-14T07:58:21.523Z] RMSE (validation) = 3.6219689545487506 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-14T07:58:23.980Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-14T07:58:27.372Z] RMSE (validation) = 1.3105189674610933 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-14T07:58:30.765Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-14T07:58:32.342Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-14T07:58:33.921Z] RMSE (validation) = 1.117495376637101 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-14T07:58:36.371Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-14T07:58:37.954Z] RMSE (validation) = 0.9001440981626694 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-14T07:58:38.717Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9073522634082535.
[2024-11-14T07:58:38.717Z] The best model improves the baseline by 14.43%.
[2024-11-14T07:58:38.717Z] Movies recommended for you:
[2024-11-14T07:58:38.717Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-14T07:58:38.717Z] There is no way to check that no silent failure occurred.
[2024-11-14T07:58:38.717Z] ====== movie-lens (apache-spark) [default], iteration 12 completed (20688.505 ms) ======
[2024-11-14T07:58:38.717Z] ====== movie-lens (apache-spark) [default], iteration 13 started ======
[2024-11-14T07:58:38.717Z] GC before operation: completed in 115.354 ms, heap usage 304.974 MB -> 53.145 MB.
[2024-11-14T07:58:42.130Z] RMSE (validation) = 3.6219689545487506 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-14T07:58:44.584Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-14T07:58:47.981Z] RMSE (validation) = 1.3105189674610935 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-14T07:58:51.371Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-14T07:58:52.944Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-14T07:58:55.393Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-14T07:58:56.969Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-14T07:58:58.560Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-14T07:58:59.320Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9073522634082535.
[2024-11-14T07:58:59.320Z] The best model improves the baseline by 14.43%.
[2024-11-14T07:58:59.320Z] Movies recommended for you:
[2024-11-14T07:58:59.320Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-14T07:58:59.320Z] There is no way to check that no silent failure occurred.
[2024-11-14T07:58:59.320Z] ====== movie-lens (apache-spark) [default], iteration 13 completed (20516.561 ms) ======
[2024-11-14T07:58:59.320Z] ====== movie-lens (apache-spark) [default], iteration 14 started ======
[2024-11-14T07:58:59.320Z] GC before operation: completed in 117.676 ms, heap usage 269.186 MB -> 52.810 MB.
[2024-11-14T07:59:02.721Z] RMSE (validation) = 3.6219689545487506 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-14T07:59:06.113Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-14T07:59:08.563Z] RMSE (validation) = 1.3105189674610933 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-14T07:59:11.955Z] RMSE (validation) = 1.004540774800519 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-14T07:59:13.541Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-14T07:59:15.990Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-14T07:59:17.570Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-14T07:59:19.143Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-14T07:59:19.910Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9073522634082535.
[2024-11-14T07:59:19.910Z] The best model improves the baseline by 14.43%.
[2024-11-14T07:59:19.910Z] Movies recommended for you:
[2024-11-14T07:59:19.910Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-14T07:59:19.910Z] There is no way to check that no silent failure occurred.
[2024-11-14T07:59:19.910Z] ====== movie-lens (apache-spark) [default], iteration 14 completed (20644.906 ms) ======
[2024-11-14T07:59:19.910Z] ====== movie-lens (apache-spark) [default], iteration 15 started ======
[2024-11-14T07:59:19.910Z] GC before operation: completed in 118.710 ms, heap usage 400.338 MB -> 53.131 MB.
[2024-11-14T07:59:23.298Z] RMSE (validation) = 3.621968954548751 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-14T07:59:26.708Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-14T07:59:30.103Z] RMSE (validation) = 1.3105189674610933 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-14T07:59:32.555Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-14T07:59:34.129Z] RMSE (validation) = 1.2218330581874073 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-14T07:59:36.592Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-14T07:59:38.176Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-14T07:59:39.760Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-14T07:59:40.521Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9073522634082535.
[2024-11-14T07:59:40.521Z] The best model improves the baseline by 14.43%.
[2024-11-14T07:59:40.521Z] Movies recommended for you:
[2024-11-14T07:59:40.521Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-14T07:59:40.521Z] There is no way to check that no silent failure occurred.
[2024-11-14T07:59:40.521Z] ====== movie-lens (apache-spark) [default], iteration 15 completed (20321.539 ms) ======
[2024-11-14T07:59:40.521Z] ====== movie-lens (apache-spark) [default], iteration 16 started ======
[2024-11-14T07:59:40.521Z] GC before operation: completed in 161.515 ms, heap usage 233.206 MB -> 53.068 MB.
