renaissance-movie-lens_0
[2024-07-31T20:33:32.443Z] Running test renaissance-movie-lens_0 ...
[2024-07-31T20:33:32.443Z] ===============================================
[2024-07-31T20:33:32.443Z] renaissance-movie-lens_0 Start Time: Wed Jul 31 16:33:32 2024 Epoch Time (ms): 1722458012296
[2024-07-31T20:33:32.443Z] variation: NoOptions
[2024-07-31T20:33:32.443Z] JVM_OPTIONS:
[2024-07-31T20:33:32.443Z] { \
[2024-07-31T20:33:32.443Z] echo ""; echo "TEST SETUP:"; \
[2024-07-31T20:33:32.443Z] echo "Nothing to be done for setup."; \
[2024-07-31T20:33:32.443Z] mkdir -p "/Users/admin/workspace/workspace/Test_openjdk17_hs_extended.perf_aarch64_mac_testList_0/aqa-tests/TKG/../TKG/output_17224577378238/renaissance-movie-lens_0"; \
[2024-07-31T20:33:32.443Z] cd "/Users/admin/workspace/workspace/Test_openjdk17_hs_extended.perf_aarch64_mac_testList_0/aqa-tests/TKG/../TKG/output_17224577378238/renaissance-movie-lens_0"; \
[2024-07-31T20:33:32.443Z] echo ""; echo "TESTING:"; \
[2024-07-31T20:33:32.443Z] "/Users/admin/workspace/workspace/Test_openjdk17_hs_extended.perf_aarch64_mac_testList_0/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_openjdk17_hs_extended.perf_aarch64_mac_testList_0/aqa-tests/TKG/../../jvmtest/perf/renaissance/renaissance.jar" --json ""/Users/admin/workspace/workspace/Test_openjdk17_hs_extended.perf_aarch64_mac_testList_0/aqa-tests/TKG/../TKG/output_17224577378238/renaissance-movie-lens_0"/movie-lens.json" movie-lens; \
[2024-07-31T20:33:32.443Z] if [ $? -eq 0 ]; then echo "-----------------------------------"; echo "renaissance-movie-lens_0""_PASSED"; echo "-----------------------------------"; cd /Users/admin/workspace/workspace/Test_openjdk17_hs_extended.perf_aarch64_mac_testList_0/aqa-tests/TKG/..; rm -f -r "/Users/admin/workspace/workspace/Test_openjdk17_hs_extended.perf_aarch64_mac_testList_0/aqa-tests/TKG/../TKG/output_17224577378238/renaissance-movie-lens_0"; else echo "-----------------------------------"; echo "renaissance-movie-lens_0""_FAILED"; echo "-----------------------------------"; fi; \
[2024-07-31T20:33:32.443Z] echo ""; echo "TEST TEARDOWN:"; \
[2024-07-31T20:33:32.443Z] echo "Nothing to be done for teardown."; \
[2024-07-31T20:33:32.443Z] } 2>&1 | tee -a "/Users/admin/workspace/workspace/Test_openjdk17_hs_extended.perf_aarch64_mac_testList_0/aqa-tests/TKG/../TKG/output_17224577378238/TestTargetResult";
[2024-07-31T20:33:32.443Z]
[2024-07-31T20:33:32.443Z] TEST SETUP:
[2024-07-31T20:33:32.443Z] Nothing to be done for setup.
[2024-07-31T20:33:32.443Z]
[2024-07-31T20:33:32.443Z] TESTING:
[2024-07-31T20:33:34.244Z] Using Spark's default log4j profile: org/apache/spark/log4j-defaults.properties
[2024-07-31T20:33:34.613Z] NOTE: 'movie-lens' benchmark uses Spark local executor with 3 (out of 3) threads.
[2024-07-31T20:33:35.894Z] Got 100004 ratings from 671 users on 9066 movies.
[2024-07-31T20:33:35.894Z] Training: 60056, validation: 20285, test: 19854
[2024-07-31T20:33:35.894Z] ====== movie-lens (apache-spark) [default], iteration 0 started ======
[2024-07-31T20:33:35.894Z] GC before operation: completed in 18.761 ms, heap usage 71.687 MB -> 36.939 MB.
