renaissance-movie-lens_0
[2024-12-05T06:28:07.774Z] Running test renaissance-movie-lens_0 ...
[2024-12-05T06:28:07.774Z] ===============================================
[2024-12-05T06:28:07.774Z] renaissance-movie-lens_0 Start Time: Wed Dec 4 22:28:04 2024 Epoch Time (ms): 1733380084969
[2024-12-05T06:28:07.774Z] variation: NoOptions
[2024-12-05T06:28:07.774Z] JVM_OPTIONS:
[2024-12-05T06:28:07.774Z] { \
[2024-12-05T06:28:07.774Z] echo ""; echo "TEST SETUP:"; \
[2024-12-05T06:28:07.774Z] echo "Nothing to be done for setup."; \
[2024-12-05T06:28:07.774Z] mkdir -p "/Users/admin/workspace/workspace/Test_openjdk17_hs_extended.perf_x86-64_mac_testList_0/aqa-tests/TKG/../TKG/output_17333796599309/renaissance-movie-lens_0"; \
[2024-12-05T06:28:07.774Z] cd "/Users/admin/workspace/workspace/Test_openjdk17_hs_extended.perf_x86-64_mac_testList_0/aqa-tests/TKG/../TKG/output_17333796599309/renaissance-movie-lens_0"; \
[2024-12-05T06:28:07.774Z] echo ""; echo "TESTING:"; \
[2024-12-05T06:28:07.774Z] "/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_17333796599309/renaissance-movie-lens_0"/movie-lens.json" movie-lens; \
[2024-12-05T06:28:07.774Z] 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_17333796599309/renaissance-movie-lens_0"; else echo "-----------------------------------"; echo "renaissance-movie-lens_0""_FAILED"; echo "-----------------------------------"; fi; \
[2024-12-05T06:28:07.774Z] echo ""; echo "TEST TEARDOWN:"; \
[2024-12-05T06:28:07.774Z] echo "Nothing to be done for teardown."; \
[2024-12-05T06:28:07.774Z] } 2>&1 | tee -a "/Users/admin/workspace/workspace/Test_openjdk17_hs_extended.perf_x86-64_mac_testList_0/aqa-tests/TKG/../TKG/output_17333796599309/TestTargetResult";
[2024-12-05T06:28:07.774Z]
[2024-12-05T06:28:07.774Z] TEST SETUP:
[2024-12-05T06:28:07.774Z] Nothing to be done for setup.
[2024-12-05T06:28:07.774Z]
[2024-12-05T06:28:07.774Z] TESTING:
[2024-12-05T06:28:13.547Z] Using Spark's default log4j profile: org/apache/spark/log4j-defaults.properties
[2024-12-05T06:28:16.438Z] NOTE: 'movie-lens' benchmark uses Spark local executor with 3 (out of 3) threads.
[2024-12-05T06:28:21.194Z] Got 100004 ratings from 671 users on 9066 movies.
[2024-12-05T06:28:21.194Z] Training: 60056, validation: 20285, test: 19854
[2024-12-05T06:28:21.194Z] ====== movie-lens (apache-spark) [default], iteration 0 started ======
[2024-12-05T06:28:21.683Z] GC before operation: completed in 134.387 ms, heap usage 155.765 MB -> 37.757 MB.
[2024-12-05T06:28:37.129Z] RMSE (validation) = 3.6219689545487603 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-12-05T06:28:45.872Z] RMSE (validation) = 2.1340923221100736 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-12-05T06:28:53.161Z] RMSE (validation) = 1.3105190428502682 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-12-05T06:28:57.966Z] RMSE (validation) = 0.9920028100571827 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-12-05T06:29:00.844Z] RMSE (validation) = 1.212631715629696 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-12-05T06:29:05.704Z] RMSE (validation) = 1.1025694034386657 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-12-05T06:29:08.680Z] RMSE (validation) = 0.9259652469249674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-12-05T06:29:12.629Z] RMSE (validation) = 0.8982232214592321 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-12-05T06:29:13.095Z] 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.
[2024-12-05T06:29:13.095Z] The best model improves the baseline by 14.52%.
[2024-12-05T06:29:13.576Z] Movies recommended for you:
[2024-12-05T06:29:13.576Z] WARNING: This benchmark provides no result that can be validated.
