renaissance-movie-lens_0
[2025-02-13T01:33:33.623Z] Running test renaissance-movie-lens_0 ...
[2025-02-13T01:33:34.042Z] ===============================================
[2025-02-13T01:33:34.042Z] renaissance-movie-lens_0 Start Time: Wed Feb 12 17:33:32 2025 Epoch Time (ms): 1739410412988
[2025-02-13T01:33:34.042Z] variation: NoOptions
[2025-02-13T01:33:34.042Z] JVM_OPTIONS:
[2025-02-13T01:33:34.042Z] { \
[2025-02-13T01:33:34.042Z] echo ""; echo "TEST SETUP:"; \
[2025-02-13T01:33:34.042Z] echo "Nothing to be done for setup."; \
[2025-02-13T01:33:34.042Z] mkdir -p "/Users/admin/workspace/workspace/Test_openjdk17_hs_extended.perf_x86-64_mac_testList_0/aqa-tests/TKG/../TKG/output_17394094624238/renaissance-movie-lens_0"; \
[2025-02-13T01:33:34.042Z] cd "/Users/admin/workspace/workspace/Test_openjdk17_hs_extended.perf_x86-64_mac_testList_0/aqa-tests/TKG/../TKG/output_17394094624238/renaissance-movie-lens_0"; \
[2025-02-13T01:33:34.042Z] echo ""; echo "TESTING:"; \
[2025-02-13T01:33:34.042Z] "/Users/admin/workspace/workspace/Test_openjdk17_hs_extended.perf_x86-64_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_x86-64_mac_testList_0/aqa-tests/TKG/../../jvmtest/perf/renaissance/renaissance.jar" --json ""/Users/admin/workspace/workspace/Test_openjdk17_hs_extended.perf_x86-64_mac_testList_0/aqa-tests/TKG/../TKG/output_17394094624238/renaissance-movie-lens_0"/movie-lens.json" movie-lens; \
[2025-02-13T01:33:34.043Z] 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_0/aqa-tests/TKG/..; rm -f -r "/Users/admin/workspace/workspace/Test_openjdk17_hs_extended.perf_x86-64_mac_testList_0/aqa-tests/TKG/../TKG/output_17394094624238/renaissance-movie-lens_0"; else echo "-----------------------------------"; echo "renaissance-movie-lens_0""_FAILED"; echo "-----------------------------------"; fi; \
[2025-02-13T01:33:34.043Z] echo ""; echo "TEST TEARDOWN:"; \
[2025-02-13T01:33:34.043Z] echo "Nothing to be done for teardown."; \
[2025-02-13T01:33:34.043Z] } 2>&1 | tee -a "/Users/admin/workspace/workspace/Test_openjdk17_hs_extended.perf_x86-64_mac_testList_0/aqa-tests/TKG/../TKG/output_17394094624238/TestTargetResult";
[2025-02-13T01:33:34.043Z]
[2025-02-13T01:33:34.043Z] TEST SETUP:
[2025-02-13T01:33:34.043Z] Nothing to be done for setup.
[2025-02-13T01:33:34.043Z]
[2025-02-13T01:33:34.043Z] TESTING:
[2025-02-13T01:33:38.536Z] Using Spark's default log4j profile: org/apache/spark/log4j-defaults.properties
[2025-02-13T01:33:39.452Z] NOTE: 'movie-lens' benchmark uses Spark local executor with 3 (out of 3) threads.
[2025-02-13T01:33:43.102Z] Got 100004 ratings from 671 users on 9066 movies.
[2025-02-13T01:33:43.102Z] Training: 60056, validation: 20285, test: 19854
[2025-02-13T01:33:43.102Z] ====== movie-lens (apache-spark) [default], iteration 0 started ======
[2025-02-13T01:33:43.102Z] GC before operation: completed in 97.965 ms, heap usage 56.340 MB -> 37.655 MB.
[2025-02-13T01:33:51.725Z] RMSE (validation) = 3.62196895454876 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2025-02-13T01:33:58.788Z] RMSE (validation) = 2.1340923221100736 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2025-02-13T01:34:03.598Z] RMSE (validation) = 1.3105190428502682 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2025-02-13T01:34:09.341Z] RMSE (validation) = 0.9920028100571827 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2025-02-13T01:34:10.827Z] RMSE (validation) = 1.212631715629696 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2025-02-13T01:34:14.525Z] RMSE (validation) = 1.1025694034386657 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2025-02-13T01:34:16.587Z] RMSE (validation) = 0.9259652469249674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2025-02-13T01:34:19.341Z] RMSE (validation) = 0.8982232214592321 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2025-02-13T01:34:19.341Z] 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.
