renaissance-movie-lens_0
[2024-09-25T21:11:46.224Z] Running test renaissance-movie-lens_0 ...
[2024-09-25T21:11:46.224Z] ===============================================
[2024-09-25T21:11:46.224Z] renaissance-movie-lens_0 Start Time: Wed Sep 25 17:11:45 2024 Epoch Time (ms): 1727298705949
[2024-09-25T21:11:46.224Z] variation: NoOptions
[2024-09-25T21:11:46.224Z] JVM_OPTIONS:
[2024-09-25T21:11:46.224Z] { \
[2024-09-25T21:11:46.224Z] echo ""; echo "TEST SETUP:"; \
[2024-09-25T21:11:46.224Z] echo "Nothing to be done for setup."; \
[2024-09-25T21:11:46.224Z] mkdir -p "/Users/admin/workspace/workspace/Test_openjdk11_hs_extended.perf_aarch64_mac/aqa-tests/TKG/../TKG/output_17272982564732/renaissance-movie-lens_0"; \
[2024-09-25T21:11:46.224Z] cd "/Users/admin/workspace/workspace/Test_openjdk11_hs_extended.perf_aarch64_mac/aqa-tests/TKG/../TKG/output_17272982564732/renaissance-movie-lens_0"; \
[2024-09-25T21:11:46.224Z] echo ""; echo "TESTING:"; \
[2024-09-25T21:11:46.224Z] "/Users/admin/workspace/workspace/Test_openjdk11_hs_extended.perf_aarch64_mac/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_aarch64_mac/aqa-tests/TKG/../../jvmtest/perf/renaissance/renaissance.jar" --json ""/Users/admin/workspace/workspace/Test_openjdk11_hs_extended.perf_aarch64_mac/aqa-tests/TKG/../TKG/output_17272982564732/renaissance-movie-lens_0"/movie-lens.json" movie-lens; \
[2024-09-25T21:11:46.224Z] if [ $? -eq 0 ]; then echo "-----------------------------------"; echo "renaissance-movie-lens_0""_PASSED"; echo "-----------------------------------"; cd /Users/admin/workspace/workspace/Test_openjdk11_hs_extended.perf_aarch64_mac/aqa-tests/TKG/..; rm -f -r "/Users/admin/workspace/workspace/Test_openjdk11_hs_extended.perf_aarch64_mac/aqa-tests/TKG/../TKG/output_17272982564732/renaissance-movie-lens_0"; else echo "-----------------------------------"; echo "renaissance-movie-lens_0""_FAILED"; echo "-----------------------------------"; fi; \
[2024-09-25T21:11:46.224Z] echo ""; echo "TEST TEARDOWN:"; \
[2024-09-25T21:11:46.224Z] echo "Nothing to be done for teardown."; \
[2024-09-25T21:11:46.224Z] } 2>&1 | tee -a "/Users/admin/workspace/workspace/Test_openjdk11_hs_extended.perf_aarch64_mac/aqa-tests/TKG/../TKG/output_17272982564732/TestTargetResult";
[2024-09-25T21:11:46.224Z]
[2024-09-25T21:11:46.224Z] TEST SETUP:
[2024-09-25T21:11:46.224Z] Nothing to be done for setup.
[2024-09-25T21:11:46.224Z]
[2024-09-25T21:11:46.224Z] TESTING:
[2024-09-25T21:11:48.095Z] Using Spark's default log4j profile: org/apache/spark/log4j-defaults.properties
[2024-09-25T21:11:49.340Z] NOTE: 'movie-lens' benchmark uses Spark local executor with 3 (out of 3) threads.
[2024-09-25T21:11:51.777Z] Got 100004 ratings from 671 users on 9066 movies.
[2024-09-25T21:11:51.777Z] Training: 60056, validation: 20285, test: 19854
[2024-09-25T21:11:51.777Z] ====== movie-lens (apache-spark) [default], iteration 0 started ======
[2024-09-25T21:11:51.777Z] GC before operation: completed in 52.206 ms, heap usage 93.087 MB -> 36.575 MB.