[2024-12-05T06:29:13.576Z] There is no way to check that no silent failure occurred.
[2024-12-05T06:29:13.576Z] ====== movie-lens (apache-spark) [default], iteration 0 completed (51999.441 ms) ======
[2024-12-05T06:29:13.577Z] ====== movie-lens (apache-spark) [default], iteration 1 started ======
[2024-12-05T06:29:13.577Z] GC before operation: completed in 164.257 ms, heap usage 333.191 MB -> 49.768 MB.
[2024-12-05T06:29:20.629Z] RMSE (validation) = 3.6219689545487603 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-12-05T06:29:27.763Z] RMSE (validation) = 2.1340923221100736 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-12-05T06:29:33.584Z] RMSE (validation) = 1.3105190428502682 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-12-05T06:29:40.661Z] RMSE (validation) = 0.9920028100571827 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-12-05T06:29:43.585Z] RMSE (validation) = 1.212631715629696 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-12-05T06:29:47.224Z] RMSE (validation) = 1.1025694034386657 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-12-05T06:29:50.853Z] RMSE (validation) = 0.9259652469249674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-12-05T06:29:54.510Z] RMSE (validation) = 0.8982232214592321 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-12-05T06:29:54.940Z] 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.
[2024-12-05T06:29:55.389Z] The best model improves the baseline by 14.52%.
[2024-12-05T06:29:55.389Z] Movies recommended for you:
[2024-12-05T06:29:55.389Z] WARNING: This benchmark provides no result that can be validated.
[2024-12-05T06:29:55.389Z] There is no way to check that no silent failure occurred.
[2024-12-05T06:29:55.389Z] ====== movie-lens (apache-spark) [default], iteration 1 completed (41617.810 ms) ======
[2024-12-05T06:29:55.389Z] ====== movie-lens (apache-spark) [default], iteration 2 started ======
[2024-12-05T06:29:55.389Z] GC before operation: completed in 120.074 ms, heap usage 535.931 MB -> 53.403 MB.
[2024-12-05T06:30:02.387Z] RMSE (validation) = 3.62196895454876 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-12-05T06:30:09.401Z] RMSE (validation) = 2.1340923221100736 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-12-05T06:30:15.234Z] RMSE (validation) = 1.3105190428502682 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-12-05T06:30:22.353Z] RMSE (validation) = 0.9920028100571827 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-12-05T06:30:25.357Z] RMSE (validation) = 1.212631715629696 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-12-05T06:30:29.146Z] RMSE (validation) = 1.1025694034386657 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-12-05T06:30:32.057Z] RMSE (validation) = 0.9259652469249674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-12-05T06:30:36.854Z] RMSE (validation) = 0.8982232214592321 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-12-05T06:30:36.855Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9063003101263983.
[2024-12-05T06:30:36.855Z] The best model improves the baseline by 14.52%.
[2024-12-05T06:30:36.855Z] Movies recommended for you:
[2024-12-05T06:30:36.855Z] WARNING: This benchmark provides no result that can be validated.
[2024-12-05T06:30:36.855Z] There is no way to check that no silent failure occurred.
[2024-12-05T06:30:36.855Z] ====== movie-lens (apache-spark) [default], iteration 2 completed (41276.051 ms) ======
[2024-12-05T06:30:36.855Z] ====== movie-lens (apache-spark) [default], iteration 3 started ======
[2024-12-05T06:30:36.855Z] GC before operation: completed in 96.451 ms, heap usage 322.385 MB -> 50.265 MB.
[2024-12-05T06:30:42.555Z] RMSE (validation) = 3.62196895454876 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-12-05T06:30:48.141Z] RMSE (validation) = 2.1340923221100736 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-12-05T06:30:53.861Z] RMSE (validation) = 1.3105190428502682 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-12-05T06:30:57.508Z] RMSE (validation) = 0.9920028100571827 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-12-05T06:30:58.938Z] RMSE (validation) = 1.212631715629696 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-12-05T06:31:00.402Z] RMSE (validation) = 1.1025694034386657 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-12-05T06:31:02.490Z] RMSE (validation) = 0.9259652469249674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-12-05T06:31:03.963Z] RMSE (validation) = 0.8982232214592321 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-12-05T06:31:04.452Z] 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.
