renaissance-movie-lens_0
[2024-06-26T20:21:22.447Z] Running test renaissance-movie-lens_0 ...
[2024-06-26T20:21:22.447Z] ===============================================
[2024-06-26T20:21:22.447Z] renaissance-movie-lens_0 Start Time: Wed Jun 26 16:21:22 2024 Epoch Time (ms): 1719433282353
[2024-06-26T20:21:22.447Z] variation: NoOptions
[2024-06-26T20:21:22.447Z] JVM_OPTIONS:
[2024-06-26T20:21:22.447Z] { \
[2024-06-26T20:21:22.447Z] echo ""; echo "TEST SETUP:"; \
[2024-06-26T20:21:22.447Z] echo "Nothing to be done for setup."; \
[2024-06-26T20:21:22.447Z] mkdir -p "/Users/admin/workspace/workspace/Test_openjdk17_hs_extended.perf_aarch64_mac_testList_0/aqa-tests/TKG/../TKG/output_17194329494866/renaissance-movie-lens_0"; \
[2024-06-26T20:21:22.447Z] cd "/Users/admin/workspace/workspace/Test_openjdk17_hs_extended.perf_aarch64_mac_testList_0/aqa-tests/TKG/../TKG/output_17194329494866/renaissance-movie-lens_0"; \
[2024-06-26T20:21:22.447Z] echo ""; echo "TESTING:"; \
[2024-06-26T20:21:22.447Z] "/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_17194329494866/renaissance-movie-lens_0"/movie-lens.json" movie-lens; \
[2024-06-26T20:21:22.447Z] 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_17194329494866/renaissance-movie-lens_0"; else echo "-----------------------------------"; echo "renaissance-movie-lens_0""_FAILED"; echo "-----------------------------------"; fi; \
[2024-06-26T20:21:22.447Z] echo ""; echo "TEST TEARDOWN:"; \
[2024-06-26T20:21:22.447Z] echo "Nothing to be done for teardown."; \
[2024-06-26T20:21:22.447Z] } 2>&1 | tee -a "/Users/admin/workspace/workspace/Test_openjdk17_hs_extended.perf_aarch64_mac_testList_0/aqa-tests/TKG/../TKG/output_17194329494866/TestTargetResult";
[2024-06-26T20:21:22.447Z]
[2024-06-26T20:21:22.447Z] TEST SETUP:
[2024-06-26T20:21:22.447Z] Nothing to be done for setup.
[2024-06-26T20:21:22.447Z]
[2024-06-26T20:21:22.447Z] TESTING:
[2024-06-26T20:21:24.822Z] Using Spark's default log4j profile: org/apache/spark/log4j-defaults.properties
[2024-06-26T20:21:25.582Z] NOTE: 'movie-lens' benchmark uses Spark local executor with 3 (out of 3) threads.
[2024-06-26T20:21:27.409Z] Got 100004 ratings from 671 users on 9066 movies.
[2024-06-26T20:21:27.409Z] Training: 60056, validation: 20285, test: 19854
[2024-06-26T20:21:27.409Z] ====== movie-lens (apache-spark) [default], iteration 0 started ======
[2024-06-26T20:21:27.409Z] GC before operation: completed in 19.770 ms, heap usage 126.142 MB -> 36.847 MB.
[2024-06-26T20:21:30.532Z] RMSE (validation) = 3.62196895454876 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-06-26T20:21:32.937Z] RMSE (validation) = 2.1340923221100736 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-06-26T20:21:34.769Z] RMSE (validation) = 1.3105190428502682 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-06-26T20:21:36.543Z] RMSE (validation) = 0.9920028100571827 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-06-26T20:21:37.760Z] RMSE (validation) = 1.212631715629696 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-06-26T20:21:38.510Z] RMSE (validation) = 1.1025694034386657 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-06-26T20:21:39.728Z] RMSE (validation) = 0.9259652469249674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-06-26T20:21:41.018Z] RMSE (validation) = 0.8982232214592321 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-06-26T20:21:41.018Z] 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.
[2024-06-26T20:21:41.018Z] The best model improves the baseline by 14.52%.
[2024-06-26T20:21:41.018Z] Movies recommended for you:
[2024-06-26T20:21:41.018Z] WARNING: This benchmark provides no result that can be validated.