[2024-09-25T21:11:55.766Z] RMSE (validation) = 3.62196895454876 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-09-25T21:11:57.561Z] RMSE (validation) = 2.1340923221100736 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-09-25T21:11:59.353Z] RMSE (validation) = 1.3105190428502682 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-09-25T21:12:01.168Z] RMSE (validation) = 0.9920028100571827 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-09-25T21:12:02.467Z] RMSE (validation) = 1.212631715629696 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-09-25T21:12:03.751Z] RMSE (validation) = 1.1025694034386657 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-09-25T21:12:04.547Z] RMSE (validation) = 0.9259652469249674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-09-25T21:12:05.828Z] RMSE (validation) = 0.8982232214592321 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-09-25T21:12:05.828Z] 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.
[2024-09-25T21:12:05.828Z] The best model improves the baseline by 14.52%.
[2024-09-25T21:12:05.828Z] Movies recommended for you:
[2024-09-25T21:12:05.828Z] WARNING: This benchmark provides no result that can be validated.
[2024-09-25T21:12:05.828Z] There is no way to check that no silent failure occurred.
[2024-09-25T21:12:05.828Z] ====== movie-lens (apache-spark) [default], iteration 0 completed (14215.919 ms) ======
[2024-09-25T21:12:05.828Z] ====== movie-lens (apache-spark) [default], iteration 1 started ======
[2024-09-25T21:12:06.187Z] GC before operation: completed in 67.651 ms, heap usage 184.377 MB -> 48.142 MB.
[2024-09-25T21:12:08.065Z] RMSE (validation) = 3.6219689545487603 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-09-25T21:12:09.319Z] RMSE (validation) = 2.1340923221100736 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-09-25T21:12:11.167Z] RMSE (validation) = 1.3105190428502682 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-09-25T21:12:12.445Z] RMSE (validation) = 0.9920028100571827 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-09-25T21:12:13.718Z] RMSE (validation) = 1.212631715629696 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-09-25T21:12:14.517Z] RMSE (validation) = 1.1025694034386657 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-09-25T21:12:15.799Z] RMSE (validation) = 0.9259652469249674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-09-25T21:12:16.601Z] RMSE (validation) = 0.8982232214592321 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-09-25T21:12:16.601Z] 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.
[2024-09-25T21:12:16.963Z] The best model improves the baseline by 14.52%.
[2024-09-25T21:12:16.963Z] Movies recommended for you:
[2024-09-25T21:12:16.963Z] WARNING: This benchmark provides no result that can be validated.
[2024-09-25T21:12:16.963Z] There is no way to check that no silent failure occurred.
[2024-09-25T21:12:16.963Z] ====== movie-lens (apache-spark) [default], iteration 1 completed (10790.968 ms) ======
[2024-09-25T21:12:16.963Z] ====== movie-lens (apache-spark) [default], iteration 2 started ======
[2024-09-25T21:12:16.963Z] GC before operation: completed in 50.060 ms, heap usage 262.792 MB -> 49.011 MB.
[2024-09-25T21:12:18.787Z] RMSE (validation) = 3.62196895454876 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-09-25T21:12:20.063Z] RMSE (validation) = 2.1340923221100736 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-09-25T21:12:21.890Z] RMSE (validation) = 1.3105190428502682 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-09-25T21:12:23.683Z] RMSE (validation) = 0.9920028100571827 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-09-25T21:12:24.958Z] RMSE (validation) = 1.212631715629696 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-09-25T21:12:25.806Z] RMSE (validation) = 1.1025694034386657 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-09-25T21:12:26.581Z] RMSE (validation) = 0.9259652469249674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-09-25T21:12:27.857Z] RMSE (validation) = 0.8982232214592321 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-09-25T21:12:27.857Z] 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.
[2024-09-25T21:12:27.857Z] The best model improves the baseline by 14.52%.
[2024-09-25T21:12:27.857Z] Movies recommended for you:
[2024-09-25T21:12:27.857Z] WARNING: This benchmark provides no result that can be validated.
[2024-09-25T21:12:27.857Z] There is no way to check that no silent failure occurred.
[2024-09-25T21:12:27.857Z] ====== movie-lens (apache-spark) [default], iteration 2 completed (11124.134 ms) ======
[2024-09-25T21:12:27.857Z] ====== movie-lens (apache-spark) [default], iteration 3 started ======
[2024-09-25T21:12:28.215Z] GC before operation: completed in 45.160 ms, heap usage 232.378 MB -> 49.285 MB.