[2024-12-05T06:31:04.452Z] The best model improves the baseline by 14.52%.
[2024-12-05T06:31:04.452Z] Movies recommended for you:
[2024-12-05T06:31:04.452Z] WARNING: This benchmark provides no result that can be validated.
[2024-12-05T06:31:04.452Z] There is no way to check that no silent failure occurred.
[2024-12-05T06:31:04.452Z] ====== movie-lens (apache-spark) [default], iteration 3 completed (27564.632 ms) ======
[2024-12-05T06:31:04.452Z] ====== movie-lens (apache-spark) [default], iteration 4 started ======
[2024-12-05T06:31:04.452Z] GC before operation: completed in 65.749 ms, heap usage 206.059 MB -> 50.518 MB.
[2024-12-05T06:31:08.022Z] RMSE (validation) = 3.6219689545487603 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-12-05T06:31:10.745Z] RMSE (validation) = 2.1340923221100736 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-12-05T06:31:13.459Z] RMSE (validation) = 1.3105190428502682 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-12-05T06:31:16.927Z] RMSE (validation) = 0.9920028100571827 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-12-05T06:31:17.841Z] RMSE (validation) = 1.212631715629696 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-12-05T06:31:19.992Z] RMSE (validation) = 1.1025694034386657 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-12-05T06:31:22.721Z] RMSE (validation) = 0.9259652469249674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-12-05T06:31:24.167Z] RMSE (validation) = 0.8982232214592321 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-12-05T06:31:24.167Z] 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.
[2024-12-05T06:31:24.167Z] The best model improves the baseline by 14.52%.
[2024-12-05T06:31:24.616Z] Movies recommended for you:
[2024-12-05T06:31:24.616Z] WARNING: This benchmark provides no result that can be validated.
[2024-12-05T06:31:24.616Z] There is no way to check that no silent failure occurred.
[2024-12-05T06:31:24.616Z] ====== movie-lens (apache-spark) [default], iteration 4 completed (20054.988 ms) ======
[2024-12-05T06:31:24.616Z] ====== movie-lens (apache-spark) [default], iteration 5 started ======
[2024-12-05T06:31:24.616Z] GC before operation: completed in 122.850 ms, heap usage 338.600 MB -> 50.860 MB.
[2024-12-05T06:31:28.111Z] RMSE (validation) = 3.62196895454876 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-12-05T06:31:31.620Z] RMSE (validation) = 2.1340923221100736 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-12-05T06:31:34.456Z] RMSE (validation) = 1.3105190428502682 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-12-05T06:31:37.193Z] RMSE (validation) = 0.9920028100571827 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-12-05T06:31:38.624Z] RMSE (validation) = 1.212631715629696 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-12-05T06:31:40.073Z] RMSE (validation) = 1.1025694034386657 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-12-05T06:31:42.080Z] RMSE (validation) = 0.9259652469249674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-12-05T06:31:44.876Z] RMSE (validation) = 0.8982232214592321 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-12-05T06:31:44.876Z] 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.
[2024-12-05T06:31:45.355Z] The best model improves the baseline by 14.52%.
[2024-12-05T06:31:45.355Z] Movies recommended for you:
[2024-12-05T06:31:45.355Z] WARNING: This benchmark provides no result that can be validated.
[2024-12-05T06:31:45.355Z] There is no way to check that no silent failure occurred.
[2024-12-05T06:31:45.355Z] ====== movie-lens (apache-spark) [default], iteration 5 completed (20767.514 ms) ======
[2024-12-05T06:31:45.355Z] ====== movie-lens (apache-spark) [default], iteration 6 started ======
[2024-12-05T06:31:45.355Z] GC before operation: completed in 110.382 ms, heap usage 64.543 MB -> 52.523 MB.
