renaissance-movie-lens_0
[2024-11-28T00:26:53.021Z] Running test renaissance-movie-lens_0 ...
[2024-11-28T00:26:53.021Z] ===============================================
[2024-11-28T00:26:53.022Z] renaissance-movie-lens_0 Start Time: Wed Nov 27 16:26:51 2024 Epoch Time (ms): 1732753611014
[2024-11-28T00:26:53.022Z] variation: NoOptions
[2024-11-28T00:26:53.022Z] JVM_OPTIONS:
[2024-11-28T00:26:53.022Z] { \
[2024-11-28T00:26:53.022Z] echo ""; echo "TEST SETUP:"; \
[2024-11-28T00:26:53.022Z] echo "Nothing to be done for setup."; \
[2024-11-28T00:26:53.022Z] mkdir -p "/Users/admin/workspace/workspace/Test_openjdk17_hs_extended.perf_x86-64_mac_testList_1/aqa-tests/TKG/../TKG/output_17327514857613/renaissance-movie-lens_0"; \
[2024-11-28T00:26:53.022Z] cd "/Users/admin/workspace/workspace/Test_openjdk17_hs_extended.perf_x86-64_mac_testList_1/aqa-tests/TKG/../TKG/output_17327514857613/renaissance-movie-lens_0"; \
[2024-11-28T00:26:53.022Z] echo ""; echo "TESTING:"; \
[2024-11-28T00:26:53.022Z] "/Users/admin/workspace/workspace/Test_openjdk17_hs_extended.perf_x86-64_mac_testList_1/jdkbinary/j2sdk-image/Contents/Home/bin/..//bin/java" --add-opens java.base/java.lang=ALL-UNNAMED --add-opens java.base/java.util=ALL-UNNAMED --add-opens java.base/java.util.concurrent=ALL-UNNAMED --add-opens java.base/java.nio=ALL-UNNAMED --add-opens java.base/sun.nio.ch=ALL-UNNAMED --add-opens java.base/java.lang.invoke=ALL-UNNAMED -jar "/Users/admin/workspace/workspace/Test_openjdk17_hs_extended.perf_x86-64_mac_testList_1/aqa-tests/TKG/../../jvmtest/perf/renaissance/renaissance.jar" --json ""/Users/admin/workspace/workspace/Test_openjdk17_hs_extended.perf_x86-64_mac_testList_1/aqa-tests/TKG/../TKG/output_17327514857613/renaissance-movie-lens_0"/movie-lens.json" movie-lens; \
[2024-11-28T00:26:53.022Z] 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_1/aqa-tests/TKG/..; rm -f -r "/Users/admin/workspace/workspace/Test_openjdk17_hs_extended.perf_x86-64_mac_testList_1/aqa-tests/TKG/../TKG/output_17327514857613/renaissance-movie-lens_0"; else echo "-----------------------------------"; echo "renaissance-movie-lens_0""_FAILED"; echo "-----------------------------------"; fi; \
[2024-11-28T00:26:53.022Z] echo ""; echo "TEST TEARDOWN:"; \
[2024-11-28T00:26:53.022Z] echo "Nothing to be done for teardown."; \
[2024-11-28T00:26:53.022Z] } 2>&1 | tee -a "/Users/admin/workspace/workspace/Test_openjdk17_hs_extended.perf_x86-64_mac_testList_1/aqa-tests/TKG/../TKG/output_17327514857613/TestTargetResult";
[2024-11-28T00:26:53.022Z]
[2024-11-28T00:26:53.022Z] TEST SETUP:
[2024-11-28T00:26:53.022Z] Nothing to be done for setup.
[2024-11-28T00:26:53.022Z]
[2024-11-28T00:26:53.022Z] TESTING:
[2024-11-28T00:27:09.190Z] Using Spark's default log4j profile: org/apache/spark/log4j-defaults.properties
[2024-11-28T00:27:15.627Z] NOTE: 'movie-lens' benchmark uses Spark local executor with 3 (out of 3) threads.