[2024-06-26T20:21:41.018Z] There is no way to check that no silent failure occurred.
[2024-06-26T20:21:41.018Z] ====== movie-lens (apache-spark) [default], iteration 0 completed (13729.557 ms) ======
[2024-06-26T20:21:41.018Z] ====== movie-lens (apache-spark) [default], iteration 1 started ======
[2024-06-26T20:21:41.018Z] GC before operation: completed in 40.146 ms, heap usage 115.524 MB -> 50.965 MB.
[2024-06-26T20:21:42.788Z] RMSE (validation) = 3.6219689545487603 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-06-26T20:21:44.585Z] RMSE (validation) = 2.1340923221100736 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-06-26T20:21:45.811Z] RMSE (validation) = 1.3105190428502682 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-06-26T20:21:47.044Z] RMSE (validation) = 0.9920028100571827 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-06-26T20:21:48.292Z] RMSE (validation) = 1.212631715629696 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-06-26T20:21:49.072Z] RMSE (validation) = 1.1025694034386657 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-06-26T20:21:49.842Z] RMSE (validation) = 0.9259652469249674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-06-26T20:21:50.606Z] RMSE (validation) = 0.8982232214592321 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-06-26T20:21:50.606Z] 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.
[2024-06-26T20:21:50.606Z] The best model improves the baseline by 14.52%.
[2024-06-26T20:21:50.606Z] Movies recommended for you:
[2024-06-26T20:21:50.606Z] WARNING: This benchmark provides no result that can be validated.
[2024-06-26T20:21:50.606Z] There is no way to check that no silent failure occurred.
[2024-06-26T20:21:50.606Z] ====== movie-lens (apache-spark) [default], iteration 1 completed (9738.283 ms) ======
[2024-06-26T20:21:50.606Z] ====== movie-lens (apache-spark) [default], iteration 2 started ======
[2024-06-26T20:21:50.959Z] GC before operation: completed in 29.557 ms, heap usage 100.060 MB -> 49.196 MB.
[2024-06-26T20:21:52.205Z] RMSE (validation) = 3.62196895454876 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-06-26T20:21:54.087Z] RMSE (validation) = 2.1340923221100736 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-06-26T20:21:55.322Z] RMSE (validation) = 1.3105190428502682 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-06-26T20:21:57.107Z] RMSE (validation) = 0.9920028100571827 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-06-26T20:21:57.893Z] RMSE (validation) = 1.212631715629696 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-06-26T20:21:58.649Z] RMSE (validation) = 1.1025694034386657 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-06-26T20:21:59.001Z] RMSE (validation) = 0.9259652469249674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-06-26T20:21:59.811Z] RMSE (validation) = 0.8982232214592321 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-06-26T20:22:00.165Z] 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.
[2024-06-26T20:22:00.165Z] The best model improves the baseline by 14.52%.
[2024-06-26T20:22:00.165Z] Movies recommended for you:
[2024-06-26T20:22:00.165Z] WARNING: This benchmark provides no result that can be validated.
[2024-06-26T20:22:00.165Z] There is no way to check that no silent failure occurred.
[2024-06-26T20:22:00.165Z] ====== movie-lens (apache-spark) [default], iteration 2 completed (9408.996 ms) ======
[2024-06-26T20:22:00.165Z] ====== movie-lens (apache-spark) [default], iteration 3 started ======
[2024-06-26T20:22:00.165Z] GC before operation: completed in 39.720 ms, heap usage 268.249 MB -> 49.676 MB.
[2024-06-26T20:22:01.953Z] RMSE (validation) = 3.62196895454876 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-06-26T20:22:03.249Z] RMSE (validation) = 2.1340923221100736 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-06-26T20:22:05.059Z] RMSE (validation) = 1.3105190428502682 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-06-26T20:22:06.288Z] RMSE (validation) = 0.9920028100571827 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-06-26T20:22:07.519Z] RMSE (validation) = 1.212631715629696 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-06-26T20:22:08.304Z] RMSE (validation) = 1.1025694034386657 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-06-26T20:22:09.072Z] RMSE (validation) = 0.9259652469249674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-06-26T20:22:09.850Z] RMSE (validation) = 0.8982232214592321 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-06-26T20:22:10.219Z] 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.
