renaissance-movie-lens_0
[2024-06-27T04:39:48.496Z] Running test renaissance-movie-lens_0 ...
[2024-06-27T04:39:48.496Z] ===============================================
[2024-06-27T04:39:48.496Z] renaissance-movie-lens_0 Start Time: Thu Jun 27 04:39:47 2024 Epoch Time (ms): 1719463187225
[2024-06-27T04:39:48.496Z] variation: NoOptions
[2024-06-27T04:39:48.496Z] JVM_OPTIONS:
[2024-06-27T04:39:48.496Z] { \
[2024-06-27T04:39:48.496Z] echo ""; echo "TEST SETUP:"; \
[2024-06-27T04:39:48.496Z] echo "Nothing to be done for setup."; \
[2024-06-27T04:39:48.496Z] mkdir -p "/home/jenkins/workspace/Test_openjdk11_hs_extended.perf_ppc64_aix_testList_0/aqa-tests/TKG/../TKG/output_17194621789584/renaissance-movie-lens_0"; \
[2024-06-27T04:39:48.496Z] cd "/home/jenkins/workspace/Test_openjdk11_hs_extended.perf_ppc64_aix_testList_0/aqa-tests/TKG/../TKG/output_17194621789584/renaissance-movie-lens_0"; \
[2024-06-27T04:39:48.496Z] echo ""; echo "TESTING:"; \
[2024-06-27T04:39:48.496Z] "/home/jenkins/workspace/Test_openjdk11_hs_extended.perf_ppc64_aix_testList_0/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_testList_0/aqa-tests/TKG/../../jvmtest/perf/renaissance/renaissance.jar" --json ""/home/jenkins/workspace/Test_openjdk11_hs_extended.perf_ppc64_aix_testList_0/aqa-tests/TKG/../TKG/output_17194621789584/renaissance-movie-lens_0"/movie-lens.json" movie-lens; \
[2024-06-27T04:39:48.496Z] if [ $? -eq 0 ]; then echo "-----------------------------------"; echo "renaissance-movie-lens_0""_PASSED"; echo "-----------------------------------"; cd /home/jenkins/workspace/Test_openjdk11_hs_extended.perf_ppc64_aix_testList_0/aqa-tests/TKG/..; rm -f -r "/home/jenkins/workspace/Test_openjdk11_hs_extended.perf_ppc64_aix_testList_0/aqa-tests/TKG/../TKG/output_17194621789584/renaissance-movie-lens_0"; else echo "-----------------------------------"; echo "renaissance-movie-lens_0""_FAILED"; echo "-----------------------------------"; fi; \
[2024-06-27T04:39:48.496Z] echo ""; echo "TEST TEARDOWN:"; \
[2024-06-27T04:39:48.496Z] echo "Nothing to be done for teardown."; \
[2024-06-27T04:39:48.496Z] } 2>&1 | tee -a "/home/jenkins/workspace/Test_openjdk11_hs_extended.perf_ppc64_aix_testList_0/aqa-tests/TKG/../TKG/output_17194621789584/TestTargetResult";
[2024-06-27T04:39:48.496Z]
[2024-06-27T04:39:48.496Z] TEST SETUP:
[2024-06-27T04:39:48.496Z] Nothing to be done for setup.
[2024-06-27T04:39:48.496Z]
[2024-06-27T04:39:48.496Z] TESTING:
[2024-06-27T04:39:52.916Z] Using Spark's default log4j profile: org/apache/spark/log4j-defaults.properties
[2024-06-27T04:39:55.362Z] NOTE: 'movie-lens' benchmark uses Spark local executor with 8 (out of 24) threads.
[2024-06-27T04:39:58.748Z] Got 100004 ratings from 671 users on 9066 movies.
[2024-06-27T04:39:59.510Z] Training: 60056, validation: 20285, test: 19854
[2024-06-27T04:39:59.510Z] ====== movie-lens (apache-spark) [default], iteration 0 started ======
[2024-06-27T04:39:59.510Z] GC before operation: completed in 58.942 ms, heap usage 58.805 MB -> 38.084 MB.
