renaissance-movie-lens_0
[2024-06-26T23:17:09.470Z] Running test renaissance-movie-lens_0 ...
[2024-06-26T23:17:09.470Z] ===============================================
[2024-06-26T23:17:09.470Z] renaissance-movie-lens_0 Start Time: Wed Jun 26 16:17:09 2024 Epoch Time (ms): 1719443829140
[2024-06-26T23:17:09.470Z] variation: NoOptions
[2024-06-26T23:17:09.470Z] JVM_OPTIONS:
[2024-06-26T23:17:09.470Z] { \
[2024-06-26T23:17:09.470Z] echo ""; echo "TEST SETUP:"; \
[2024-06-26T23:17:09.470Z] echo "Nothing to be done for setup."; \
[2024-06-26T23:17:09.470Z] mkdir -p "/Users/admin/workspace/workspace/Test_openjdk17_hs_extended.perf_x86-64_mac_testList_1/aqa-tests/TKG/../TKG/output_17194430781820/renaissance-movie-lens_0"; \
[2024-06-26T23:17:09.470Z] cd "/Users/admin/workspace/workspace/Test_openjdk17_hs_extended.perf_x86-64_mac_testList_1/aqa-tests/TKG/../TKG/output_17194430781820/renaissance-movie-lens_0"; \
[2024-06-26T23:17:09.470Z] echo ""; echo "TESTING:"; \
[2024-06-26T23:17:09.470Z] "/Users/admin/workspace/workspace/Test_openjdk17_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_openjdk17_hs_extended.perf_x86-64_mac_testList_1/aqa-tests/TKG/../../jvmtest/perf/renaissance/renaissance.jar" --json ""/Users/admin/workspace/workspace/Test_openjdk17_hs_extended.perf_x86-64_mac_testList_1/aqa-tests/TKG/../TKG/output_17194430781820/renaissance-movie-lens_0"/movie-lens.json" movie-lens; \
[2024-06-26T23:17:09.470Z] if [ $? -eq 0 ]; then echo "-----------------------------------"; echo "renaissance-movie-lens_0""_PASSED"; echo "-----------------------------------"; cd /Users/admin/workspace/workspace/Test_openjdk17_hs_extended.perf_x86-64_mac_testList_1/aqa-tests/TKG/..; rm -f -r "/Users/admin/workspace/workspace/Test_openjdk17_hs_extended.perf_x86-64_mac_testList_1/aqa-tests/TKG/../TKG/output_17194430781820/renaissance-movie-lens_0"; else echo "-----------------------------------"; echo "renaissance-movie-lens_0""_FAILED"; echo "-----------------------------------"; fi; \
[2024-06-26T23:17:09.470Z] echo ""; echo "TEST TEARDOWN:"; \
[2024-06-26T23:17:09.470Z] echo "Nothing to be done for teardown."; \
[2024-06-26T23:17:09.470Z] } 2>&1 | tee -a "/Users/admin/workspace/workspace/Test_openjdk17_hs_extended.perf_x86-64_mac_testList_1/aqa-tests/TKG/../TKG/output_17194430781820/TestTargetResult";
[2024-06-26T23:17:09.470Z]
[2024-06-26T23:17:09.470Z] TEST SETUP:
[2024-06-26T23:17:09.470Z] Nothing to be done for setup.
[2024-06-26T23:17:09.470Z]
[2024-06-26T23:17:09.470Z] TESTING:
[2024-06-26T23:17:13.840Z] Using Spark's default log4j profile: org/apache/spark/log4j-defaults.properties
[2024-06-26T23:17:15.943Z] NOTE: 'movie-lens' benchmark uses Spark local executor with 3 (out of 3) threads.
[2024-06-26T23:17:20.656Z] Got 100004 ratings from 671 users on 9066 movies.
[2024-06-26T23:17:21.735Z] Training: 60056, validation: 20285, test: 19854
[2024-06-26T23:17:21.735Z] ====== movie-lens (apache-spark) [default], iteration 0 started ======
[2024-06-26T23:17:22.320Z] GC before operation: completed in 217.835 ms, heap usage 69.672 MB -> 37.672 MB.