[2024-11-28T00:27:29.279Z] Got 100004 ratings from 671 users on 9066 movies.
[2024-11-28T00:27:30.380Z] Training: 60056, validation: 20285, test: 19854
[2024-11-28T00:27:30.380Z] ====== movie-lens (apache-spark) [default], iteration 0 started ======
[2024-11-28T00:27:30.380Z] GC before operation: completed in 157.248 ms, heap usage 61.182 MB -> 37.428 MB.
[2024-11-28T00:28:08.688Z] RMSE (validation) = 3.62196895454876 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-28T00:28:37.382Z] RMSE (validation) = 2.1340923221100736 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-28T00:28:57.384Z] RMSE (validation) = 1.3105190428502682 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-28T00:29:20.803Z] RMSE (validation) = 0.9920028100571827 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-28T00:29:29.460Z] RMSE (validation) = 1.212631715629696 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-28T00:29:41.339Z] RMSE (validation) = 1.1025694034386657 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-28T00:29:51.910Z] RMSE (validation) = 0.9259652469249674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-28T00:30:01.972Z] RMSE (validation) = 0.8982232214592321 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-28T00:30:07.764Z] 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.
[2024-11-28T00:30:09.330Z] The best model improves the baseline by 14.52%.
[2024-11-28T00:30:10.896Z] Movies recommended for you:
[2024-11-28T00:30:10.896Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-28T00:30:10.896Z] There is no way to check that no silent failure occurred.
[2024-11-28T00:30:10.896Z] ====== movie-lens (apache-spark) [default], iteration 0 completed (160696.151 ms) ======
[2024-11-28T00:30:10.896Z] ====== movie-lens (apache-spark) [default], iteration 1 started ======
[2024-11-28T00:30:12.354Z] GC before operation: completed in 1063.589 ms, heap usage 279.556 MB -> 49.418 MB.
[2024-11-28T00:30:32.338Z] RMSE (validation) = 3.62196895454876 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-28T00:30:49.097Z] RMSE (validation) = 2.1340923221100736 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-28T00:31:09.794Z] RMSE (validation) = 1.3105190428502682 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-28T00:31:24.025Z] RMSE (validation) = 0.9920028100571827 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-28T00:31:34.860Z] RMSE (validation) = 1.212631715629696 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-28T00:31:45.706Z] RMSE (validation) = 1.1025694034386657 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-28T00:31:55.612Z] RMSE (validation) = 0.9259652469249674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-28T00:32:04.727Z] RMSE (validation) = 0.8982232214592321 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-28T00:32:06.960Z] 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.
[2024-11-28T00:32:06.960Z] The best model improves the baseline by 14.52%.
[2024-11-28T00:32:06.960Z] Movies recommended for you:
[2024-11-28T00:32:06.960Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-28T00:32:06.960Z] There is no way to check that no silent failure occurred.
[2024-11-28T00:32:06.960Z] ====== movie-lens (apache-spark) [default], iteration 1 completed (114687.706 ms) ======
[2024-11-28T00:32:06.960Z] ====== movie-lens (apache-spark) [default], iteration 2 started ======
[2024-11-28T00:32:07.543Z] GC before operation: completed in 338.607 ms, heap usage 239.301 MB -> 49.696 MB.
[2024-11-28T00:32:30.950Z] RMSE (validation) = 3.62196895454876 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-28T00:32:43.752Z] RMSE (validation) = 2.1340923221100736 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-28T00:33:03.428Z] RMSE (validation) = 1.3105190428502682 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-28T00:33:23.336Z] RMSE (validation) = 0.9920028100571827 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-28T00:33:31.291Z] RMSE (validation) = 1.212631715629696 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-28T00:33:41.533Z] RMSE (validation) = 1.1025694034386657 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-28T00:33:53.669Z] RMSE (validation) = 0.9259652469249674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-28T00:34:03.640Z] RMSE (validation) = 0.8982232214592321 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-28T00:34:04.785Z] 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.
[2024-11-28T00:34:04.785Z] The best model improves the baseline by 14.52%.