[2024-06-26T20:22:10.219Z] The best model improves the baseline by 14.52%.
[2024-06-26T20:22:10.219Z] Movies recommended for you:
[2024-06-26T20:22:10.219Z] WARNING: This benchmark provides no result that can be validated.
[2024-06-26T20:22:10.219Z] There is no way to check that no silent failure occurred.
[2024-06-26T20:22:10.219Z] ====== movie-lens (apache-spark) [default], iteration 3 completed (9916.563 ms) ======
[2024-06-26T20:22:10.219Z] ====== movie-lens (apache-spark) [default], iteration 4 started ======
[2024-06-26T20:22:10.219Z] GC before operation: completed in 35.585 ms, heap usage 290.417 MB -> 50.069 MB.
[2024-06-26T20:22:11.487Z] RMSE (validation) = 3.62196895454876 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-06-26T20:22:13.247Z] RMSE (validation) = 2.1340923221100736 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-06-26T20:22:15.030Z] RMSE (validation) = 1.3105190428502682 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-06-26T20:22:15.800Z] RMSE (validation) = 0.9920028100571827 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-06-26T20:22:17.033Z] RMSE (validation) = 1.212631715629696 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-06-26T20:22:17.798Z] RMSE (validation) = 1.1025694034386657 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-06-26T20:22:18.567Z] RMSE (validation) = 0.9259652469249674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-06-26T20:22:19.346Z] RMSE (validation) = 0.8982232214592321 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-06-26T20:22:19.706Z] 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.
[2024-06-26T20:22:19.706Z] The best model improves the baseline by 14.52%.
[2024-06-26T20:22:19.706Z] Movies recommended for you:
[2024-06-26T20:22:19.706Z] WARNING: This benchmark provides no result that can be validated.
[2024-06-26T20:22:19.706Z] There is no way to check that no silent failure occurred.
[2024-06-26T20:22:19.706Z] ====== movie-lens (apache-spark) [default], iteration 4 completed (9498.499 ms) ======
[2024-06-26T20:22:19.706Z] ====== movie-lens (apache-spark) [default], iteration 5 started ======
[2024-06-26T20:22:19.706Z] GC before operation: completed in 40.811 ms, heap usage 145.875 MB -> 50.142 MB.
[2024-06-26T20:22:21.490Z] RMSE (validation) = 3.62196895454876 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-06-26T20:22:22.717Z] RMSE (validation) = 2.1340923221100736 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-06-26T20:22:23.980Z] RMSE (validation) = 1.3105190428502682 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-06-26T20:22:25.217Z] RMSE (validation) = 0.9920028100571827 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-06-26T20:22:26.456Z] RMSE (validation) = 1.212631715629696 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-06-26T20:22:27.235Z] RMSE (validation) = 1.1025694034386657 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-06-26T20:22:28.024Z] RMSE (validation) = 0.9259652469249674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-06-26T20:22:28.795Z] RMSE (validation) = 0.8982232214592321 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-06-26T20:22:29.162Z] 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.
[2024-06-26T20:22:29.162Z] The best model improves the baseline by 14.52%.
[2024-06-26T20:22:29.162Z] Movies recommended for you:
[2024-06-26T20:22:29.162Z] WARNING: This benchmark provides no result that can be validated.
[2024-06-26T20:22:29.162Z] There is no way to check that no silent failure occurred.
[2024-06-26T20:22:29.162Z] ====== movie-lens (apache-spark) [default], iteration 5 completed (9396.750 ms) ======
[2024-06-26T20:22:29.162Z] ====== movie-lens (apache-spark) [default], iteration 6 started ======
[2024-06-26T20:22:29.162Z] GC before operation: completed in 30.799 ms, heap usage 63.494 MB -> 52.336 MB.