[2024-06-27T04:40:05.074Z] RMSE (validation) = 3.621968954548751 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-06-27T04:40:09.502Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-06-27T04:40:12.884Z] RMSE (validation) = 1.3105189674610935 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-06-27T04:40:16.436Z] RMSE (validation) = 1.004540774800519 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-06-27T04:40:18.008Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-06-27T04:40:20.475Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-06-27T04:40:22.933Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-06-27T04:40:24.512Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-06-27T04:40:24.512Z] 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.
[2024-06-27T04:40:24.512Z] The best model improves the baseline by 14.43%.
[2024-06-27T04:40:25.272Z] Movies recommended for you:
[2024-06-27T04:40:25.272Z] WARNING: This benchmark provides no result that can be validated.
[2024-06-27T04:40:25.272Z] There is no way to check that no silent failure occurred.
[2024-06-27T04:40:25.272Z] ====== movie-lens (apache-spark) [default], iteration 0 completed (25479.303 ms) ======
[2024-06-27T04:40:25.272Z] ====== movie-lens (apache-spark) [default], iteration 1 started ======
[2024-06-27T04:40:25.272Z] GC before operation: completed in 101.642 ms, heap usage 124.222 MB -> 50.304 MB.
[2024-06-27T04:40:28.657Z] RMSE (validation) = 3.6219689545487506 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-06-27T04:40:32.045Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-06-27T04:40:35.439Z] RMSE (validation) = 1.3105189674610933 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-06-27T04:40:37.891Z] RMSE (validation) = 1.004540774800519 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-06-27T04:40:40.340Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-06-27T04:40:41.911Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-06-27T04:40:44.359Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-06-27T04:40:45.946Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-06-27T04:40:45.946Z] 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.
[2024-06-27T04:40:45.946Z] The best model improves the baseline by 14.43%.
[2024-06-27T04:40:45.946Z] Movies recommended for you:
[2024-06-27T04:40:45.946Z] WARNING: This benchmark provides no result that can be validated.
[2024-06-27T04:40:45.946Z] There is no way to check that no silent failure occurred.
[2024-06-27T04:40:45.946Z] ====== movie-lens (apache-spark) [default], iteration 1 completed (21182.938 ms) ======
[2024-06-27T04:40:45.946Z] ====== movie-lens (apache-spark) [default], iteration 2 started ======
[2024-06-27T04:40:46.707Z] GC before operation: completed in 113.024 ms, heap usage 445.433 MB -> 51.891 MB.
[2024-06-27T04:40:49.149Z] RMSE (validation) = 3.621968954548751 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-06-27T04:40:52.540Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-06-27T04:40:55.924Z] RMSE (validation) = 1.3105189674610933 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-06-27T04:40:59.320Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-06-27T04:41:00.891Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-06-27T04:41:02.463Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-06-27T04:41:04.912Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-06-27T04:41:06.501Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-06-27T04:41:06.501Z] 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.
[2024-06-27T04:41:06.501Z] The best model improves the baseline by 14.43%.
[2024-06-27T04:41:06.501Z] Movies recommended for you:
[2024-06-27T04:41:06.501Z] WARNING: This benchmark provides no result that can be validated.
[2024-06-27T04:41:06.501Z] There is no way to check that no silent failure occurred.
[2024-06-27T04:41:06.501Z] ====== movie-lens (apache-spark) [default], iteration 2 completed (20373.139 ms) ======
[2024-06-27T04:41:06.501Z] ====== movie-lens (apache-spark) [default], iteration 3 started ======
[2024-06-27T04:41:07.272Z] GC before operation: completed in 109.945 ms, heap usage 355.469 MB -> 52.193 MB.
[2024-06-27T04:41:09.714Z] RMSE (validation) = 3.621968954548751 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-06-27T04:41:13.104Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-06-27T04:41:16.606Z] RMSE (validation) = 1.3105189674610935 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-06-27T04:41:19.071Z] RMSE (validation) = 1.004540774800519 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-06-27T04:41:21.515Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-06-27T04:41:23.095Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-06-27T04:41:24.681Z] RMSE (validation) = 0.927571739133814 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-06-27T04:41:26.263Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-06-27T04:41:27.027Z] 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.
[2024-06-27T04:41:27.027Z] The best model improves the baseline by 14.43%.
