renaissance-movie-lens_0
[2025-02-06T02:35:30.635Z] Running test renaissance-movie-lens_0 ...
[2025-02-06T02:35:30.635Z] ===============================================
[2025-02-06T02:35:30.635Z] renaissance-movie-lens_0 Start Time: Thu Feb 6 02:35:30 2025 Epoch Time (ms): 1738809330490
[2025-02-06T02:35:30.635Z] variation: NoOptions
[2025-02-06T02:35:30.635Z] JVM_OPTIONS:
[2025-02-06T02:35:30.635Z] { \
[2025-02-06T02:35:30.635Z] echo ""; echo "TEST SETUP:"; \
[2025-02-06T02:35:30.635Z] echo "Nothing to be done for setup."; \
[2025-02-06T02:35:30.635Z] mkdir -p "/home/jenkins/workspace/Test_openjdk11_hs_extended.perf_ppc64_aix/aqa-tests/TKG/../TKG/output_17388083385047/renaissance-movie-lens_0"; \
[2025-02-06T02:35:30.635Z] cd "/home/jenkins/workspace/Test_openjdk11_hs_extended.perf_ppc64_aix/aqa-tests/TKG/../TKG/output_17388083385047/renaissance-movie-lens_0"; \
[2025-02-06T02:35:30.635Z] echo ""; echo "TESTING:"; \
[2025-02-06T02:35:30.635Z] "/home/jenkins/workspace/Test_openjdk11_hs_extended.perf_ppc64_aix/jdkbinary/j2sdk-image/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 "/home/jenkins/workspace/Test_openjdk11_hs_extended.perf_ppc64_aix/aqa-tests/TKG/../../jvmtest/perf/renaissance/renaissance.jar" --json ""/home/jenkins/workspace/Test_openjdk11_hs_extended.perf_ppc64_aix/aqa-tests/TKG/../TKG/output_17388083385047/renaissance-movie-lens_0"/movie-lens.json" movie-lens; \
[2025-02-06T02:35:30.635Z] if [ $? -eq 0 ]; then echo "-----------------------------------"; echo "renaissance-movie-lens_0""_PASSED"; echo "-----------------------------------"; cd /home/jenkins/workspace/Test_openjdk11_hs_extended.perf_ppc64_aix/aqa-tests/TKG/..; rm -f -r "/home/jenkins/workspace/Test_openjdk11_hs_extended.perf_ppc64_aix/aqa-tests/TKG/../TKG/output_17388083385047/renaissance-movie-lens_0"; else echo "-----------------------------------"; echo "renaissance-movie-lens_0""_FAILED"; echo "-----------------------------------"; fi; \
[2025-02-06T02:35:30.635Z] echo ""; echo "TEST TEARDOWN:"; \
[2025-02-06T02:35:30.635Z] echo "Nothing to be done for teardown."; \
[2025-02-06T02:35:30.635Z] } 2>&1 | tee -a "/home/jenkins/workspace/Test_openjdk11_hs_extended.perf_ppc64_aix/aqa-tests/TKG/../TKG/output_17388083385047/TestTargetResult";
[2025-02-06T02:35:30.635Z]
[2025-02-06T02:35:30.635Z] TEST SETUP:
[2025-02-06T02:35:30.635Z] Nothing to be done for setup.
[2025-02-06T02:35:30.635Z]
[2025-02-06T02:35:30.635Z] TESTING:
[2025-02-06T02:35:36.232Z] Using Spark's default log4j profile: org/apache/spark/log4j-defaults.properties
[2025-02-06T02:35:38.703Z] NOTE: 'movie-lens' benchmark uses Spark local executor with 8 (out of 24) threads.
[2025-02-06T02:35:42.141Z] Got 100004 ratings from 671 users on 9066 movies.
[2025-02-06T02:35:42.913Z] Training: 60056, validation: 20285, test: 19854
[2025-02-06T02:35:42.913Z] ====== movie-lens (apache-spark) [default], iteration 0 started ======
[2025-02-06T02:35:42.913Z] GC before operation: completed in 53.834 ms, heap usage 145.198 MB -> 37.963 MB.