[2024-09-25T21:12:30.034Z] RMSE (validation) = 3.62196895454876 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-09-25T21:12:31.310Z] RMSE (validation) = 2.1340923221100736 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-09-25T21:12:33.123Z] RMSE (validation) = 1.3105190428502682 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-09-25T21:12:34.401Z] RMSE (validation) = 0.9920028100571827 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-09-25T21:12:35.677Z] RMSE (validation) = 1.212631715629696 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-09-25T21:12:36.445Z] RMSE (validation) = 1.1025694034386657 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-09-25T21:12:37.700Z] RMSE (validation) = 0.9259652469249674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-09-25T21:12:38.471Z] RMSE (validation) = 0.8982232214592321 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-09-25T21:12:38.471Z] 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.
[2024-09-25T21:12:38.471Z] The best model improves the baseline by 14.52%.
[2024-09-25T21:12:38.471Z] Movies recommended for you:
[2024-09-25T21:12:38.471Z] WARNING: This benchmark provides no result that can be validated.
[2024-09-25T21:12:38.472Z] There is no way to check that no silent failure occurred.
[2024-09-25T21:12:38.472Z] ====== movie-lens (apache-spark) [default], iteration 3 completed (10586.160 ms) ======
[2024-09-25T21:12:38.472Z] ====== movie-lens (apache-spark) [default], iteration 4 started ======
[2024-09-25T21:12:38.832Z] GC before operation: completed in 56.698 ms, heap usage 259.282 MB -> 49.640 MB.
[2024-09-25T21:12:40.073Z] RMSE (validation) = 3.62196895454876 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-09-25T21:12:41.904Z] RMSE (validation) = 2.1340923221100736 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-09-25T21:12:43.162Z] RMSE (validation) = 1.3105190428502682 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-09-25T21:12:44.405Z] RMSE (validation) = 0.9920028100571827 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-09-25T21:12:45.178Z] RMSE (validation) = 1.212631715629696 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-09-25T21:12:45.966Z] RMSE (validation) = 1.1025694034386657 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-09-25T21:12:46.742Z] RMSE (validation) = 0.9259652469249674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-09-25T21:12:47.988Z] RMSE (validation) = 0.8982232214592321 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-09-25T21:12:47.988Z] 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.
[2024-09-25T21:12:47.988Z] The best model improves the baseline by 14.52%.
[2024-09-25T21:12:47.988Z] Movies recommended for you:
[2024-09-25T21:12:47.988Z] WARNING: This benchmark provides no result that can be validated.
[2024-09-25T21:12:47.988Z] There is no way to check that no silent failure occurred.
[2024-09-25T21:12:47.988Z] ====== movie-lens (apache-spark) [default], iteration 4 completed (9366.650 ms) ======
[2024-09-25T21:12:47.988Z] ====== movie-lens (apache-spark) [default], iteration 5 started ======
[2024-09-25T21:12:47.989Z] GC before operation: completed in 63.403 ms, heap usage 258.195 MB -> 49.829 MB.
[2024-09-25T21:12:49.791Z] RMSE (validation) = 3.6219689545487603 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-09-25T21:12:51.634Z] RMSE (validation) = 2.1340923221100736 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-09-25T21:12:52.897Z] RMSE (validation) = 1.3105190428502682 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-09-25T21:12:54.704Z] RMSE (validation) = 0.9920028100571827 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-09-25T21:12:55.487Z] RMSE (validation) = 1.212631715629696 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-09-25T21:12:56.262Z] RMSE (validation) = 1.1025694034386657 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-09-25T21:12:57.046Z] RMSE (validation) = 0.9259652469249674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-09-25T21:12:58.299Z] RMSE (validation) = 0.8982232214592321 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-09-25T21:12:58.299Z] 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.
[2024-09-25T21:12:58.299Z] The best model improves the baseline by 14.52%.
[2024-09-25T21:12:58.299Z] Movies recommended for you:
[2024-09-25T21:12:58.299Z] WARNING: This benchmark provides no result that can be validated.
[2024-09-25T21:12:58.299Z] There is no way to check that no silent failure occurred.
[2024-09-25T21:12:58.299Z] ====== movie-lens (apache-spark) [default], iteration 5 completed (10251.066 ms) ======
[2024-09-25T21:12:58.299Z] ====== movie-lens (apache-spark) [default], iteration 6 started ======
[2024-09-25T21:12:58.299Z] GC before operation: completed in 67.529 ms, heap usage 137.361 MB -> 49.647 MB.