[2025-02-13T01:34:19.341Z] The best model improves the baseline by 14.52%.
[2025-02-13T01:34:19.341Z] Movies recommended for you:
[2025-02-13T01:34:19.341Z] WARNING: This benchmark provides no result that can be validated.
[2025-02-13T01:34:19.341Z] There is no way to check that no silent failure occurred.
[2025-02-13T01:34:19.341Z] ====== movie-lens (apache-spark) [default], iteration 0 completed (36216.814 ms) ======
[2025-02-13T01:34:19.341Z] ====== movie-lens (apache-spark) [default], iteration 1 started ======
[2025-02-13T01:34:19.341Z] GC before operation: completed in 66.282 ms, heap usage 66.048 MB -> 51.702 MB.
[2025-02-13T01:34:23.908Z] RMSE (validation) = 3.6219689545487603 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2025-02-13T01:34:27.494Z] RMSE (validation) = 2.1340923221100736 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2025-02-13T01:34:31.116Z] RMSE (validation) = 1.3105190428502682 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2025-02-13T01:34:34.624Z] RMSE (validation) = 0.9920028100571827 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2025-02-13T01:34:37.417Z] RMSE (validation) = 1.212631715629696 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2025-02-13T01:34:39.428Z] RMSE (validation) = 1.1025694034386657 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2025-02-13T01:34:41.486Z] RMSE (validation) = 0.9259652469249674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2025-02-13T01:34:43.494Z] RMSE (validation) = 0.8982232214592321 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2025-02-13T01:34:43.924Z] 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.
[2025-02-13T01:34:43.924Z] The best model improves the baseline by 14.52%.
[2025-02-13T01:34:43.924Z] Movies recommended for you:
[2025-02-13T01:34:43.924Z] WARNING: This benchmark provides no result that can be validated.
[2025-02-13T01:34:43.924Z] There is no way to check that no silent failure occurred.
[2025-02-13T01:34:43.924Z] ====== movie-lens (apache-spark) [default], iteration 1 completed (24635.151 ms) ======
[2025-02-13T01:34:43.924Z] ====== movie-lens (apache-spark) [default], iteration 2 started ======
[2025-02-13T01:34:44.342Z] GC before operation: completed in 69.707 ms, heap usage 389.597 MB -> 53.231 MB.
[2025-02-13T01:34:48.829Z] RMSE (validation) = 3.6219689545487603 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2025-02-13T01:34:52.347Z] RMSE (validation) = 2.1340923221100736 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2025-02-13T01:34:56.848Z] RMSE (validation) = 1.3105190428502682 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2025-02-13T01:35:01.510Z] RMSE (validation) = 0.9920028100571827 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2025-02-13T01:35:04.335Z] RMSE (validation) = 1.212631715629696 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2025-02-13T01:35:06.297Z] RMSE (validation) = 1.1025694034386657 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2025-02-13T01:35:07.715Z] RMSE (validation) = 0.9259652469249674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2025-02-13T01:35:09.755Z] RMSE (validation) = 0.8982232214592321 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2025-02-13T01:35:09.755Z] 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.
[2025-02-13T01:35:09.755Z] The best model improves the baseline by 14.52%.
[2025-02-13T01:35:09.755Z] Movies recommended for you:
[2025-02-13T01:35:09.755Z] WARNING: This benchmark provides no result that can be validated.
[2025-02-13T01:35:09.755Z] There is no way to check that no silent failure occurred.
[2025-02-13T01:35:09.755Z] ====== movie-lens (apache-spark) [default], iteration 2 completed (25643.936 ms) ======
[2025-02-13T01:35:09.755Z] ====== movie-lens (apache-spark) [default], iteration 3 started ======
[2025-02-13T01:35:09.755Z] GC before operation: completed in 46.626 ms, heap usage 196.271 MB -> 50.224 MB.
