renaissance-movie-lens_0
[2024-10-03T03:10:09.151Z] Running test renaissance-movie-lens_0 ...
[2024-10-03T03:10:09.151Z] ===============================================
[2024-10-03T03:10:09.151Z] renaissance-movie-lens_0 Start Time: Wed Oct 2 20:10:07 2024 Epoch Time (ms): 1727925007771
[2024-10-03T03:10:09.151Z] variation: NoOptions
[2024-10-03T03:10:09.151Z] JVM_OPTIONS:
[2024-10-03T03:10:09.151Z] { \
[2024-10-03T03:10:09.151Z] echo ""; echo "TEST SETUP:"; \
[2024-10-03T03:10:09.151Z] echo "Nothing to be done for setup."; \
[2024-10-03T03:10:09.151Z] mkdir -p "/Users/admin/workspace/workspace/Test_openjdk11_hs_extended.perf_x86-64_mac_testList_1/aqa-tests/TKG/../TKG/output_17279235211789/renaissance-movie-lens_0"; \
[2024-10-03T03:10:09.151Z] cd "/Users/admin/workspace/workspace/Test_openjdk11_hs_extended.perf_x86-64_mac_testList_1/aqa-tests/TKG/../TKG/output_17279235211789/renaissance-movie-lens_0"; \
[2024-10-03T03:10:09.151Z] echo ""; echo "TESTING:"; \
[2024-10-03T03:10:09.151Z] "/Users/admin/workspace/workspace/Test_openjdk11_hs_extended.perf_x86-64_mac_testList_1/jdkbinary/j2sdk-image/Contents/Home/bin/..//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 "/Users/admin/workspace/workspace/Test_openjdk11_hs_extended.perf_x86-64_mac_testList_1/aqa-tests/TKG/../../jvmtest/perf/renaissance/renaissance.jar" --json ""/Users/admin/workspace/workspace/Test_openjdk11_hs_extended.perf_x86-64_mac_testList_1/aqa-tests/TKG/../TKG/output_17279235211789/renaissance-movie-lens_0"/movie-lens.json" movie-lens; \
[2024-10-03T03:10:09.151Z] if [ $? -eq 0 ]; then echo "-----------------------------------"; echo "renaissance-movie-lens_0""_PASSED"; echo "-----------------------------------"; cd /Users/admin/workspace/workspace/Test_openjdk11_hs_extended.perf_x86-64_mac_testList_1/aqa-tests/TKG/..; rm -f -r "/Users/admin/workspace/workspace/Test_openjdk11_hs_extended.perf_x86-64_mac_testList_1/aqa-tests/TKG/../TKG/output_17279235211789/renaissance-movie-lens_0"; else echo "-----------------------------------"; echo "renaissance-movie-lens_0""_FAILED"; echo "-----------------------------------"; fi; \
[2024-10-03T03:10:09.151Z] echo ""; echo "TEST TEARDOWN:"; \
[2024-10-03T03:10:09.151Z] echo "Nothing to be done for teardown."; \
[2024-10-03T03:10:09.151Z] } 2>&1 | tee -a "/Users/admin/workspace/workspace/Test_openjdk11_hs_extended.perf_x86-64_mac_testList_1/aqa-tests/TKG/../TKG/output_17279235211789/TestTargetResult";
[2024-10-03T03:10:09.151Z]
[2024-10-03T03:10:09.151Z] TEST SETUP:
[2024-10-03T03:10:09.151Z] Nothing to be done for setup.
[2024-10-03T03:10:09.151Z]
[2024-10-03T03:10:09.151Z] TESTING:
[2024-10-03T03:10:17.717Z] Using Spark's default log4j profile: org/apache/spark/log4j-defaults.properties
[2024-10-03T03:10:22.293Z] NOTE: 'movie-lens' benchmark uses Spark local executor with 3 (out of 3) threads.
[2024-10-03T03:10:30.653Z] Got 100004 ratings from 671 users on 9066 movies.
[2024-10-03T03:10:31.052Z] Training: 60056, validation: 20285, test: 19854
[2024-10-03T03:10:31.052Z] ====== movie-lens (apache-spark) [default], iteration 0 started ======
[2024-10-03T03:10:31.052Z] GC before operation: completed in 215.082 ms, heap usage 306.943 MB -> 36.701 MB.