[2024-12-05T06:31:49.916Z] RMSE (validation) = 3.62196895454876 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-12-05T06:31:52.625Z] RMSE (validation) = 2.1340923221100736 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-12-05T06:31:55.367Z] RMSE (validation) = 1.3105190428502682 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-12-05T06:31:58.916Z] RMSE (validation) = 0.9920028100571827 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-12-05T06:32:00.359Z] RMSE (validation) = 1.212631715629696 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-12-05T06:32:02.469Z] RMSE (validation) = 1.1025694034386657 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-12-05T06:32:03.905Z] RMSE (validation) = 0.9259652469249674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-12-05T06:32:06.133Z] RMSE (validation) = 0.8982232214592321 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-12-05T06:32:06.133Z] 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.
[2024-12-05T06:32:06.133Z] The best model improves the baseline by 14.52%.
[2024-12-05T06:32:06.557Z] Movies recommended for you:
[2024-12-05T06:32:06.557Z] WARNING: This benchmark provides no result that can be validated.
[2024-12-05T06:32:06.557Z] There is no way to check that no silent failure occurred.
[2024-12-05T06:32:06.557Z] ====== movie-lens (apache-spark) [default], iteration 6 completed (20886.519 ms) ======
[2024-12-05T06:32:06.557Z] ====== movie-lens (apache-spark) [default], iteration 7 started ======
[2024-12-05T06:32:06.557Z] GC before operation: completed in 47.968 ms, heap usage 96.460 MB -> 52.122 MB.
[2024-12-05T06:32:09.253Z] RMSE (validation) = 3.62196895454876 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-12-05T06:32:13.694Z] RMSE (validation) = 2.1340923221100736 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-12-05T06:32:18.322Z] RMSE (validation) = 1.3105190428502682 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-12-05T06:32:21.038Z] RMSE (validation) = 0.9920028100571827 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-12-05T06:32:23.148Z] RMSE (validation) = 1.212631715629696 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-12-05T06:32:25.173Z] RMSE (validation) = 1.1025694034386657 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-12-05T06:32:26.670Z] RMSE (validation) = 0.9259652469249674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-12-05T06:32:28.687Z] RMSE (validation) = 0.8982232214592321 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-12-05T06:32:28.687Z] 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.
[2024-12-05T06:32:28.687Z] The best model improves the baseline by 14.52%.
[2024-12-05T06:32:28.687Z] Movies recommended for you:
[2024-12-05T06:32:28.687Z] WARNING: This benchmark provides no result that can be validated.
[2024-12-05T06:32:28.687Z] There is no way to check that no silent failure occurred.
[2024-12-05T06:32:28.687Z] ====== movie-lens (apache-spark) [default], iteration 7 completed (22355.038 ms) ======
[2024-12-05T06:32:28.687Z] ====== movie-lens (apache-spark) [default], iteration 8 started ======
[2024-12-05T06:32:28.687Z] GC before operation: completed in 52.925 ms, heap usage 285.694 MB -> 51.146 MB.
[2024-12-05T06:32:32.165Z] RMSE (validation) = 3.6219689545487603 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-12-05T06:32:36.688Z] RMSE (validation) = 2.1340923221100736 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-12-05T06:32:42.558Z] RMSE (validation) = 1.3105190428502682 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-12-05T06:32:47.262Z] RMSE (validation) = 0.9920028100571827 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-12-05T06:32:51.044Z] RMSE (validation) = 1.212631715629696 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-12-05T06:32:54.706Z] RMSE (validation) = 1.1025694034386657 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-12-05T06:32:57.749Z] RMSE (validation) = 0.9259652469249674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-12-05T06:33:01.723Z] RMSE (validation) = 0.8982232214592321 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-12-05T06:33:02.137Z] 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.
[2024-12-05T06:33:02.551Z] The best model improves the baseline by 14.52%.
[2024-12-05T06:33:02.989Z] Movies recommended for you:
[2024-12-05T06:33:02.989Z] WARNING: This benchmark provides no result that can be validated.
[2024-12-05T06:33:02.989Z] There is no way to check that no silent failure occurred.
[2024-12-05T06:33:02.989Z] ====== movie-lens (apache-spark) [default], iteration 8 completed (33985.721 ms) ======
[2024-12-05T06:33:02.989Z] ====== movie-lens (apache-spark) [default], iteration 9 started ======
[2024-12-05T06:33:02.989Z] GC before operation: completed in 137.208 ms, heap usage 196.863 MB -> 50.939 MB.