[2024-09-25T21:13:00.094Z] RMSE (validation) = 3.62196895454876 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-09-25T21:13:01.424Z] RMSE (validation) = 2.1340923221100736 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-09-25T21:13:03.233Z] RMSE (validation) = 1.3105190428502682 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-09-25T21:13:04.526Z] RMSE (validation) = 0.9920028100571827 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-09-25T21:13:05.793Z] RMSE (validation) = 1.212631715629696 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-09-25T21:13:06.587Z] RMSE (validation) = 1.1025694034386657 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-09-25T21:13:07.388Z] RMSE (validation) = 0.9259652469249674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-09-25T21:13:08.629Z] RMSE (validation) = 0.8982232214592321 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-09-25T21:13:08.629Z] 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.
[2024-09-25T21:13:08.629Z] The best model improves the baseline by 14.52%.
[2024-09-25T21:13:08.629Z] Movies recommended for you:
[2024-09-25T21:13:08.629Z] WARNING: This benchmark provides no result that can be validated.
[2024-09-25T21:13:08.629Z] There is no way to check that no silent failure occurred.
[2024-09-25T21:13:08.629Z] ====== movie-lens (apache-spark) [default], iteration 6 completed (10263.592 ms) ======
[2024-09-25T21:13:08.629Z] ====== movie-lens (apache-spark) [default], iteration 7 started ======
[2024-09-25T21:13:08.629Z] GC before operation: completed in 51.717 ms, heap usage 168.669 MB -> 49.875 MB.
[2024-09-25T21:13:10.416Z] RMSE (validation) = 3.62196895454876 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-09-25T21:13:11.736Z] RMSE (validation) = 2.1340923221100736 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-09-25T21:13:13.560Z] RMSE (validation) = 1.3105190428502682 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-09-25T21:13:14.816Z] RMSE (validation) = 0.9920028100571827 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-09-25T21:13:16.058Z] RMSE (validation) = 1.212631715629696 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-09-25T21:13:16.829Z] RMSE (validation) = 1.1025694034386657 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-09-25T21:13:18.091Z] RMSE (validation) = 0.9259652469249674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-09-25T21:13:18.920Z] RMSE (validation) = 0.8982232214592321 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-09-25T21:13:19.285Z] 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.
[2024-09-25T21:13:19.285Z] The best model improves the baseline by 14.52%.
[2024-09-25T21:13:19.285Z] Movies recommended for you:
[2024-09-25T21:13:19.285Z] WARNING: This benchmark provides no result that can be validated.
[2024-09-25T21:13:19.285Z] There is no way to check that no silent failure occurred.
[2024-09-25T21:13:19.285Z] ====== movie-lens (apache-spark) [default], iteration 7 completed (10425.970 ms) ======
[2024-09-25T21:13:19.285Z] ====== movie-lens (apache-spark) [default], iteration 8 started ======
[2024-09-25T21:13:19.285Z] GC before operation: completed in 56.933 ms, heap usage 126.398 MB -> 50.071 MB.
[2024-09-25T21:13:21.093Z] RMSE (validation) = 3.6219689545487603 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-09-25T21:13:22.956Z] RMSE (validation) = 2.1340923221100736 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-09-25T21:13:24.232Z] RMSE (validation) = 1.3105190428502682 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-09-25T21:13:26.028Z] RMSE (validation) = 0.9920028100571827 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-09-25T21:13:26.825Z] RMSE (validation) = 1.212631715629696 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-09-25T21:13:27.614Z] RMSE (validation) = 1.1025694034386657 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-09-25T21:13:28.861Z] RMSE (validation) = 0.9259652469249674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-09-25T21:13:30.136Z] RMSE (validation) = 0.8982232214592321 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-09-25T21:13:30.136Z] 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.
[2024-09-25T21:13:30.136Z] The best model improves the baseline by 14.52%.
[2024-09-25T21:13:30.136Z] Movies recommended for you:
[2024-09-25T21:13:30.136Z] WARNING: This benchmark provides no result that can be validated.
[2024-09-25T21:13:30.136Z] There is no way to check that no silent failure occurred.