[2024-10-03T03:10:52.151Z] RMSE (validation) = 3.6219689545487603 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-10-03T03:11:02.815Z] RMSE (validation) = 2.1340923221100736 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-10-03T03:11:15.405Z] RMSE (validation) = 1.3105190428502682 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-10-03T03:11:25.911Z] RMSE (validation) = 0.9920028100571827 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-10-03T03:11:30.587Z] RMSE (validation) = 1.212631715629696 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-10-03T03:11:37.718Z] RMSE (validation) = 1.1025694034386657 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-10-03T03:11:43.504Z] RMSE (validation) = 0.9259652469249674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-10-03T03:11:48.533Z] RMSE (validation) = 0.8982232214592321 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-10-03T03:11:48.947Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9063003101263983.
[2024-10-03T03:11:48.948Z] The best model improves the baseline by 14.52%.
[2024-10-03T03:11:49.443Z] Movies recommended for you:
[2024-10-03T03:11:49.443Z] WARNING: This benchmark provides no result that can be validated.
[2024-10-03T03:11:49.443Z] There is no way to check that no silent failure occurred.
[2024-10-03T03:11:49.443Z] ====== movie-lens (apache-spark) [default], iteration 0 completed (78326.793 ms) ======
[2024-10-03T03:11:49.443Z] ====== movie-lens (apache-spark) [default], iteration 1 started ======
[2024-10-03T03:11:49.933Z] GC before operation: completed in 345.108 ms, heap usage 91.797 MB -> 50.836 MB.
[2024-10-03T03:12:00.243Z] RMSE (validation) = 3.62196895454876 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-10-03T03:12:08.748Z] RMSE (validation) = 2.1340923221100736 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-10-03T03:12:19.066Z] RMSE (validation) = 1.3105190428502682 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-10-03T03:12:26.081Z] RMSE (validation) = 0.9920028100571827 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-10-03T03:12:30.981Z] RMSE (validation) = 1.212631715629696 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-10-03T03:12:35.606Z] RMSE (validation) = 1.1025694034386657 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-10-03T03:12:41.469Z] RMSE (validation) = 0.9259652469249674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-10-03T03:12:47.512Z] RMSE (validation) = 0.8982232214592321 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-10-03T03:12:48.510Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9063003101263983.
[2024-10-03T03:12:48.510Z] The best model improves the baseline by 14.52%.
[2024-10-03T03:12:48.937Z] Movies recommended for you:
[2024-10-03T03:12:48.937Z] WARNING: This benchmark provides no result that can be validated.
[2024-10-03T03:12:48.937Z] There is no way to check that no silent failure occurred.
[2024-10-03T03:12:48.937Z] ====== movie-lens (apache-spark) [default], iteration 1 completed (58982.067 ms) ======
[2024-10-03T03:12:48.937Z] ====== movie-lens (apache-spark) [default], iteration 2 started ======
[2024-10-03T03:12:48.937Z] GC before operation: completed in 178.092 ms, heap usage 169.131 MB -> 48.882 MB.
[2024-10-03T03:12:59.423Z] RMSE (validation) = 3.62196895454876 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-10-03T03:13:08.141Z] RMSE (validation) = 2.1340923221100736 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-10-03T03:13:16.511Z] RMSE (validation) = 1.3105190428502682 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-10-03T03:13:25.314Z] RMSE (validation) = 0.9920028100571827 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-10-03T03:13:29.916Z] RMSE (validation) = 1.212631715629696 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-10-03T03:13:34.596Z] RMSE (validation) = 1.1025694034386657 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-10-03T03:13:39.290Z] RMSE (validation) = 0.9259652469249674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-10-03T03:13:45.090Z] RMSE (validation) = 0.8982232214592321 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-10-03T03:13:45.576Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9063003101263983.
[2024-10-03T03:13:45.576Z] The best model improves the baseline by 14.52%.
[2024-10-03T03:13:45.970Z] Movies recommended for you:
[2024-10-03T03:13:45.970Z] WARNING: This benchmark provides no result that can be validated.
[2024-10-03T03:13:45.970Z] There is no way to check that no silent failure occurred.
[2024-10-03T03:13:45.970Z] ====== movie-lens (apache-spark) [default], iteration 2 completed (56939.608 ms) ======
[2024-10-03T03:13:45.970Z] ====== movie-lens (apache-spark) [default], iteration 3 started ======
[2024-10-03T03:13:45.970Z] GC before operation: completed in 213.811 ms, heap usage 185.982 MB -> 49.145 MB.
