renaissance-movie-lens_0
[2024-09-26T02:47:25.365Z] Running test renaissance-movie-lens_0 ...
[2024-09-26T02:47:25.365Z] ===============================================
[2024-09-26T02:47:25.365Z] renaissance-movie-lens_0 Start Time: Wed Sep 25 19:47:24 2024 Epoch Time (ms): 1727318844611
[2024-09-26T02:47:25.365Z] variation: NoOptions
[2024-09-26T02:47:25.365Z] JVM_OPTIONS:
[2024-09-26T02:47:25.365Z] { \
[2024-09-26T02:47:25.365Z] echo ""; echo "TEST SETUP:"; \
[2024-09-26T02:47:25.366Z] echo "Nothing to be done for setup."; \
[2024-09-26T02:47:25.366Z] mkdir -p "/Users/admin/workspace/workspace/Test_openjdk11_hs_extended.perf_x86-64_mac_testList_1/aqa-tests/TKG/../TKG/output_17273160556125/renaissance-movie-lens_0"; \
[2024-09-26T02:47:25.366Z] cd "/Users/admin/workspace/workspace/Test_openjdk11_hs_extended.perf_x86-64_mac_testList_1/aqa-tests/TKG/../TKG/output_17273160556125/renaissance-movie-lens_0"; \
[2024-09-26T02:47:25.366Z] echo ""; echo "TESTING:"; \
[2024-09-26T02:47:25.366Z] "/Users/admin/workspace/workspace/Test_openjdk11_hs_extended.perf_x86-64_mac_testList_1/jdkbinary/j2sdk-image/Contents/Home/bin/..//bin/java" --add-opens java.base/java.lang=ALL-UNNAMED --add-opens java.base/java.util=ALL-UNNAMED --add-opens java.base/java.util.concurrent=ALL-UNNAMED --add-opens java.base/java.nio=ALL-UNNAMED --add-opens java.base/sun.nio.ch=ALL-UNNAMED --add-opens java.base/java.lang.invoke=ALL-UNNAMED -jar "/Users/admin/workspace/workspace/Test_openjdk11_hs_extended.perf_x86-64_mac_testList_1/aqa-tests/TKG/../../jvmtest/perf/renaissance/renaissance.jar" --json ""/Users/admin/workspace/workspace/Test_openjdk11_hs_extended.perf_x86-64_mac_testList_1/aqa-tests/TKG/../TKG/output_17273160556125/renaissance-movie-lens_0"/movie-lens.json" movie-lens; \
[2024-09-26T02:47:25.366Z] if [ $? -eq 0 ]; then echo "-----------------------------------"; echo "renaissance-movie-lens_0""_PASSED"; echo "-----------------------------------"; cd /Users/admin/workspace/workspace/Test_openjdk11_hs_extended.perf_x86-64_mac_testList_1/aqa-tests/TKG/..; rm -f -r "/Users/admin/workspace/workspace/Test_openjdk11_hs_extended.perf_x86-64_mac_testList_1/aqa-tests/TKG/../TKG/output_17273160556125/renaissance-movie-lens_0"; else echo "-----------------------------------"; echo "renaissance-movie-lens_0""_FAILED"; echo "-----------------------------------"; fi; \
[2024-09-26T02:47:25.366Z] echo ""; echo "TEST TEARDOWN:"; \
[2024-09-26T02:47:25.366Z] echo "Nothing to be done for teardown."; \
[2024-09-26T02:47:25.366Z] } 2>&1 | tee -a "/Users/admin/workspace/workspace/Test_openjdk11_hs_extended.perf_x86-64_mac_testList_1/aqa-tests/TKG/../TKG/output_17273160556125/TestTargetResult";
[2024-09-26T02:47:25.366Z]
[2024-09-26T02:47:25.366Z] TEST SETUP:
[2024-09-26T02:47:25.366Z] Nothing to be done for setup.
[2024-09-26T02:47:25.366Z]
[2024-09-26T02:47:25.366Z] TESTING:
[2024-09-26T02:47:35.563Z] Using Spark's default log4j profile: org/apache/spark/log4j-defaults.properties
[2024-09-26T02:47:42.543Z] NOTE: 'movie-lens' benchmark uses Spark local executor with 3 (out of 3) threads.
[2024-09-26T02:47:55.204Z] Got 100004 ratings from 671 users on 9066 movies.