[2024-11-28T00:34:05.282Z] Movies recommended for you:
[2024-11-28T00:34:05.282Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-28T00:34:05.282Z] There is no way to check that no silent failure occurred.
[2024-11-28T00:34:05.282Z] ====== movie-lens (apache-spark) [default], iteration 2 completed (118244.210 ms) ======
[2024-11-28T00:34:05.282Z] ====== movie-lens (apache-spark) [default], iteration 3 started ======
[2024-11-28T00:34:05.823Z] GC before operation: completed in 297.826 ms, heap usage 278.757 MB -> 49.982 MB.
[2024-11-28T00:34:26.219Z] RMSE (validation) = 3.62196895454876 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-28T00:34:40.812Z] RMSE (validation) = 2.1340923221100736 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-28T00:35:01.537Z] RMSE (validation) = 1.3105190428502682 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-28T00:35:20.802Z] RMSE (validation) = 0.9920028100571827 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-28T00:35:29.410Z] RMSE (validation) = 1.212631715629696 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-28T00:35:38.761Z] RMSE (validation) = 1.1025694034386657 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-28T00:35:50.092Z] RMSE (validation) = 0.9259652469249674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-28T00:36:00.977Z] RMSE (validation) = 0.8982232214592321 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-28T00:36:01.461Z] 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.
[2024-11-28T00:36:01.462Z] The best model improves the baseline by 14.52%.
[2024-11-28T00:36:02.221Z] Movies recommended for you:
[2024-11-28T00:36:02.221Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-28T00:36:02.221Z] There is no way to check that no silent failure occurred.
[2024-11-28T00:36:02.221Z] ====== movie-lens (apache-spark) [default], iteration 3 completed (116035.226 ms) ======
[2024-11-28T00:36:02.221Z] ====== movie-lens (apache-spark) [default], iteration 4 started ======
[2024-11-28T00:36:03.343Z] GC before operation: completed in 1024.819 ms, heap usage 285.980 MB -> 50.325 MB.
[2024-11-28T00:36:23.085Z] RMSE (validation) = 3.6219689545487603 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-28T00:36:39.865Z] RMSE (validation) = 2.1340923221100736 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-28T00:36:56.214Z] RMSE (validation) = 1.3105190428502682 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-28T00:37:13.144Z] RMSE (validation) = 0.9920028100571827 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-28T00:37:19.634Z] RMSE (validation) = 1.212631715629696 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-28T00:37:27.322Z] RMSE (validation) = 1.1025694034386657 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-28T00:37:36.687Z] RMSE (validation) = 0.9259652469249674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-28T00:37:44.736Z] RMSE (validation) = 0.8982232214592321 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-28T00:37:46.589Z] 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.
[2024-11-28T00:37:46.589Z] The best model improves the baseline by 14.52%.
[2024-11-28T00:37:47.154Z] Movies recommended for you:
[2024-11-28T00:37:47.154Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-28T00:37:47.154Z] There is no way to check that no silent failure occurred.
[2024-11-28T00:37:47.154Z] ====== movie-lens (apache-spark) [default], iteration 4 completed (104141.053 ms) ======
[2024-11-28T00:37:47.154Z] ====== movie-lens (apache-spark) [default], iteration 5 started ======
[2024-11-28T00:37:47.154Z] GC before operation: completed in 185.589 ms, heap usage 320.191 MB -> 50.585 MB.
[2024-11-28T00:38:03.648Z] RMSE (validation) = 3.62196895454876 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-28T00:38:20.258Z] RMSE (validation) = 2.1340923221100736 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-28T00:38:39.992Z] RMSE (validation) = 1.3105190428502682 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-28T00:38:56.176Z] RMSE (validation) = 0.9920028100571827 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-28T00:39:04.333Z] RMSE (validation) = 1.212631715629696 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-28T00:39:12.422Z] RMSE (validation) = 1.1025694034386657 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-28T00:39:22.914Z] RMSE (validation) = 0.9259652469249674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-28T00:39:34.425Z] RMSE (validation) = 0.8982232214592321 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-28T00:39:36.144Z] 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.