[2025-02-13T01:35:12.398Z] RMSE (validation) = 3.6219689545487603 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2025-02-13T01:35:15.073Z] RMSE (validation) = 2.1340923221100736 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2025-02-13T01:35:18.577Z] RMSE (validation) = 1.3105190428502682 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2025-02-13T01:35:20.577Z] RMSE (validation) = 0.9920028100571827 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2025-02-13T01:35:22.562Z] RMSE (validation) = 1.212631715629696 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2025-02-13T01:35:23.968Z] RMSE (validation) = 1.1025694034386657 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2025-02-13T01:35:25.948Z] RMSE (validation) = 0.9259652469249674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2025-02-13T01:35:27.395Z] RMSE (validation) = 0.8982232214592321 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2025-02-13T01:35:27.806Z] 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.
[2025-02-13T01:35:27.806Z] The best model improves the baseline by 14.52%.
[2025-02-13T01:35:27.806Z] Movies recommended for you:
[2025-02-13T01:35:27.806Z] WARNING: This benchmark provides no result that can be validated.
[2025-02-13T01:35:27.806Z] There is no way to check that no silent failure occurred.
[2025-02-13T01:35:27.806Z] ====== movie-lens (apache-spark) [default], iteration 3 completed (17941.611 ms) ======
[2025-02-13T01:35:27.806Z] ====== movie-lens (apache-spark) [default], iteration 4 started ======
[2025-02-13T01:35:27.806Z] GC before operation: completed in 49.582 ms, heap usage 272.344 MB -> 50.638 MB.
[2025-02-13T01:35:30.534Z] RMSE (validation) = 3.62196895454876 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2025-02-13T01:35:33.264Z] RMSE (validation) = 2.1340923221100736 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2025-02-13T01:35:36.728Z] RMSE (validation) = 1.3105190428502682 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2025-02-13T01:35:39.436Z] RMSE (validation) = 0.9920028100571827 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2025-02-13T01:35:40.455Z] RMSE (validation) = 1.212631715629696 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2025-02-13T01:35:42.436Z] RMSE (validation) = 1.1025694034386657 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2025-02-13T01:35:43.820Z] RMSE (validation) = 0.9259652469249674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2025-02-13T01:35:45.227Z] RMSE (validation) = 0.8982232214592321 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2025-02-13T01:35:45.638Z] 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.
[2025-02-13T01:35:45.638Z] The best model improves the baseline by 14.52%.
[2025-02-13T01:35:45.638Z] Movies recommended for you:
[2025-02-13T01:35:45.638Z] WARNING: This benchmark provides no result that can be validated.
[2025-02-13T01:35:45.638Z] There is no way to check that no silent failure occurred.
[2025-02-13T01:35:45.638Z] ====== movie-lens (apache-spark) [default], iteration 4 completed (17778.034 ms) ======
[2025-02-13T01:35:45.638Z] ====== movie-lens (apache-spark) [default], iteration 5 started ======
[2025-02-13T01:35:45.638Z] GC before operation: completed in 42.206 ms, heap usage 273.035 MB -> 50.785 MB.
[2025-02-13T01:35:48.303Z] RMSE (validation) = 3.62196895454876 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2025-02-13T01:35:50.996Z] RMSE (validation) = 2.1340923221100736 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2025-02-13T01:35:53.673Z] RMSE (validation) = 1.3105190428502682 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2025-02-13T01:35:55.705Z] RMSE (validation) = 0.9920028100571827 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2025-02-13T01:35:57.699Z] RMSE (validation) = 1.212631715629696 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2025-02-13T01:35:58.589Z] RMSE (validation) = 1.1025694034386657 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2025-02-13T01:36:00.556Z] RMSE (validation) = 0.9259652469249674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2025-02-13T01:36:01.962Z] RMSE (validation) = 0.8982232214592321 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2025-02-13T01:36:01.962Z] 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.
[2025-02-13T01:36:01.962Z] The best model improves the baseline by 14.52%.
[2025-02-13T01:36:02.384Z] Movies recommended for you:
[2025-02-13T01:36:02.384Z] WARNING: This benchmark provides no result that can be validated.
[2025-02-13T01:36:02.384Z] There is no way to check that no silent failure occurred.
[2025-02-13T01:36:02.384Z] ====== movie-lens (apache-spark) [default], iteration 5 completed (16503.913 ms) ======
[2025-02-13T01:36:02.384Z] ====== movie-lens (apache-spark) [default], iteration 6 started ======
[2025-02-13T01:36:02.384Z] GC before operation: completed in 43.913 ms, heap usage 202.972 MB -> 50.689 MB.