[2024-09-26T02:47:56.241Z] Training: 60056, validation: 20285, test: 19854
[2024-09-26T02:47:56.241Z] ====== movie-lens (apache-spark) [default], iteration 0 started ======
[2024-09-26T02:47:56.649Z] GC before operation: completed in 486.957 ms, heap usage 54.636 MB -> 36.589 MB.
[2024-09-26T02:48:28.237Z] RMSE (validation) = 3.6219689545487603 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-09-26T02:48:47.273Z] RMSE (validation) = 2.1340923221100736 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-09-26T02:49:06.029Z] RMSE (validation) = 1.3105190428502682 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-09-26T02:49:19.134Z] RMSE (validation) = 0.9920028100571827 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-09-26T02:49:28.018Z] RMSE (validation) = 1.212631715629696 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-09-26T02:49:37.559Z] RMSE (validation) = 1.1025694034386657 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-09-26T02:49:47.023Z] RMSE (validation) = 0.9259652469249674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-09-26T02:49:54.691Z] RMSE (validation) = 0.8982232214592321 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-09-26T02:49:55.699Z] 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.
[2024-09-26T02:49:56.367Z] The best model improves the baseline by 14.52%.
[2024-09-26T02:49:57.459Z] Movies recommended for you:
[2024-09-26T02:49:57.459Z] WARNING: This benchmark provides no result that can be validated.
[2024-09-26T02:49:57.459Z] There is no way to check that no silent failure occurred.
[2024-09-26T02:49:57.459Z] ====== movie-lens (apache-spark) [default], iteration 0 completed (120837.123 ms) ======
[2024-09-26T02:49:57.459Z] ====== movie-lens (apache-spark) [default], iteration 1 started ======
[2024-09-26T02:49:58.071Z] GC before operation: completed in 335.445 ms, heap usage 185.672 MB -> 48.563 MB.
[2024-09-26T02:50:14.209Z] RMSE (validation) = 3.6219689545487603 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-09-26T02:50:32.715Z] RMSE (validation) = 2.1340923221100736 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-09-26T02:50:48.648Z] RMSE (validation) = 1.3105190428502682 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-09-26T02:51:03.873Z] RMSE (validation) = 0.9920028100571827 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-09-26T02:51:10.234Z] RMSE (validation) = 1.212631715629696 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-09-26T02:51:20.917Z] RMSE (validation) = 1.1025694034386657 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-09-26T02:51:30.088Z] RMSE (validation) = 0.9259652469249674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-09-26T02:51:36.320Z] RMSE (validation) = 0.8982232214592321 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-09-26T02:51:38.021Z] 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.
[2024-09-26T02:51:38.415Z] The best model improves the baseline by 14.52%.
[2024-09-26T02:51:38.834Z] Movies recommended for you:
[2024-09-26T02:51:38.834Z] WARNING: This benchmark provides no result that can be validated.
[2024-09-26T02:51:38.834Z] There is no way to check that no silent failure occurred.
[2024-09-26T02:51:38.834Z] ====== movie-lens (apache-spark) [default], iteration 1 completed (100977.537 ms) ======
[2024-09-26T02:51:38.834Z] ====== movie-lens (apache-spark) [default], iteration 2 started ======
[2024-09-26T02:51:38.834Z] GC before operation: completed in 204.434 ms, heap usage 252.214 MB -> 48.940 MB.
[2024-09-26T02:51:57.049Z] RMSE (validation) = 3.6219689545487603 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-09-26T02:52:12.619Z] RMSE (validation) = 2.1340923221100736 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-09-26T02:52:31.066Z] RMSE (validation) = 1.3105190428502682 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-09-26T02:52:43.917Z] RMSE (validation) = 0.9920028100571827 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-09-26T02:52:54.454Z] RMSE (validation) = 1.212631715629696 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-09-26T02:53:00.640Z] RMSE (validation) = 1.1025694034386657 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-09-26T02:53:11.247Z] RMSE (validation) = 0.9259652469249674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-09-26T02:53:20.253Z] RMSE (validation) = 0.8982232214592321 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-09-26T02:53:22.021Z] 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.
[2024-09-26T02:53:22.021Z] The best model improves the baseline by 14.52%.
[2024-09-26T02:53:22.511Z] Movies recommended for you:
[2024-09-26T02:53:22.511Z] WARNING: This benchmark provides no result that can be validated.