[2024-07-31T20:33:38.373Z] RMSE (validation) = 3.6219689545487603 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-07-31T20:33:39.621Z] RMSE (validation) = 2.1340923221100736 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-07-31T20:33:41.462Z] RMSE (validation) = 1.3105190428502682 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-07-31T20:33:42.252Z] RMSE (validation) = 0.9920028100571827 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-07-31T20:33:43.030Z] RMSE (validation) = 1.212631715629696 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-07-31T20:33:43.815Z] RMSE (validation) = 1.1025694034386657 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-07-31T20:33:44.610Z] RMSE (validation) = 0.9259652469249674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-07-31T20:33:45.401Z] RMSE (validation) = 0.8982232214592321 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-07-31T20:33:45.401Z] 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.
[2024-07-31T20:33:45.401Z] The best model improves the baseline by 14.52%.
[2024-07-31T20:33:45.401Z] Movies recommended for you:
[2024-07-31T20:33:45.401Z] WARNING: This benchmark provides no result that can be validated.
[2024-07-31T20:33:45.401Z] There is no way to check that no silent failure occurred.
[2024-07-31T20:33:45.401Z] ====== movie-lens (apache-spark) [default], iteration 0 completed (9552.474 ms) ======
[2024-07-31T20:33:45.401Z] ====== movie-lens (apache-spark) [default], iteration 1 started ======
[2024-07-31T20:33:45.401Z] GC before operation: completed in 28.966 ms, heap usage 142.218 MB -> 52.910 MB.
[2024-07-31T20:33:46.676Z] RMSE (validation) = 3.62196895454876 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-07-31T20:33:47.459Z] RMSE (validation) = 2.1340923221100736 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-07-31T20:33:48.877Z] RMSE (validation) = 1.3105190428502682 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-07-31T20:33:49.660Z] RMSE (validation) = 0.9920028100571827 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-07-31T20:33:50.457Z] RMSE (validation) = 1.212631715629696 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-07-31T20:33:51.243Z] RMSE (validation) = 1.1025694034386657 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-07-31T20:33:51.609Z] RMSE (validation) = 0.9259652469249674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-07-31T20:33:52.405Z] RMSE (validation) = 0.8982232214592321 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-07-31T20:33:52.405Z] 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.
[2024-07-31T20:33:52.405Z] The best model improves the baseline by 14.52%.
[2024-07-31T20:33:52.405Z] Movies recommended for you:
[2024-07-31T20:33:52.405Z] WARNING: This benchmark provides no result that can be validated.
[2024-07-31T20:33:52.405Z] There is no way to check that no silent failure occurred.
[2024-07-31T20:33:52.405Z] ====== movie-lens (apache-spark) [default], iteration 1 completed (7016.300 ms) ======
[2024-07-31T20:33:52.405Z] ====== movie-lens (apache-spark) [default], iteration 2 started ======
[2024-07-31T20:33:52.405Z] GC before operation: completed in 27.081 ms, heap usage 65.660 MB -> 49.272 MB.
[2024-07-31T20:33:53.667Z] RMSE (validation) = 3.62196895454876 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-07-31T20:33:54.451Z] RMSE (validation) = 2.1340923221100736 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-07-31T20:33:55.712Z] RMSE (validation) = 1.3105190428502682 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-07-31T20:33:56.497Z] RMSE (validation) = 0.9920028100571827 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-07-31T20:33:56.862Z] RMSE (validation) = 1.212631715629696 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-07-31T20:33:57.640Z] RMSE (validation) = 1.1025694034386657 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-07-31T20:33:58.056Z] RMSE (validation) = 0.9259652469249674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-07-31T20:33:58.855Z] RMSE (validation) = 0.8982232214592321 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-07-31T20:33:58.855Z] 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.
[2024-07-31T20:33:58.855Z] The best model improves the baseline by 14.52%.
[2024-07-31T20:33:58.855Z] Movies recommended for you:
[2024-07-31T20:33:58.855Z] WARNING: This benchmark provides no result that can be validated.
[2024-07-31T20:33:58.855Z] There is no way to check that no silent failure occurred.
[2024-07-31T20:33:58.855Z] ====== movie-lens (apache-spark) [default], iteration 2 completed (6456.536 ms) ======
[2024-07-31T20:33:58.855Z] ====== movie-lens (apache-spark) [default], iteration 3 started ======
[2024-07-31T20:33:58.855Z] GC before operation: completed in 29.953 ms, heap usage 98.680 MB -> 49.510 MB.