[2024-10-03T03:13:56.681Z] RMSE (validation) = 3.62196895454876 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-10-03T03:14:05.438Z] RMSE (validation) = 2.1340923221100736 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-10-03T03:14:14.334Z] RMSE (validation) = 1.3105190428502682 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-10-03T03:14:23.022Z] RMSE (validation) = 0.9920028100571827 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-10-03T03:14:26.961Z] RMSE (validation) = 1.212631715629696 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-10-03T03:14:31.651Z] RMSE (validation) = 1.1025694034386657 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-10-03T03:14:35.466Z] RMSE (validation) = 0.9259652469249674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-10-03T03:14:40.104Z] RMSE (validation) = 0.8982232214592321 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-10-03T03:14:40.104Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9063003101263983.
[2024-10-03T03:14:40.104Z] The best model improves the baseline by 14.52%.
[2024-10-03T03:14:40.565Z] Movies recommended for you:
[2024-10-03T03:14:40.565Z] WARNING: This benchmark provides no result that can be validated.
[2024-10-03T03:14:40.565Z] There is no way to check that no silent failure occurred.
[2024-10-03T03:14:40.565Z] ====== movie-lens (apache-spark) [default], iteration 3 completed (54397.659 ms) ======
[2024-10-03T03:14:40.565Z] ====== movie-lens (apache-spark) [default], iteration 4 started ======
[2024-10-03T03:14:40.977Z] GC before operation: completed in 268.542 ms, heap usage 87.093 MB -> 51.169 MB.
[2024-10-03T03:14:49.627Z] RMSE (validation) = 3.6219689545487603 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-10-03T03:14:58.080Z] RMSE (validation) = 2.1340923221100736 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-10-03T03:15:08.412Z] RMSE (validation) = 1.3105190428502682 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-10-03T03:15:15.482Z] RMSE (validation) = 0.9920028100571827 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-10-03T03:15:20.278Z] RMSE (validation) = 1.212631715629696 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-10-03T03:15:26.282Z] RMSE (validation) = 1.1025694034386657 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-10-03T03:15:31.222Z] RMSE (validation) = 0.9259652469249674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-10-03T03:15:35.846Z] RMSE (validation) = 0.8982232214592321 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-10-03T03:15:35.846Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9063003101263983.
[2024-10-03T03:15:35.846Z] The best model improves the baseline by 14.52%.
[2024-10-03T03:15:35.846Z] Movies recommended for you:
[2024-10-03T03:15:35.846Z] WARNING: This benchmark provides no result that can be validated.
[2024-10-03T03:15:35.847Z] There is no way to check that no silent failure occurred.
[2024-10-03T03:15:35.847Z] ====== movie-lens (apache-spark) [default], iteration 4 completed (55040.170 ms) ======
[2024-10-03T03:15:35.847Z] ====== movie-lens (apache-spark) [default], iteration 5 started ======
[2024-10-03T03:15:36.322Z] GC before operation: completed in 262.432 ms, heap usage 574.417 MB -> 53.310 MB.
[2024-10-03T03:15:46.779Z] RMSE (validation) = 3.62196895454876 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-10-03T03:15:55.285Z] RMSE (validation) = 2.1340923221100736 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-10-03T03:16:05.865Z] RMSE (validation) = 1.3105190428502682 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-10-03T03:16:18.145Z] RMSE (validation) = 0.9920028100571827 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-10-03T03:16:21.974Z] RMSE (validation) = 1.212631715629696 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-10-03T03:16:26.592Z] RMSE (validation) = 1.1025694034386657 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-10-03T03:16:36.842Z] RMSE (validation) = 0.9259652469249674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-10-03T03:16:40.517Z] RMSE (validation) = 0.8982232214592321 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-10-03T03:16:41.541Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9063003101263983.
[2024-10-03T03:16:41.541Z] The best model improves the baseline by 14.52%.
[2024-10-03T03:16:42.131Z] Movies recommended for you:
[2024-10-03T03:16:42.572Z] WARNING: This benchmark provides no result that can be validated.
[2024-10-03T03:16:42.572Z] There is no way to check that no silent failure occurred.