[2024-09-26T02:53:22.511Z] There is no way to check that no silent failure occurred.
[2024-09-26T02:53:22.511Z] ====== movie-lens (apache-spark) [default], iteration 2 completed (103355.481 ms) ======
[2024-09-26T02:53:22.511Z] ====== movie-lens (apache-spark) [default], iteration 3 started ======
[2024-09-26T02:53:22.511Z] GC before operation: completed in 312.422 ms, heap usage 382.659 MB -> 52.485 MB.
[2024-09-26T02:53:40.608Z] RMSE (validation) = 3.6219689545487603 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-09-26T02:53:53.375Z] RMSE (validation) = 2.1340923221100736 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-09-26T02:54:04.352Z] RMSE (validation) = 1.3105190428502682 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-09-26T02:54:17.308Z] RMSE (validation) = 0.9920028100571827 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-09-26T02:54:26.191Z] RMSE (validation) = 1.212631715629696 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-09-26T02:54:32.154Z] RMSE (validation) = 1.1025694034386657 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-09-26T02:54:41.221Z] RMSE (validation) = 0.9259652469249674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-09-26T02:54:52.021Z] RMSE (validation) = 0.8982232214592321 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-09-26T02:54:52.021Z] 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.
[2024-09-26T02:54:52.021Z] The best model improves the baseline by 14.52%.
[2024-09-26T02:54:52.021Z] Movies recommended for you:
[2024-09-26T02:54:52.021Z] WARNING: This benchmark provides no result that can be validated.
[2024-09-26T02:54:52.021Z] There is no way to check that no silent failure occurred.
[2024-09-26T02:54:52.021Z] ====== movie-lens (apache-spark) [default], iteration 3 completed (88824.691 ms) ======
[2024-09-26T02:54:52.021Z] ====== movie-lens (apache-spark) [default], iteration 4 started ======
[2024-09-26T02:54:52.021Z] GC before operation: completed in 205.663 ms, heap usage 403.909 MB -> 52.818 MB.
[2024-09-26T02:55:07.040Z] RMSE (validation) = 3.62196895454876 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-09-26T02:55:19.428Z] RMSE (validation) = 2.1340923221100736 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-09-26T02:55:31.936Z] RMSE (validation) = 1.3105190428502682 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-09-26T02:55:53.976Z] RMSE (validation) = 0.9920028100571827 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-09-26T02:55:59.193Z] RMSE (validation) = 1.212631715629696 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-09-26T02:56:06.799Z] RMSE (validation) = 1.1025694034386657 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-09-26T02:56:14.002Z] RMSE (validation) = 0.9259652469249674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-09-26T02:56:19.613Z] RMSE (validation) = 0.8982232214592321 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-09-26T02:56:20.477Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9063003101263983.
[2024-09-26T02:56:20.477Z] The best model improves the baseline by 14.52%.
[2024-09-26T02:56:20.903Z] Movies recommended for you:
[2024-09-26T02:56:20.903Z] WARNING: This benchmark provides no result that can be validated.
[2024-09-26T02:56:20.903Z] There is no way to check that no silent failure occurred.
[2024-09-26T02:56:20.903Z] ====== movie-lens (apache-spark) [default], iteration 4 completed (89174.613 ms) ======
[2024-09-26T02:56:20.903Z] ====== movie-lens (apache-spark) [default], iteration 5 started ======
[2024-09-26T02:56:21.382Z] GC before operation: completed in 267.106 ms, heap usage 276.050 MB -> 49.755 MB.
[2024-09-26T02:56:37.313Z] RMSE (validation) = 3.6219689545487603 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-09-26T02:56:48.006Z] RMSE (validation) = 2.1340923221100736 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-09-26T02:57:00.389Z] RMSE (validation) = 1.3105190428502682 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-09-26T02:57:16.079Z] RMSE (validation) = 0.9920028100571827 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-09-26T02:57:24.849Z] RMSE (validation) = 1.212631715629696 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-09-26T02:57:29.456Z] RMSE (validation) = 1.1025694034386657 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-09-26T02:57:39.998Z] RMSE (validation) = 0.9259652469249674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-09-26T02:57:45.859Z] RMSE (validation) = 0.8982232214592321 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-09-26T02:57:46.237Z] 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.
[2024-09-26T02:57:46.237Z] The best model improves the baseline by 14.52%.
[2024-09-26T02:57:46.237Z] Movies recommended for you:
[2024-09-26T02:57:46.237Z] WARNING: This benchmark provides no result that can be validated.