[2024-06-27T04:41:27.027Z] Movies recommended for you:
[2024-06-27T04:41:27.027Z] WARNING: This benchmark provides no result that can be validated.
[2024-06-27T04:41:27.027Z] There is no way to check that no silent failure occurred.
[2024-06-27T04:41:27.027Z] ====== movie-lens (apache-spark) [default], iteration 3 completed (20170.803 ms) ======
[2024-06-27T04:41:27.027Z] ====== movie-lens (apache-spark) [default], iteration 4 started ======
[2024-06-27T04:41:27.027Z] GC before operation: completed in 125.322 ms, heap usage 249.686 MB -> 52.481 MB.
[2024-06-27T04:41:30.412Z] RMSE (validation) = 3.6219689545487506 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-06-27T04:41:32.867Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-06-27T04:41:36.249Z] RMSE (validation) = 1.3105189674610933 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-06-27T04:41:39.647Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-06-27T04:41:41.223Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-06-27T04:41:42.801Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-06-27T04:41:45.250Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-06-27T04:41:46.829Z] RMSE (validation) = 0.9001440981626694 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-06-27T04:41:46.829Z] 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.
[2024-06-27T04:41:46.829Z] The best model improves the baseline by 14.43%.
[2024-06-27T04:41:47.590Z] Movies recommended for you:
[2024-06-27T04:41:47.590Z] WARNING: This benchmark provides no result that can be validated.
[2024-06-27T04:41:47.590Z] There is no way to check that no silent failure occurred.
[2024-06-27T04:41:47.590Z] ====== movie-lens (apache-spark) [default], iteration 4 completed (20105.344 ms) ======
[2024-06-27T04:41:47.590Z] ====== movie-lens (apache-spark) [default], iteration 5 started ======
[2024-06-27T04:41:47.590Z] GC before operation: completed in 107.799 ms, heap usage 520.864 MB -> 56.038 MB.
[2024-06-27T04:41:50.583Z] RMSE (validation) = 3.621968954548751 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-06-27T04:41:53.944Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-06-27T04:41:56.955Z] RMSE (validation) = 1.3105189674610933 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-06-27T04:41:59.399Z] RMSE (validation) = 1.004540774800519 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-06-27T04:42:00.970Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-06-27T04:42:03.419Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-06-27T04:42:04.988Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-06-27T04:42:06.571Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-06-27T04:42:07.331Z] 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.
[2024-06-27T04:42:07.331Z] The best model improves the baseline by 14.43%.
[2024-06-27T04:42:07.331Z] Movies recommended for you:
[2024-06-27T04:42:07.331Z] WARNING: This benchmark provides no result that can be validated.
[2024-06-27T04:42:07.331Z] There is no way to check that no silent failure occurred.
[2024-06-27T04:42:07.331Z] ====== movie-lens (apache-spark) [default], iteration 5 completed (19803.186 ms) ======
[2024-06-27T04:42:07.331Z] ====== movie-lens (apache-spark) [default], iteration 6 started ======
[2024-06-27T04:42:07.331Z] GC before operation: completed in 144.500 ms, heap usage 330.826 MB -> 52.586 MB.
[2024-06-27T04:42:10.724Z] RMSE (validation) = 3.6219689545487506 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-06-27T04:42:13.169Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-06-27T04:42:16.660Z] RMSE (validation) = 1.3105189674610933 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-06-27T04:42:19.119Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-06-27T04:42:21.569Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-06-27T04:42:23.142Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-06-27T04:42:24.717Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-06-27T04:42:27.159Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-06-27T04:42:27.159Z] 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.
[2024-06-27T04:42:27.159Z] The best model improves the baseline by 14.43%.
[2024-06-27T04:42:27.159Z] Movies recommended for you:
[2024-06-27T04:42:27.159Z] WARNING: This benchmark provides no result that can be validated.
[2024-06-27T04:42:27.159Z] There is no way to check that no silent failure occurred.
[2024-06-27T04:42:27.160Z] ====== movie-lens (apache-spark) [default], iteration 6 completed (19858.227 ms) ======
[2024-06-27T04:42:27.160Z] ====== movie-lens (apache-spark) [default], iteration 7 started ======
[2024-06-27T04:42:27.160Z] GC before operation: completed in 146.773 ms, heap usage 230.653 MB -> 52.674 MB.