[2024-10-03T03:16:42.572Z] ====== movie-lens (apache-spark) [default], iteration 5 completed (65687.170 ms) ======
[2024-10-03T03:16:42.572Z] ====== movie-lens (apache-spark) [default], iteration 6 started ======
[2024-10-03T03:16:42.572Z] GC before operation: completed in 275.366 ms, heap usage 433.331 MB -> 53.035 MB.
[2024-10-03T03:16:53.144Z] RMSE (validation) = 3.6219689545487603 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-10-03T03:17:03.176Z] RMSE (validation) = 2.1340923221100736 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-10-03T03:17:11.871Z] RMSE (validation) = 1.3105190428502682 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-10-03T03:17:22.126Z] RMSE (validation) = 0.9920028100571827 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-10-03T03:17:26.811Z] RMSE (validation) = 1.212631715629696 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-10-03T03:17:32.435Z] RMSE (validation) = 1.1025694034386657 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-10-03T03:17:37.006Z] RMSE (validation) = 0.9259652469249674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-10-03T03:17:42.535Z] RMSE (validation) = 0.8982232214592321 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-10-03T03:17:43.386Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9063003101263983.
[2024-10-03T03:17:43.386Z] The best model improves the baseline by 14.52%.
[2024-10-03T03:17:43.800Z] Movies recommended for you:
[2024-10-03T03:17:43.800Z] WARNING: This benchmark provides no result that can be validated.
[2024-10-03T03:17:43.800Z] There is no way to check that no silent failure occurred.
[2024-10-03T03:17:43.800Z] ====== movie-lens (apache-spark) [default], iteration 6 completed (61646.666 ms) ======
[2024-10-03T03:17:43.800Z] ====== movie-lens (apache-spark) [default], iteration 7 started ======
[2024-10-03T03:17:44.331Z] GC before operation: completed in 333.987 ms, heap usage 76.857 MB -> 53.120 MB.
[2024-10-03T03:17:54.408Z] RMSE (validation) = 3.62196895454876 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-10-03T03:18:04.462Z] RMSE (validation) = 2.1340923221100736 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-10-03T03:18:09.005Z] 24/10/02 20:18:08 WARN BlockManager: Asked to remove block rdd_18694_2, which does not exist
[2024-10-03T03:18:13.617Z] RMSE (validation) = 1.3105190428502682 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-10-03T03:18:21.899Z] RMSE (validation) = 0.9920028100571827 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-10-03T03:18:27.440Z] RMSE (validation) = 1.212631715629696 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-10-03T03:18:32.896Z] RMSE (validation) = 1.1025694034386657 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-10-03T03:18:37.494Z] RMSE (validation) = 0.9259652469249674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-10-03T03:18:43.032Z] RMSE (validation) = 0.8982232214592321 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-10-03T03:18:43.932Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9063003101263983.
[2024-10-03T03:18:43.932Z] The best model improves the baseline by 14.52%.
[2024-10-03T03:18:44.362Z] Movies recommended for you:
[2024-10-03T03:18:44.362Z] WARNING: This benchmark provides no result that can be validated.
[2024-10-03T03:18:44.362Z] There is no way to check that no silent failure occurred.
[2024-10-03T03:18:44.362Z] ====== movie-lens (apache-spark) [default], iteration 7 completed (60038.677 ms) ======
[2024-10-03T03:18:44.362Z] ====== movie-lens (apache-spark) [default], iteration 8 started ======
[2024-10-03T03:18:44.362Z] GC before operation: completed in 264.688 ms, heap usage 81.888 MB -> 52.321 MB.
[2024-10-03T03:18:54.546Z] RMSE (validation) = 3.62196895454876 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-10-03T03:19:01.552Z] RMSE (validation) = 2.1340923221100736 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-10-03T03:19:11.799Z] RMSE (validation) = 1.3105190428502682 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-10-03T03:19:22.000Z] RMSE (validation) = 0.9920028100571827 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-10-03T03:19:26.508Z] RMSE (validation) = 1.212631715629696 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-10-03T03:19:31.376Z] RMSE (validation) = 1.1025694034386657 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-10-03T03:19:35.837Z] RMSE (validation) = 0.9259652469249674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-10-03T03:19:41.477Z] RMSE (validation) = 0.8982232214592321 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-10-03T03:19:41.477Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9063003101263983.
[2024-10-03T03:19:41.477Z] The best model improves the baseline by 14.52%.
[2024-10-03T03:19:41.900Z] Movies recommended for you:
[2024-10-03T03:19:41.900Z] WARNING: This benchmark provides no result that can be validated.