[2024-09-26T02:57:46.237Z] There is no way to check that no silent failure occurred.
[2024-09-26T02:57:46.237Z] ====== movie-lens (apache-spark) [default], iteration 5 completed (85408.836 ms) ======
[2024-09-26T02:57:46.237Z] ====== movie-lens (apache-spark) [default], iteration 6 started ======
[2024-09-26T02:57:46.703Z] GC before operation: completed in 305.280 ms, heap usage 402.945 MB -> 52.973 MB.
[2024-09-26T02:58:01.560Z] RMSE (validation) = 3.62196895454876 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-09-26T02:58:14.770Z] RMSE (validation) = 2.1340923221100736 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-09-26T02:58:27.957Z] RMSE (validation) = 1.3105190428502682 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-09-26T02:58:43.642Z] RMSE (validation) = 0.9920028100571827 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-09-26T02:58:52.461Z] RMSE (validation) = 1.212631715629696 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-09-26T02:58:59.420Z] RMSE (validation) = 1.1025694034386657 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-09-26T02:59:06.707Z] RMSE (validation) = 0.9259652469249674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-09-26T02:59:15.967Z] RMSE (validation) = 0.8982232214592321 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-09-26T02:59:17.006Z] 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.
[2024-09-26T02:59:17.006Z] The best model improves the baseline by 14.52%.
[2024-09-26T02:59:17.006Z] Movies recommended for you:
[2024-09-26T02:59:17.006Z] WARNING: This benchmark provides no result that can be validated.
[2024-09-26T02:59:17.006Z] There is no way to check that no silent failure occurred.
[2024-09-26T02:59:17.006Z] ====== movie-lens (apache-spark) [default], iteration 6 completed (90184.640 ms) ======
[2024-09-26T02:59:17.006Z] ====== movie-lens (apache-spark) [default], iteration 7 started ======
[2024-09-26T02:59:17.006Z] GC before operation: completed in 218.111 ms, heap usage 124.342 MB -> 49.935 MB.
[2024-09-26T02:59:32.067Z] RMSE (validation) = 3.6219689545487603 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-09-26T02:59:47.100Z] RMSE (validation) = 2.1340923221100736 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-09-26T02:59:59.909Z] RMSE (validation) = 1.3105190428502682 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-09-26T03:00:16.261Z] RMSE (validation) = 0.9920028100571827 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-09-26T03:00:25.630Z] RMSE (validation) = 1.212631715629696 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-09-26T03:00:34.536Z] RMSE (validation) = 1.1025694034386657 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-09-26T03:00:45.003Z] RMSE (validation) = 0.9259652469249674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-09-26T03:00:50.406Z] RMSE (validation) = 0.8982232214592321 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-09-26T03:00:50.912Z] 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.
[2024-09-26T03:00:51.461Z] The best model improves the baseline by 14.52%.
[2024-09-26T03:00:51.461Z] Movies recommended for you:
[2024-09-26T03:00:51.461Z] WARNING: This benchmark provides no result that can be validated.
[2024-09-26T03:00:51.461Z] There is no way to check that no silent failure occurred.
[2024-09-26T03:00:51.461Z] ====== movie-lens (apache-spark) [default], iteration 7 completed (94274.954 ms) ======
[2024-09-26T03:00:51.461Z] ====== movie-lens (apache-spark) [default], iteration 8 started ======
[2024-09-26T03:00:52.493Z] GC before operation: completed in 715.151 ms, heap usage 236.312 MB -> 50.087 MB.
[2024-09-26T03:01:04.932Z] RMSE (validation) = 3.62196895454876 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-09-26T03:01:19.997Z] RMSE (validation) = 2.1340923221100736 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-09-26T03:01:32.484Z] RMSE (validation) = 1.3105190428502682 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-09-26T03:01:42.767Z] RMSE (validation) = 0.9920028100571827 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-09-26T03:01:50.554Z] RMSE (validation) = 1.212631715629696 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-09-26T03:01:59.917Z] RMSE (validation) = 1.1025694034386657 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-09-26T03:02:10.268Z] RMSE (validation) = 0.9259652469249674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-09-26T03:02:19.052Z] RMSE (validation) = 0.8982232214592321 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-09-26T03:02:20.217Z] 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.
[2024-09-26T03:02:21.268Z] The best model improves the baseline by 14.52%.