[2024-06-26T23:17:37.265Z] RMSE (validation) = 3.62196895454876 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-06-26T23:17:46.030Z] RMSE (validation) = 2.1340923221100736 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-06-26T23:17:54.728Z] RMSE (validation) = 1.3105190428502682 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-06-26T23:18:03.297Z] RMSE (validation) = 0.9920028100571827 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-06-26T23:18:06.867Z] RMSE (validation) = 1.212631715629696 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-06-26T23:18:12.513Z] RMSE (validation) = 1.1025694034386657 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-06-26T23:18:16.260Z] RMSE (validation) = 0.9259652469249674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-06-26T23:18:20.756Z] RMSE (validation) = 0.8982232214592321 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-06-26T23:18:21.193Z] 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.
[2024-06-26T23:18:21.609Z] The best model improves the baseline by 14.52%.
[2024-06-26T23:18:22.049Z] Movies recommended for you:
[2024-06-26T23:18:22.049Z] WARNING: This benchmark provides no result that can be validated.
[2024-06-26T23:18:22.049Z] There is no way to check that no silent failure occurred.
[2024-06-26T23:18:22.049Z] ====== movie-lens (apache-spark) [default], iteration 0 completed (59812.288 ms) ======
[2024-06-26T23:18:22.049Z] ====== movie-lens (apache-spark) [default], iteration 1 started ======
[2024-06-26T23:18:22.049Z] GC before operation: completed in 174.534 ms, heap usage 143.132 MB -> 49.569 MB.
[2024-06-26T23:18:29.197Z] RMSE (validation) = 3.62196895454876 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-06-26T23:18:37.584Z] RMSE (validation) = 2.1340923221100736 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-06-26T23:18:45.693Z] RMSE (validation) = 1.3105190428502682 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-06-26T23:18:52.662Z] RMSE (validation) = 0.9920028100571827 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-06-26T23:18:56.226Z] RMSE (validation) = 1.212631715629696 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-06-26T23:19:00.613Z] RMSE (validation) = 1.1025694034386657 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-06-26T23:19:05.182Z] RMSE (validation) = 0.9259652469249674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-06-26T23:19:08.903Z] RMSE (validation) = 0.8982232214592321 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-06-26T23:19:09.819Z] 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.
[2024-06-26T23:19:09.819Z] The best model improves the baseline by 14.52%.
[2024-06-26T23:19:09.819Z] Movies recommended for you:
[2024-06-26T23:19:09.819Z] WARNING: This benchmark provides no result that can be validated.
[2024-06-26T23:19:09.819Z] There is no way to check that no silent failure occurred.
[2024-06-26T23:19:09.819Z] ====== movie-lens (apache-spark) [default], iteration 1 completed (47801.212 ms) ======
[2024-06-26T23:19:09.819Z] ====== movie-lens (apache-spark) [default], iteration 2 started ======
[2024-06-26T23:19:09.819Z] GC before operation: completed in 64.073 ms, heap usage 405.736 MB -> 53.225 MB.
[2024-06-26T23:19:19.597Z] RMSE (validation) = 3.62196895454876 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-06-26T23:19:24.226Z] RMSE (validation) = 2.1340923221100736 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-06-26T23:19:30.810Z] RMSE (validation) = 1.3105190428502682 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-06-26T23:19:35.031Z] RMSE (validation) = 0.9920028100571827 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-06-26T23:19:38.379Z] RMSE (validation) = 1.212631715629696 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-06-26T23:19:41.102Z] RMSE (validation) = 1.1025694034386657 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-06-26T23:19:43.048Z] RMSE (validation) = 0.9259652469249674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-06-26T23:19:45.629Z] RMSE (validation) = 0.8982232214592321 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-06-26T23:19:46.018Z] 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.
[2024-06-26T23:19:46.018Z] The best model improves the baseline by 14.52%.
[2024-06-26T23:19:46.379Z] Movies recommended for you:
[2024-06-26T23:19:46.379Z] WARNING: This benchmark provides no result that can be validated.
[2024-06-26T23:19:46.379Z] There is no way to check that no silent failure occurred.
[2024-06-26T23:19:46.379Z] ====== movie-lens (apache-spark) [default], iteration 2 completed (36418.943 ms) ======
[2024-06-26T23:19:46.379Z] ====== movie-lens (apache-spark) [default], iteration 3 started ======
[2024-06-26T23:19:46.379Z] GC before operation: completed in 58.214 ms, heap usage 393.919 MB -> 50.300 MB.