[2024-06-26T20:22:30.939Z] RMSE (validation) = 3.62196895454876 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-06-26T20:22:32.167Z] RMSE (validation) = 2.1340923221100736 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-06-26T20:22:33.430Z] RMSE (validation) = 1.3105190428502682 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-06-26T20:22:34.773Z] RMSE (validation) = 0.9920028100571827 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-06-26T20:22:35.998Z] RMSE (validation) = 1.212631715629696 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-06-26T20:22:36.770Z] RMSE (validation) = 1.1025694034386657 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-06-26T20:22:37.564Z] RMSE (validation) = 0.9259652469249674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-06-26T20:22:38.318Z] RMSE (validation) = 0.8982232214592321 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-06-26T20:22:38.675Z] 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.
[2024-06-26T20:22:38.675Z] The best model improves the baseline by 14.52%.
[2024-06-26T20:22:38.675Z] Movies recommended for you:
[2024-06-26T20:22:38.675Z] WARNING: This benchmark provides no result that can be validated.
[2024-06-26T20:22:38.675Z] There is no way to check that no silent failure occurred.
[2024-06-26T20:22:38.675Z] ====== movie-lens (apache-spark) [default], iteration 6 completed (9558.436 ms) ======
[2024-06-26T20:22:38.675Z] ====== movie-lens (apache-spark) [default], iteration 7 started ======
[2024-06-26T20:22:38.675Z] GC before operation: completed in 39.642 ms, heap usage 208.052 MB -> 50.267 MB.
[2024-06-26T20:22:40.443Z] RMSE (validation) = 3.62196895454876 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-06-26T20:22:41.737Z] RMSE (validation) = 2.1340923221100736 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-06-26T20:22:43.500Z] RMSE (validation) = 1.3105190428502682 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-06-26T20:22:44.736Z] RMSE (validation) = 0.9920028100571827 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-06-26T20:22:45.981Z] RMSE (validation) = 1.212631715629696 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-06-26T20:22:46.761Z] RMSE (validation) = 1.1025694034386657 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-06-26T20:22:47.528Z] RMSE (validation) = 0.9259652469249674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-06-26T20:22:48.301Z] RMSE (validation) = 0.8982232214592321 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-06-26T20:22:48.660Z] 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.
[2024-06-26T20:22:48.660Z] The best model improves the baseline by 14.52%.
[2024-06-26T20:22:48.660Z] Movies recommended for you:
[2024-06-26T20:22:48.660Z] WARNING: This benchmark provides no result that can be validated.
[2024-06-26T20:22:48.660Z] There is no way to check that no silent failure occurred.
[2024-06-26T20:22:48.660Z] ====== movie-lens (apache-spark) [default], iteration 7 completed (9896.466 ms) ======
[2024-06-26T20:22:48.660Z] ====== movie-lens (apache-spark) [default], iteration 8 started ======
[2024-06-26T20:22:48.660Z] GC before operation: completed in 38.985 ms, heap usage 266.941 MB -> 50.609 MB.
[2024-06-26T20:22:50.417Z] RMSE (validation) = 3.62196895454876 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-06-26T20:22:51.658Z] RMSE (validation) = 2.1340923221100736 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-06-26T20:22:53.449Z] RMSE (validation) = 1.3105190428502682 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-06-26T20:22:54.682Z] RMSE (validation) = 0.9920028100571827 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-06-26T20:22:55.438Z] RMSE (validation) = 1.212631715629696 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-06-26T20:22:56.222Z] RMSE (validation) = 1.1025694034386657 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-06-26T20:22:57.453Z] RMSE (validation) = 0.9259652469249674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-06-26T20:22:58.233Z] RMSE (validation) = 0.8982232214592321 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-06-26T20:22:58.583Z] 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.
[2024-06-26T20:22:58.583Z] The best model improves the baseline by 14.52%.
[2024-06-26T20:22:58.583Z] Movies recommended for you:
[2024-06-26T20:22:58.583Z] WARNING: This benchmark provides no result that can be validated.
[2024-06-26T20:22:58.583Z] There is no way to check that no silent failure occurred.
[2024-06-26T20:22:58.583Z] ====== movie-lens (apache-spark) [default], iteration 8 completed (9820.936 ms) ======
[2024-06-26T20:22:58.583Z] ====== movie-lens (apache-spark) [default], iteration 9 started ======
[2024-06-26T20:22:58.583Z] GC before operation: completed in 37.252 ms, heap usage 95.402 MB -> 50.378 MB.