[2024-10-03T03:19:41.900Z] There is no way to check that no silent failure occurred.
[2024-10-03T03:19:41.900Z] ====== movie-lens (apache-spark) [default], iteration 8 completed (57458.464 ms) ======
[2024-10-03T03:19:41.900Z] ====== movie-lens (apache-spark) [default], iteration 9 started ======
[2024-10-03T03:19:42.341Z] GC before operation: completed in 288.223 ms, heap usage 207.865 MB -> 49.889 MB.
[2024-10-03T03:19:52.305Z] RMSE (validation) = 3.62196895454876 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-10-03T03:20:02.365Z] RMSE (validation) = 2.1340923221100736 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-10-03T03:20:12.400Z] RMSE (validation) = 1.3105190428502682 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-10-03T03:20:20.655Z] RMSE (validation) = 0.9920028100571827 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-10-03T03:20:26.160Z] RMSE (validation) = 1.212631715629696 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-10-03T03:20:30.774Z] RMSE (validation) = 1.1025694034386657 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-10-03T03:20:35.431Z] RMSE (validation) = 0.9259652469249674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-10-03T03:20:41.591Z] RMSE (validation) = 0.8982232214592321 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-10-03T03:20:41.591Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9063003101263983.
[2024-10-03T03:20:41.591Z] The best model improves the baseline by 14.52%.
[2024-10-03T03:20:42.004Z] Movies recommended for you:
[2024-10-03T03:20:42.004Z] WARNING: This benchmark provides no result that can be validated.
[2024-10-03T03:20:42.004Z] There is no way to check that no silent failure occurred.
[2024-10-03T03:20:42.004Z] ====== movie-lens (apache-spark) [default], iteration 9 completed (59748.452 ms) ======
[2024-10-03T03:20:42.004Z] ====== movie-lens (apache-spark) [default], iteration 10 started ======
[2024-10-03T03:20:42.390Z] GC before operation: completed in 280.373 ms, heap usage 397.791 MB -> 53.344 MB.
[2024-10-03T03:20:52.681Z] RMSE (validation) = 3.62196895454876 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-10-03T03:21:02.846Z] RMSE (validation) = 2.1340923221100736 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-10-03T03:21:11.288Z] RMSE (validation) = 1.3105190428502682 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-10-03T03:21:21.768Z] RMSE (validation) = 0.9920028100571827 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-10-03T03:21:26.739Z] RMSE (validation) = 1.212631715629696 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-10-03T03:21:33.122Z] RMSE (validation) = 1.1025694034386657 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-10-03T03:21:39.171Z] RMSE (validation) = 0.9259652469249674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-10-03T03:21:45.162Z] RMSE (validation) = 0.8982232214592321 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-10-03T03:21:45.605Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9063003101263983.
[2024-10-03T03:21:46.075Z] The best model improves the baseline by 14.52%.
[2024-10-03T03:21:46.075Z] Movies recommended for you:
[2024-10-03T03:21:46.075Z] WARNING: This benchmark provides no result that can be validated.
[2024-10-03T03:21:46.075Z] There is no way to check that no silent failure occurred.
[2024-10-03T03:21:46.075Z] ====== movie-lens (apache-spark) [default], iteration 10 completed (63861.239 ms) ======
[2024-10-03T03:21:46.075Z] ====== movie-lens (apache-spark) [default], iteration 11 started ======
[2024-10-03T03:21:46.583Z] GC before operation: completed in 333.345 ms, heap usage 410.320 MB -> 53.082 MB.
[2024-10-03T03:21:56.715Z] RMSE (validation) = 3.6219689545487603 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-10-03T03:22:05.442Z] RMSE (validation) = 2.1340923221100736 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-10-03T03:22:15.339Z] RMSE (validation) = 1.3105190428502682 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-10-03T03:22:23.585Z] RMSE (validation) = 0.9920028100571827 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-10-03T03:22:29.313Z] RMSE (validation) = 1.212631715629696 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-10-03T03:22:35.001Z] RMSE (validation) = 1.1025694034386657 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-10-03T03:22:40.654Z] RMSE (validation) = 0.9259652469249674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-10-03T03:22:47.333Z] RMSE (validation) = 0.8982232214592321 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-10-03T03:22:47.333Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9063003101263983.