[2024-11-14T07:59:43.925Z] RMSE (validation) = 3.6219689545487506 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-14T07:59:47.318Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-14T07:59:49.760Z] RMSE (validation) = 1.3105189674610933 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-14T07:59:53.156Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-14T07:59:54.731Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-14T07:59:57.178Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-14T07:59:58.763Z] RMSE (validation) = 0.927571739133814 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-14T08:00:00.348Z] RMSE (validation) = 0.9001440981626694 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-14T08:00:01.108Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9073522634082535.
[2024-11-14T08:00:01.108Z] The best model improves the baseline by 14.43%.
[2024-11-14T08:00:01.108Z] Movies recommended for you:
[2024-11-14T08:00:01.108Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-14T08:00:01.108Z] There is no way to check that no silent failure occurred.
[2024-11-14T08:00:01.108Z] ====== movie-lens (apache-spark) [default], iteration 16 completed (20357.096 ms) ======
[2024-11-14T08:00:01.108Z] ====== movie-lens (apache-spark) [default], iteration 17 started ======
[2024-11-14T08:00:01.108Z] GC before operation: completed in 115.790 ms, heap usage 365.102 MB -> 52.916 MB.
[2024-11-14T08:00:04.527Z] RMSE (validation) = 3.6219689545487506 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-14T08:00:06.978Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-14T08:00:10.379Z] RMSE (validation) = 1.3105189674610933 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-14T08:00:13.789Z] RMSE (validation) = 1.004540774800519 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-14T08:00:15.386Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-14T08:00:16.958Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-14T08:00:19.410Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-14T08:00:20.996Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-14T08:00:20.996Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9073522634082535.
[2024-11-14T08:00:20.996Z] The best model improves the baseline by 14.43%.
[2024-11-14T08:00:21.757Z] Movies recommended for you:
[2024-11-14T08:00:21.757Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-14T08:00:21.757Z] There is no way to check that no silent failure occurred.
[2024-11-14T08:00:21.757Z] ====== movie-lens (apache-spark) [default], iteration 17 completed (20271.693 ms) ======
[2024-11-14T08:00:21.757Z] ====== movie-lens (apache-spark) [default], iteration 18 started ======
[2024-11-14T08:00:21.757Z] GC before operation: completed in 118.536 ms, heap usage 513.766 MB -> 56.351 MB.
[2024-11-14T08:00:24.200Z] RMSE (validation) = 3.621968954548751 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-14T08:00:27.589Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-14T08:00:30.983Z] RMSE (validation) = 1.3105189674610935 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-14T08:00:33.430Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-14T08:00:35.898Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-14T08:00:37.495Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-14T08:00:39.067Z] RMSE (validation) = 0.927571739133814 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-14T08:00:40.645Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-14T08:00:41.407Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9073522634082535.
[2024-11-14T08:00:41.407Z] The best model improves the baseline by 14.43%.
[2024-11-14T08:00:41.407Z] Movies recommended for you:
[2024-11-14T08:00:41.407Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-14T08:00:41.407Z] There is no way to check that no silent failure occurred.
[2024-11-14T08:00:41.407Z] ====== movie-lens (apache-spark) [default], iteration 18 completed (19909.402 ms) ======
[2024-11-14T08:00:41.407Z] ====== movie-lens (apache-spark) [default], iteration 19 started ======
[2024-11-14T08:00:41.407Z] GC before operation: completed in 113.334 ms, heap usage 219.118 MB -> 53.106 MB.
[2024-11-14T08:00:44.824Z] RMSE (validation) = 3.621968954548751 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-14T08:00:47.877Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-14T08:00:51.048Z] RMSE (validation) = 1.3105189674610933 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-14T08:00:53.496Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-14T08:00:55.191Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-14T08:00:56.766Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-14T08:00:59.211Z] RMSE (validation) = 0.927571739133814 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-14T08:01:00.795Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-14T08:01:00.795Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9073522634082535.
[2024-11-14T08:01:00.795Z] The best model improves the baseline by 14.43%.
[2024-11-14T08:01:00.795Z] Movies recommended for you:
[2024-11-14T08:01:00.795Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-14T08:01:00.795Z] There is no way to check that no silent failure occurred.
[2024-11-14T08:01:00.795Z] ====== movie-lens (apache-spark) [default], iteration 19 completed (19644.242 ms) ======
[2024-11-14T08:01:01.557Z] -----------------------------------
[2024-11-14T08:01:01.557Z] renaissance-movie-lens_0_PASSED
[2024-11-14T08:01:01.557Z] -----------------------------------
[2024-11-14T08:01:01.557Z]
[2024-11-14T08:01:01.557Z] TEST TEARDOWN:
[2024-11-14T08:01:01.557Z] Nothing to be done for teardown.
[2024-11-14T08:01:01.557Z] renaissance-movie-lens_0 Finish Time: Thu Nov 14 08:01:01 2024 Epoch Time (ms): 1731571261339