[2025-02-13T01:36:05.078Z] RMSE (validation) = 3.6219689545487603 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2025-02-13T01:36:07.739Z] RMSE (validation) = 2.1340923221100736 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2025-02-13T01:36:10.434Z] RMSE (validation) = 1.3105190428502682 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2025-02-13T01:36:13.110Z] RMSE (validation) = 0.9920028100571827 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2025-02-13T01:36:13.981Z] RMSE (validation) = 1.212631715629696 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2025-02-13T01:36:15.370Z] RMSE (validation) = 1.1025694034386657 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2025-02-13T01:36:17.414Z] RMSE (validation) = 0.9259652469249674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2025-02-13T01:36:18.789Z] RMSE (validation) = 0.8982232214592321 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2025-02-13T01:36:18.789Z] 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.
[2025-02-13T01:36:18.789Z] The best model improves the baseline by 14.52%.
[2025-02-13T01:36:19.191Z] Movies recommended for you:
[2025-02-13T01:36:19.191Z] WARNING: This benchmark provides no result that can be validated.
[2025-02-13T01:36:19.191Z] There is no way to check that no silent failure occurred.
[2025-02-13T01:36:19.191Z] ====== movie-lens (apache-spark) [default], iteration 6 completed (16807.128 ms) ======
[2025-02-13T01:36:19.191Z] ====== movie-lens (apache-spark) [default], iteration 7 started ======
[2025-02-13T01:36:19.191Z] GC before operation: completed in 48.298 ms, heap usage 273.531 MB -> 51.023 MB.
[2025-02-13T01:36:21.858Z] RMSE (validation) = 3.62196895454876 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2025-02-13T01:36:24.528Z] RMSE (validation) = 2.1340923221100736 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2025-02-13T01:36:27.218Z] RMSE (validation) = 1.3105190428502682 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2025-02-13T01:36:29.915Z] RMSE (validation) = 0.9920028100571827 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2025-02-13T01:36:31.876Z] RMSE (validation) = 1.212631715629696 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2025-02-13T01:36:33.269Z] RMSE (validation) = 1.1025694034386657 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2025-02-13T01:36:34.666Z] RMSE (validation) = 0.9259652469249674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2025-02-13T01:36:36.630Z] RMSE (validation) = 0.8982232214592321 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2025-02-13T01:36:36.630Z] 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.
[2025-02-13T01:36:36.630Z] The best model improves the baseline by 14.52%.
[2025-02-13T01:36:36.630Z] Movies recommended for you:
[2025-02-13T01:36:36.630Z] WARNING: This benchmark provides no result that can be validated.
[2025-02-13T01:36:36.630Z] There is no way to check that no silent failure occurred.
[2025-02-13T01:36:36.630Z] ====== movie-lens (apache-spark) [default], iteration 7 completed (17727.217 ms) ======
[2025-02-13T01:36:36.630Z] ====== movie-lens (apache-spark) [default], iteration 8 started ======
[2025-02-13T01:36:37.062Z] GC before operation: completed in 44.789 ms, heap usage 399.442 MB -> 54.598 MB.
[2025-02-13T01:36:39.740Z] RMSE (validation) = 3.6219689545487603 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2025-02-13T01:36:42.435Z] RMSE (validation) = 2.1340923221100736 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2025-02-13T01:36:45.116Z] RMSE (validation) = 1.3105190428502682 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2025-02-13T01:36:47.786Z] RMSE (validation) = 0.9920028100571827 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2025-02-13T01:36:49.765Z] RMSE (validation) = 1.212631715629696 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2025-02-13T01:36:51.190Z] RMSE (validation) = 1.1025694034386657 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2025-02-13T01:36:52.582Z] RMSE (validation) = 0.9259652469249674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2025-02-13T01:36:53.986Z] RMSE (validation) = 0.8982232214592321 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2025-02-13T01:36:54.417Z] 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.
[2025-02-13T01:36:54.417Z] The best model improves the baseline by 14.52%.
[2025-02-13T01:36:54.417Z] Movies recommended for you:
[2025-02-13T01:36:54.417Z] WARNING: This benchmark provides no result that can be validated.
[2025-02-13T01:36:54.417Z] There is no way to check that no silent failure occurred.