[2025-02-06T02:35:48.528Z] RMSE (validation) = 3.621968954548751 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2025-02-06T02:35:52.994Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2025-02-06T02:35:56.417Z] RMSE (validation) = 1.3105189674610933 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2025-02-06T02:35:59.842Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2025-02-06T02:36:01.437Z] RMSE (validation) = 1.2218330581874073 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2025-02-06T02:36:03.961Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2025-02-06T02:36:05.552Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2025-02-06T02:36:08.028Z] RMSE (validation) = 0.9001440981626694 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2025-02-06T02:36:08.028Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9073522634082535.
[2025-02-06T02:36:08.028Z] The best model improves the baseline by 14.43%.
[2025-02-06T02:36:08.028Z] Movies recommended for you:
[2025-02-06T02:36:08.028Z] WARNING: This benchmark provides no result that can be validated.
[2025-02-06T02:36:08.028Z] There is no way to check that no silent failure occurred.
[2025-02-06T02:36:08.028Z] ====== movie-lens (apache-spark) [default], iteration 0 completed (25578.844 ms) ======
[2025-02-06T02:36:08.028Z] ====== movie-lens (apache-spark) [default], iteration 1 started ======
[2025-02-06T02:36:08.797Z] GC before operation: completed in 114.683 ms, heap usage 335.244 MB -> 50.393 MB.
[2025-02-06T02:36:11.278Z] RMSE (validation) = 3.6219689545487506 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2025-02-06T02:36:14.710Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2025-02-06T02:36:18.141Z] RMSE (validation) = 1.3105189674610933 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2025-02-06T02:36:21.577Z] RMSE (validation) = 1.004540774800519 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2025-02-06T02:36:23.169Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2025-02-06T02:36:25.652Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2025-02-06T02:36:27.242Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2025-02-06T02:36:28.860Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2025-02-06T02:36:29.632Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9073522634082535.
[2025-02-06T02:36:29.632Z] The best model improves the baseline by 14.43%.
[2025-02-06T02:36:29.632Z] Movies recommended for you:
[2025-02-06T02:36:29.632Z] WARNING: This benchmark provides no result that can be validated.
[2025-02-06T02:36:29.632Z] There is no way to check that no silent failure occurred.
[2025-02-06T02:36:29.632Z] ====== movie-lens (apache-spark) [default], iteration 1 completed (21153.161 ms) ======
[2025-02-06T02:36:29.632Z] ====== movie-lens (apache-spark) [default], iteration 2 started ======
[2025-02-06T02:36:29.632Z] GC before operation: completed in 129.877 ms, heap usage 464.954 MB -> 51.869 MB.
[2025-02-06T02:36:33.064Z] RMSE (validation) = 3.6219689545487506 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2025-02-06T02:36:36.485Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2025-02-06T02:36:38.955Z] RMSE (validation) = 1.3105189674610933 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2025-02-06T02:36:42.376Z] RMSE (validation) = 1.004540774800519 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2025-02-06T02:36:43.969Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2025-02-06T02:36:45.568Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2025-02-06T02:36:48.061Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2025-02-06T02:36:49.663Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2025-02-06T02:36:49.663Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9073522634082535.
[2025-02-06T02:36:49.663Z] The best model improves the baseline by 14.43%.
[2025-02-06T02:36:50.435Z] Movies recommended for you:
[2025-02-06T02:36:50.435Z] WARNING: This benchmark provides no result that can be validated.
[2025-02-06T02:36:50.435Z] There is no way to check that no silent failure occurred.
[2025-02-06T02:36:50.435Z] ====== movie-lens (apache-spark) [default], iteration 2 completed (20431.536 ms) ======
[2025-02-06T02:36:50.435Z] ====== movie-lens (apache-spark) [default], iteration 3 started ======
[2025-02-06T02:36:50.435Z] GC before operation: completed in 113.671 ms, heap usage 143.440 MB -> 52.036 MB.
[2025-02-06T02:36:53.868Z] RMSE (validation) = 3.6219689545487506 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2025-02-06T02:36:56.348Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2025-02-06T02:36:59.787Z] RMSE (validation) = 1.3105189674610933 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2025-02-06T02:37:02.259Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2025-02-06T02:37:04.736Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2025-02-06T02:37:06.341Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2025-02-06T02:37:07.933Z] RMSE (validation) = 0.927571739133814 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2025-02-06T02:37:10.411Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2025-02-06T02:37:10.411Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9073522634082535.