[2024-06-26T23:19:51.653Z] RMSE (validation) = 3.6219689545487603 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-06-26T23:19:56.075Z] RMSE (validation) = 2.1340923221100736 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-06-26T23:20:00.337Z] RMSE (validation) = 1.3105190428502682 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-06-26T23:20:03.771Z] RMSE (validation) = 0.9920028100571827 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-06-26T23:20:05.716Z] RMSE (validation) = 1.212631715629696 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-06-26T23:20:08.938Z] RMSE (validation) = 1.1025694034386657 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-06-26T23:20:11.506Z] RMSE (validation) = 0.9259652469249674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-06-26T23:20:14.182Z] RMSE (validation) = 0.8982232214592321 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-06-26T23:20:14.182Z] 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.
[2024-06-26T23:20:14.182Z] The best model improves the baseline by 14.52%.
[2024-06-26T23:20:14.182Z] Movies recommended for you:
[2024-06-26T23:20:14.182Z] WARNING: This benchmark provides no result that can be validated.
[2024-06-26T23:20:14.182Z] There is no way to check that no silent failure occurred.
[2024-06-26T23:20:14.182Z] ====== movie-lens (apache-spark) [default], iteration 3 completed (27764.398 ms) ======
[2024-06-26T23:20:14.182Z] ====== movie-lens (apache-spark) [default], iteration 4 started ======
[2024-06-26T23:20:14.182Z] GC before operation: completed in 64.764 ms, heap usage 264.309 MB -> 50.510 MB.
[2024-06-26T23:20:18.431Z] RMSE (validation) = 3.6219689545487603 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-06-26T23:20:23.960Z] RMSE (validation) = 2.1340923221100736 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-06-26T23:20:28.262Z] RMSE (validation) = 1.3105190428502682 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-06-26T23:20:35.031Z] RMSE (validation) = 0.9920028100571827 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-06-26T23:20:37.725Z] RMSE (validation) = 1.212631715629696 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-06-26T23:20:40.351Z] RMSE (validation) = 1.1025694034386657 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-06-26T23:20:43.009Z] RMSE (validation) = 0.9259652469249674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-06-26T23:20:45.015Z] RMSE (validation) = 0.8982232214592321 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-06-26T23:20:45.015Z] 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.
[2024-06-26T23:20:45.015Z] The best model improves the baseline by 14.52%.
[2024-06-26T23:20:45.380Z] Movies recommended for you:
[2024-06-26T23:20:45.380Z] WARNING: This benchmark provides no result that can be validated.
[2024-06-26T23:20:45.380Z] There is no way to check that no silent failure occurred.
[2024-06-26T23:20:45.380Z] ====== movie-lens (apache-spark) [default], iteration 4 completed (31020.243 ms) ======
[2024-06-26T23:20:45.380Z] ====== movie-lens (apache-spark) [default], iteration 5 started ======
[2024-06-26T23:20:45.380Z] GC before operation: completed in 86.491 ms, heap usage 567.581 MB -> 54.132 MB.
[2024-06-26T23:20:49.652Z] RMSE (validation) = 3.6219689545487603 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-06-26T23:20:56.095Z] RMSE (validation) = 2.1340923221100736 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-06-26T23:21:00.428Z] RMSE (validation) = 1.3105190428502682 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-06-26T23:21:05.986Z] RMSE (validation) = 0.9920028100571827 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-06-26T23:21:11.420Z] RMSE (validation) = 1.212631715629696 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-06-26T23:21:15.002Z] RMSE (validation) = 1.1025694034386657 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-06-26T23:21:17.686Z] RMSE (validation) = 0.9259652469249674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-06-26T23:21:22.010Z] RMSE (validation) = 0.8982232214592321 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-06-26T23:21:22.948Z] 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.
[2024-06-26T23:21:22.948Z] The best model improves the baseline by 14.52%.
[2024-06-26T23:21:23.392Z] Movies recommended for you:
[2024-06-26T23:21:23.392Z] WARNING: This benchmark provides no result that can be validated.
[2024-06-26T23:21:23.392Z] There is no way to check that no silent failure occurred.