[2024-07-31T20:34:00.116Z] RMSE (validation) = 3.62196895454876 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-07-31T20:34:00.904Z] RMSE (validation) = 2.1340923221100736 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-07-31T20:34:02.184Z] RMSE (validation) = 1.3105190428502682 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-07-31T20:34:02.977Z] RMSE (validation) = 0.9920028100571827 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-07-31T20:34:03.775Z] RMSE (validation) = 1.212631715629696 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-07-31T20:34:04.591Z] RMSE (validation) = 1.1025694034386657 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-07-31T20:34:04.955Z] RMSE (validation) = 0.9259652469249674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-07-31T20:34:05.741Z] RMSE (validation) = 0.8982232214592321 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-07-31T20:34:05.741Z] 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.
[2024-07-31T20:34:05.741Z] The best model improves the baseline by 14.52%.
[2024-07-31T20:34:05.741Z] Movies recommended for you:
[2024-07-31T20:34:05.741Z] WARNING: This benchmark provides no result that can be validated.
[2024-07-31T20:34:05.741Z] There is no way to check that no silent failure occurred.
[2024-07-31T20:34:05.741Z] ====== movie-lens (apache-spark) [default], iteration 3 completed (6635.976 ms) ======
[2024-07-31T20:34:05.741Z] ====== movie-lens (apache-spark) [default], iteration 4 started ======
[2024-07-31T20:34:05.741Z] GC before operation: completed in 29.482 ms, heap usage 172.378 MB -> 49.913 MB.
[2024-07-31T20:34:06.591Z] RMSE (validation) = 3.6219689545487603 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-07-31T20:34:07.874Z] RMSE (validation) = 2.1340923221100736 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-07-31T20:34:08.657Z] RMSE (validation) = 1.3105190428502682 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-07-31T20:34:09.915Z] RMSE (validation) = 0.9920028100571827 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-07-31T20:34:10.281Z] RMSE (validation) = 1.212631715629696 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-07-31T20:34:11.073Z] RMSE (validation) = 1.1025694034386657 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-07-31T20:34:11.443Z] RMSE (validation) = 0.9259652469249674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-07-31T20:34:11.809Z] RMSE (validation) = 0.8982232214592321 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-07-31T20:34:12.179Z] 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.
[2024-07-31T20:34:12.179Z] The best model improves the baseline by 14.52%.
[2024-07-31T20:34:12.179Z] Movies recommended for you:
[2024-07-31T20:34:12.179Z] WARNING: This benchmark provides no result that can be validated.
[2024-07-31T20:34:12.179Z] There is no way to check that no silent failure occurred.
[2024-07-31T20:34:12.179Z] ====== movie-lens (apache-spark) [default], iteration 4 completed (6466.234 ms) ======
[2024-07-31T20:34:12.179Z] ====== movie-lens (apache-spark) [default], iteration 5 started ======
[2024-07-31T20:34:12.179Z] GC before operation: completed in 28.250 ms, heap usage 210.805 MB -> 50.044 MB.
[2024-07-31T20:34:13.437Z] RMSE (validation) = 3.62196895454876 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-07-31T20:34:14.223Z] RMSE (validation) = 2.1340923221100736 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-07-31T20:34:15.035Z] RMSE (validation) = 1.3105190428502682 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-07-31T20:34:16.397Z] RMSE (validation) = 0.9920028100571827 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-07-31T20:34:16.761Z] RMSE (validation) = 1.212631715629696 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-07-31T20:34:17.126Z] RMSE (validation) = 1.1025694034386657 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-07-31T20:34:17.907Z] RMSE (validation) = 0.9259652469249674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-07-31T20:34:18.685Z] RMSE (validation) = 0.8982232214592321 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-07-31T20:34:18.685Z] 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.
[2024-07-31T20:34:18.685Z] The best model improves the baseline by 14.52%.
[2024-07-31T20:34:18.686Z] Movies recommended for you:
[2024-07-31T20:34:18.686Z] WARNING: This benchmark provides no result that can be validated.
[2024-07-31T20:34:18.686Z] There is no way to check that no silent failure occurred.