[2025-02-06T02:37:10.411Z] The best model improves the baseline by 14.43%.
[2025-02-06T02:37:10.411Z] Movies recommended for you:
[2025-02-06T02:37:10.411Z] WARNING: This benchmark provides no result that can be validated.
[2025-02-06T02:37:10.411Z] There is no way to check that no silent failure occurred.
[2025-02-06T02:37:10.411Z] ====== movie-lens (apache-spark) [default], iteration 3 completed (20361.320 ms) ======
[2025-02-06T02:37:10.411Z] ====== movie-lens (apache-spark) [default], iteration 4 started ======
[2025-02-06T02:37:10.411Z] GC before operation: completed in 118.565 ms, heap usage 246.090 MB -> 52.465 MB.
[2025-02-06T02:37:13.848Z] RMSE (validation) = 3.621968954548751 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2025-02-06T02:37:17.269Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2025-02-06T02:37:19.747Z] RMSE (validation) = 1.3105189674610935 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2025-02-06T02:37:23.190Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2025-02-06T02:37:24.787Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2025-02-06T02:37:26.385Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2025-02-06T02:37:28.878Z] RMSE (validation) = 0.9275717391338142 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2025-02-06T02:37:30.473Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2025-02-06T02:37:30.473Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9073522634082535.
[2025-02-06T02:37:30.473Z] The best model improves the baseline by 14.43%.
[2025-02-06T02:37:30.473Z] Movies recommended for you:
[2025-02-06T02:37:30.473Z] WARNING: This benchmark provides no result that can be validated.
[2025-02-06T02:37:30.473Z] There is no way to check that no silent failure occurred.
[2025-02-06T02:37:30.473Z] ====== movie-lens (apache-spark) [default], iteration 4 completed (19971.006 ms) ======
[2025-02-06T02:37:30.473Z] ====== movie-lens (apache-spark) [default], iteration 5 started ======
[2025-02-06T02:37:30.473Z] GC before operation: completed in 110.076 ms, heap usage 465.607 MB -> 52.774 MB.
[2025-02-06T02:37:33.906Z] RMSE (validation) = 3.621968954548751 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2025-02-06T02:37:37.341Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2025-02-06T02:37:39.826Z] RMSE (validation) = 1.3105189674610935 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2025-02-06T02:37:43.253Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2025-02-06T02:37:44.856Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2025-02-06T02:37:46.451Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2025-02-06T02:37:48.046Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2025-02-06T02:37:50.516Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2025-02-06T02:37:50.516Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9073522634082535.
[2025-02-06T02:37:50.516Z] The best model improves the baseline by 14.43%.
[2025-02-06T02:37:50.516Z] Movies recommended for you:
[2025-02-06T02:37:50.516Z] WARNING: This benchmark provides no result that can be validated.
[2025-02-06T02:37:50.516Z] There is no way to check that no silent failure occurred.
[2025-02-06T02:37:50.516Z] ====== movie-lens (apache-spark) [default], iteration 5 completed (19743.581 ms) ======
[2025-02-06T02:37:50.516Z] ====== movie-lens (apache-spark) [default], iteration 6 started ======
[2025-02-06T02:37:50.516Z] GC before operation: completed in 120.433 ms, heap usage 366.600 MB -> 52.621 MB.
[2025-02-06T02:37:53.958Z] RMSE (validation) = 3.621968954548751 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2025-02-06T02:37:56.437Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2025-02-06T02:37:59.873Z] RMSE (validation) = 1.3105189674610935 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2025-02-06T02:38:03.309Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2025-02-06T02:38:04.905Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2025-02-06T02:38:06.494Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2025-02-06T02:38:08.089Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2025-02-06T02:38:10.566Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2025-02-06T02:38:10.566Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9073522634082535.
[2025-02-06T02:38:10.566Z] The best model improves the baseline by 14.43%.
[2025-02-06T02:38:10.566Z] Movies recommended for you:
[2025-02-06T02:38:10.566Z] WARNING: This benchmark provides no result that can be validated.
[2025-02-06T02:38:10.566Z] There is no way to check that no silent failure occurred.