[2024-11-28T00:39:36.664Z] The best model improves the baseline by 14.52%.
[2024-11-28T00:39:36.664Z] Movies recommended for you:
[2024-11-28T00:39:36.664Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-28T00:39:36.664Z] There is no way to check that no silent failure occurred.
[2024-11-28T00:39:36.664Z] ====== movie-lens (apache-spark) [default], iteration 5 completed (109572.592 ms) ======
[2024-11-28T00:39:36.664Z] ====== movie-lens (apache-spark) [default], iteration 6 started ======
[2024-11-28T00:39:37.240Z] GC before operation: completed in 202.193 ms, heap usage 384.756 MB -> 53.748 MB.
[2024-11-28T00:39:56.481Z] RMSE (validation) = 3.62196895454876 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-28T00:40:12.920Z] RMSE (validation) = 2.1340923221100736 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-28T00:40:31.661Z] RMSE (validation) = 1.3105190428502682 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-28T00:40:51.488Z] RMSE (validation) = 0.9920028100571827 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-28T00:40:59.897Z] RMSE (validation) = 1.212631715629696 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-28T00:41:10.278Z] RMSE (validation) = 1.1025694034386657 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-28T00:41:21.781Z] RMSE (validation) = 0.9259652469249674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-28T00:41:31.768Z] RMSE (validation) = 0.8982232214592321 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-28T00:41:32.874Z] 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.
[2024-11-28T00:41:32.874Z] The best model improves the baseline by 14.52%.
[2024-11-28T00:41:33.362Z] Movies recommended for you:
[2024-11-28T00:41:33.362Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-28T00:41:33.362Z] There is no way to check that no silent failure occurred.
[2024-11-28T00:41:33.362Z] ====== movie-lens (apache-spark) [default], iteration 6 completed (116198.184 ms) ======
[2024-11-28T00:41:33.362Z] ====== movie-lens (apache-spark) [default], iteration 7 started ======
[2024-11-28T00:41:33.362Z] GC before operation: completed in 200.945 ms, heap usage 182.611 MB -> 50.592 MB.
[2024-11-28T00:41:53.249Z] RMSE (validation) = 3.62196895454876 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-28T00:42:13.415Z] RMSE (validation) = 2.1340923221100736 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-28T00:42:26.773Z] RMSE (validation) = 1.3105190428502682 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-28T00:42:46.293Z] RMSE (validation) = 0.9920028100571827 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-28T00:42:56.464Z] RMSE (validation) = 1.212631715629696 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-28T00:43:05.602Z] RMSE (validation) = 1.1025694034386657 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-28T00:43:15.895Z] RMSE (validation) = 0.9259652469249674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-28T00:43:32.627Z] RMSE (validation) = 0.8982232214592321 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-28T00:43:33.128Z] 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.
[2024-11-28T00:43:33.128Z] The best model improves the baseline by 14.52%.
[2024-11-28T00:43:33.649Z] Movies recommended for you:
[2024-11-28T00:43:33.649Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-28T00:43:33.649Z] There is no way to check that no silent failure occurred.
[2024-11-28T00:43:33.649Z] ====== movie-lens (apache-spark) [default], iteration 7 completed (120263.877 ms) ======
[2024-11-28T00:43:33.649Z] ====== movie-lens (apache-spark) [default], iteration 8 started ======
[2024-11-28T00:43:33.649Z] GC before operation: completed in 244.921 ms, heap usage 273.011 MB -> 50.906 MB.
[2024-11-28T00:43:52.546Z] RMSE (validation) = 3.62196895454876 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-28T00:44:08.959Z] RMSE (validation) = 2.1340923221100736 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-28T00:44:28.203Z] RMSE (validation) = 1.3105190428502682 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-28T00:44:42.255Z] RMSE (validation) = 0.9920028100571827 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-28T00:44:53.153Z] RMSE (validation) = 1.212631715629696 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-28T00:45:03.217Z] RMSE (validation) = 1.1025694034386657 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-28T00:45:15.871Z] RMSE (validation) = 0.9259652469249674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-28T00:45:25.670Z] RMSE (validation) = 0.8982232214592321 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-28T00:45:27.414Z] 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.