[2024-06-26T23:21:23.392Z] ====== movie-lens (apache-spark) [default], iteration 5 completed (38054.360 ms) ======
[2024-06-26T23:21:23.392Z] ====== movie-lens (apache-spark) [default], iteration 6 started ======
[2024-06-26T23:21:23.392Z] GC before operation: completed in 141.564 ms, heap usage 342.424 MB -> 50.711 MB.
[2024-06-26T23:21:31.529Z] RMSE (validation) = 3.62196895454876 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-06-26T23:21:39.500Z] RMSE (validation) = 2.1340923221100736 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-06-26T23:21:47.973Z] RMSE (validation) = 1.3105190428502682 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-06-26T23:21:56.496Z] RMSE (validation) = 0.9920028100571827 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-06-26T23:22:02.022Z] RMSE (validation) = 1.212631715629696 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-06-26T23:22:05.597Z] RMSE (validation) = 1.1025694034386657 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-06-26T23:22:12.388Z] RMSE (validation) = 0.9259652469249674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-06-26T23:22:16.837Z] RMSE (validation) = 0.8982232214592321 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-06-26T23:22:17.263Z] 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.
[2024-06-26T23:22:17.263Z] The best model improves the baseline by 14.52%.
[2024-06-26T23:22:17.693Z] Movies recommended for you:
[2024-06-26T23:22:17.693Z] WARNING: This benchmark provides no result that can be validated.
[2024-06-26T23:22:17.693Z] There is no way to check that no silent failure occurred.
[2024-06-26T23:22:17.693Z] ====== movie-lens (apache-spark) [default], iteration 6 completed (53997.503 ms) ======
[2024-06-26T23:22:17.693Z] ====== movie-lens (apache-spark) [default], iteration 7 started ======
[2024-06-26T23:22:17.693Z] GC before operation: completed in 107.673 ms, heap usage 68.011 MB -> 52.096 MB.
[2024-06-26T23:22:25.976Z] RMSE (validation) = 3.62196895454876 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-06-26T23:22:34.092Z] RMSE (validation) = 2.1340923221100736 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-06-26T23:22:41.004Z] RMSE (validation) = 1.3105190428502682 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-06-26T23:22:47.682Z] RMSE (validation) = 0.9920028100571827 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-06-26T23:22:53.123Z] RMSE (validation) = 1.212631715629696 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-06-26T23:22:57.454Z] RMSE (validation) = 1.1025694034386657 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-06-26T23:23:02.800Z] RMSE (validation) = 0.9259652469249674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-06-26T23:23:07.153Z] RMSE (validation) = 0.8982232214592321 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-06-26T23:23:07.650Z] 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.
[2024-06-26T23:23:07.650Z] The best model improves the baseline by 14.52%.
[2024-06-26T23:23:07.650Z] Movies recommended for you:
[2024-06-26T23:23:07.650Z] WARNING: This benchmark provides no result that can be validated.
[2024-06-26T23:23:07.650Z] There is no way to check that no silent failure occurred.
[2024-06-26T23:23:07.650Z] ====== movie-lens (apache-spark) [default], iteration 7 completed (49961.679 ms) ======
[2024-06-26T23:23:07.650Z] ====== movie-lens (apache-spark) [default], iteration 8 started ======
[2024-06-26T23:23:07.650Z] GC before operation: completed in 125.823 ms, heap usage 211.664 MB -> 51.052 MB.
[2024-06-26T23:23:17.971Z] RMSE (validation) = 3.6219689545487603 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-06-26T23:23:23.423Z] RMSE (validation) = 2.1340923221100736 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-06-26T23:23:33.132Z] RMSE (validation) = 1.3105190428502682 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-06-26T23:23:42.942Z] RMSE (validation) = 0.9920028100571827 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-06-26T23:23:47.383Z] RMSE (validation) = 1.212631715629696 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-06-26T23:23:52.032Z] RMSE (validation) = 1.1025694034386657 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-06-26T23:23:58.804Z] RMSE (validation) = 0.9259652469249674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-06-26T23:24:04.502Z] RMSE (validation) = 0.8982232214592321 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-06-26T23:24:05.064Z] 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.
[2024-06-26T23:24:05.064Z] The best model improves the baseline by 14.52%.
[2024-06-26T23:24:05.508Z] Movies recommended for you:
[2024-06-26T23:24:05.508Z] WARNING: This benchmark provides no result that can be validated.
[2024-06-26T23:24:05.508Z] There is no way to check that no silent failure occurred.