[2025-02-06T02:38:10.566Z] ====== movie-lens (apache-spark) [default], iteration 6 completed (19954.886 ms) ======
[2025-02-06T02:38:10.566Z] ====== movie-lens (apache-spark) [default], iteration 7 started ======
[2025-02-06T02:38:10.566Z] GC before operation: completed in 115.458 ms, heap usage 492.626 MB -> 56.166 MB.
[2025-02-06T02:38:14.007Z] RMSE (validation) = 3.6219689545487506 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2025-02-06T02:38:16.473Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2025-02-06T02:38:19.892Z] RMSE (validation) = 1.3105189674610935 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2025-02-06T02:38:23.315Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2025-02-06T02:38:24.907Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2025-02-06T02:38:26.499Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2025-02-06T02:38:28.088Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2025-02-06T02:38:30.584Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2025-02-06T02:38:30.584Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9073522634082535.
[2025-02-06T02:38:30.584Z] The best model improves the baseline by 14.43%.
[2025-02-06T02:38:30.584Z] Movies recommended for you:
[2025-02-06T02:38:30.584Z] WARNING: This benchmark provides no result that can be validated.
[2025-02-06T02:38:30.584Z] There is no way to check that no silent failure occurred.
[2025-02-06T02:38:30.584Z] ====== movie-lens (apache-spark) [default], iteration 7 completed (19827.698 ms) ======
[2025-02-06T02:38:30.584Z] ====== movie-lens (apache-spark) [default], iteration 8 started ======
[2025-02-06T02:38:30.584Z] GC before operation: completed in 115.851 ms, heap usage 272.292 MB -> 53.007 MB.
[2025-02-06T02:38:34.038Z] RMSE (validation) = 3.621968954548751 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2025-02-06T02:38:36.514Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2025-02-06T02:38:39.938Z] RMSE (validation) = 1.3105189674610935 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2025-02-06T02:38:43.362Z] RMSE (validation) = 1.004540774800519 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2025-02-06T02:38:44.956Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2025-02-06T02:38:46.557Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2025-02-06T02:38:48.160Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2025-02-06T02:38:50.644Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2025-02-06T02:38:50.644Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9073522634082535.
[2025-02-06T02:38:50.644Z] The best model improves the baseline by 14.43%.
[2025-02-06T02:38:50.644Z] Movies recommended for you:
[2025-02-06T02:38:50.644Z] WARNING: This benchmark provides no result that can be validated.
[2025-02-06T02:38:50.644Z] There is no way to check that no silent failure occurred.
[2025-02-06T02:38:50.644Z] ====== movie-lens (apache-spark) [default], iteration 8 completed (19966.107 ms) ======
[2025-02-06T02:38:50.644Z] ====== movie-lens (apache-spark) [default], iteration 9 started ======
[2025-02-06T02:38:50.644Z] GC before operation: completed in 123.822 ms, heap usage 313.300 MB -> 52.866 MB.
[2025-02-06T02:38:54.065Z] RMSE (validation) = 3.621968954548751 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2025-02-06T02:38:56.542Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2025-02-06T02:38:59.973Z] RMSE (validation) = 1.3105189674610933 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2025-02-06T02:39:02.642Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2025-02-06T02:39:05.120Z] RMSE (validation) = 1.2218330581874073 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2025-02-06T02:39:06.713Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2025-02-06T02:39:08.313Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2025-02-06T02:39:10.795Z] RMSE (validation) = 0.9001440981626694 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2025-02-06T02:39:10.796Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9073522634082535.
[2025-02-06T02:39:10.796Z] The best model improves the baseline by 14.43%.
[2025-02-06T02:39:10.796Z] Movies recommended for you:
[2025-02-06T02:39:10.796Z] WARNING: This benchmark provides no result that can be validated.
[2025-02-06T02:39:10.796Z] There is no way to check that no silent failure occurred.
[2025-02-06T02:39:10.796Z] ====== movie-lens (apache-spark) [default], iteration 9 completed (20001.682 ms) ======
[2025-02-06T02:39:10.796Z] ====== movie-lens (apache-spark) [default], iteration 10 started ======
[2025-02-06T02:39:10.796Z] GC before operation: completed in 141.722 ms, heap usage 223.608 MB -> 52.933 MB.