[2024-07-31T20:34:18.686Z] ====== movie-lens (apache-spark) [default], iteration 5 completed (6545.317 ms) ======
[2024-07-31T20:34:18.686Z] ====== movie-lens (apache-spark) [default], iteration 6 started ======
[2024-07-31T20:34:18.686Z] GC before operation: completed in 29.166 ms, heap usage 250.002 MB -> 50.193 MB.
[2024-07-31T20:34:19.948Z] RMSE (validation) = 3.62196895454876 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-07-31T20:34:20.734Z] RMSE (validation) = 2.1340923221100736 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-07-31T20:34:22.001Z] RMSE (validation) = 1.3105190428502682 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-07-31T20:34:22.789Z] RMSE (validation) = 0.9920028100571827 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-07-31T20:34:23.165Z] RMSE (validation) = 1.212631715629696 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-07-31T20:34:23.953Z] RMSE (validation) = 1.1025694034386657 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-07-31T20:34:24.329Z] RMSE (validation) = 0.9259652469249674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-07-31T20:34:25.105Z] RMSE (validation) = 0.8982232214592321 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-07-31T20:34:25.105Z] 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.
[2024-07-31T20:34:25.105Z] The best model improves the baseline by 14.52%.
[2024-07-31T20:34:25.105Z] Movies recommended for you:
[2024-07-31T20:34:25.105Z] WARNING: This benchmark provides no result that can be validated.
[2024-07-31T20:34:25.105Z] There is no way to check that no silent failure occurred.
[2024-07-31T20:34:25.105Z] ====== movie-lens (apache-spark) [default], iteration 6 completed (6472.122 ms) ======
[2024-07-31T20:34:25.105Z] ====== movie-lens (apache-spark) [default], iteration 7 started ======
[2024-07-31T20:34:25.105Z] GC before operation: completed in 30.812 ms, heap usage 173.928 MB -> 50.320 MB.
[2024-07-31T20:34:26.365Z] RMSE (validation) = 3.6219689545487603 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-07-31T20:34:28.189Z] RMSE (validation) = 2.1340923221100736 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-07-31T20:34:29.468Z] RMSE (validation) = 1.3105190428502682 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-07-31T20:34:30.255Z] RMSE (validation) = 0.9920028100571827 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-07-31T20:34:31.039Z] RMSE (validation) = 1.212631715629696 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-07-31T20:34:31.404Z] RMSE (validation) = 1.1025694034386657 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-07-31T20:34:32.188Z] RMSE (validation) = 0.9259652469249674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-07-31T20:34:32.990Z] RMSE (validation) = 0.8982232214592321 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-07-31T20:34:32.990Z] 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.
[2024-07-31T20:34:32.990Z] The best model improves the baseline by 14.52%.
[2024-07-31T20:34:32.990Z] Movies recommended for you:
[2024-07-31T20:34:32.990Z] WARNING: This benchmark provides no result that can be validated.
[2024-07-31T20:34:32.990Z] There is no way to check that no silent failure occurred.
[2024-07-31T20:34:32.990Z] ====== movie-lens (apache-spark) [default], iteration 7 completed (7692.880 ms) ======
[2024-07-31T20:34:32.990Z] ====== movie-lens (apache-spark) [default], iteration 8 started ======
[2024-07-31T20:34:32.990Z] GC before operation: completed in 27.939 ms, heap usage 128.216 MB -> 50.421 MB.
[2024-07-31T20:34:33.773Z] RMSE (validation) = 3.62196895454876 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-07-31T20:34:34.599Z] RMSE (validation) = 2.1340923221100736 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-07-31T20:34:35.887Z] RMSE (validation) = 1.3105190428502682 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-07-31T20:34:36.676Z] RMSE (validation) = 0.9920028100571827 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-07-31T20:34:37.461Z] RMSE (validation) = 1.212631715629696 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-07-31T20:34:37.827Z] RMSE (validation) = 1.1025694034386657 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-07-31T20:34:38.606Z] RMSE (validation) = 0.9259652469249674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-07-31T20:34:38.974Z] RMSE (validation) = 0.8982232214592321 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-07-31T20:34:39.376Z] 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.
[2024-07-31T20:34:39.376Z] The best model improves the baseline by 14.52%.
[2024-07-31T20:34:39.376Z] Movies recommended for you:
[2024-07-31T20:34:39.376Z] WARNING: This benchmark provides no result that can be validated.
[2024-07-31T20:34:39.376Z] There is no way to check that no silent failure occurred.