[2024-11-28T00:45:27.414Z] The best model improves the baseline by 14.52%.
[2024-11-28T00:45:28.738Z] Movies recommended for you:
[2024-11-28T00:45:28.738Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-28T00:45:28.738Z] There is no way to check that no silent failure occurred.
[2024-11-28T00:45:28.738Z] ====== movie-lens (apache-spark) [default], iteration 8 completed (113577.808 ms) ======
[2024-11-28T00:45:28.738Z] ====== movie-lens (apache-spark) [default], iteration 9 started ======
[2024-11-28T00:45:28.738Z] GC before operation: completed in 364.636 ms, heap usage 869.055 MB -> 55.274 MB.
[2024-11-28T00:45:48.156Z] RMSE (validation) = 3.62196895454876 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-28T00:45:58.159Z] RMSE (validation) = 2.1340923221100736 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-28T00:46:17.560Z] RMSE (validation) = 1.3105190428502682 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-28T00:46:31.032Z] RMSE (validation) = 0.9920028100571827 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-28T00:46:42.565Z] RMSE (validation) = 1.212631715629696 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-28T00:46:52.583Z] RMSE (validation) = 1.1025694034386657 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-28T00:47:04.310Z] RMSE (validation) = 0.9259652469249674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-28T00:47:12.962Z] RMSE (validation) = 0.8982232214592321 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-28T00:47:16.464Z] 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.
[2024-11-28T00:47:16.464Z] The best model improves the baseline by 14.52%.
[2024-11-28T00:47:16.464Z] Movies recommended for you:
[2024-11-28T00:47:16.464Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-28T00:47:16.464Z] There is no way to check that no silent failure occurred.
[2024-11-28T00:47:16.464Z] ====== movie-lens (apache-spark) [default], iteration 9 completed (108603.905 ms) ======
[2024-11-28T00:47:16.464Z] ====== movie-lens (apache-spark) [default], iteration 10 started ======
[2024-11-28T00:47:17.056Z] GC before operation: completed in 295.529 ms, heap usage 291.261 MB -> 50.997 MB.
[2024-11-28T00:47:36.808Z] RMSE (validation) = 3.62196895454876 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-28T00:47:53.123Z] RMSE (validation) = 2.1340923221100736 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-28T00:48:09.842Z] RMSE (validation) = 1.3105190428502682 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-28T00:48:24.176Z] RMSE (validation) = 0.9920028100571827 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-28T00:48:34.464Z] RMSE (validation) = 1.212631715629696 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-28T00:48:41.510Z] RMSE (validation) = 1.1025694034386657 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-28T00:48:50.180Z] RMSE (validation) = 0.9259652469249674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-28T00:48:57.981Z] RMSE (validation) = 0.8982232214592321 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-28T00:48:59.438Z] 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.
[2024-11-28T00:49:00.847Z] The best model improves the baseline by 14.52%.
[2024-11-28T00:49:01.420Z] Movies recommended for you:
[2024-11-28T00:49:01.420Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-28T00:49:01.420Z] There is no way to check that no silent failure occurred.
[2024-11-28T00:49:01.420Z] ====== movie-lens (apache-spark) [default], iteration 10 completed (104550.829 ms) ======
[2024-11-28T00:49:01.420Z] ====== movie-lens (apache-spark) [default], iteration 11 started ======
[2024-11-28T00:49:01.420Z] GC before operation: completed in 234.482 ms, heap usage 240.767 MB -> 50.833 MB.