[2024-09-26T03:02:21.710Z] Movies recommended for you:
[2024-09-26T03:02:21.710Z] WARNING: This benchmark provides no result that can be validated.
[2024-09-26T03:02:21.710Z] There is no way to check that no silent failure occurred.
[2024-09-26T03:02:21.710Z] ====== movie-lens (apache-spark) [default], iteration 8 completed (89524.036 ms) ======
[2024-09-26T03:02:21.710Z] ====== movie-lens (apache-spark) [default], iteration 9 started ======
[2024-09-26T03:02:22.196Z] GC before operation: completed in 194.971 ms, heap usage 160.832 MB -> 49.867 MB.
[2024-09-26T03:02:35.080Z] RMSE (validation) = 3.62196895454876 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-09-26T03:02:50.850Z] RMSE (validation) = 2.1340923221100736 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-09-26T03:03:06.157Z] RMSE (validation) = 1.3105190428502682 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-09-26T03:03:16.315Z] RMSE (validation) = 0.9920028100571827 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-09-26T03:03:20.821Z] RMSE (validation) = 1.212631715629696 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-09-26T03:03:26.971Z] RMSE (validation) = 1.1025694034386657 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-09-26T03:03:33.409Z] RMSE (validation) = 0.9259652469249674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-09-26T03:03:42.452Z] RMSE (validation) = 0.8982232214592321 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-09-26T03:03:44.635Z] 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.
[2024-09-26T03:03:44.636Z] The best model improves the baseline by 14.52%.
[2024-09-26T03:03:45.105Z] Movies recommended for you:
[2024-09-26T03:03:45.105Z] WARNING: This benchmark provides no result that can be validated.
[2024-09-26T03:03:45.105Z] There is no way to check that no silent failure occurred.
[2024-09-26T03:03:45.105Z] ====== movie-lens (apache-spark) [default], iteration 9 completed (82952.374 ms) ======
[2024-09-26T03:03:45.105Z] ====== movie-lens (apache-spark) [default], iteration 10 started ======
[2024-09-26T03:03:45.105Z] GC before operation: completed in 289.045 ms, heap usage 449.596 MB -> 53.371 MB.
[2024-09-26T03:03:58.338Z] RMSE (validation) = 3.6219689545487603 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-09-26T03:04:13.760Z] RMSE (validation) = 2.1340923221100736 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-09-26T03:04:31.324Z] RMSE (validation) = 1.3105190428502682 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-09-26T03:04:43.920Z] RMSE (validation) = 0.9920028100571827 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-09-26T03:04:49.969Z] RMSE (validation) = 1.212631715629696 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-09-26T03:04:57.148Z] RMSE (validation) = 1.1025694034386657 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-09-26T03:05:06.224Z] RMSE (validation) = 0.9259652469249674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-09-26T03:05:13.512Z] RMSE (validation) = 0.8982232214592321 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-09-26T03:05:15.142Z] 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.
[2024-09-26T03:05:15.142Z] The best model improves the baseline by 14.52%.
[2024-09-26T03:05:15.142Z] Movies recommended for you:
[2024-09-26T03:05:15.142Z] WARNING: This benchmark provides no result that can be validated.
[2024-09-26T03:05:15.142Z] There is no way to check that no silent failure occurred.
[2024-09-26T03:05:15.142Z] ====== movie-lens (apache-spark) [default], iteration 10 completed (90157.661 ms) ======
[2024-09-26T03:05:15.142Z] ====== movie-lens (apache-spark) [default], iteration 11 started ======
[2024-09-26T03:05:15.638Z] GC before operation: completed in 168.051 ms, heap usage 178.012 MB -> 49.707 MB.
[2024-09-26T03:05:30.430Z] RMSE (validation) = 3.62196895454876 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-09-26T03:05:40.912Z] RMSE (validation) = 2.1340923221100736 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-09-26T03:05:49.580Z] RMSE (validation) = 1.3105190428502682 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-09-26T03:06:04.625Z] RMSE (validation) = 0.9920028100571827 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-09-26T03:06:13.358Z] RMSE (validation) = 1.212631715629696 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-09-26T03:06:19.337Z] RMSE (validation) = 1.1025694034386657 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-09-26T03:06:27.895Z] RMSE (validation) = 0.9259652469249674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-09-26T03:06:33.836Z] RMSE (validation) = 0.8982232214592321 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-09-26T03:06:34.312Z] 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.