[2024-06-26T20:22:59.819Z] RMSE (validation) = 3.62196895454876 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-06-26T20:23:01.624Z] RMSE (validation) = 2.1340923221100736 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-06-26T20:23:02.882Z] RMSE (validation) = 1.3105190428502682 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-06-26T20:23:04.127Z] RMSE (validation) = 0.9920028100571827 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-06-26T20:23:04.905Z] RMSE (validation) = 1.212631715629696 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-06-26T20:23:05.707Z] RMSE (validation) = 1.1025694034386657 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-06-26T20:23:06.943Z] RMSE (validation) = 0.9259652469249674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-06-26T20:23:07.712Z] RMSE (validation) = 0.8982232214592321 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-06-26T20:23:07.712Z] 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.
[2024-06-26T20:23:07.712Z] The best model improves the baseline by 14.52%.
[2024-06-26T20:23:07.712Z] Movies recommended for you:
[2024-06-26T20:23:07.712Z] WARNING: This benchmark provides no result that can be validated.
[2024-06-26T20:23:07.712Z] There is no way to check that no silent failure occurred.
[2024-06-26T20:23:07.712Z] ====== movie-lens (apache-spark) [default], iteration 9 completed (9219.035 ms) ======
[2024-06-26T20:23:07.712Z] ====== movie-lens (apache-spark) [default], iteration 10 started ======
[2024-06-26T20:23:07.712Z] GC before operation: completed in 42.849 ms, heap usage 261.280 MB -> 50.516 MB.
[2024-06-26T20:23:09.496Z] RMSE (validation) = 3.62196895454876 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-06-26T20:23:10.733Z] RMSE (validation) = 2.1340923221100736 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-06-26T20:23:11.955Z] RMSE (validation) = 1.3105190428502682 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-06-26T20:23:13.217Z] RMSE (validation) = 0.9920028100571827 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-06-26T20:23:13.985Z] RMSE (validation) = 1.212631715629696 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-06-26T20:23:14.759Z] RMSE (validation) = 1.1025694034386657 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-06-26T20:23:15.565Z] RMSE (validation) = 0.9259652469249674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-06-26T20:23:16.841Z] RMSE (validation) = 0.8982232214592321 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-06-26T20:23:16.841Z] 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.
[2024-06-26T20:23:16.841Z] The best model improves the baseline by 14.52%.
[2024-06-26T20:23:16.841Z] Movies recommended for you:
[2024-06-26T20:23:16.841Z] WARNING: This benchmark provides no result that can be validated.
[2024-06-26T20:23:16.841Z] There is no way to check that no silent failure occurred.
[2024-06-26T20:23:16.842Z] ====== movie-lens (apache-spark) [default], iteration 10 completed (8973.981 ms) ======
[2024-06-26T20:23:16.842Z] ====== movie-lens (apache-spark) [default], iteration 11 started ======
[2024-06-26T20:23:16.842Z] GC before operation: completed in 30.648 ms, heap usage 114.347 MB -> 51.273 MB.
[2024-06-26T20:23:18.063Z] RMSE (validation) = 3.6219689545487603 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-06-26T20:23:19.292Z] RMSE (validation) = 2.1340923221100736 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-06-26T20:23:20.521Z] RMSE (validation) = 1.3105190428502682 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-06-26T20:23:21.742Z] RMSE (validation) = 0.9920028100571827 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-06-26T20:23:22.512Z] RMSE (validation) = 1.212631715629696 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-06-26T20:23:23.277Z] RMSE (validation) = 1.1025694034386657 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-06-26T20:23:24.031Z] RMSE (validation) = 0.9259652469249674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-06-26T20:23:24.793Z] RMSE (validation) = 0.8982232214592321 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-06-26T20:23:24.793Z] 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.
[2024-06-26T20:23:24.793Z] The best model improves the baseline by 14.52%.
[2024-06-26T20:23:24.793Z] Movies recommended for you:
[2024-06-26T20:23:24.793Z] WARNING: This benchmark provides no result that can be validated.
[2024-06-26T20:23:24.793Z] There is no way to check that no silent failure occurred.