[2025-02-06T02:39:14.250Z] RMSE (validation) = 3.6219689545487506 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2025-02-06T02:39:16.725Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2025-02-06T02:39:20.147Z] RMSE (validation) = 1.3105189674610935 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2025-02-06T02:39:22.628Z] RMSE (validation) = 1.004540774800519 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2025-02-06T02:39:25.101Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2025-02-06T02:39:26.702Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2025-02-06T02:39:28.293Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2025-02-06T02:39:30.789Z] RMSE (validation) = 0.9001440981626694 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2025-02-06T02:39:30.789Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9073522634082535.
[2025-02-06T02:39:30.789Z] The best model improves the baseline by 14.43%.
[2025-02-06T02:39:30.789Z] Movies recommended for you:
[2025-02-06T02:39:30.789Z] WARNING: This benchmark provides no result that can be validated.
[2025-02-06T02:39:30.789Z] There is no way to check that no silent failure occurred.
[2025-02-06T02:39:30.789Z] ====== movie-lens (apache-spark) [default], iteration 10 completed (19856.444 ms) ======
[2025-02-06T02:39:30.789Z] ====== movie-lens (apache-spark) [default], iteration 11 started ======
[2025-02-06T02:39:30.789Z] GC before operation: completed in 122.060 ms, heap usage 105.737 MB -> 54.380 MB.
[2025-02-06T02:39:34.216Z] RMSE (validation) = 3.6219689545487506 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2025-02-06T02:39:36.693Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2025-02-06T02:39:40.125Z] RMSE (validation) = 1.3105189674610933 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2025-02-06T02:39:42.618Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2025-02-06T02:39:45.096Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2025-02-06T02:39:46.685Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2025-02-06T02:39:48.282Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2025-02-06T02:39:49.884Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2025-02-06T02:39:50.654Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9073522634082535.
[2025-02-06T02:39:50.654Z] The best model improves the baseline by 14.43%.
[2025-02-06T02:39:50.654Z] Movies recommended for you:
[2025-02-06T02:39:50.654Z] WARNING: This benchmark provides no result that can be validated.
[2025-02-06T02:39:50.654Z] There is no way to check that no silent failure occurred.
[2025-02-06T02:39:50.654Z] ====== movie-lens (apache-spark) [default], iteration 11 completed (19792.139 ms) ======
[2025-02-06T02:39:50.654Z] ====== movie-lens (apache-spark) [default], iteration 12 started ======
[2025-02-06T02:39:50.654Z] GC before operation: completed in 116.804 ms, heap usage 261.614 MB -> 52.845 MB.
[2025-02-06T02:39:54.090Z] RMSE (validation) = 3.6219689545487506 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2025-02-06T02:39:56.573Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2025-02-06T02:39:59.996Z] RMSE (validation) = 1.3105189674610933 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2025-02-06T02:40:02.548Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2025-02-06T02:40:05.054Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2025-02-06T02:40:06.644Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2025-02-06T02:40:08.233Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2025-02-06T02:40:09.835Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2025-02-06T02:40:10.612Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9073522634082535.
[2025-02-06T02:40:10.612Z] The best model improves the baseline by 14.43%.
[2025-02-06T02:40:10.612Z] Movies recommended for you:
[2025-02-06T02:40:10.612Z] WARNING: This benchmark provides no result that can be validated.
[2025-02-06T02:40:10.612Z] There is no way to check that no silent failure occurred.
[2025-02-06T02:40:10.612Z] ====== movie-lens (apache-spark) [default], iteration 12 completed (19786.211 ms) ======
[2025-02-06T02:40:10.612Z] ====== movie-lens (apache-spark) [default], iteration 13 started ======
[2025-02-06T02:40:10.612Z] GC before operation: completed in 124.118 ms, heap usage 551.862 MB -> 56.476 MB.
[2025-02-06T02:40:14.044Z] RMSE (validation) = 3.6219689545487506 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2025-02-06T02:40:16.518Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2025-02-06T02:40:19.941Z] RMSE (validation) = 1.3105189674610935 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2025-02-06T02:40:22.408Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2025-02-06T02:40:24.888Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2025-02-06T02:40:26.499Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2025-02-06T02:40:28.822Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2025-02-06T02:40:29.763Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2025-02-06T02:40:30.714Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9073522634082535.
[2025-02-06T02:40:30.714Z] The best model improves the baseline by 14.43%.
[2025-02-06T02:40:30.714Z] Movies recommended for you:
[2025-02-06T02:40:30.714Z] WARNING: This benchmark provides no result that can be validated.