[2025-02-13T01:36:54.417Z] ====== movie-lens (apache-spark) [default], iteration 8 completed (17622.178 ms) ======
[2025-02-13T01:36:54.417Z] ====== movie-lens (apache-spark) [default], iteration 9 started ======
[2025-02-13T01:36:54.417Z] GC before operation: completed in 43.102 ms, heap usage 98.123 MB -> 53.901 MB.
[2025-02-13T01:36:57.120Z] RMSE (validation) = 3.62196895454876 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2025-02-13T01:36:59.832Z] RMSE (validation) = 2.1340923221100736 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2025-02-13T01:37:03.375Z] RMSE (validation) = 1.3105190428502682 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2025-02-13T01:37:06.075Z] RMSE (validation) = 0.9920028100571827 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2025-02-13T01:37:07.468Z] RMSE (validation) = 1.212631715629696 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2025-02-13T01:37:08.892Z] RMSE (validation) = 1.1025694034386657 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2025-02-13T01:37:10.872Z] RMSE (validation) = 0.9259652469249674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2025-02-13T01:37:12.280Z] RMSE (validation) = 0.8982232214592321 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2025-02-13T01:37:12.280Z] 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.
[2025-02-13T01:37:12.280Z] The best model improves the baseline by 14.52%.
[2025-02-13T01:37:12.280Z] Movies recommended for you:
[2025-02-13T01:37:12.280Z] WARNING: This benchmark provides no result that can be validated.
[2025-02-13T01:37:12.280Z] There is no way to check that no silent failure occurred.
[2025-02-13T01:37:12.280Z] ====== movie-lens (apache-spark) [default], iteration 9 completed (17824.818 ms) ======
[2025-02-13T01:37:12.280Z] ====== movie-lens (apache-spark) [default], iteration 10 started ======
[2025-02-13T01:37:12.280Z] GC before operation: completed in 46.367 ms, heap usage 202.875 MB -> 51.207 MB.
[2025-02-13T01:37:15.715Z] RMSE (validation) = 3.62196895454876 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2025-02-13T01:37:17.684Z] RMSE (validation) = 2.1340923221100736 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2025-02-13T01:37:20.381Z] RMSE (validation) = 1.3105190428502682 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2025-02-13T01:37:23.119Z] RMSE (validation) = 0.9920028100571827 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2025-02-13T01:37:24.007Z] RMSE (validation) = 1.212631715629696 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2025-02-13T01:37:26.000Z] RMSE (validation) = 1.1025694034386657 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2025-02-13T01:37:27.395Z] RMSE (validation) = 0.9259652469249674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2025-02-13T01:37:28.785Z] RMSE (validation) = 0.8982232214592321 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2025-02-13T01:37:29.189Z] 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.
[2025-02-13T01:37:29.189Z] The best model improves the baseline by 14.52%.
[2025-02-13T01:37:29.189Z] Movies recommended for you:
[2025-02-13T01:37:29.189Z] WARNING: This benchmark provides no result that can be validated.
[2025-02-13T01:37:29.189Z] There is no way to check that no silent failure occurred.
[2025-02-13T01:37:29.189Z] ====== movie-lens (apache-spark) [default], iteration 10 completed (16726.433 ms) ======
[2025-02-13T01:37:29.189Z] ====== movie-lens (apache-spark) [default], iteration 11 started ======
[2025-02-13T01:37:29.189Z] GC before operation: completed in 44.650 ms, heap usage 61.357 MB -> 50.883 MB.
[2025-02-13T01:37:31.866Z] RMSE (validation) = 3.6219689545487603 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2025-02-13T01:37:34.542Z] RMSE (validation) = 2.1340923221100736 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2025-02-13T01:37:37.188Z] RMSE (validation) = 1.3105190428502682 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2025-02-13T01:37:39.924Z] RMSE (validation) = 0.9920028100571827 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2025-02-13T01:37:41.304Z] RMSE (validation) = 1.212631715629696 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2025-02-13T01:37:42.689Z] RMSE (validation) = 1.1025694034386657 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2025-02-13T01:37:44.124Z] RMSE (validation) = 0.9259652469249674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2025-02-13T01:37:46.107Z] RMSE (validation) = 0.8982232214592321 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2025-02-13T01:37:46.107Z] 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.
[2025-02-13T01:37:46.107Z] The best model improves the baseline by 14.52%.
[2025-02-13T01:37:46.515Z] Movies recommended for you:
[2025-02-13T01:37:46.515Z] WARNING: This benchmark provides no result that can be validated.