[2024-06-26T20:23:24.793Z] ====== movie-lens (apache-spark) [default], iteration 11 completed (8029.959 ms) ======
[2024-06-26T20:23:24.793Z] ====== movie-lens (apache-spark) [default], iteration 12 started ======
[2024-06-26T20:23:24.793Z] GC before operation: completed in 37.602 ms, heap usage 210.977 MB -> 50.342 MB.
[2024-06-26T20:23:26.018Z] RMSE (validation) = 3.6219689545487603 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-06-26T20:23:27.238Z] RMSE (validation) = 2.1340923221100736 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-06-26T20:23:28.505Z] RMSE (validation) = 1.3105190428502682 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-06-26T20:23:29.727Z] RMSE (validation) = 0.9920028100571827 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-06-26T20:23:30.502Z] RMSE (validation) = 1.212631715629696 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-06-26T20:23:30.861Z] RMSE (validation) = 1.1025694034386657 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-06-26T20:23:31.619Z] RMSE (validation) = 0.9259652469249674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-06-26T20:23:32.388Z] RMSE (validation) = 0.8982232214592321 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-06-26T20:23:32.388Z] 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.
[2024-06-26T20:23:32.388Z] The best model improves the baseline by 14.52%.
[2024-06-26T20:23:32.388Z] Movies recommended for you:
[2024-06-26T20:23:32.388Z] WARNING: This benchmark provides no result that can be validated.
[2024-06-26T20:23:32.388Z] There is no way to check that no silent failure occurred.
[2024-06-26T20:23:32.388Z] ====== movie-lens (apache-spark) [default], iteration 12 completed (7446.020 ms) ======
[2024-06-26T20:23:32.388Z] ====== movie-lens (apache-spark) [default], iteration 13 started ======
[2024-06-26T20:23:32.388Z] GC before operation: completed in 28.679 ms, heap usage 114.083 MB -> 50.370 MB.
[2024-06-26T20:23:33.611Z] RMSE (validation) = 3.62196895454876 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-06-26T20:23:34.844Z] RMSE (validation) = 2.1340923221100736 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-06-26T20:23:35.625Z] RMSE (validation) = 1.3105190428502682 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-06-26T20:23:36.887Z] RMSE (validation) = 0.9920028100571827 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-06-26T20:23:37.247Z] RMSE (validation) = 1.212631715629696 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-06-26T20:23:38.008Z] RMSE (validation) = 1.1025694034386657 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-06-26T20:23:38.773Z] RMSE (validation) = 0.9259652469249674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-06-26T20:23:39.535Z] RMSE (validation) = 0.8982232214592321 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-06-26T20:23:39.535Z] 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.
[2024-06-26T20:23:39.535Z] The best model improves the baseline by 14.52%.
[2024-06-26T20:23:39.535Z] Movies recommended for you:
[2024-06-26T20:23:39.535Z] WARNING: This benchmark provides no result that can be validated.
[2024-06-26T20:23:39.535Z] There is no way to check that no silent failure occurred.
[2024-06-26T20:23:39.535Z] ====== movie-lens (apache-spark) [default], iteration 13 completed (7324.477 ms) ======
[2024-06-26T20:23:39.535Z] ====== movie-lens (apache-spark) [default], iteration 14 started ======
[2024-06-26T20:23:39.894Z] GC before operation: completed in 30.441 ms, heap usage 236.300 MB -> 50.383 MB.
[2024-06-26T20:23:40.656Z] RMSE (validation) = 3.62196895454876 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-06-26T20:23:41.873Z] RMSE (validation) = 2.1340923221100736 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-06-26T20:23:43.110Z] RMSE (validation) = 1.3105190428502682 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-06-26T20:23:44.321Z] RMSE (validation) = 0.9920028100571827 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-06-26T20:23:44.691Z] RMSE (validation) = 1.212631715629696 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-06-26T20:23:45.446Z] RMSE (validation) = 1.1025694034386657 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-06-26T20:23:46.207Z] RMSE (validation) = 0.9259652469249674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-06-26T20:23:46.978Z] RMSE (validation) = 0.8982232214592321 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-06-26T20:23:46.978Z] 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.
[2024-06-26T20:23:46.978Z] The best model improves the baseline by 14.52%.
[2024-06-26T20:23:46.978Z] Movies recommended for you:
[2024-06-26T20:23:46.978Z] WARNING: This benchmark provides no result that can be validated.