[2024-10-03T03:22:47.333Z] The best model improves the baseline by 14.52%.
[2024-10-03T03:22:47.333Z] Movies recommended for you:
[2024-10-03T03:22:47.333Z] WARNING: This benchmark provides no result that can be validated.
[2024-10-03T03:22:47.333Z] There is no way to check that no silent failure occurred.
[2024-10-03T03:22:47.333Z] ====== movie-lens (apache-spark) [default], iteration 11 completed (60997.257 ms) ======
[2024-10-03T03:22:47.333Z] ====== movie-lens (apache-spark) [default], iteration 12 started ======
[2024-10-03T03:22:48.089Z] GC before operation: completed in 347.174 ms, heap usage 440.203 MB -> 53.319 MB.
[2024-10-03T03:22:58.466Z] RMSE (validation) = 3.62196895454876 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-10-03T03:23:08.679Z] RMSE (validation) = 2.1340923221100736 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-10-03T03:23:18.760Z] RMSE (validation) = 1.3105190428502682 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-10-03T03:23:27.262Z] RMSE (validation) = 0.9920028100571827 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-10-03T03:23:33.173Z] RMSE (validation) = 1.212631715629696 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-10-03T03:23:38.817Z] RMSE (validation) = 1.1025694034386657 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-10-03T03:23:44.764Z] RMSE (validation) = 0.9259652469249674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-10-03T03:23:49.185Z] RMSE (validation) = 0.8982232214592321 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-10-03T03:23:50.019Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9063003101263983.
[2024-10-03T03:23:50.019Z] The best model improves the baseline by 14.52%.
[2024-10-03T03:23:50.438Z] Movies recommended for you:
[2024-10-03T03:23:50.438Z] WARNING: This benchmark provides no result that can be validated.
[2024-10-03T03:23:50.438Z] There is no way to check that no silent failure occurred.
[2024-10-03T03:23:50.438Z] ====== movie-lens (apache-spark) [default], iteration 12 completed (62733.163 ms) ======
[2024-10-03T03:23:50.438Z] ====== movie-lens (apache-spark) [default], iteration 13 started ======
[2024-10-03T03:23:50.880Z] GC before operation: completed in 240.378 ms, heap usage 376.261 MB -> 53.425 MB.
[2024-10-03T03:24:00.904Z] RMSE (validation) = 3.62196895454876 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-10-03T03:24:10.980Z] RMSE (validation) = 2.1340923221100736 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-10-03T03:24:22.818Z] RMSE (validation) = 1.3105190428502682 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-10-03T03:24:29.684Z] RMSE (validation) = 0.9920028100571827 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-10-03T03:24:35.687Z] RMSE (validation) = 1.212631715629696 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-10-03T03:24:41.219Z] RMSE (validation) = 1.1025694034386657 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-10-03T03:24:46.967Z] RMSE (validation) = 0.9259652469249674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-10-03T03:24:51.334Z] RMSE (validation) = 0.8982232214592321 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-10-03T03:24:52.245Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9063003101263983.
[2024-10-03T03:24:52.245Z] The best model improves the baseline by 14.52%.
[2024-10-03T03:24:52.245Z] Movies recommended for you:
[2024-10-03T03:24:52.245Z] WARNING: This benchmark provides no result that can be validated.
[2024-10-03T03:24:52.245Z] There is no way to check that no silent failure occurred.
[2024-10-03T03:24:52.245Z] ====== movie-lens (apache-spark) [default], iteration 13 completed (61666.044 ms) ======
[2024-10-03T03:24:52.245Z] ====== movie-lens (apache-spark) [default], iteration 14 started ======
[2024-10-03T03:24:52.806Z] GC before operation: completed in 176.263 ms, heap usage 253.639 MB -> 49.869 MB.
[2024-10-03T03:25:01.372Z] RMSE (validation) = 3.62196895454876 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-10-03T03:25:11.511Z] RMSE (validation) = 2.1340923221100736 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-10-03T03:25:21.560Z] RMSE (validation) = 1.3105190428502682 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-10-03T03:25:29.827Z] RMSE (validation) = 0.9920028100571827 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-10-03T03:25:33.498Z] RMSE (validation) = 1.212631715629696 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-10-03T03:25:38.237Z] RMSE (validation) = 1.1025694034386657 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-10-03T03:25:43.909Z] RMSE (validation) = 0.9259652469249674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-10-03T03:25:48.407Z] RMSE (validation) = 0.8982232214592321 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-10-03T03:25:49.419Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9063003101263983.