[2024-06-27T04:42:30.566Z] RMSE (validation) = 3.621968954548751 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-06-27T04:42:33.019Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-06-27T04:42:36.411Z] RMSE (validation) = 1.3105189674610933 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-06-27T04:42:39.822Z] RMSE (validation) = 1.004540774800519 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-06-27T04:42:41.397Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-06-27T04:42:43.002Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-06-27T04:42:44.578Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-06-27T04:42:47.031Z] RMSE (validation) = 0.9001440981626694 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-06-27T04:42:47.031Z] 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.
[2024-06-27T04:42:47.031Z] The best model improves the baseline by 14.43%.
[2024-06-27T04:42:47.031Z] Movies recommended for you:
[2024-06-27T04:42:47.031Z] WARNING: This benchmark provides no result that can be validated.
[2024-06-27T04:42:47.031Z] There is no way to check that no silent failure occurred.
[2024-06-27T04:42:47.031Z] ====== movie-lens (apache-spark) [default], iteration 7 completed (19853.443 ms) ======
[2024-06-27T04:42:47.031Z] ====== movie-lens (apache-spark) [default], iteration 8 started ======
[2024-06-27T04:42:47.031Z] GC before operation: completed in 118.287 ms, heap usage 219.385 MB -> 53.019 MB.
[2024-06-27T04:42:50.433Z] RMSE (validation) = 3.6219689545487506 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-06-27T04:42:53.834Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-06-27T04:42:56.279Z] RMSE (validation) = 1.3105189674610935 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-06-27T04:42:59.680Z] RMSE (validation) = 1.004540774800519 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-06-27T04:43:01.426Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-06-27T04:43:02.998Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-06-27T04:43:04.573Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-06-27T04:43:07.016Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-06-27T04:43:07.016Z] 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.
[2024-06-27T04:43:07.016Z] The best model improves the baseline by 14.43%.
[2024-06-27T04:43:07.016Z] Movies recommended for you:
[2024-06-27T04:43:07.016Z] WARNING: This benchmark provides no result that can be validated.
[2024-06-27T04:43:07.016Z] There is no way to check that no silent failure occurred.
[2024-06-27T04:43:07.016Z] ====== movie-lens (apache-spark) [default], iteration 8 completed (19898.064 ms) ======
[2024-06-27T04:43:07.016Z] ====== movie-lens (apache-spark) [default], iteration 9 started ======
[2024-06-27T04:43:07.016Z] GC before operation: completed in 123.662 ms, heap usage 337.841 MB -> 52.884 MB.
[2024-06-27T04:43:10.419Z] RMSE (validation) = 3.6219689545487506 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-06-27T04:43:13.817Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-06-27T04:43:16.358Z] RMSE (validation) = 1.3105189674610933 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-06-27T04:43:19.752Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-06-27T04:43:21.325Z] RMSE (validation) = 1.2218330581874073 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-06-27T04:43:22.898Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-06-27T04:43:25.346Z] RMSE (validation) = 0.9275717391338142 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-06-27T04:43:26.920Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-06-27T04:43:26.920Z] 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.
[2024-06-27T04:43:26.920Z] The best model improves the baseline by 14.43%.
[2024-06-27T04:43:27.682Z] Movies recommended for you:
[2024-06-27T04:43:27.682Z] WARNING: This benchmark provides no result that can be validated.
[2024-06-27T04:43:27.682Z] There is no way to check that no silent failure occurred.
[2024-06-27T04:43:27.682Z] ====== movie-lens (apache-spark) [default], iteration 9 completed (19975.487 ms) ======
[2024-06-27T04:43:27.682Z] ====== movie-lens (apache-spark) [default], iteration 10 started ======
[2024-06-27T04:43:27.682Z] GC before operation: completed in 117.926 ms, heap usage 647.274 MB -> 56.454 MB.