[2024-12-05T06:33:11.426Z] RMSE (validation) = 3.6219689545487603 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-12-05T06:33:16.023Z] RMSE (validation) = 2.1340923221100736 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-12-05T06:33:23.084Z] RMSE (validation) = 1.3105190428502682 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-12-05T06:33:27.682Z] RMSE (validation) = 0.9920028100571827 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-12-05T06:33:31.465Z] RMSE (validation) = 1.212631715629696 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-12-05T06:33:35.138Z] RMSE (validation) = 1.1025694034386657 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-12-05T06:33:39.108Z] RMSE (validation) = 0.9259652469249674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-12-05T06:33:42.127Z] RMSE (validation) = 0.8982232214592321 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-12-05T06:33:43.095Z] 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.
[2024-12-05T06:33:43.095Z] The best model improves the baseline by 14.52%.
[2024-12-05T06:33:43.095Z] Movies recommended for you:
[2024-12-05T06:33:43.095Z] WARNING: This benchmark provides no result that can be validated.
[2024-12-05T06:33:43.095Z] There is no way to check that no silent failure occurred.
[2024-12-05T06:33:43.095Z] ====== movie-lens (apache-spark) [default], iteration 9 completed (40191.884 ms) ======
[2024-12-05T06:33:43.095Z] ====== movie-lens (apache-spark) [default], iteration 10 started ======
[2024-12-05T06:33:43.095Z] GC before operation: completed in 132.330 ms, heap usage 822.572 MB -> 55.477 MB.
[2024-12-05T06:33:50.186Z] RMSE (validation) = 3.62196895454876 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-12-05T06:33:56.132Z] RMSE (validation) = 2.1340923221100736 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-12-05T06:34:03.159Z] RMSE (validation) = 1.3105190428502682 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-12-05T06:34:06.706Z] RMSE (validation) = 0.9920028100571827 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-12-05T06:34:08.115Z] RMSE (validation) = 1.212631715629696 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-12-05T06:34:10.145Z] RMSE (validation) = 1.1025694034386657 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-12-05T06:34:11.562Z] RMSE (validation) = 0.9259652469249674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-12-05T06:34:12.962Z] RMSE (validation) = 0.8982232214592321 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-12-05T06:34:12.962Z] 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.
[2024-12-05T06:34:12.962Z] The best model improves the baseline by 14.52%.
[2024-12-05T06:34:13.379Z] Movies recommended for you:
[2024-12-05T06:34:13.379Z] WARNING: This benchmark provides no result that can be validated.
[2024-12-05T06:34:13.379Z] There is no way to check that no silent failure occurred.
[2024-12-05T06:34:13.379Z] ====== movie-lens (apache-spark) [default], iteration 10 completed (29941.750 ms) ======
[2024-12-05T06:34:13.379Z] ====== movie-lens (apache-spark) [default], iteration 11 started ======
[2024-12-05T06:34:13.379Z] GC before operation: completed in 47.271 ms, heap usage 331.211 MB -> 50.996 MB.
[2024-12-05T06:34:16.048Z] RMSE (validation) = 3.62196895454876 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-12-05T06:34:18.728Z] RMSE (validation) = 2.1340923221100736 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-12-05T06:34:21.491Z] RMSE (validation) = 1.3105190428502682 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-12-05T06:34:25.004Z] RMSE (validation) = 0.9920028100571827 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-12-05T06:34:27.761Z] RMSE (validation) = 1.212631715629696 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-12-05T06:34:30.673Z] RMSE (validation) = 1.1025694034386657 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-12-05T06:34:32.733Z] RMSE (validation) = 0.9259652469249674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-12-05T06:34:34.321Z] RMSE (validation) = 0.8982232214592321 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-12-05T06:34:34.767Z] 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.
[2024-12-05T06:34:34.767Z] The best model improves the baseline by 14.52%.
[2024-12-05T06:34:34.767Z] Movies recommended for you:
[2024-12-05T06:34:34.767Z] WARNING: This benchmark provides no result that can be validated.
[2024-12-05T06:34:34.767Z] There is no way to check that no silent failure occurred.