[2024-09-26T03:06:34.312Z] The best model improves the baseline by 14.52%.
[2024-09-26T03:06:34.721Z] Movies recommended for you:
[2024-09-26T03:06:34.721Z] WARNING: This benchmark provides no result that can be validated.
[2024-09-26T03:06:34.721Z] There is no way to check that no silent failure occurred.
[2024-09-26T03:06:34.721Z] ====== movie-lens (apache-spark) [default], iteration 11 completed (79066.445 ms) ======
[2024-09-26T03:06:34.721Z] ====== movie-lens (apache-spark) [default], iteration 12 started ======
[2024-09-26T03:06:34.721Z] GC before operation: completed in 220.132 ms, heap usage 180.711 MB -> 52.105 MB.
[2024-09-26T03:06:46.970Z] RMSE (validation) = 3.62196895454876 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-09-26T03:06:55.819Z] RMSE (validation) = 2.1340923221100736 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-09-26T03:07:08.718Z] RMSE (validation) = 1.3105190428502682 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-09-26T03:07:21.478Z] RMSE (validation) = 0.9920028100571827 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-09-26T03:07:27.577Z] RMSE (validation) = 1.212631715629696 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-09-26T03:07:33.553Z] RMSE (validation) = 1.1025694034386657 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-09-26T03:07:39.735Z] RMSE (validation) = 0.9259652469249674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-09-26T03:07:47.148Z] RMSE (validation) = 0.8982232214592321 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-09-26T03:07:47.149Z] 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.
[2024-09-26T03:07:47.545Z] The best model improves the baseline by 14.52%.
[2024-09-26T03:07:48.033Z] Movies recommended for you:
[2024-09-26T03:07:48.033Z] WARNING: This benchmark provides no result that can be validated.
[2024-09-26T03:07:48.033Z] There is no way to check that no silent failure occurred.
[2024-09-26T03:07:48.033Z] ====== movie-lens (apache-spark) [default], iteration 12 completed (73026.387 ms) ======
[2024-09-26T03:07:48.033Z] ====== movie-lens (apache-spark) [default], iteration 13 started ======
[2024-09-26T03:07:48.627Z] GC before operation: completed in 293.462 ms, heap usage 100.117 MB -> 51.031 MB.
[2024-09-26T03:08:00.850Z] RMSE (validation) = 3.6219689545487603 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-09-26T03:08:12.913Z] RMSE (validation) = 2.1340923221100736 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-09-26T03:08:25.420Z] RMSE (validation) = 1.3105190428502682 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-09-26T03:08:33.964Z] RMSE (validation) = 0.9920028100571827 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-09-26T03:08:39.655Z] RMSE (validation) = 1.212631715629696 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-09-26T03:08:53.547Z] RMSE (validation) = 1.1025694034386657 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-09-26T03:08:59.264Z] RMSE (validation) = 0.9259652469249674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-09-26T03:09:05.444Z] RMSE (validation) = 0.8982232214592321 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-09-26T03:09:06.455Z] 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.
[2024-09-26T03:09:06.455Z] The best model improves the baseline by 14.52%.
[2024-09-26T03:09:06.455Z] Movies recommended for you:
[2024-09-26T03:09:06.455Z] WARNING: This benchmark provides no result that can be validated.
[2024-09-26T03:09:06.455Z] There is no way to check that no silent failure occurred.
[2024-09-26T03:09:06.455Z] ====== movie-lens (apache-spark) [default], iteration 13 completed (78346.570 ms) ======
[2024-09-26T03:09:06.455Z] ====== movie-lens (apache-spark) [default], iteration 14 started ======
[2024-09-26T03:09:06.455Z] GC before operation: completed in 205.069 ms, heap usage 212.383 MB -> 49.806 MB.
[2024-09-26T03:09:32.345Z] RMSE (validation) = 3.62196895454876 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-09-26T03:09:42.652Z] RMSE (validation) = 2.1340923221100736 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-09-26T03:09:58.171Z] RMSE (validation) = 1.3105190428502682 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-09-26T03:10:11.061Z] RMSE (validation) = 0.9920028100571827 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-09-26T03:10:29.630Z] RMSE (validation) = 1.212631715629696 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-09-26T03:10:35.342Z] RMSE (validation) = 1.1025694034386657 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-09-26T03:10:42.404Z] RMSE (validation) = 0.9259652469249674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-09-26T03:10:49.748Z] RMSE (validation) = 0.8982232214592321 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-09-26T03:10:50.967Z] 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.