[2024-06-27T04:43:30.135Z] RMSE (validation) = 3.6219689545487506 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-06-27T04:43:33.530Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-06-27T04:43:36.932Z] RMSE (validation) = 1.3105189674610935 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-06-27T04:43:39.385Z] RMSE (validation) = 1.004540774800519 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-06-27T04:43:40.962Z] RMSE (validation) = 1.2218330581874073 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-06-27T04:43:43.435Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-06-27T04:43:45.740Z] RMSE (validation) = 0.9275717391338142 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-06-27T04:43:46.664Z] RMSE (validation) = 0.9001440981626694 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-06-27T04:43:47.425Z] 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.
[2024-06-27T04:43:47.425Z] The best model improves the baseline by 14.43%.
[2024-06-27T04:43:47.426Z] Movies recommended for you:
[2024-06-27T04:43:47.426Z] WARNING: This benchmark provides no result that can be validated.
[2024-06-27T04:43:47.426Z] There is no way to check that no silent failure occurred.
[2024-06-27T04:43:47.426Z] ====== movie-lens (apache-spark) [default], iteration 10 completed (19814.110 ms) ======
[2024-06-27T04:43:47.426Z] ====== movie-lens (apache-spark) [default], iteration 11 started ======
[2024-06-27T04:43:47.426Z] GC before operation: completed in 121.214 ms, heap usage 307.071 MB -> 52.717 MB.
[2024-06-27T04:43:50.839Z] RMSE (validation) = 3.6219689545487506 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-06-27T04:43:53.303Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-06-27T04:43:56.704Z] RMSE (validation) = 1.3105189674610933 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-06-27T04:43:59.146Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-06-27T04:44:01.595Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-06-27T04:44:03.165Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-06-27T04:44:04.744Z] RMSE (validation) = 0.9275717391338142 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-06-27T04:44:07.210Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-06-27T04:44:07.210Z] 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.
[2024-06-27T04:44:07.210Z] The best model improves the baseline by 14.43%.
[2024-06-27T04:44:07.210Z] Movies recommended for you:
[2024-06-27T04:44:07.210Z] WARNING: This benchmark provides no result that can be validated.
[2024-06-27T04:44:07.210Z] There is no way to check that no silent failure occurred.
[2024-06-27T04:44:07.210Z] ====== movie-lens (apache-spark) [default], iteration 11 completed (19972.988 ms) ======
[2024-06-27T04:44:07.210Z] ====== movie-lens (apache-spark) [default], iteration 12 started ======
[2024-06-27T04:44:07.210Z] GC before operation: completed in 109.384 ms, heap usage 330.525 MB -> 52.917 MB.
[2024-06-27T04:44:10.615Z] RMSE (validation) = 3.6219689545487506 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-06-27T04:44:13.068Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-06-27T04:44:16.600Z] RMSE (validation) = 1.3105189674610933 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-06-27T04:44:19.994Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-06-27T04:44:21.563Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-06-27T04:44:23.131Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-06-27T04:44:24.703Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-06-27T04:44:27.141Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-06-27T04:44:27.141Z] 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.
[2024-06-27T04:44:27.141Z] The best model improves the baseline by 14.43%.
[2024-06-27T04:44:27.141Z] Movies recommended for you:
[2024-06-27T04:44:27.141Z] WARNING: This benchmark provides no result that can be validated.
[2024-06-27T04:44:27.141Z] There is no way to check that no silent failure occurred.
[2024-06-27T04:44:27.141Z] ====== movie-lens (apache-spark) [default], iteration 12 completed (19871.072 ms) ======
[2024-06-27T04:44:27.141Z] ====== movie-lens (apache-spark) [default], iteration 13 started ======
[2024-06-27T04:44:27.141Z] GC before operation: completed in 112.801 ms, heap usage 310.469 MB -> 53.104 MB.
[2024-06-27T04:44:30.529Z] RMSE (validation) = 3.6219689545487506 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-06-27T04:44:33.927Z] RMSE (validation) = 2.1340923217118064 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-06-27T04:44:36.378Z] RMSE (validation) = 1.3105189674610935 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-06-27T04:44:39.766Z] RMSE (validation) = 1.004540774800519 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-06-27T04:44:41.587Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-06-27T04:44:43.161Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-06-27T04:44:45.613Z] RMSE (validation) = 0.927571739133814 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-06-27T04:44:47.196Z] RMSE (validation) = 0.9001440981626694 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-06-27T04:44:47.196Z] 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.