[2024-10-03T03:25:49.419Z] The best model improves the baseline by 14.52%.
[2024-10-03T03:25:49.419Z] Movies recommended for you:
[2024-10-03T03:25:49.419Z] WARNING: This benchmark provides no result that can be validated.
[2024-10-03T03:25:49.419Z] There is no way to check that no silent failure occurred.
[2024-10-03T03:25:49.419Z] ====== movie-lens (apache-spark) [default], iteration 14 completed (56969.411 ms) ======
[2024-10-03T03:25:49.419Z] ====== movie-lens (apache-spark) [default], iteration 15 started ======
[2024-10-03T03:25:49.419Z] GC before operation: completed in 34.944 ms, heap usage 120.926 MB -> 121.207 MB.
[2024-10-03T03:25:57.882Z] RMSE (validation) = 3.62196895454876 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-10-03T03:26:07.819Z] RMSE (validation) = 2.1340923221100736 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-10-03T03:26:18.098Z] RMSE (validation) = 1.3105190428502682 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-10-03T03:26:26.369Z] RMSE (validation) = 0.9920028100571827 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-10-03T03:26:32.176Z] RMSE (validation) = 1.212631715629696 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-10-03T03:26:36.683Z] RMSE (validation) = 1.1025694034386657 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-10-03T03:26:42.343Z] RMSE (validation) = 0.9259652469249674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-10-03T03:26:46.683Z] RMSE (validation) = 0.8982232214592321 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-10-03T03:26:46.683Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9063003101263983.
[2024-10-03T03:26:46.683Z] The best model improves the baseline by 14.52%.
[2024-10-03T03:26:46.683Z] Movies recommended for you:
[2024-10-03T03:26:46.683Z] WARNING: This benchmark provides no result that can be validated.
[2024-10-03T03:26:46.683Z] There is no way to check that no silent failure occurred.
[2024-10-03T03:26:46.683Z] ====== movie-lens (apache-spark) [default], iteration 15 completed (57263.349 ms) ======
[2024-10-03T03:26:46.683Z] ====== movie-lens (apache-spark) [default], iteration 16 started ======
[2024-10-03T03:26:47.065Z] GC before operation: completed in 205.614 ms, heap usage 372.733 MB -> 53.429 MB.
[2024-10-03T03:26:57.052Z] RMSE (validation) = 3.62196895454876 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-10-03T03:27:07.395Z] RMSE (validation) = 2.1340923221100736 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-10-03T03:27:15.532Z] RMSE (validation) = 1.3105190428502682 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-10-03T03:27:23.970Z] RMSE (validation) = 0.9920028100571827 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-10-03T03:27:28.367Z] RMSE (validation) = 1.212631715629696 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-10-03T03:27:32.697Z] RMSE (validation) = 1.1025694034386657 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-10-03T03:27:38.218Z] RMSE (validation) = 0.9259652469249674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-10-03T03:27:44.016Z] RMSE (validation) = 0.8982232214592321 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-10-03T03:27:44.955Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9063003101263983.
[2024-10-03T03:27:44.955Z] The best model improves the baseline by 14.52%.
[2024-10-03T03:27:44.955Z] Movies recommended for you:
[2024-10-03T03:27:44.955Z] WARNING: This benchmark provides no result that can be validated.
[2024-10-03T03:27:44.955Z] There is no way to check that no silent failure occurred.
[2024-10-03T03:27:44.955Z] ====== movie-lens (apache-spark) [default], iteration 16 completed (57830.998 ms) ======
[2024-10-03T03:27:44.955Z] ====== movie-lens (apache-spark) [default], iteration 17 started ======
[2024-10-03T03:27:44.955Z] GC before operation: completed in 259.454 ms, heap usage 241.641 MB -> 49.970 MB.
[2024-10-03T03:27:57.059Z] RMSE (validation) = 3.62196895454876 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-10-03T03:28:05.419Z] RMSE (validation) = 2.1340923221100736 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-10-03T03:28:15.541Z] RMSE (validation) = 1.3105190428502682 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-10-03T03:28:22.312Z] RMSE (validation) = 0.9920028100571827 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-10-03T03:28:27.807Z] RMSE (validation) = 1.212631715629696 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-10-03T03:28:33.233Z] RMSE (validation) = 1.1025694034386657 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-10-03T03:28:37.236Z] RMSE (validation) = 0.9259652469249674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-10-03T03:28:42.724Z] RMSE (validation) = 0.8982232214592321 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-10-03T03:28:43.764Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9063003101263983.