[2024-09-26T03:10:51.460Z] The best model improves the baseline by 14.52%.
[2024-09-26T03:10:51.460Z] Movies recommended for you:
[2024-09-26T03:10:51.460Z] WARNING: This benchmark provides no result that can be validated.
[2024-09-26T03:10:51.460Z] There is no way to check that no silent failure occurred.
[2024-09-26T03:10:51.460Z] ====== movie-lens (apache-spark) [default], iteration 14 completed (104722.695 ms) ======
[2024-09-26T03:10:51.460Z] ====== movie-lens (apache-spark) [default], iteration 15 started ======
[2024-09-26T03:10:51.460Z] GC before operation: completed in 263.889 ms, heap usage 453.119 MB -> 53.397 MB.
[2024-09-26T03:11:04.088Z] RMSE (validation) = 3.6219689545487603 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-09-26T03:11:17.133Z] RMSE (validation) = 2.1340923221100736 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-09-26T03:11:27.283Z] RMSE (validation) = 1.3105190428502682 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-09-26T03:11:35.897Z] RMSE (validation) = 0.9920028100571827 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-09-26T03:11:40.789Z] RMSE (validation) = 1.212631715629696 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-09-26T03:11:48.055Z] RMSE (validation) = 1.1025694034386657 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-09-26T03:11:54.132Z] RMSE (validation) = 0.9259652469249674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-09-26T03:11:58.647Z] RMSE (validation) = 0.8982232214592321 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-09-26T03:11:58.647Z] 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.
[2024-09-26T03:11:58.647Z] The best model improves the baseline by 14.52%.
[2024-09-26T03:11:59.066Z] Movies recommended for you:
[2024-09-26T03:11:59.066Z] WARNING: This benchmark provides no result that can be validated.
[2024-09-26T03:11:59.066Z] There is no way to check that no silent failure occurred.
[2024-09-26T03:11:59.066Z] ====== movie-lens (apache-spark) [default], iteration 15 completed (67345.194 ms) ======
[2024-09-26T03:11:59.066Z] ====== movie-lens (apache-spark) [default], iteration 16 started ======
[2024-09-26T03:11:59.066Z] GC before operation: completed in 218.503 ms, heap usage 342.695 MB -> 52.500 MB.
[2024-09-26T03:12:11.336Z] RMSE (validation) = 3.6219689545487603 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-09-26T03:12:29.508Z] RMSE (validation) = 2.1340923221100736 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-09-26T03:12:34.159Z] RMSE (validation) = 1.3105190428502682 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-09-26T03:12:44.420Z] RMSE (validation) = 0.9920028100571827 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-09-26T03:12:49.004Z] RMSE (validation) = 1.212631715629696 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-09-26T03:12:53.693Z] RMSE (validation) = 1.1025694034386657 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-09-26T03:12:59.814Z] RMSE (validation) = 0.9259652469249674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-09-26T03:13:06.808Z] RMSE (validation) = 0.8982232214592321 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-09-26T03:13:07.729Z] 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.
[2024-09-26T03:13:07.729Z] The best model improves the baseline by 14.52%.
[2024-09-26T03:13:07.729Z] Movies recommended for you:
[2024-09-26T03:13:07.729Z] WARNING: This benchmark provides no result that can be validated.
[2024-09-26T03:13:07.729Z] There is no way to check that no silent failure occurred.
[2024-09-26T03:13:07.729Z] ====== movie-lens (apache-spark) [default], iteration 16 completed (68672.808 ms) ======
[2024-09-26T03:13:07.729Z] ====== movie-lens (apache-spark) [default], iteration 17 started ======
[2024-09-26T03:13:08.128Z] GC before operation: completed in 105.087 ms, heap usage 102.486 MB -> 49.810 MB.
[2024-09-26T03:13:14.831Z] RMSE (validation) = 3.6219689545487603 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-09-26T03:13:19.384Z] RMSE (validation) = 2.1340923221100736 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-09-26T03:13:34.322Z] RMSE (validation) = 1.3105190428502682 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-09-26T03:13:49.245Z] RMSE (validation) = 0.9920028100571827 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-09-26T03:13:53.971Z] RMSE (validation) = 1.212631715629696 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-09-26T03:14:01.036Z] RMSE (validation) = 1.1025694034386657 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-09-26T03:14:09.030Z] RMSE (validation) = 0.9259652469249674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-09-26T03:14:22.332Z] RMSE (validation) = 0.8982232214592321 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-09-26T03:14:22.761Z] 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.