[2024-06-26T23:24:05.508Z] ====== movie-lens (apache-spark) [default], iteration 8 completed (57799.847 ms) ======
[2024-06-26T23:24:05.508Z] ====== movie-lens (apache-spark) [default], iteration 9 started ======
[2024-06-26T23:24:05.962Z] GC before operation: completed in 193.417 ms, heap usage 311.913 MB -> 51.003 MB.
[2024-06-26T23:24:16.010Z] RMSE (validation) = 3.62196895454876 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-06-26T23:24:24.499Z] RMSE (validation) = 2.1340923221100736 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-06-26T23:24:31.185Z] RMSE (validation) = 1.3105190428502682 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-06-26T23:24:36.727Z] RMSE (validation) = 0.9920028100571827 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-06-26T23:24:41.196Z] RMSE (validation) = 1.212631715629696 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-06-26T23:24:45.918Z] RMSE (validation) = 1.1025694034386657 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-06-26T23:24:51.307Z] RMSE (validation) = 0.9259652469249674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-06-26T23:24:55.829Z] RMSE (validation) = 0.8982232214592321 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-06-26T23:24:55.829Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9063003101263983.
[2024-06-26T23:24:56.311Z] The best model improves the baseline by 14.52%.
[2024-06-26T23:24:56.311Z] Movies recommended for you:
[2024-06-26T23:24:56.311Z] WARNING: This benchmark provides no result that can be validated.
[2024-06-26T23:24:56.311Z] There is no way to check that no silent failure occurred.
[2024-06-26T23:24:56.311Z] ====== movie-lens (apache-spark) [default], iteration 9 completed (50508.538 ms) ======
[2024-06-26T23:24:56.311Z] ====== movie-lens (apache-spark) [default], iteration 10 started ======
[2024-06-26T23:24:56.780Z] GC before operation: completed in 177.222 ms, heap usage 402.698 MB -> 54.356 MB.
[2024-06-26T23:25:04.773Z] RMSE (validation) = 3.6219689545487603 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-06-26T23:25:13.055Z] RMSE (validation) = 2.1340923221100736 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-06-26T23:25:23.044Z] RMSE (validation) = 1.3105190428502682 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-06-26T23:25:29.976Z] RMSE (validation) = 0.9920028100571827 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-06-26T23:25:34.540Z] RMSE (validation) = 1.212631715629696 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-06-26T23:25:38.917Z] RMSE (validation) = 1.1025694034386657 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-06-26T23:25:43.174Z] RMSE (validation) = 0.9259652469249674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-06-26T23:25:48.655Z] RMSE (validation) = 0.8982232214592321 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-06-26T23:25:48.655Z] 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.
[2024-06-26T23:25:48.655Z] The best model improves the baseline by 14.52%.
[2024-06-26T23:25:48.655Z] Movies recommended for you:
[2024-06-26T23:25:48.655Z] WARNING: This benchmark provides no result that can be validated.
[2024-06-26T23:25:48.655Z] There is no way to check that no silent failure occurred.
[2024-06-26T23:25:48.655Z] ====== movie-lens (apache-spark) [default], iteration 10 completed (52310.805 ms) ======
[2024-06-26T23:25:48.655Z] ====== movie-lens (apache-spark) [default], iteration 11 started ======
[2024-06-26T23:25:49.086Z] GC before operation: completed in 230.802 ms, heap usage 191.797 MB -> 50.761 MB.
[2024-06-26T23:25:57.268Z] RMSE (validation) = 3.62196895454876 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-06-26T23:26:05.406Z] RMSE (validation) = 2.1340923221100736 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-06-26T23:26:11.967Z] RMSE (validation) = 1.3105190428502682 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-06-26T23:26:20.504Z] RMSE (validation) = 0.9920028100571827 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-06-26T23:26:25.864Z] RMSE (validation) = 1.212631715629696 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-06-26T23:26:30.438Z] RMSE (validation) = 1.1025694034386657 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-06-26T23:26:37.373Z] RMSE (validation) = 0.9259652469249674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-06-26T23:26:41.605Z] RMSE (validation) = 0.8982232214592321 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-06-26T23:26:42.023Z] 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.
[2024-06-26T23:26:42.023Z] The best model improves the baseline by 14.52%.