[2024-09-25T21:13:30.136Z] ====== movie-lens (apache-spark) [default], iteration 8 completed (10883.535 ms) ======
[2024-09-25T21:13:30.136Z] ====== movie-lens (apache-spark) [default], iteration 9 started ======
[2024-09-25T21:13:30.136Z] GC before operation: completed in 60.527 ms, heap usage 263.408 MB -> 50.057 MB.
[2024-09-25T21:13:31.941Z] RMSE (validation) = 3.6219689545487603 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-09-25T21:13:33.220Z] RMSE (validation) = 2.1340923221100736 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-09-25T21:13:35.051Z] RMSE (validation) = 1.3105190428502682 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-09-25T21:13:36.889Z] RMSE (validation) = 0.9920028100571827 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-09-25T21:13:37.284Z] RMSE (validation) = 1.212631715629696 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-09-25T21:13:38.537Z] RMSE (validation) = 1.1025694034386657 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-09-25T21:13:39.312Z] RMSE (validation) = 0.9259652469249674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-09-25T21:13:40.102Z] RMSE (validation) = 0.8982232214592321 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-09-25T21:13:40.461Z] 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.
[2024-09-25T21:13:40.461Z] The best model improves the baseline by 14.52%.
[2024-09-25T21:13:40.461Z] Movies recommended for you:
[2024-09-25T21:13:40.461Z] WARNING: This benchmark provides no result that can be validated.
[2024-09-25T21:13:40.461Z] There is no way to check that no silent failure occurred.
[2024-09-25T21:13:40.461Z] ====== movie-lens (apache-spark) [default], iteration 9 completed (10285.604 ms) ======
[2024-09-25T21:13:40.461Z] ====== movie-lens (apache-spark) [default], iteration 10 started ======
[2024-09-25T21:13:40.461Z] GC before operation: completed in 54.173 ms, heap usage 169.552 MB -> 49.994 MB.
[2024-09-25T21:13:42.272Z] RMSE (validation) = 3.62196895454876 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-09-25T21:13:43.555Z] RMSE (validation) = 2.1340923221100736 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-09-25T21:13:45.345Z] RMSE (validation) = 1.3105190428502682 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-09-25T21:13:46.606Z] RMSE (validation) = 0.9920028100571827 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-09-25T21:13:47.884Z] RMSE (validation) = 1.212631715629696 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-09-25T21:13:48.661Z] RMSE (validation) = 1.1025694034386657 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-09-25T21:13:49.450Z] RMSE (validation) = 0.9259652469249674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-09-25T21:13:50.244Z] RMSE (validation) = 0.8982232214592321 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-09-25T21:13:50.616Z] 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.
[2024-09-25T21:13:50.616Z] The best model improves the baseline by 14.52%.
[2024-09-25T21:13:50.616Z] Movies recommended for you:
[2024-09-25T21:13:50.616Z] WARNING: This benchmark provides no result that can be validated.
[2024-09-25T21:13:50.616Z] There is no way to check that no silent failure occurred.
[2024-09-25T21:13:50.616Z] ====== movie-lens (apache-spark) [default], iteration 10 completed (10011.410 ms) ======
[2024-09-25T21:13:50.616Z] ====== movie-lens (apache-spark) [default], iteration 11 started ======
[2024-09-25T21:13:50.616Z] GC before operation: completed in 52.713 ms, heap usage 187.659 MB -> 49.740 MB.
[2024-09-25T21:13:52.422Z] RMSE (validation) = 3.6219689545487603 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-09-25T21:13:54.237Z] RMSE (validation) = 2.1340923221100736 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-09-25T21:13:56.040Z] RMSE (validation) = 1.3105190428502682 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-09-25T21:13:57.295Z] RMSE (validation) = 0.9920028100571827 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-09-25T21:13:58.185Z] RMSE (validation) = 1.212631715629696 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-09-25T21:13:59.425Z] RMSE (validation) = 1.1025694034386657 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-09-25T21:14:00.207Z] RMSE (validation) = 0.9259652469249674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-09-25T21:14:01.006Z] RMSE (validation) = 0.8982232214592321 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-09-25T21:14:01.371Z] 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.
[2024-09-25T21:14:01.371Z] The best model improves the baseline by 14.52%.
[2024-09-25T21:14:01.371Z] Movies recommended for you:
[2024-09-25T21:14:01.371Z] WARNING: This benchmark provides no result that can be validated.