[2025-02-13T01:37:46.515Z] There is no way to check that no silent failure occurred.
[2025-02-13T01:37:46.515Z] ====== movie-lens (apache-spark) [default], iteration 11 completed (17191.764 ms) ======
[2025-02-13T01:37:46.515Z] ====== movie-lens (apache-spark) [default], iteration 12 started ======
[2025-02-13T01:37:46.515Z] GC before operation: completed in 56.464 ms, heap usage 361.610 MB -> 51.245 MB.
[2025-02-13T01:37:49.985Z] RMSE (validation) = 3.6219689545487603 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2025-02-13T01:37:52.011Z] RMSE (validation) = 2.1340923221100736 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2025-02-13T01:37:54.666Z] RMSE (validation) = 1.3105190428502682 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2025-02-13T01:37:57.331Z] RMSE (validation) = 0.9920028100571827 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2025-02-13T01:37:58.740Z] RMSE (validation) = 1.212631715629696 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2025-02-13T01:38:00.129Z] RMSE (validation) = 1.1025694034386657 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2025-02-13T01:38:01.578Z] RMSE (validation) = 0.9259652469249674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2025-02-13T01:38:02.488Z] RMSE (validation) = 0.8982232214592321 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2025-02-13T01:38:02.957Z] 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.
[2025-02-13T01:38:02.957Z] The best model improves the baseline by 14.52%.
[2025-02-13T01:38:02.957Z] Movies recommended for you:
[2025-02-13T01:38:02.957Z] WARNING: This benchmark provides no result that can be validated.
[2025-02-13T01:38:02.957Z] There is no way to check that no silent failure occurred.
[2025-02-13T01:38:02.957Z] ====== movie-lens (apache-spark) [default], iteration 12 completed (16476.392 ms) ======
[2025-02-13T01:38:02.957Z] ====== movie-lens (apache-spark) [default], iteration 13 started ======
[2025-02-13T01:38:02.957Z] GC before operation: completed in 46.823 ms, heap usage 280.162 MB -> 51.360 MB.
[2025-02-13T01:38:05.015Z] RMSE (validation) = 3.62196895454876 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2025-02-13T01:38:06.988Z] RMSE (validation) = 2.1340923221100736 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2025-02-13T01:38:09.685Z] RMSE (validation) = 1.3105190428502682 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2025-02-13T01:38:11.076Z] RMSE (validation) = 0.9920028100571827 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2025-02-13T01:38:13.066Z] RMSE (validation) = 1.212631715629696 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2025-02-13T01:38:14.485Z] RMSE (validation) = 1.1025694034386657 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2025-02-13T01:38:15.866Z] RMSE (validation) = 0.9259652469249674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2025-02-13T01:38:17.881Z] RMSE (validation) = 0.8982232214592321 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2025-02-13T01:38:17.881Z] 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.
[2025-02-13T01:38:17.881Z] The best model improves the baseline by 14.52%.
[2025-02-13T01:38:17.881Z] Movies recommended for you:
[2025-02-13T01:38:17.881Z] WARNING: This benchmark provides no result that can be validated.
[2025-02-13T01:38:17.881Z] There is no way to check that no silent failure occurred.
[2025-02-13T01:38:17.881Z] ====== movie-lens (apache-spark) [default], iteration 13 completed (14855.469 ms) ======
[2025-02-13T01:38:17.881Z] ====== movie-lens (apache-spark) [default], iteration 14 started ======
[2025-02-13T01:38:17.881Z] GC before operation: completed in 41.380 ms, heap usage 163.654 MB -> 51.092 MB.
[2025-02-13T01:38:20.540Z] RMSE (validation) = 3.62196895454876 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2025-02-13T01:38:23.187Z] RMSE (validation) = 2.1340923221100736 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2025-02-13T01:38:25.875Z] RMSE (validation) = 1.3105190428502682 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2025-02-13T01:38:28.576Z] RMSE (validation) = 0.9920028100571827 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2025-02-13T01:38:29.967Z] RMSE (validation) = 1.212631715629696 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2025-02-13T01:38:31.346Z] RMSE (validation) = 1.1025694034386657 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2025-02-13T01:38:32.777Z] RMSE (validation) = 0.9259652469249674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2025-02-13T01:38:33.643Z] RMSE (validation) = 0.8982232214592321 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2025-02-13T01:38:34.045Z] 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.