[2024-07-31T20:34:39.376Z] ====== movie-lens (apache-spark) [default], iteration 8 completed (6325.234 ms) ======
[2024-07-31T20:34:39.376Z] ====== movie-lens (apache-spark) [default], iteration 9 started ======
[2024-07-31T20:34:39.376Z] GC before operation: completed in 28.836 ms, heap usage 183.177 MB -> 50.456 MB.
[2024-07-31T20:34:40.151Z] RMSE (validation) = 3.62196895454876 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-07-31T20:34:41.416Z] RMSE (validation) = 2.1340923221100736 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-07-31T20:34:42.202Z] RMSE (validation) = 1.3105190428502682 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-07-31T20:34:42.990Z] RMSE (validation) = 0.9920028100571827 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-07-31T20:34:43.780Z] RMSE (validation) = 1.212631715629696 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-07-31T20:34:44.569Z] RMSE (validation) = 1.1025694034386657 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-07-31T20:34:44.945Z] RMSE (validation) = 0.9259652469249674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-07-31T20:34:45.724Z] RMSE (validation) = 0.8982232214592321 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-07-31T20:34:45.724Z] 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.
[2024-07-31T20:34:45.724Z] The best model improves the baseline by 14.52%.
[2024-07-31T20:34:45.724Z] Movies recommended for you:
[2024-07-31T20:34:45.724Z] WARNING: This benchmark provides no result that can be validated.
[2024-07-31T20:34:45.724Z] There is no way to check that no silent failure occurred.
[2024-07-31T20:34:45.724Z] ====== movie-lens (apache-spark) [default], iteration 9 completed (6477.400 ms) ======
[2024-07-31T20:34:45.724Z] ====== movie-lens (apache-spark) [default], iteration 10 started ======
[2024-07-31T20:34:45.724Z] GC before operation: completed in 28.665 ms, heap usage 136.684 MB -> 50.466 MB.
[2024-07-31T20:34:46.994Z] RMSE (validation) = 3.62196895454876 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-07-31T20:34:47.788Z] RMSE (validation) = 2.1340923221100736 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-07-31T20:34:48.565Z] RMSE (validation) = 1.3105190428502682 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-07-31T20:34:49.831Z] RMSE (validation) = 0.9920028100571827 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-07-31T20:34:50.198Z] RMSE (validation) = 1.212631715629696 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-07-31T20:34:50.572Z] RMSE (validation) = 1.1025694034386657 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-07-31T20:34:51.351Z] RMSE (validation) = 0.9259652469249674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-07-31T20:34:52.139Z] RMSE (validation) = 0.8982232214592321 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-07-31T20:34:52.139Z] 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.
[2024-07-31T20:34:52.139Z] The best model improves the baseline by 14.52%.
[2024-07-31T20:34:52.139Z] Movies recommended for you:
[2024-07-31T20:34:52.139Z] WARNING: This benchmark provides no result that can be validated.
[2024-07-31T20:34:52.139Z] There is no way to check that no silent failure occurred.
[2024-07-31T20:34:52.139Z] ====== movie-lens (apache-spark) [default], iteration 10 completed (6260.618 ms) ======
[2024-07-31T20:34:52.139Z] ====== movie-lens (apache-spark) [default], iteration 11 started ======
[2024-07-31T20:34:52.139Z] GC before operation: completed in 32.729 ms, heap usage 292.158 MB -> 50.311 MB.
[2024-07-31T20:34:52.927Z] RMSE (validation) = 3.62196895454876 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-07-31T20:34:54.196Z] RMSE (validation) = 2.1340923221100736 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-07-31T20:34:54.980Z] RMSE (validation) = 1.3105190428502682 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-07-31T20:34:56.258Z] RMSE (validation) = 0.9920028100571827 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-07-31T20:34:56.642Z] RMSE (validation) = 1.212631715629696 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-07-31T20:34:57.012Z] RMSE (validation) = 1.1025694034386657 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-07-31T20:34:57.807Z] RMSE (validation) = 0.9259652469249674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-07-31T20:34:58.179Z] RMSE (validation) = 0.8982232214592321 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-07-31T20:34:58.179Z] 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.
[2024-07-31T20:34:58.179Z] The best model improves the baseline by 14.52%.