[2024-12-05T06:34:34.767Z] ====== movie-lens (apache-spark) [default], iteration 11 completed (21511.372 ms) ======
[2024-12-05T06:34:34.767Z] ====== movie-lens (apache-spark) [default], iteration 12 started ======
[2024-12-05T06:34:35.258Z] GC before operation: completed in 58.369 ms, heap usage 128.431 MB -> 50.964 MB.
[2024-12-05T06:34:41.025Z] RMSE (validation) = 3.62196895454876 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-12-05T06:34:48.044Z] RMSE (validation) = 2.1340923221100736 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-12-05T06:34:53.735Z] RMSE (validation) = 1.3105190428502682 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-12-05T06:34:58.374Z] RMSE (validation) = 0.9920028100571827 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-12-05T06:35:01.420Z] RMSE (validation) = 1.212631715629696 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-12-05T06:35:04.260Z] RMSE (validation) = 1.1025694034386657 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-12-05T06:35:05.709Z] RMSE (validation) = 0.9259652469249674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-12-05T06:35:07.108Z] RMSE (validation) = 0.8982232214592321 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-12-05T06:35:07.108Z] 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.
[2024-12-05T06:35:07.108Z] The best model improves the baseline by 14.52%.
[2024-12-05T06:35:07.521Z] Movies recommended for you:
[2024-12-05T06:35:07.521Z] WARNING: This benchmark provides no result that can be validated.
[2024-12-05T06:35:07.521Z] There is no way to check that no silent failure occurred.
[2024-12-05T06:35:07.521Z] ====== movie-lens (apache-spark) [default], iteration 12 completed (32527.496 ms) ======
[2024-12-05T06:35:07.521Z] ====== movie-lens (apache-spark) [default], iteration 13 started ======
[2024-12-05T06:35:07.521Z] GC before operation: completed in 57.168 ms, heap usage 265.487 MB -> 51.241 MB.
[2024-12-05T06:35:09.557Z] RMSE (validation) = 3.62196895454876 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-12-05T06:35:12.995Z] RMSE (validation) = 2.1340923221100736 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-12-05T06:35:15.771Z] RMSE (validation) = 1.3105190428502682 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-12-05T06:35:17.921Z] RMSE (validation) = 0.9920028100571827 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-12-05T06:35:19.311Z] RMSE (validation) = 1.212631715629696 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-12-05T06:35:20.743Z] RMSE (validation) = 1.1025694034386657 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-12-05T06:35:22.733Z] RMSE (validation) = 0.9259652469249674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-12-05T06:35:24.097Z] RMSE (validation) = 0.8982232214592321 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-12-05T06:35:24.097Z] 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.
[2024-12-05T06:35:24.097Z] The best model improves the baseline by 14.52%.
[2024-12-05T06:35:24.097Z] Movies recommended for you:
[2024-12-05T06:35:24.097Z] WARNING: This benchmark provides no result that can be validated.
[2024-12-05T06:35:24.097Z] There is no way to check that no silent failure occurred.
[2024-12-05T06:35:24.097Z] ====== movie-lens (apache-spark) [default], iteration 13 completed (16893.947 ms) ======
[2024-12-05T06:35:24.097Z] ====== movie-lens (apache-spark) [default], iteration 14 started ======
[2024-12-05T06:35:24.507Z] GC before operation: completed in 40.287 ms, heap usage 251.509 MB -> 51.057 MB.
[2024-12-05T06:35:27.263Z] RMSE (validation) = 3.62196895454876 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-12-05T06:35:29.976Z] RMSE (validation) = 2.1340923221100736 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-12-05T06:35:32.706Z] RMSE (validation) = 1.3105190428502682 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-12-05T06:35:34.711Z] RMSE (validation) = 0.9920028100571827 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-12-05T06:35:36.105Z] RMSE (validation) = 1.212631715629696 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-12-05T06:35:37.510Z] RMSE (validation) = 1.1025694034386657 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-12-05T06:35:38.921Z] RMSE (validation) = 0.9259652469249674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-12-05T06:35:40.877Z] RMSE (validation) = 0.8982232214592321 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-12-05T06:35:40.877Z] 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.
[2024-12-05T06:35:40.877Z] The best model improves the baseline by 14.52%.
[2024-12-05T06:35:40.877Z] Movies recommended for you:
[2024-12-05T06:35:40.877Z] WARNING: This benchmark provides no result that can be validated.