[2025-02-13T01:38:34.045Z] The best model improves the baseline by 14.52%.
[2025-02-13T01:38:34.045Z] Movies recommended for you:
[2025-02-13T01:38:34.045Z] WARNING: This benchmark provides no result that can be validated.
[2025-02-13T01:38:34.045Z] There is no way to check that no silent failure occurred.
[2025-02-13T01:38:34.045Z] ====== movie-lens (apache-spark) [default], iteration 14 completed (16109.932 ms) ======
[2025-02-13T01:38:34.045Z] ====== movie-lens (apache-spark) [default], iteration 15 started ======
[2025-02-13T01:38:34.045Z] GC before operation: completed in 42.837 ms, heap usage 214.252 MB -> 51.218 MB.
[2025-02-13T01:38:36.078Z] RMSE (validation) = 3.62196895454876 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2025-02-13T01:38:38.731Z] RMSE (validation) = 2.1340923221100736 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2025-02-13T01:38:40.741Z] RMSE (validation) = 1.3105190428502682 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2025-02-13T01:38:43.428Z] RMSE (validation) = 0.9920028100571827 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2025-02-13T01:38:44.852Z] RMSE (validation) = 1.212631715629696 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2025-02-13T01:38:46.249Z] RMSE (validation) = 1.1025694034386657 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2025-02-13T01:38:48.219Z] RMSE (validation) = 0.9259652469249674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2025-02-13T01:38:49.592Z] RMSE (validation) = 0.8982232214592321 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2025-02-13T01:38:50.000Z] 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.
[2025-02-13T01:38:50.000Z] The best model improves the baseline by 14.52%.
[2025-02-13T01:38:50.000Z] Movies recommended for you:
[2025-02-13T01:38:50.000Z] WARNING: This benchmark provides no result that can be validated.
[2025-02-13T01:38:50.000Z] There is no way to check that no silent failure occurred.
[2025-02-13T01:38:50.000Z] ====== movie-lens (apache-spark) [default], iteration 15 completed (16012.735 ms) ======
[2025-02-13T01:38:50.000Z] ====== movie-lens (apache-spark) [default], iteration 16 started ======
[2025-02-13T01:38:50.000Z] GC before operation: completed in 47.126 ms, heap usage 202.737 MB -> 51.208 MB.
[2025-02-13T01:38:53.488Z] RMSE (validation) = 3.62196895454876 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2025-02-13T01:38:55.510Z] RMSE (validation) = 2.1340923221100736 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2025-02-13T01:38:58.166Z] RMSE (validation) = 1.3105190428502682 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2025-02-13T01:39:00.880Z] RMSE (validation) = 0.9920028100571827 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2025-02-13T01:39:02.279Z] RMSE (validation) = 1.212631715629696 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2025-02-13T01:39:03.717Z] RMSE (validation) = 1.1025694034386657 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2025-02-13T01:39:05.153Z] RMSE (validation) = 0.9259652469249674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2025-02-13T01:39:07.142Z] RMSE (validation) = 0.8982232214592321 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2025-02-13T01:39:07.142Z] 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.
[2025-02-13T01:39:07.142Z] The best model improves the baseline by 14.52%.
[2025-02-13T01:39:07.556Z] Movies recommended for you:
[2025-02-13T01:39:07.557Z] WARNING: This benchmark provides no result that can be validated.
[2025-02-13T01:39:07.557Z] There is no way to check that no silent failure occurred.
[2025-02-13T01:39:07.557Z] ====== movie-lens (apache-spark) [default], iteration 16 completed (17323.290 ms) ======
[2025-02-13T01:39:07.557Z] ====== movie-lens (apache-spark) [default], iteration 17 started ======
[2025-02-13T01:39:07.557Z] GC before operation: completed in 43.019 ms, heap usage 252.437 MB -> 51.132 MB.
[2025-02-13T01:39:10.231Z] RMSE (validation) = 3.6219689545487603 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2025-02-13T01:39:12.261Z] RMSE (validation) = 2.1340923221100736 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2025-02-13T01:39:14.944Z] RMSE (validation) = 1.3105190428502682 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2025-02-13T01:39:17.605Z] RMSE (validation) = 0.9920028100571827 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2025-02-13T01:39:19.002Z] RMSE (validation) = 1.212631715629696 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2025-02-13T01:39:20.405Z] RMSE (validation) = 1.1025694034386657 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2025-02-13T01:39:21.822Z] RMSE (validation) = 0.9259652469249674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2025-02-13T01:39:23.179Z] RMSE (validation) = 0.8982232214592321 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2025-02-13T01:39:23.591Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9063003101263983.