[2024-07-31T20:34:58.179Z] Movies recommended for you:
[2024-07-31T20:34:58.179Z] WARNING: This benchmark provides no result that can be validated.
[2024-07-31T20:34:58.179Z] There is no way to check that no silent failure occurred.
[2024-07-31T20:34:58.179Z] ====== movie-lens (apache-spark) [default], iteration 11 completed (6195.994 ms) ======
[2024-07-31T20:34:58.179Z] ====== movie-lens (apache-spark) [default], iteration 12 started ======
[2024-07-31T20:34:58.179Z] GC before operation: completed in 29.337 ms, heap usage 71.797 MB -> 50.584 MB.
[2024-07-31T20:34:59.445Z] RMSE (validation) = 3.62196895454876 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-07-31T20:35:00.251Z] RMSE (validation) = 2.1340923221100736 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-07-31T20:35:01.534Z] RMSE (validation) = 1.3105190428502682 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-07-31T20:35:02.326Z] RMSE (validation) = 0.9920028100571827 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-07-31T20:35:03.133Z] RMSE (validation) = 1.212631715629696 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-07-31T20:35:03.580Z] RMSE (validation) = 1.1025694034386657 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-07-31T20:35:03.951Z] RMSE (validation) = 0.9259652469249674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-07-31T20:35:04.337Z] RMSE (validation) = 0.8982232214592321 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-07-31T20:35:04.724Z] 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.
[2024-07-31T20:35:04.724Z] The best model improves the baseline by 14.52%.
[2024-07-31T20:35:04.724Z] Movies recommended for you:
[2024-07-31T20:35:04.724Z] WARNING: This benchmark provides no result that can be validated.
[2024-07-31T20:35:04.724Z] There is no way to check that no silent failure occurred.
[2024-07-31T20:35:04.724Z] ====== movie-lens (apache-spark) [default], iteration 12 completed (6282.173 ms) ======
[2024-07-31T20:35:04.724Z] ====== movie-lens (apache-spark) [default], iteration 13 started ======
[2024-07-31T20:35:04.724Z] GC before operation: completed in 30.250 ms, heap usage 415.542 MB -> 53.928 MB.
[2024-07-31T20:35:05.510Z] RMSE (validation) = 3.62196895454876 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-07-31T20:35:06.420Z] RMSE (validation) = 2.1340923221100736 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-07-31T20:35:07.208Z] RMSE (validation) = 1.3105190428502682 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-07-31T20:35:08.480Z] RMSE (validation) = 0.9920028100571827 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-07-31T20:35:08.848Z] RMSE (validation) = 1.212631715629696 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-07-31T20:35:09.657Z] RMSE (validation) = 1.1025694034386657 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-07-31T20:35:10.024Z] RMSE (validation) = 0.9259652469249674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-07-31T20:35:10.841Z] RMSE (validation) = 0.8982232214592321 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-07-31T20:35:10.841Z] 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.
[2024-07-31T20:35:10.841Z] The best model improves the baseline by 14.52%.
[2024-07-31T20:35:10.841Z] Movies recommended for you:
[2024-07-31T20:35:10.841Z] WARNING: This benchmark provides no result that can be validated.
[2024-07-31T20:35:10.841Z] There is no way to check that no silent failure occurred.
[2024-07-31T20:35:10.841Z] ====== movie-lens (apache-spark) [default], iteration 13 completed (6109.269 ms) ======
[2024-07-31T20:35:10.841Z] ====== movie-lens (apache-spark) [default], iteration 14 started ======
[2024-07-31T20:35:10.841Z] GC before operation: completed in 30.345 ms, heap usage 74.216 MB -> 50.286 MB.
[2024-07-31T20:35:11.619Z] RMSE (validation) = 3.6219689545487603 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-07-31T20:35:12.881Z] RMSE (validation) = 2.1340923221100736 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-07-31T20:35:13.675Z] RMSE (validation) = 1.3105190428502682 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-07-31T20:35:14.933Z] RMSE (validation) = 0.9920028100571827 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-07-31T20:35:15.295Z] RMSE (validation) = 1.212631715629696 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-07-31T20:35:16.077Z] RMSE (validation) = 1.1025694034386657 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-07-31T20:35:16.447Z] RMSE (validation) = 0.9259652469249674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-07-31T20:35:17.240Z] RMSE (validation) = 0.8982232214592321 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-07-31T20:35:17.240Z] 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.