[2025-02-06T02:40:30.714Z] There is no way to check that no silent failure occurred.
[2025-02-06T02:40:30.714Z] ====== movie-lens (apache-spark) [default], iteration 13 completed (19679.690 ms) ======
[2025-02-06T02:40:30.714Z] ====== movie-lens (apache-spark) [default], iteration 14 started ======
[2025-02-06T02:40:30.714Z] GC before operation: completed in 118.018 ms, heap usage 280.354 MB -> 52.753 MB.
[2025-02-06T02:40:33.747Z] RMSE (validation) = 3.6219689545487506 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2025-02-06T02:40:36.218Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2025-02-06T02:40:39.701Z] RMSE (validation) = 1.3105189674610935 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2025-02-06T02:40:42.173Z] RMSE (validation) = 1.004540774800519 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2025-02-06T02:40:44.652Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2025-02-06T02:40:46.246Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2025-02-06T02:40:47.838Z] RMSE (validation) = 0.927571739133814 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2025-02-06T02:40:49.439Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2025-02-06T02:40:50.209Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9073522634082535.
[2025-02-06T02:40:50.209Z] The best model improves the baseline by 14.43%.
[2025-02-06T02:40:50.209Z] Movies recommended for you:
[2025-02-06T02:40:50.209Z] WARNING: This benchmark provides no result that can be validated.
[2025-02-06T02:40:50.209Z] There is no way to check that no silent failure occurred.
[2025-02-06T02:40:50.209Z] ====== movie-lens (apache-spark) [default], iteration 14 completed (19648.516 ms) ======
[2025-02-06T02:40:50.209Z] ====== movie-lens (apache-spark) [default], iteration 15 started ======
[2025-02-06T02:40:50.209Z] GC before operation: completed in 111.886 ms, heap usage 573.083 MB -> 56.452 MB.
[2025-02-06T02:40:53.654Z] RMSE (validation) = 3.6219689545487506 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2025-02-06T02:40:56.122Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2025-02-06T02:40:59.551Z] RMSE (validation) = 1.3105189674610933 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2025-02-06T02:41:02.026Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2025-02-06T02:41:04.505Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2025-02-06T02:41:06.106Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2025-02-06T02:41:07.695Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2025-02-06T02:41:09.288Z] RMSE (validation) = 0.9001440981626694 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2025-02-06T02:41:10.058Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9073522634082535.
[2025-02-06T02:41:10.058Z] The best model improves the baseline by 14.43%.
[2025-02-06T02:41:10.058Z] Movies recommended for you:
[2025-02-06T02:41:10.058Z] WARNING: This benchmark provides no result that can be validated.
[2025-02-06T02:41:10.058Z] There is no way to check that no silent failure occurred.
[2025-02-06T02:41:10.058Z] ====== movie-lens (apache-spark) [default], iteration 15 completed (19798.930 ms) ======
[2025-02-06T02:41:10.058Z] ====== movie-lens (apache-spark) [default], iteration 16 started ======
[2025-02-06T02:41:10.058Z] GC before operation: completed in 122.601 ms, heap usage 157.923 MB -> 52.926 MB.
[2025-02-06T02:41:13.518Z] RMSE (validation) = 3.621968954548751 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2025-02-06T02:41:15.997Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2025-02-06T02:41:19.420Z] RMSE (validation) = 1.3105189674610933 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2025-02-06T02:41:21.892Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2025-02-06T02:41:24.467Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2025-02-06T02:41:26.057Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2025-02-06T02:41:27.658Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2025-02-06T02:41:29.255Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2025-02-06T02:41:30.024Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9073522634082535.
[2025-02-06T02:41:30.024Z] The best model improves the baseline by 14.43%.
[2025-02-06T02:41:30.024Z] Movies recommended for you:
[2025-02-06T02:41:30.024Z] WARNING: This benchmark provides no result that can be validated.
[2025-02-06T02:41:30.024Z] There is no way to check that no silent failure occurred.
[2025-02-06T02:41:30.024Z] ====== movie-lens (apache-spark) [default], iteration 16 completed (19758.193 ms) ======
[2025-02-06T02:41:30.024Z] ====== movie-lens (apache-spark) [default], iteration 17 started ======
[2025-02-06T02:41:30.024Z] GC before operation: completed in 119.389 ms, heap usage 512.094 MB -> 56.298 MB.