[2024-06-26T23:26:42.023Z] Movies recommended for you:
[2024-06-26T23:26:42.023Z] WARNING: This benchmark provides no result that can be validated.
[2024-06-26T23:26:42.023Z] There is no way to check that no silent failure occurred.
[2024-06-26T23:26:42.023Z] ====== movie-lens (apache-spark) [default], iteration 11 completed (53099.488 ms) ======
[2024-06-26T23:26:42.023Z] ====== movie-lens (apache-spark) [default], iteration 12 started ======
[2024-06-26T23:26:42.023Z] GC before operation: completed in 114.704 ms, heap usage 115.573 MB -> 50.858 MB.
[2024-06-26T23:26:50.100Z] RMSE (validation) = 3.62196895454876 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-06-26T23:26:55.437Z] RMSE (validation) = 2.1340923221100736 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-06-26T23:27:02.042Z] RMSE (validation) = 1.3105190428502682 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-06-26T23:27:08.823Z] RMSE (validation) = 0.9920028100571827 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-06-26T23:27:12.427Z] RMSE (validation) = 1.212631715629696 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-06-26T23:27:15.297Z] RMSE (validation) = 1.1025694034386657 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-06-26T23:27:20.790Z] RMSE (validation) = 0.9259652469249674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-06-26T23:27:24.250Z] RMSE (validation) = 0.8982232214592321 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-06-26T23:27:24.707Z] 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.
[2024-06-26T23:27:25.205Z] The best model improves the baseline by 14.52%.
[2024-06-26T23:27:25.205Z] Movies recommended for you:
[2024-06-26T23:27:25.205Z] WARNING: This benchmark provides no result that can be validated.
[2024-06-26T23:27:25.205Z] There is no way to check that no silent failure occurred.
[2024-06-26T23:27:25.205Z] ====== movie-lens (apache-spark) [default], iteration 12 completed (42922.991 ms) ======
[2024-06-26T23:27:25.205Z] ====== movie-lens (apache-spark) [default], iteration 13 started ======
[2024-06-26T23:27:25.205Z] GC before operation: completed in 137.398 ms, heap usage 201.326 MB -> 51.090 MB.
[2024-06-26T23:27:31.814Z] RMSE (validation) = 3.62196895454876 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-06-26T23:27:36.047Z] RMSE (validation) = 2.1340923221100736 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-06-26T23:27:44.256Z] RMSE (validation) = 1.3105190428502682 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-06-26T23:27:51.190Z] RMSE (validation) = 0.9920028100571827 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-06-26T23:27:55.593Z] RMSE (validation) = 1.212631715629696 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-06-26T23:27:59.156Z] RMSE (validation) = 1.1025694034386657 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-06-26T23:28:03.609Z] RMSE (validation) = 0.9259652469249674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-06-26T23:28:09.146Z] RMSE (validation) = 0.8982232214592321 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-06-26T23:28:09.146Z] 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.
[2024-06-26T23:28:09.527Z] The best model improves the baseline by 14.52%.
[2024-06-26T23:28:09.527Z] Movies recommended for you:
[2024-06-26T23:28:09.527Z] WARNING: This benchmark provides no result that can be validated.
[2024-06-26T23:28:09.527Z] There is no way to check that no silent failure occurred.
[2024-06-26T23:28:09.527Z] ====== movie-lens (apache-spark) [default], iteration 13 completed (44304.158 ms) ======
[2024-06-26T23:28:09.527Z] ====== movie-lens (apache-spark) [default], iteration 14 started ======
[2024-06-26T23:28:09.527Z] GC before operation: completed in 130.016 ms, heap usage 171.418 MB -> 50.826 MB.
[2024-06-26T23:28:19.547Z] RMSE (validation) = 3.62196895454876 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-06-26T23:28:28.093Z] RMSE (validation) = 2.1340923221100736 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-06-26T23:28:36.372Z] RMSE (validation) = 1.3105190428502682 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-06-26T23:28:43.211Z] RMSE (validation) = 0.9920028100571827 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-06-26T23:28:47.728Z] RMSE (validation) = 1.212631715629696 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-06-26T23:28:51.033Z] RMSE (validation) = 1.1025694034386657 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-06-26T23:28:53.021Z] RMSE (validation) = 0.9259652469249674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-06-26T23:28:56.338Z] RMSE (validation) = 0.8982232214592321 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-06-26T23:28:56.338Z] 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.