[2024-07-31T20:35:17.240Z] The best model improves the baseline by 14.52%.
[2024-07-31T20:35:17.240Z] Movies recommended for you:
[2024-07-31T20:35:17.240Z] WARNING: This benchmark provides no result that can be validated.
[2024-07-31T20:35:17.240Z] There is no way to check that no silent failure occurred.
[2024-07-31T20:35:17.240Z] ====== movie-lens (apache-spark) [default], iteration 14 completed (6518.370 ms) ======
[2024-07-31T20:35:17.240Z] ====== movie-lens (apache-spark) [default], iteration 15 started ======
[2024-07-31T20:35:17.240Z] GC before operation: completed in 29.535 ms, heap usage 195.029 MB -> 50.547 MB.
[2024-07-31T20:35:18.507Z] RMSE (validation) = 3.62196895454876 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-07-31T20:35:19.303Z] RMSE (validation) = 2.1340923221100736 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-07-31T20:35:20.096Z] RMSE (validation) = 1.3105190428502682 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-07-31T20:35:21.357Z] RMSE (validation) = 0.9920028100571827 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-07-31T20:35:21.725Z] RMSE (validation) = 1.212631715629696 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-07-31T20:35:22.511Z] RMSE (validation) = 1.1025694034386657 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-07-31T20:35:22.877Z] RMSE (validation) = 0.9259652469249674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-07-31T20:35:23.657Z] RMSE (validation) = 0.8982232214592321 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-07-31T20:35:23.657Z] 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.
[2024-07-31T20:35:23.657Z] The best model improves the baseline by 14.52%.
[2024-07-31T20:35:23.657Z] Movies recommended for you:
[2024-07-31T20:35:23.657Z] WARNING: This benchmark provides no result that can be validated.
[2024-07-31T20:35:23.657Z] There is no way to check that no silent failure occurred.
[2024-07-31T20:35:23.657Z] ====== movie-lens (apache-spark) [default], iteration 15 completed (6315.465 ms) ======
[2024-07-31T20:35:23.657Z] ====== movie-lens (apache-spark) [default], iteration 16 started ======
[2024-07-31T20:35:23.657Z] GC before operation: completed in 28.922 ms, heap usage 79.895 MB -> 50.740 MB.
[2024-07-31T20:35:24.915Z] RMSE (validation) = 3.6219689545487603 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-07-31T20:35:25.294Z] RMSE (validation) = 2.1340923221100736 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-07-31T20:35:26.549Z] RMSE (validation) = 1.3105190428502682 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-07-31T20:35:27.334Z] RMSE (validation) = 0.9920028100571827 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-07-31T20:35:27.704Z] RMSE (validation) = 1.212631715629696 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-07-31T20:35:28.503Z] RMSE (validation) = 1.1025694034386657 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-07-31T20:35:28.869Z] RMSE (validation) = 0.9259652469249674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-07-31T20:35:29.654Z] RMSE (validation) = 0.8982232214592321 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-07-31T20:35:29.654Z] 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.
[2024-07-31T20:35:29.654Z] The best model improves the baseline by 14.52%.
[2024-07-31T20:35:29.654Z] Movies recommended for you:
[2024-07-31T20:35:29.654Z] WARNING: This benchmark provides no result that can be validated.
[2024-07-31T20:35:29.654Z] There is no way to check that no silent failure occurred.
[2024-07-31T20:35:29.654Z] ====== movie-lens (apache-spark) [default], iteration 16 completed (5873.447 ms) ======
[2024-07-31T20:35:29.654Z] ====== movie-lens (apache-spark) [default], iteration 17 started ======
[2024-07-31T20:35:29.654Z] GC before operation: completed in 31.033 ms, heap usage 396.983 MB -> 53.946 MB.
[2024-07-31T20:35:30.436Z] RMSE (validation) = 3.62196895454876 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-07-31T20:35:31.243Z] RMSE (validation) = 2.1340923221100736 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-07-31T20:35:32.094Z] RMSE (validation) = 1.3105190428502682 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-07-31T20:35:33.351Z] RMSE (validation) = 0.9920028100571827 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-07-31T20:35:33.713Z] RMSE (validation) = 1.212631715629696 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-07-31T20:35:34.098Z] RMSE (validation) = 1.1025694034386657 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-07-31T20:35:34.911Z] RMSE (validation) = 0.9259652469249674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-07-31T20:35:35.288Z] RMSE (validation) = 0.8982232214592321 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-07-31T20:35:35.288Z] 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.