[2025-02-06T02:41:33.445Z] RMSE (validation) = 3.6219689545487506 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2025-02-06T02:41:35.914Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2025-02-06T02:41:39.360Z] RMSE (validation) = 1.3105189674610935 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2025-02-06T02:41:41.834Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2025-02-06T02:41:44.306Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2025-02-06T02:41:45.910Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2025-02-06T02:41:47.500Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2025-02-06T02:41:49.093Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2025-02-06T02:41:49.863Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9073522634082535.
[2025-02-06T02:41:49.863Z] The best model improves the baseline by 14.43%.
[2025-02-06T02:41:49.863Z] Movies recommended for you:
[2025-02-06T02:41:49.863Z] WARNING: This benchmark provides no result that can be validated.
[2025-02-06T02:41:49.863Z] There is no way to check that no silent failure occurred.
[2025-02-06T02:41:49.863Z] ====== movie-lens (apache-spark) [default], iteration 17 completed (19631.035 ms) ======
[2025-02-06T02:41:49.863Z] ====== movie-lens (apache-spark) [default], iteration 18 started ======
[2025-02-06T02:41:49.863Z] GC before operation: completed in 126.794 ms, heap usage 296.924 MB -> 52.967 MB.
[2025-02-06T02:41:53.282Z] RMSE (validation) = 3.6219689545487506 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2025-02-06T02:41:55.760Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2025-02-06T02:41:59.190Z] RMSE (validation) = 1.3105189674610935 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2025-02-06T02:42:01.673Z] RMSE (validation) = 1.004540774800519 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2025-02-06T02:42:03.275Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2025-02-06T02:42:05.746Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2025-02-06T02:42:07.336Z] RMSE (validation) = 0.927571739133814 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2025-02-06T02:42:08.934Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2025-02-06T02:42:08.934Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9073522634082535.
[2025-02-06T02:42:09.705Z] The best model improves the baseline by 14.43%.
[2025-02-06T02:42:09.705Z] Movies recommended for you:
[2025-02-06T02:42:09.705Z] WARNING: This benchmark provides no result that can be validated.
[2025-02-06T02:42:09.705Z] There is no way to check that no silent failure occurred.
[2025-02-06T02:42:09.705Z] ====== movie-lens (apache-spark) [default], iteration 18 completed (19586.445 ms) ======
[2025-02-06T02:42:09.705Z] ====== movie-lens (apache-spark) [default], iteration 19 started ======
[2025-02-06T02:42:09.705Z] GC before operation: completed in 119.536 ms, heap usage 489.874 MB -> 56.545 MB.
[2025-02-06T02:42:12.175Z] RMSE (validation) = 3.6219689545487506 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2025-02-06T02:42:15.595Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2025-02-06T02:42:19.067Z] RMSE (validation) = 1.3105189674610935 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2025-02-06T02:42:21.536Z] RMSE (validation) = 1.004540774800519 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2025-02-06T02:42:23.123Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2025-02-06T02:42:24.711Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2025-02-06T02:42:27.210Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2025-02-06T02:42:28.800Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2025-02-06T02:42:28.800Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9073522634082535.
[2025-02-06T02:42:28.800Z] The best model improves the baseline by 14.43%.
[2025-02-06T02:42:28.800Z] Movies recommended for you:
[2025-02-06T02:42:28.800Z] WARNING: This benchmark provides no result that can be validated.
[2025-02-06T02:42:28.800Z] There is no way to check that no silent failure occurred.
[2025-02-06T02:42:28.800Z] ====== movie-lens (apache-spark) [default], iteration 19 completed (19488.460 ms) ======
[2025-02-06T02:42:29.571Z] -----------------------------------
[2025-02-06T02:42:29.571Z] renaissance-movie-lens_0_PASSED
[2025-02-06T02:42:29.571Z] -----------------------------------
[2025-02-06T02:42:29.571Z]
[2025-02-06T02:42:29.571Z] TEST TEARDOWN:
[2025-02-06T02:42:29.571Z] Nothing to be done for teardown.
[2025-02-06T02:42:29.571Z] renaissance-movie-lens_0 Finish Time: Thu Feb 6 02:42:29 2025 Epoch Time (ms): 1738809749087