[2024-12-05T06:35:40.877Z] There is no way to check that no silent failure occurred.
[2024-12-05T06:35:40.877Z] ====== movie-lens (apache-spark) [default], iteration 14 completed (16643.691 ms) ======
[2024-12-05T06:35:40.877Z] ====== movie-lens (apache-spark) [default], iteration 15 started ======
[2024-12-05T06:35:40.877Z] GC before operation: completed in 51.325 ms, heap usage 82.802 MB -> 53.368 MB.
[2024-12-05T06:35:45.307Z] RMSE (validation) = 3.62196895454876 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-12-05T06:35:49.840Z] RMSE (validation) = 2.1340923221100736 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-12-05T06:35:54.415Z] RMSE (validation) = 1.3105190428502682 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-12-05T06:35:58.933Z] RMSE (validation) = 0.9920028100571827 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-12-05T06:36:01.104Z] RMSE (validation) = 1.212631715629696 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-12-05T06:36:03.898Z] RMSE (validation) = 1.1025694034386657 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-12-05T06:36:05.319Z] RMSE (validation) = 0.9259652469249674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-12-05T06:36:06.193Z] RMSE (validation) = 0.8982232214592321 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-12-05T06:36:06.193Z] 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.
[2024-12-05T06:36:06.193Z] The best model improves the baseline by 14.52%.
[2024-12-05T06:36:06.193Z] Movies recommended for you:
[2024-12-05T06:36:06.193Z] WARNING: This benchmark provides no result that can be validated.
[2024-12-05T06:36:06.193Z] There is no way to check that no silent failure occurred.
[2024-12-05T06:36:06.193Z] ====== movie-lens (apache-spark) [default], iteration 15 completed (25237.413 ms) ======
[2024-12-05T06:36:06.193Z] ====== movie-lens (apache-spark) [default], iteration 16 started ======
[2024-12-05T06:36:06.193Z] GC before operation: completed in 45.845 ms, heap usage 105.673 MB -> 51.080 MB.
[2024-12-05T06:36:08.881Z] RMSE (validation) = 3.6219689545487603 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-12-05T06:36:11.546Z] RMSE (validation) = 2.1340923221100736 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-12-05T06:36:15.253Z] RMSE (validation) = 1.3105190428502682 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-12-05T06:36:20.886Z] RMSE (validation) = 0.9920028100571827 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-12-05T06:36:23.001Z] RMSE (validation) = 1.212631715629696 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-12-05T06:36:25.199Z] RMSE (validation) = 1.1025694034386657 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-12-05T06:36:26.649Z] RMSE (validation) = 0.9259652469249674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-12-05T06:36:28.130Z] RMSE (validation) = 0.8982232214592321 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-12-05T06:36:28.538Z] 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.
[2024-12-05T06:36:28.538Z] The best model improves the baseline by 14.52%.
[2024-12-05T06:36:28.538Z] Movies recommended for you:
[2024-12-05T06:36:28.539Z] WARNING: This benchmark provides no result that can be validated.
[2024-12-05T06:36:28.539Z] There is no way to check that no silent failure occurred.
[2024-12-05T06:36:28.539Z] ====== movie-lens (apache-spark) [default], iteration 16 completed (22318.807 ms) ======
[2024-12-05T06:36:28.539Z] ====== movie-lens (apache-spark) [default], iteration 17 started ======
[2024-12-05T06:36:28.539Z] GC before operation: completed in 44.747 ms, heap usage 212.912 MB -> 51.033 MB.
[2024-12-05T06:36:32.986Z] RMSE (validation) = 3.6219689545487603 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-12-05T06:36:35.748Z] RMSE (validation) = 2.1340923221100736 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-12-05T06:36:38.470Z] RMSE (validation) = 1.3105190428502682 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-12-05T06:36:41.176Z] RMSE (validation) = 0.9920028100571827 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-12-05T06:36:42.546Z] RMSE (validation) = 1.212631715629696 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-12-05T06:36:43.924Z] RMSE (validation) = 1.1025694034386657 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-12-05T06:36:45.335Z] RMSE (validation) = 0.9259652469249674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-12-05T06:36:46.724Z] RMSE (validation) = 0.8982232214592321 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-12-05T06:36:46.724Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9063003101263983.