[2024-07-31T20:35:35.288Z] The best model improves the baseline by 14.52%.
[2024-07-31T20:35:35.665Z] Movies recommended for you:
[2024-07-31T20:35:35.665Z] WARNING: This benchmark provides no result that can be validated.
[2024-07-31T20:35:35.666Z] There is no way to check that no silent failure occurred.
[2024-07-31T20:35:35.666Z] ====== movie-lens (apache-spark) [default], iteration 17 completed (5879.881 ms) ======
[2024-07-31T20:35:35.666Z] ====== movie-lens (apache-spark) [default], iteration 18 started ======
[2024-07-31T20:35:35.666Z] GC before operation: completed in 29.645 ms, heap usage 72.552 MB -> 50.396 MB.
[2024-07-31T20:35:36.468Z] RMSE (validation) = 3.62196895454876 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-07-31T20:35:37.254Z] RMSE (validation) = 2.1340923221100736 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-07-31T20:35:38.035Z] RMSE (validation) = 1.3105190428502682 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-07-31T20:35:38.851Z] RMSE (validation) = 0.9920028100571827 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-07-31T20:35:39.213Z] RMSE (validation) = 1.212631715629696 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-07-31T20:35:39.591Z] RMSE (validation) = 1.1025694034386657 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-07-31T20:35:40.388Z] RMSE (validation) = 0.9259652469249674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-07-31T20:35:40.758Z] RMSE (validation) = 0.8982232214592321 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-07-31T20:35:41.136Z] 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.
[2024-07-31T20:35:41.136Z] The best model improves the baseline by 14.52%.
[2024-07-31T20:35:41.136Z] Movies recommended for you:
[2024-07-31T20:35:41.136Z] WARNING: This benchmark provides no result that can be validated.
[2024-07-31T20:35:41.136Z] There is no way to check that no silent failure occurred.
[2024-07-31T20:35:41.136Z] ====== movie-lens (apache-spark) [default], iteration 18 completed (5543.077 ms) ======
[2024-07-31T20:35:41.136Z] ====== movie-lens (apache-spark) [default], iteration 19 started ======
[2024-07-31T20:35:41.136Z] GC before operation: completed in 30.075 ms, heap usage 178.636 MB -> 50.639 MB.
[2024-07-31T20:35:41.933Z] RMSE (validation) = 3.6219689545487603 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-07-31T20:35:43.197Z] RMSE (validation) = 2.1340923221100736 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-07-31T20:35:43.992Z] RMSE (validation) = 1.3105190428502682 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-07-31T20:35:44.850Z] RMSE (validation) = 0.9920028100571827 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-07-31T20:35:45.637Z] RMSE (validation) = 1.212631715629696 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-07-31T20:35:45.999Z] RMSE (validation) = 1.1025694034386657 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-07-31T20:35:46.789Z] RMSE (validation) = 0.9259652469249674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-07-31T20:35:47.157Z] RMSE (validation) = 0.8982232214592321 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-07-31T20:35:47.157Z] 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.
[2024-07-31T20:35:47.157Z] The best model improves the baseline by 14.52%.
[2024-07-31T20:35:47.157Z] Movies recommended for you:
[2024-07-31T20:35:47.157Z] WARNING: This benchmark provides no result that can be validated.
[2024-07-31T20:35:47.157Z] There is no way to check that no silent failure occurred.
[2024-07-31T20:35:47.157Z] ====== movie-lens (apache-spark) [default], iteration 19 completed (6109.254 ms) ======
[2024-07-31T20:35:47.519Z] -----------------------------------
[2024-07-31T20:35:47.519Z] renaissance-movie-lens_0_PASSED
[2024-07-31T20:35:47.519Z] -----------------------------------
[2024-07-31T20:35:47.519Z]
[2024-07-31T20:35:47.519Z] TEST TEARDOWN:
[2024-07-31T20:35:47.519Z] Nothing to be done for teardown.
[2024-07-31T20:35:47.519Z] renaissance-movie-lens_0 Finish Time: Wed Jul 31 16:35:47 2024 Epoch Time (ms): 1722458147195