[2024-06-26T20:23:46.978Z] There is no way to check that no silent failure occurred.
[2024-06-26T20:23:46.978Z] ====== movie-lens (apache-spark) [default], iteration 14 completed (7185.027 ms) ======
[2024-06-26T20:23:46.978Z] ====== movie-lens (apache-spark) [default], iteration 15 started ======
[2024-06-26T20:23:46.978Z] GC before operation: completed in 29.540 ms, heap usage 114.753 MB -> 50.362 MB.
[2024-06-26T20:23:48.210Z] RMSE (validation) = 3.62196895454876 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-06-26T20:23:49.447Z] RMSE (validation) = 2.1340923221100736 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-06-26T20:23:51.222Z] RMSE (validation) = 1.3105190428502682 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-06-26T20:23:52.479Z] RMSE (validation) = 0.9920028100571827 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-06-26T20:23:53.249Z] RMSE (validation) = 1.212631715629696 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-06-26T20:23:54.029Z] RMSE (validation) = 1.1025694034386657 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-06-26T20:23:54.853Z] RMSE (validation) = 0.9259652469249674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-06-26T20:23:55.620Z] RMSE (validation) = 0.8982232214592321 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-06-26T20:23:55.620Z] 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.
[2024-06-26T20:23:55.620Z] The best model improves the baseline by 14.52%.
[2024-06-26T20:23:55.620Z] Movies recommended for you:
[2024-06-26T20:23:55.620Z] WARNING: This benchmark provides no result that can be validated.
[2024-06-26T20:23:55.620Z] There is no way to check that no silent failure occurred.
[2024-06-26T20:23:55.620Z] ====== movie-lens (apache-spark) [default], iteration 15 completed (8805.160 ms) ======
[2024-06-26T20:23:55.620Z] ====== movie-lens (apache-spark) [default], iteration 16 started ======
[2024-06-26T20:23:55.620Z] GC before operation: completed in 31.262 ms, heap usage 237.589 MB -> 50.582 MB.
[2024-06-26T20:23:57.386Z] RMSE (validation) = 3.6219689545487603 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-06-26T20:23:58.605Z] RMSE (validation) = 2.1340923221100736 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-06-26T20:23:59.849Z] RMSE (validation) = 1.3105190428502682 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-06-26T20:24:00.636Z] RMSE (validation) = 0.9920028100571827 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-06-26T20:24:01.397Z] RMSE (validation) = 1.212631715629696 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-06-26T20:24:02.154Z] RMSE (validation) = 1.1025694034386657 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-06-26T20:24:02.929Z] RMSE (validation) = 0.9259652469249674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-06-26T20:24:03.709Z] RMSE (validation) = 0.8982232214592321 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-06-26T20:24:03.709Z] 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.
[2024-06-26T20:24:03.709Z] The best model improves the baseline by 14.52%.
[2024-06-26T20:24:03.709Z] Movies recommended for you:
[2024-06-26T20:24:03.709Z] WARNING: This benchmark provides no result that can be validated.
[2024-06-26T20:24:03.709Z] There is no way to check that no silent failure occurred.
[2024-06-26T20:24:03.709Z] ====== movie-lens (apache-spark) [default], iteration 16 completed (8036.293 ms) ======
[2024-06-26T20:24:03.709Z] ====== movie-lens (apache-spark) [default], iteration 17 started ======
[2024-06-26T20:24:03.709Z] GC before operation: completed in 31.094 ms, heap usage 59.941 MB -> 50.203 MB.
[2024-06-26T20:24:04.952Z] RMSE (validation) = 3.62196895454876 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-06-26T20:24:06.171Z] RMSE (validation) = 2.1340923221100736 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-06-26T20:24:07.385Z] RMSE (validation) = 1.3105190428502682 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-06-26T20:24:08.610Z] RMSE (validation) = 0.9920028100571827 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-06-26T20:24:08.963Z] RMSE (validation) = 1.212631715629696 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-06-26T20:24:09.727Z] RMSE (validation) = 1.1025694034386657 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-06-26T20:24:10.494Z] RMSE (validation) = 0.9259652469249674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-06-26T20:24:10.859Z] RMSE (validation) = 0.8982232214592321 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-06-26T20:24:10.859Z] 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.