[2024-10-03T03:28:43.764Z] The best model improves the baseline by 14.52%.
[2024-10-03T03:28:43.764Z] Movies recommended for you:
[2024-10-03T03:28:43.764Z] WARNING: This benchmark provides no result that can be validated.
[2024-10-03T03:28:43.764Z] There is no way to check that no silent failure occurred.
[2024-10-03T03:28:43.764Z] ====== movie-lens (apache-spark) [default], iteration 17 completed (58816.235 ms) ======
[2024-10-03T03:28:43.764Z] ====== movie-lens (apache-spark) [default], iteration 18 started ======
[2024-10-03T03:28:44.175Z] GC before operation: completed in 220.897 ms, heap usage 410.555 MB -> 53.329 MB.
[2024-10-03T03:28:54.155Z] RMSE (validation) = 3.6219689545487603 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-10-03T03:29:02.631Z] RMSE (validation) = 2.1340923221100736 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-10-03T03:29:12.744Z] RMSE (validation) = 1.3105190428502682 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-10-03T03:29:21.071Z] RMSE (validation) = 0.9920028100571827 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-10-03T03:29:25.509Z] RMSE (validation) = 1.212631715629696 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-10-03T03:29:31.150Z] RMSE (validation) = 1.1025694034386657 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-10-03T03:29:36.964Z] RMSE (validation) = 0.9259652469249674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-10-03T03:29:42.565Z] RMSE (validation) = 0.8982232214592321 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-10-03T03:29:42.946Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9063003101263983.
[2024-10-03T03:29:42.946Z] The best model improves the baseline by 14.52%.
[2024-10-03T03:29:43.836Z] Movies recommended for you:
[2024-10-03T03:29:43.836Z] WARNING: This benchmark provides no result that can be validated.
[2024-10-03T03:29:43.836Z] There is no way to check that no silent failure occurred.
[2024-10-03T03:29:43.836Z] ====== movie-lens (apache-spark) [default], iteration 18 completed (59211.038 ms) ======
[2024-10-03T03:29:43.836Z] ====== movie-lens (apache-spark) [default], iteration 19 started ======
[2024-10-03T03:29:43.836Z] GC before operation: completed in 399.993 ms, heap usage 442.536 MB -> 53.560 MB.
[2024-10-03T03:29:53.844Z] RMSE (validation) = 3.62196895454876 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-10-03T03:30:01.966Z] RMSE (validation) = 2.1340923221100736 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-10-03T03:30:10.551Z] RMSE (validation) = 1.3105190428502682 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-10-03T03:30:18.551Z] RMSE (validation) = 0.9920028100571827 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-10-03T03:30:23.131Z] RMSE (validation) = 1.212631715629696 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-10-03T03:30:29.977Z] RMSE (validation) = 1.1025694034386657 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-10-03T03:30:34.412Z] RMSE (validation) = 0.9259652469249674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-10-03T03:30:38.949Z] RMSE (validation) = 0.8982232214592321 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-10-03T03:30:39.819Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9063003101263983.
[2024-10-03T03:30:39.819Z] The best model improves the baseline by 14.52%.
[2024-10-03T03:30:40.219Z] Movies recommended for you:
[2024-10-03T03:30:40.219Z] WARNING: This benchmark provides no result that can be validated.
[2024-10-03T03:30:40.219Z] There is no way to check that no silent failure occurred.
[2024-10-03T03:30:40.219Z] ====== movie-lens (apache-spark) [default], iteration 19 completed (56344.680 ms) ======
[2024-10-03T03:30:42.225Z] -----------------------------------
[2024-10-03T03:30:42.225Z] renaissance-movie-lens_0_PASSED
[2024-10-03T03:30:42.225Z] -----------------------------------
[2024-10-03T03:30:42.225Z]
[2024-10-03T03:30:42.225Z] TEST TEARDOWN:
[2024-10-03T03:30:42.225Z] Nothing to be done for teardown.
[2024-10-03T03:30:42.225Z] renaissance-movie-lens_0 Finish Time: Wed Oct 2 20:30:41 2024 Epoch Time (ms): 1727926241759