[2024-06-27T04:44:47.196Z] The best model improves the baseline by 14.43%.
[2024-06-27T04:44:47.196Z] Movies recommended for you:
[2024-06-27T04:44:47.196Z] WARNING: This benchmark provides no result that can be validated.
[2024-06-27T04:44:47.196Z] There is no way to check that no silent failure occurred.
[2024-06-27T04:44:47.196Z] ====== movie-lens (apache-spark) [default], iteration 13 completed (19966.684 ms) ======
[2024-06-27T04:44:47.196Z] ====== movie-lens (apache-spark) [default], iteration 14 started ======
[2024-06-27T04:44:47.196Z] GC before operation: completed in 114.734 ms, heap usage 638.676 MB -> 56.274 MB.
[2024-06-27T04:44:50.636Z] RMSE (validation) = 3.6219689545487506 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-06-27T04:44:54.024Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-06-27T04:44:56.465Z] RMSE (validation) = 1.3105189674610935 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-06-27T04:44:59.856Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-06-27T04:45:01.435Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-06-27T04:45:03.023Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-06-27T04:45:04.591Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-06-27T04:45:07.042Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-06-27T04:45:07.042Z] 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.
[2024-06-27T04:45:07.042Z] The best model improves the baseline by 14.43%.
[2024-06-27T04:45:07.042Z] Movies recommended for you:
[2024-06-27T04:45:07.042Z] WARNING: This benchmark provides no result that can be validated.
[2024-06-27T04:45:07.042Z] There is no way to check that no silent failure occurred.
[2024-06-27T04:45:07.042Z] ====== movie-lens (apache-spark) [default], iteration 14 completed (19651.638 ms) ======
[2024-06-27T04:45:07.042Z] ====== movie-lens (apache-spark) [default], iteration 15 started ======
[2024-06-27T04:45:07.043Z] GC before operation: completed in 150.042 ms, heap usage 293.298 MB -> 53.020 MB.
[2024-06-27T04:45:10.430Z] RMSE (validation) = 3.6219689545487506 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-06-27T04:45:12.879Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-06-27T04:45:16.425Z] RMSE (validation) = 1.3105189674610933 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-06-27T04:45:19.813Z] RMSE (validation) = 1.004540774800519 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-06-27T04:45:21.388Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-06-27T04:45:22.976Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-06-27T04:45:24.562Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-06-27T04:45:27.011Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-06-27T04:45:27.011Z] 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.
[2024-06-27T04:45:27.011Z] The best model improves the baseline by 14.43%.
[2024-06-27T04:45:27.011Z] Movies recommended for you:
[2024-06-27T04:45:27.011Z] WARNING: This benchmark provides no result that can be validated.
[2024-06-27T04:45:27.011Z] There is no way to check that no silent failure occurred.
[2024-06-27T04:45:27.011Z] ====== movie-lens (apache-spark) [default], iteration 15 completed (19851.011 ms) ======
[2024-06-27T04:45:27.011Z] ====== movie-lens (apache-spark) [default], iteration 16 started ======
[2024-06-27T04:45:27.011Z] GC before operation: completed in 122.524 ms, heap usage 524.831 MB -> 56.473 MB.
[2024-06-27T04:45:30.406Z] RMSE (validation) = 3.6219689545487506 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-06-27T04:45:32.862Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-06-27T04:45:36.248Z] RMSE (validation) = 1.3105189674610935 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-06-27T04:45:39.641Z] RMSE (validation) = 1.004540774800519 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-06-27T04:45:41.219Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-06-27T04:45:42.790Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-06-27T04:45:44.361Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-06-27T04:45:46.814Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-06-27T04:45:46.814Z] 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.
[2024-06-27T04:45:46.814Z] The best model improves the baseline by 14.43%.
[2024-06-27T04:45:46.814Z] Movies recommended for you:
[2024-06-27T04:45:46.814Z] WARNING: This benchmark provides no result that can be validated.
[2024-06-27T04:45:46.814Z] There is no way to check that no silent failure occurred.
[2024-06-27T04:45:46.814Z] ====== movie-lens (apache-spark) [default], iteration 16 completed (19670.910 ms) ======
[2024-06-27T04:45:46.814Z] ====== movie-lens (apache-spark) [default], iteration 17 started ======
[2024-06-27T04:45:46.814Z] GC before operation: completed in 107.610 ms, heap usage 272.479 MB -> 52.870 MB.