[2024-12-05T06:36:46.724Z] The best model improves the baseline by 14.52%.
[2024-12-05T06:36:46.724Z] Movies recommended for you:
[2024-12-05T06:36:46.724Z] WARNING: This benchmark provides no result that can be validated.
[2024-12-05T06:36:46.724Z] There is no way to check that no silent failure occurred.
[2024-12-05T06:36:46.724Z] ====== movie-lens (apache-spark) [default], iteration 17 completed (18167.503 ms) ======
[2024-12-05T06:36:46.724Z] ====== movie-lens (apache-spark) [default], iteration 18 started ======
[2024-12-05T06:36:46.724Z] GC before operation: completed in 41.112 ms, heap usage 291.467 MB -> 51.243 MB.
[2024-12-05T06:36:49.399Z] RMSE (validation) = 3.62196895454876 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-12-05T06:36:53.080Z] RMSE (validation) = 2.1340923221100736 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-12-05T06:36:58.832Z] RMSE (validation) = 1.3105190428502682 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-12-05T06:37:01.585Z] RMSE (validation) = 0.9920028100571827 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-12-05T06:37:02.512Z] RMSE (validation) = 1.212631715629696 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-12-05T06:37:03.994Z] RMSE (validation) = 1.1025694034386657 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-12-05T06:37:06.004Z] RMSE (validation) = 0.9259652469249674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-12-05T06:37:07.406Z] RMSE (validation) = 0.8982232214592321 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-12-05T06:37:07.842Z] 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.
[2024-12-05T06:37:07.842Z] The best model improves the baseline by 14.52%.
[2024-12-05T06:37:07.842Z] Movies recommended for you:
[2024-12-05T06:37:07.842Z] WARNING: This benchmark provides no result that can be validated.
[2024-12-05T06:37:07.842Z] There is no way to check that no silent failure occurred.
[2024-12-05T06:37:07.842Z] ====== movie-lens (apache-spark) [default], iteration 18 completed (20895.281 ms) ======
[2024-12-05T06:37:07.842Z] ====== movie-lens (apache-spark) [default], iteration 19 started ======
[2024-12-05T06:37:07.842Z] GC before operation: completed in 48.012 ms, heap usage 74.380 MB -> 51.679 MB.
[2024-12-05T06:37:10.517Z] RMSE (validation) = 3.6219689545487603 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-12-05T06:37:13.190Z] RMSE (validation) = 2.1340923221100736 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-12-05T06:37:17.773Z] RMSE (validation) = 1.3105190428502682 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-12-05T06:37:21.303Z] RMSE (validation) = 0.9920028100571827 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-12-05T06:37:23.299Z] RMSE (validation) = 1.212631715629696 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-12-05T06:37:25.378Z] RMSE (validation) = 1.1025694034386657 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-12-05T06:37:26.860Z] RMSE (validation) = 0.9259652469249674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-12-05T06:37:28.264Z] RMSE (validation) = 0.8982232214592321 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-12-05T06:37:28.661Z] 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.
[2024-12-05T06:37:28.661Z] The best model improves the baseline by 14.52%.
[2024-12-05T06:37:28.661Z] Movies recommended for you:
[2024-12-05T06:37:28.661Z] WARNING: This benchmark provides no result that can be validated.
[2024-12-05T06:37:28.661Z] There is no way to check that no silent failure occurred.
[2024-12-05T06:37:28.661Z] ====== movie-lens (apache-spark) [default], iteration 19 completed (20917.597 ms) ======
[2024-12-05T06:37:29.547Z] -----------------------------------
[2024-12-05T06:37:29.547Z] renaissance-movie-lens_0_PASSED
[2024-12-05T06:37:29.547Z] -----------------------------------
[2024-12-05T06:37:29.547Z]
[2024-12-05T06:37:29.547Z] TEST TEARDOWN:
[2024-12-05T06:37:29.547Z] Nothing to be done for teardown.
[2024-12-05T06:37:29.547Z] renaissance-movie-lens_0 Finish Time: Wed Dec 4 22:37:28 2024 Epoch Time (ms): 1733380648226