[2024-06-26T20:24:10.859Z] The best model improves the baseline by 14.52%.
[2024-06-26T20:24:10.859Z] Movies recommended for you:
[2024-06-26T20:24:10.859Z] WARNING: This benchmark provides no result that can be validated.
[2024-06-26T20:24:10.859Z] There is no way to check that no silent failure occurred.
[2024-06-26T20:24:10.859Z] ====== movie-lens (apache-spark) [default], iteration 17 completed (7151.134 ms) ======
[2024-06-26T20:24:10.859Z] ====== movie-lens (apache-spark) [default], iteration 18 started ======
[2024-06-26T20:24:10.859Z] GC before operation: completed in 29.577 ms, heap usage 212.050 MB -> 50.356 MB.
[2024-06-26T20:24:12.082Z] RMSE (validation) = 3.6219689545487603 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-06-26T20:24:13.300Z] RMSE (validation) = 2.1340923221100736 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-06-26T20:24:14.076Z] RMSE (validation) = 1.3105190428502682 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-06-26T20:24:15.303Z] RMSE (validation) = 0.9920028100571827 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-06-26T20:24:16.072Z] RMSE (validation) = 1.212631715629696 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-06-26T20:24:16.870Z] RMSE (validation) = 1.1025694034386657 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-06-26T20:24:17.222Z] RMSE (validation) = 0.9259652469249674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-06-26T20:24:18.012Z] RMSE (validation) = 0.8982232214592321 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-06-26T20:24:18.364Z] 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.
[2024-06-26T20:24:18.364Z] The best model improves the baseline by 14.52%.
[2024-06-26T20:24:18.364Z] Movies recommended for you:
[2024-06-26T20:24:18.364Z] WARNING: This benchmark provides no result that can be validated.
[2024-06-26T20:24:18.364Z] There is no way to check that no silent failure occurred.
[2024-06-26T20:24:18.364Z] ====== movie-lens (apache-spark) [default], iteration 18 completed (7364.834 ms) ======
[2024-06-26T20:24:18.364Z] ====== movie-lens (apache-spark) [default], iteration 19 started ======
[2024-06-26T20:24:18.364Z] GC before operation: completed in 48.295 ms, heap usage 107.337 MB -> 50.637 MB.
[2024-06-26T20:24:19.581Z] RMSE (validation) = 3.62196895454876 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-06-26T20:24:20.816Z] RMSE (validation) = 2.1340923221100736 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-06-26T20:24:22.596Z] RMSE (validation) = 1.3105190428502682 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-06-26T20:24:23.823Z] RMSE (validation) = 0.9920028100571827 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-06-26T20:24:25.049Z] RMSE (validation) = 1.212631715629696 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-06-26T20:24:25.807Z] RMSE (validation) = 1.1025694034386657 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-06-26T20:24:26.574Z] RMSE (validation) = 0.9259652469249674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-06-26T20:24:27.363Z] RMSE (validation) = 0.8982232214592321 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-06-26T20:24:27.364Z] 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.
[2024-06-26T20:24:27.364Z] The best model improves the baseline by 14.52%.
[2024-06-26T20:24:27.720Z] Movies recommended for you:
[2024-06-26T20:24:27.720Z] WARNING: This benchmark provides no result that can be validated.
[2024-06-26T20:24:27.720Z] There is no way to check that no silent failure occurred.
[2024-06-26T20:24:27.720Z] ====== movie-lens (apache-spark) [default], iteration 19 completed (9140.292 ms) ======
[2024-06-26T20:24:27.720Z] -----------------------------------
[2024-06-26T20:24:27.720Z] renaissance-movie-lens_0_PASSED
[2024-06-26T20:24:27.720Z] -----------------------------------
[2024-06-26T20:24:27.720Z]
[2024-06-26T20:24:27.720Z] TEST TEARDOWN:
[2024-06-26T20:24:27.720Z] Nothing to be done for teardown.
[2024-06-26T20:24:27.720Z] renaissance-movie-lens_0 Finish Time: Wed Jun 26 16:24:27 2024 Epoch Time (ms): 1719433467599