[2024-06-27T04:45:50.199Z] RMSE (validation) = 3.6219689545487506 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-06-27T04:45:53.610Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-06-27T04:45:56.058Z] RMSE (validation) = 1.3105189674610935 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-06-27T04:45:59.031Z] RMSE (validation) = 1.004540774800519 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-06-27T04:46:00.605Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-06-27T04:46:03.056Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-06-27T04:46:04.628Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-06-27T04:46:06.199Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-06-27T04:46:06.960Z] 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.
[2024-06-27T04:46:06.960Z] The best model improves the baseline by 14.43%.
[2024-06-27T04:46:06.960Z] Movies recommended for you:
[2024-06-27T04:46:06.960Z] WARNING: This benchmark provides no result that can be validated.
[2024-06-27T04:46:06.960Z] There is no way to check that no silent failure occurred.
[2024-06-27T04:46:06.960Z] ====== movie-lens (apache-spark) [default], iteration 17 completed (19827.139 ms) ======
[2024-06-27T04:46:06.960Z] ====== movie-lens (apache-spark) [default], iteration 18 started ======
[2024-06-27T04:46:06.960Z] GC before operation: completed in 115.459 ms, heap usage 377.926 MB -> 52.984 MB.
[2024-06-27T04:46:10.362Z] RMSE (validation) = 3.6219689545487506 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-06-27T04:46:12.821Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-06-27T04:46:16.211Z] RMSE (validation) = 1.3105189674610935 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-06-27T04:46:18.718Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-06-27T04:46:21.159Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-06-27T04:46:22.730Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-06-27T04:46:24.303Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-06-27T04:46:26.762Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-06-27T04:46:26.762Z] 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.
[2024-06-27T04:46:26.762Z] The best model improves the baseline by 14.43%.
[2024-06-27T04:46:26.762Z] Movies recommended for you:
[2024-06-27T04:46:26.762Z] WARNING: This benchmark provides no result that can be validated.
[2024-06-27T04:46:26.762Z] There is no way to check that no silent failure occurred.
[2024-06-27T04:46:26.762Z] ====== movie-lens (apache-spark) [default], iteration 18 completed (19757.062 ms) ======
[2024-06-27T04:46:26.762Z] ====== movie-lens (apache-spark) [default], iteration 19 started ======
[2024-06-27T04:46:26.762Z] GC before operation: completed in 110.890 ms, heap usage 305.691 MB -> 53.182 MB.
[2024-06-27T04:46:30.151Z] RMSE (validation) = 3.621968954548751 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-06-27T04:46:32.598Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-06-27T04:46:35.991Z] RMSE (validation) = 1.3105189674610933 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-06-27T04:46:38.442Z] RMSE (validation) = 1.004540774800519 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-06-27T04:46:40.897Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-06-27T04:46:42.479Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-06-27T04:46:44.049Z] RMSE (validation) = 0.927571739133814 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-06-27T04:46:46.489Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-06-27T04:46:46.489Z] 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.
[2024-06-27T04:46:46.489Z] The best model improves the baseline by 14.43%.
[2024-06-27T04:46:46.489Z] Movies recommended for you:
[2024-06-27T04:46:46.489Z] WARNING: This benchmark provides no result that can be validated.
[2024-06-27T04:46:46.489Z] There is no way to check that no silent failure occurred.
[2024-06-27T04:46:46.489Z] ====== movie-lens (apache-spark) [default], iteration 19 completed (19643.769 ms) ======
[2024-06-27T04:46:47.249Z] -----------------------------------
[2024-06-27T04:46:47.249Z] renaissance-movie-lens_0_PASSED
[2024-06-27T04:46:47.249Z] -----------------------------------
[2024-06-27T04:46:47.249Z]
[2024-06-27T04:46:47.249Z] TEST TEARDOWN:
[2024-06-27T04:46:47.249Z] Nothing to be done for teardown.
[2024-06-27T04:46:47.249Z] renaissance-movie-lens_0 Finish Time: Thu Jun 27 04:46:46 2024 Epoch Time (ms): 1719463606722