[2024-09-25T21:14:01.371Z] There is no way to check that no silent failure occurred.
[2024-09-25T21:14:01.371Z] ====== movie-lens (apache-spark) [default], iteration 11 completed (10721.161 ms) ======
[2024-09-25T21:14:01.371Z] ====== movie-lens (apache-spark) [default], iteration 12 started ======
[2024-09-25T21:14:01.371Z] GC before operation: completed in 55.712 ms, heap usage 136.413 MB -> 49.857 MB.
[2024-09-25T21:14:03.209Z] RMSE (validation) = 3.62196895454876 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-09-25T21:14:04.495Z] RMSE (validation) = 2.1340923221100736 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-09-25T21:14:06.281Z] RMSE (validation) = 1.3105190428502682 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-09-25T21:14:07.545Z] RMSE (validation) = 0.9920028100571827 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-09-25T21:14:08.830Z] RMSE (validation) = 1.212631715629696 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-09-25T21:14:09.603Z] RMSE (validation) = 1.1025694034386657 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-09-25T21:14:10.856Z] RMSE (validation) = 0.9259652469249674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-09-25T21:14:11.636Z] RMSE (validation) = 0.8982232214592321 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-09-25T21:14:11.636Z] 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.
[2024-09-25T21:14:11.636Z] The best model improves the baseline by 14.52%.
[2024-09-25T21:14:11.636Z] Movies recommended for you:
[2024-09-25T21:14:11.636Z] WARNING: This benchmark provides no result that can be validated.
[2024-09-25T21:14:11.636Z] There is no way to check that no silent failure occurred.
[2024-09-25T21:14:11.636Z] ====== movie-lens (apache-spark) [default], iteration 12 completed (10291.859 ms) ======
[2024-09-25T21:14:11.636Z] ====== movie-lens (apache-spark) [default], iteration 13 started ======
[2024-09-25T21:14:11.636Z] GC before operation: completed in 45.157 ms, heap usage 167.843 MB -> 50.094 MB.
[2024-09-25T21:14:13.485Z] RMSE (validation) = 3.62196895454876 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-09-25T21:14:14.747Z] RMSE (validation) = 2.1340923221100736 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-09-25T21:14:16.570Z] RMSE (validation) = 1.3105190428502682 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-09-25T21:14:17.844Z] RMSE (validation) = 0.9920028100571827 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-09-25T21:14:18.618Z] RMSE (validation) = 1.212631715629696 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-09-25T21:14:19.399Z] RMSE (validation) = 1.1025694034386657 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-09-25T21:14:20.182Z] RMSE (validation) = 0.9259652469249674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-09-25T21:14:20.978Z] RMSE (validation) = 0.8982232214592321 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-09-25T21:14:21.345Z] 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.
[2024-09-25T21:14:21.345Z] The best model improves the baseline by 14.52%.
[2024-09-25T21:14:21.345Z] Movies recommended for you:
[2024-09-25T21:14:21.345Z] WARNING: This benchmark provides no result that can be validated.
[2024-09-25T21:14:21.345Z] There is no way to check that no silent failure occurred.
[2024-09-25T21:14:21.345Z] ====== movie-lens (apache-spark) [default], iteration 13 completed (9608.110 ms) ======
[2024-09-25T21:14:21.345Z] ====== movie-lens (apache-spark) [default], iteration 14 started ======
[2024-09-25T21:14:21.345Z] GC before operation: completed in 55.894 ms, heap usage 136.174 MB -> 49.775 MB.
[2024-09-25T21:14:23.127Z] RMSE (validation) = 3.62196895454876 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-09-25T21:14:24.377Z] RMSE (validation) = 2.1340923221100736 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-09-25T21:14:25.637Z] RMSE (validation) = 1.3105190428502682 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-09-25T21:14:27.418Z] RMSE (validation) = 0.9920028100571827 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-09-25T21:14:28.203Z] RMSE (validation) = 1.212631715629696 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-09-25T21:14:28.985Z] RMSE (validation) = 1.1025694034386657 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-09-25T21:14:30.241Z] RMSE (validation) = 0.9259652469249674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-09-25T21:14:30.603Z] RMSE (validation) = 0.8982232214592321 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-09-25T21:14:30.968Z] 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.