[2024-06-26T23:28:56.338Z] The best model improves the baseline by 14.52%.
[2024-06-26T23:28:56.338Z] Movies recommended for you:
[2024-06-26T23:28:56.338Z] WARNING: This benchmark provides no result that can be validated.
[2024-06-26T23:28:56.338Z] There is no way to check that no silent failure occurred.
[2024-06-26T23:28:56.338Z] ====== movie-lens (apache-spark) [default], iteration 14 completed (46764.424 ms) ======
[2024-06-26T23:28:56.338Z] ====== movie-lens (apache-spark) [default], iteration 15 started ======
[2024-06-26T23:28:56.338Z] GC before operation: completed in 60.641 ms, heap usage 264.837 MB -> 51.182 MB.
[2024-06-26T23:29:01.669Z] RMSE (validation) = 3.62196895454876 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-06-26T23:29:05.942Z] RMSE (validation) = 2.1340923221100736 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-06-26T23:29:12.794Z] RMSE (validation) = 1.3105190428502682 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-06-26T23:29:18.264Z] RMSE (validation) = 0.9920028100571827 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-06-26T23:29:21.043Z] RMSE (validation) = 1.212631715629696 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-06-26T23:29:24.578Z] RMSE (validation) = 1.1025694034386657 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-06-26T23:29:28.899Z] RMSE (validation) = 0.9259652469249674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-06-26T23:29:33.252Z] RMSE (validation) = 0.8982232214592321 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-06-26T23:29:34.267Z] 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.
[2024-06-26T23:29:34.267Z] The best model improves the baseline by 14.52%.
[2024-06-26T23:29:34.268Z] Movies recommended for you:
[2024-06-26T23:29:34.268Z] WARNING: This benchmark provides no result that can be validated.
[2024-06-26T23:29:34.268Z] There is no way to check that no silent failure occurred.
[2024-06-26T23:29:34.268Z] ====== movie-lens (apache-spark) [default], iteration 15 completed (37844.784 ms) ======
[2024-06-26T23:29:34.268Z] ====== movie-lens (apache-spark) [default], iteration 16 started ======
[2024-06-26T23:29:34.697Z] GC before operation: completed in 142.701 ms, heap usage 105.521 MB -> 51.690 MB.
[2024-06-26T23:29:41.266Z] RMSE (validation) = 3.62196895454876 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-06-26T23:29:51.303Z] RMSE (validation) = 2.1340923221100736 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-06-26T23:29:56.907Z] RMSE (validation) = 1.3105190428502682 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-06-26T23:30:03.737Z] RMSE (validation) = 0.9920028100571827 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-06-26T23:30:09.324Z] RMSE (validation) = 1.212631715629696 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-06-26T23:30:13.755Z] RMSE (validation) = 1.1025694034386657 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-06-26T23:30:18.189Z] RMSE (validation) = 0.9259652469249674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-06-26T23:30:23.742Z] RMSE (validation) = 0.8982232214592321 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-06-26T23:30:24.166Z] 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.
[2024-06-26T23:30:24.167Z] The best model improves the baseline by 14.52%.
[2024-06-26T23:30:24.622Z] Movies recommended for you:
[2024-06-26T23:30:24.622Z] WARNING: This benchmark provides no result that can be validated.
[2024-06-26T23:30:24.622Z] There is no way to check that no silent failure occurred.
[2024-06-26T23:30:24.622Z] ====== movie-lens (apache-spark) [default], iteration 16 completed (49958.756 ms) ======
[2024-06-26T23:30:24.622Z] ====== movie-lens (apache-spark) [default], iteration 17 started ======
[2024-06-26T23:30:24.622Z] GC before operation: completed in 205.416 ms, heap usage 425.314 MB -> 54.290 MB.
[2024-06-26T23:30:34.611Z] RMSE (validation) = 3.6219689545487603 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-06-26T23:30:43.101Z] RMSE (validation) = 2.1340923221100736 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-06-26T23:30:51.550Z] RMSE (validation) = 1.3105190428502682 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-06-26T23:30:59.763Z] RMSE (validation) = 0.9920028100571827 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-06-26T23:31:04.472Z] RMSE (validation) = 1.212631715629696 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-06-26T23:31:09.888Z] RMSE (validation) = 1.1025694034386657 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-06-26T23:31:15.415Z] RMSE (validation) = 0.9259652469249674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-06-26T23:31:19.822Z] RMSE (validation) = 0.8982232214592321 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-06-26T23:31:20.234Z] 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.