[2024-09-26T03:14:22.761Z] The best model improves the baseline by 14.52%.
[2024-09-26T03:14:23.154Z] Movies recommended for you:
[2024-09-26T03:14:23.154Z] WARNING: This benchmark provides no result that can be validated.
[2024-09-26T03:14:23.154Z] There is no way to check that no silent failure occurred.
[2024-09-26T03:14:23.154Z] ====== movie-lens (apache-spark) [default], iteration 17 completed (75085.171 ms) ======
[2024-09-26T03:14:23.154Z] ====== movie-lens (apache-spark) [default], iteration 18 started ======
[2024-09-26T03:14:23.154Z] GC before operation: completed in 151.351 ms, heap usage 334.351 MB -> 50.122 MB.
[2024-09-26T03:14:33.058Z] RMSE (validation) = 3.6219689545487603 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-09-26T03:14:43.697Z] RMSE (validation) = 2.1340923221100736 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-09-26T03:15:01.806Z] RMSE (validation) = 1.3105190428502682 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-09-26T03:15:12.300Z] RMSE (validation) = 0.9920028100571827 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-09-26T03:15:19.705Z] RMSE (validation) = 1.212631715629696 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-09-26T03:15:26.791Z] RMSE (validation) = 1.1025694034386657 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-09-26T03:15:36.103Z] RMSE (validation) = 0.9259652469249674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-09-26T03:15:44.474Z] RMSE (validation) = 0.8982232214592321 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-09-26T03:15:45.529Z] 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.
[2024-09-26T03:15:45.529Z] The best model improves the baseline by 14.52%.
[2024-09-26T03:15:45.942Z] Movies recommended for you:
[2024-09-26T03:15:45.942Z] WARNING: This benchmark provides no result that can be validated.
[2024-09-26T03:15:45.942Z] There is no way to check that no silent failure occurred.
[2024-09-26T03:15:45.942Z] ====== movie-lens (apache-spark) [default], iteration 18 completed (82635.069 ms) ======
[2024-09-26T03:15:45.942Z] ====== movie-lens (apache-spark) [default], iteration 19 started ======
[2024-09-26T03:15:45.942Z] GC before operation: completed in 235.091 ms, heap usage 246.990 MB -> 50.188 MB.
[2024-09-26T03:16:01.290Z] RMSE (validation) = 3.6219689545487603 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-09-26T03:16:14.181Z] RMSE (validation) = 2.1340923221100736 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-09-26T03:16:27.275Z] RMSE (validation) = 1.3105190428502682 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-09-26T03:16:40.059Z] RMSE (validation) = 0.9920028100571827 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-09-26T03:16:49.295Z] RMSE (validation) = 1.212631715629696 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-09-26T03:16:55.136Z] RMSE (validation) = 1.1025694034386657 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-09-26T03:17:02.638Z] RMSE (validation) = 0.9259652469249674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-09-26T03:17:11.391Z] RMSE (validation) = 0.8982232214592321 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-09-26T03:17:12.609Z] 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.
[2024-09-26T03:17:12.609Z] The best model improves the baseline by 14.52%.
[2024-09-26T03:17:13.070Z] Movies recommended for you:
[2024-09-26T03:17:13.070Z] WARNING: This benchmark provides no result that can be validated.
[2024-09-26T03:17:13.070Z] There is no way to check that no silent failure occurred.
[2024-09-26T03:17:13.070Z] ====== movie-lens (apache-spark) [default], iteration 19 completed (86777.721 ms) ======
[2024-09-26T03:17:17.815Z] -----------------------------------
[2024-09-26T03:17:17.815Z] renaissance-movie-lens_0_PASSED
[2024-09-26T03:17:17.815Z] -----------------------------------
[2024-09-26T03:17:17.815Z]
[2024-09-26T03:17:17.815Z] TEST TEARDOWN:
[2024-09-26T03:17:17.815Z] Nothing to be done for teardown.
[2024-09-26T03:17:17.815Z] renaissance-movie-lens_0 Finish Time: Wed Sep 25 20:17:16 2024 Epoch Time (ms): 1727320636644