[2024-09-25T21:14:30.968Z] The best model improves the baseline by 14.52%.
[2024-09-25T21:14:30.968Z] Movies recommended for you:
[2024-09-25T21:14:30.968Z] WARNING: This benchmark provides no result that can be validated.
[2024-09-25T21:14:30.968Z] There is no way to check that no silent failure occurred.
[2024-09-25T21:14:30.968Z] ====== movie-lens (apache-spark) [default], iteration 14 completed (9557.780 ms) ======
[2024-09-25T21:14:30.968Z] ====== movie-lens (apache-spark) [default], iteration 15 started ======
[2024-09-25T21:14:30.968Z] GC before operation: completed in 47.621 ms, heap usage 165.801 MB -> 50.011 MB.
[2024-09-25T21:14:32.759Z] RMSE (validation) = 3.6219689545487603 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-09-25T21:14:34.007Z] RMSE (validation) = 2.1340923221100736 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-09-25T21:14:35.796Z] RMSE (validation) = 1.3105190428502682 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-09-25T21:14:37.028Z] RMSE (validation) = 0.9920028100571827 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-09-25T21:14:37.819Z] RMSE (validation) = 1.212631715629696 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-09-25T21:14:38.602Z] RMSE (validation) = 1.1025694034386657 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-09-25T21:14:39.372Z] RMSE (validation) = 0.9259652469249674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-09-25T21:14:40.156Z] RMSE (validation) = 0.8982232214592321 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-09-25T21:14:40.524Z] 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.
[2024-09-25T21:14:40.524Z] The best model improves the baseline by 14.52%.
[2024-09-25T21:14:40.524Z] Movies recommended for you:
[2024-09-25T21:14:40.524Z] WARNING: This benchmark provides no result that can be validated.
[2024-09-25T21:14:40.524Z] There is no way to check that no silent failure occurred.
[2024-09-25T21:14:40.524Z] ====== movie-lens (apache-spark) [default], iteration 15 completed (9444.133 ms) ======
[2024-09-25T21:14:40.524Z] ====== movie-lens (apache-spark) [default], iteration 16 started ======
[2024-09-25T21:14:40.524Z] GC before operation: completed in 50.950 ms, heap usage 89.044 MB -> 49.995 MB.
[2024-09-25T21:14:41.787Z] RMSE (validation) = 3.6219689545487603 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-09-25T21:14:43.616Z] RMSE (validation) = 2.1340923221100736 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-09-25T21:14:44.866Z] RMSE (validation) = 1.3105190428502682 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-09-25T21:14:46.110Z] RMSE (validation) = 0.9920028100571827 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-09-25T21:14:46.939Z] RMSE (validation) = 1.212631715629696 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-09-25T21:14:48.206Z] RMSE (validation) = 1.1025694034386657 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-09-25T21:14:48.995Z] RMSE (validation) = 0.9259652469249674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-09-25T21:14:49.782Z] RMSE (validation) = 0.8982232214592321 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-09-25T21:14:49.782Z] 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.
[2024-09-25T21:14:49.782Z] The best model improves the baseline by 14.52%.
[2024-09-25T21:14:50.179Z] Movies recommended for you:
[2024-09-25T21:14:50.179Z] WARNING: This benchmark provides no result that can be validated.
[2024-09-25T21:14:50.179Z] There is no way to check that no silent failure occurred.
[2024-09-25T21:14:50.179Z] ====== movie-lens (apache-spark) [default], iteration 16 completed (9520.844 ms) ======
[2024-09-25T21:14:50.179Z] ====== movie-lens (apache-spark) [default], iteration 17 started ======
[2024-09-25T21:14:50.179Z] GC before operation: completed in 56.819 ms, heap usage 169.538 MB -> 50.046 MB.
[2024-09-25T21:14:51.463Z] RMSE (validation) = 3.62196895454876 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-09-25T21:14:53.286Z] RMSE (validation) = 2.1340923221100736 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-09-25T21:14:54.546Z] RMSE (validation) = 1.3105190428502682 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-09-25T21:14:55.810Z] RMSE (validation) = 0.9920028100571827 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-09-25T21:14:56.596Z] RMSE (validation) = 1.212631715629696 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-09-25T21:14:57.882Z] RMSE (validation) = 1.1025694034386657 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-09-25T21:14:58.661Z] RMSE (validation) = 0.9259652469249674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-09-25T21:14:59.476Z] RMSE (validation) = 0.8982232214592321 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-09-25T21:14:59.834Z] 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.