[2024-06-26T23:31:20.235Z] The best model improves the baseline by 14.52%.
[2024-06-26T23:31:20.658Z] Movies recommended for you:
[2024-06-26T23:31:20.658Z] WARNING: This benchmark provides no result that can be validated.
[2024-06-26T23:31:20.658Z] There is no way to check that no silent failure occurred.
[2024-06-26T23:31:20.658Z] ====== movie-lens (apache-spark) [default], iteration 17 completed (55953.203 ms) ======
[2024-06-26T23:31:20.658Z] ====== movie-lens (apache-spark) [default], iteration 18 started ======
[2024-06-26T23:31:20.658Z] GC before operation: completed in 120.646 ms, heap usage 97.054 MB -> 52.059 MB.
[2024-06-26T23:31:28.827Z] RMSE (validation) = 3.62196895454876 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-06-26T23:31:37.303Z] RMSE (validation) = 2.1340923221100736 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-06-26T23:31:45.206Z] RMSE (validation) = 1.3105190428502682 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-06-26T23:31:51.912Z] RMSE (validation) = 0.9920028100571827 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-06-26T23:31:54.553Z] RMSE (validation) = 1.212631715629696 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-06-26T23:31:57.978Z] RMSE (validation) = 1.1025694034386657 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-06-26T23:31:59.285Z] RMSE (validation) = 0.9259652469249674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-06-26T23:32:01.269Z] RMSE (validation) = 0.8982232214592321 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-06-26T23:32:01.269Z] 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.
[2024-06-26T23:32:01.269Z] The best model improves the baseline by 14.52%.
[2024-06-26T23:32:01.269Z] Movies recommended for you:
[2024-06-26T23:32:01.269Z] WARNING: This benchmark provides no result that can be validated.
[2024-06-26T23:32:01.269Z] There is no way to check that no silent failure occurred.
[2024-06-26T23:32:01.269Z] ====== movie-lens (apache-spark) [default], iteration 18 completed (40636.197 ms) ======
[2024-06-26T23:32:01.269Z] ====== movie-lens (apache-spark) [default], iteration 19 started ======
[2024-06-26T23:32:01.269Z] GC before operation: completed in 58.777 ms, heap usage 290.588 MB -> 51.245 MB.
[2024-06-26T23:32:07.817Z] RMSE (validation) = 3.62196895454876 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-06-26T23:32:12.015Z] RMSE (validation) = 2.1340923221100736 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-06-26T23:32:17.270Z] RMSE (validation) = 1.3105190428502682 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-06-26T23:32:21.650Z] RMSE (validation) = 0.9920028100571827 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-06-26T23:32:24.324Z] RMSE (validation) = 1.212631715629696 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-06-26T23:32:28.671Z] RMSE (validation) = 1.1025694034386657 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-06-26T23:32:32.441Z] RMSE (validation) = 0.9259652469249674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-06-26T23:32:35.908Z] RMSE (validation) = 0.8982232214592321 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-06-26T23:32:36.737Z] 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.
[2024-06-26T23:32:36.737Z] The best model improves the baseline by 14.52%.
[2024-06-26T23:32:36.737Z] Movies recommended for you:
[2024-06-26T23:32:36.737Z] WARNING: This benchmark provides no result that can be validated.
[2024-06-26T23:32:36.737Z] There is no way to check that no silent failure occurred.
[2024-06-26T23:32:36.737Z] ====== movie-lens (apache-spark) [default], iteration 19 completed (35461.595 ms) ======
[2024-06-26T23:32:38.058Z] -----------------------------------
[2024-06-26T23:32:38.058Z] renaissance-movie-lens_0_PASSED
[2024-06-26T23:32:38.058Z] -----------------------------------
[2024-06-26T23:32:38.058Z]
[2024-06-26T23:32:38.058Z] TEST TEARDOWN:
[2024-06-26T23:32:38.058Z] Nothing to be done for teardown.
[2024-06-26T23:32:38.058Z] renaissance-movie-lens_0 Finish Time: Wed Jun 26 16:32:37 2024 Epoch Time (ms): 1719444757716