[2025-02-13T01:39:23.591Z] The best model improves the baseline by 14.52%.
[2025-02-13T01:39:23.591Z] Movies recommended for you:
[2025-02-13T01:39:23.591Z] WARNING: This benchmark provides no result that can be validated.
[2025-02-13T01:39:23.591Z] There is no way to check that no silent failure occurred.
[2025-02-13T01:39:23.591Z] ====== movie-lens (apache-spark) [default], iteration 17 completed (16255.719 ms) ======
[2025-02-13T01:39:23.591Z] ====== movie-lens (apache-spark) [default], iteration 18 started ======
[2025-02-13T01:39:23.591Z] GC before operation: completed in 47.251 ms, heap usage 222.619 MB -> 51.065 MB.
[2025-02-13T01:39:27.017Z] RMSE (validation) = 3.6219689545487603 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2025-02-13T01:39:29.713Z] RMSE (validation) = 2.1340923221100736 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2025-02-13T01:39:31.701Z] RMSE (validation) = 1.3105190428502682 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2025-02-13T01:39:34.446Z] RMSE (validation) = 0.9920028100571827 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2025-02-13T01:39:35.353Z] RMSE (validation) = 1.212631715629696 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2025-02-13T01:39:37.311Z] RMSE (validation) = 1.1025694034386657 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2025-02-13T01:39:38.723Z] RMSE (validation) = 0.9259652469249674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2025-02-13T01:39:40.755Z] RMSE (validation) = 0.8982232214592321 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2025-02-13T01:39:40.755Z] 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.
[2025-02-13T01:39:40.755Z] The best model improves the baseline by 14.52%.
[2025-02-13T01:39:40.755Z] Movies recommended for you:
[2025-02-13T01:39:40.755Z] WARNING: This benchmark provides no result that can be validated.
[2025-02-13T01:39:40.755Z] There is no way to check that no silent failure occurred.
[2025-02-13T01:39:40.755Z] ====== movie-lens (apache-spark) [default], iteration 18 completed (16996.975 ms) ======
[2025-02-13T01:39:40.755Z] ====== movie-lens (apache-spark) [default], iteration 19 started ======
[2025-02-13T01:39:40.755Z] GC before operation: completed in 43.582 ms, heap usage 180.871 MB -> 51.349 MB.
[2025-02-13T01:39:43.420Z] RMSE (validation) = 3.62196895454876 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2025-02-13T01:39:46.155Z] RMSE (validation) = 2.1340923221100736 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2025-02-13T01:39:48.820Z] RMSE (validation) = 1.3105190428502682 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2025-02-13T01:39:50.792Z] RMSE (validation) = 0.9920028100571827 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2025-02-13T01:39:52.765Z] RMSE (validation) = 1.212631715629696 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2025-02-13T01:39:54.129Z] RMSE (validation) = 1.1025694034386657 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2025-02-13T01:39:55.552Z] RMSE (validation) = 0.9259652469249674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2025-02-13T01:39:56.948Z] RMSE (validation) = 0.8982232214592321 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2025-02-13T01:39:57.348Z] 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.
[2025-02-13T01:39:57.348Z] The best model improves the baseline by 14.52%.
[2025-02-13T01:39:57.348Z] Movies recommended for you:
[2025-02-13T01:39:57.348Z] WARNING: This benchmark provides no result that can be validated.
[2025-02-13T01:39:57.348Z] There is no way to check that no silent failure occurred.
[2025-02-13T01:39:57.348Z] ====== movie-lens (apache-spark) [default], iteration 19 completed (16688.395 ms) ======
[2025-02-13T01:39:57.761Z] -----------------------------------
[2025-02-13T01:39:57.761Z] renaissance-movie-lens_0_PASSED
[2025-02-13T01:39:57.761Z] -----------------------------------
[2025-02-13T01:39:58.169Z]
[2025-02-13T01:39:58.169Z] TEST TEARDOWN:
[2025-02-13T01:39:58.169Z] Nothing to be done for teardown.
[2025-02-13T01:39:58.169Z] renaissance-movie-lens_0 Finish Time: Wed Feb 12 17:39:57 2025 Epoch Time (ms): 1739410797157