[2024-09-25T21:14:59.834Z] The best model improves the baseline by 14.52%.
[2024-09-25T21:14:59.834Z] Movies recommended for you:
[2024-09-25T21:14:59.834Z] WARNING: This benchmark provides no result that can be validated.
[2024-09-25T21:14:59.834Z] There is no way to check that no silent failure occurred.
[2024-09-25T21:14:59.834Z] ====== movie-lens (apache-spark) [default], iteration 17 completed (9661.717 ms) ======
[2024-09-25T21:14:59.834Z] ====== movie-lens (apache-spark) [default], iteration 18 started ======
[2024-09-25T21:14:59.834Z] GC before operation: completed in 48.878 ms, heap usage 228.279 MB -> 50.167 MB.
[2024-09-25T21:15:01.098Z] RMSE (validation) = 3.6219689545487603 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-09-25T21:15:02.907Z] RMSE (validation) = 2.1340923221100736 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-09-25T21:15:04.169Z] RMSE (validation) = 1.3105190428502682 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-09-25T21:15:06.021Z] RMSE (validation) = 0.9920028100571827 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-09-25T21:15:06.798Z] RMSE (validation) = 1.212631715629696 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-09-25T21:15:07.580Z] RMSE (validation) = 1.1025694034386657 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-09-25T21:15:08.374Z] RMSE (validation) = 0.9259652469249674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-09-25T21:15:09.613Z] RMSE (validation) = 0.8982232214592321 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-09-25T21:15:09.613Z] 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.
[2024-09-25T21:15:09.613Z] The best model improves the baseline by 14.52%.
[2024-09-25T21:15:09.613Z] Movies recommended for you:
[2024-09-25T21:15:09.613Z] WARNING: This benchmark provides no result that can be validated.
[2024-09-25T21:15:09.613Z] There is no way to check that no silent failure occurred.
[2024-09-25T21:15:09.613Z] ====== movie-lens (apache-spark) [default], iteration 18 completed (9918.494 ms) ======
[2024-09-25T21:15:09.613Z] ====== movie-lens (apache-spark) [default], iteration 19 started ======
[2024-09-25T21:15:09.613Z] GC before operation: completed in 63.086 ms, heap usage 231.233 MB -> 50.336 MB.
[2024-09-25T21:15:11.416Z] RMSE (validation) = 3.62196895454876 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-09-25T21:15:12.672Z] RMSE (validation) = 2.1340923221100736 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-09-25T21:15:14.489Z] RMSE (validation) = 1.3105190428502682 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-09-25T21:15:15.738Z] RMSE (validation) = 0.9920028100571827 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-09-25T21:15:16.576Z] RMSE (validation) = 1.212631715629696 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-09-25T21:15:17.357Z] RMSE (validation) = 1.1025694034386657 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-09-25T21:15:18.611Z] RMSE (validation) = 0.9259652469249674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-09-25T21:15:19.395Z] RMSE (validation) = 0.8982232214592321 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-09-25T21:15:19.395Z] 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.
[2024-09-25T21:15:19.395Z] The best model improves the baseline by 14.52%.
[2024-09-25T21:15:19.755Z] Movies recommended for you:
[2024-09-25T21:15:19.755Z] WARNING: This benchmark provides no result that can be validated.
[2024-09-25T21:15:19.755Z] There is no way to check that no silent failure occurred.
[2024-09-25T21:15:19.755Z] ====== movie-lens (apache-spark) [default], iteration 19 completed (9835.583 ms) ======
[2024-09-25T21:15:19.755Z] -----------------------------------
[2024-09-25T21:15:19.755Z] renaissance-movie-lens_0_PASSED
[2024-09-25T21:15:19.755Z] -----------------------------------
[2024-09-25T21:15:19.755Z]
[2024-09-25T21:15:19.755Z] TEST TEARDOWN:
[2024-09-25T21:15:19.755Z] Nothing to be done for teardown.
[2024-09-25T21:15:19.755Z] renaissance-movie-lens_0 Finish Time: Wed Sep 25 17:15:19 2024 Epoch Time (ms): 1727298919670