[2024-11-28T00:49:20.761Z] RMSE (validation) = 3.62196895454876 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-28T00:49:35.106Z] RMSE (validation) = 2.1340923221100736 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-28T00:49:54.039Z] RMSE (validation) = 1.3105190428502682 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-28T00:50:10.888Z] RMSE (validation) = 0.9920028100571827 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-28T00:50:18.517Z] RMSE (validation) = 1.212631715629696 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-28T00:50:27.137Z] RMSE (validation) = 1.1025694034386657 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-28T00:50:39.271Z] RMSE (validation) = 0.9259652469249674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-28T00:50:47.500Z] RMSE (validation) = 0.8982232214592321 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-28T00:50:48.053Z] 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.
[2024-11-28T00:50:48.053Z] The best model improves the baseline by 14.52%.
[2024-11-28T00:50:48.535Z] Movies recommended for you:
[2024-11-28T00:50:48.535Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-28T00:50:48.535Z] There is no way to check that no silent failure occurred.
[2024-11-28T00:50:48.535Z] ====== movie-lens (apache-spark) [default], iteration 11 completed (107038.859 ms) ======
[2024-11-28T00:50:48.535Z] ====== movie-lens (apache-spark) [default], iteration 12 started ======
[2024-11-28T00:50:49.100Z] GC before operation: completed in 271.005 ms, heap usage 114.321 MB -> 52.063 MB.
[2024-11-28T00:51:08.263Z] RMSE (validation) = 3.62196895454876 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-28T00:51:24.377Z] RMSE (validation) = 2.1340923221100736 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-28T00:51:40.884Z] RMSE (validation) = 1.3105190428502682 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-28T00:51:54.483Z] RMSE (validation) = 0.9920028100571827 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-28T00:52:04.591Z] RMSE (validation) = 1.212631715629696 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-28T00:52:14.818Z] RMSE (validation) = 1.1025694034386657 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-28T00:52:24.551Z] RMSE (validation) = 0.9259652469249674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-28T00:52:35.707Z] RMSE (validation) = 0.8982232214592321 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-28T00:52:35.707Z] 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.
[2024-11-28T00:52:36.218Z] The best model improves the baseline by 14.52%.
[2024-11-28T00:52:37.465Z] Movies recommended for you:
[2024-11-28T00:52:37.465Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-28T00:52:37.465Z] There is no way to check that no silent failure occurred.
[2024-11-28T00:52:37.465Z] ====== movie-lens (apache-spark) [default], iteration 12 completed (108481.165 ms) ======
[2024-11-28T00:52:37.465Z] ====== movie-lens (apache-spark) [default], iteration 13 started ======
[2024-11-28T00:52:37.465Z] GC before operation: completed in 214.060 ms, heap usage 240.612 MB -> 51.082 MB.
[2024-11-28T00:52:51.549Z] RMSE (validation) = 3.62196895454876 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-28T00:53:11.232Z] RMSE (validation) = 2.1340923221100736 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-28T00:53:35.542Z] RMSE (validation) = 1.3105190428502682 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-28T00:53:49.801Z] RMSE (validation) = 0.9920028100571827 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-28T00:53:57.740Z] RMSE (validation) = 1.212631715629696 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-28T00:54:09.294Z] RMSE (validation) = 1.1025694034386657 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-28T00:54:19.301Z] RMSE (validation) = 0.9259652469249674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-28T00:54:27.715Z] RMSE (validation) = 0.8982232214592321 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-28T00:54:29.275Z] 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.
[2024-11-28T00:54:29.276Z] The best model improves the baseline by 14.52%.
[2024-11-28T00:54:30.647Z] Movies recommended for you:
[2024-11-28T00:54:30.647Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-28T00:54:30.647Z] There is no way to check that no silent failure occurred.
[2024-11-28T00:54:30.647Z] ====== movie-lens (apache-spark) [default], iteration 13 completed (113271.858 ms) ======
[2024-11-28T00:54:30.647Z] ====== movie-lens (apache-spark) [default], iteration 14 started ======
[2024-11-28T00:54:31.168Z] GC before operation: completed in 197.082 ms, heap usage 250.752 MB -> 50.927 MB.
[2024-11-28T00:54:47.686Z] RMSE (validation) = 3.62196895454876 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-28T00:55:04.210Z] RMSE (validation) = 2.1340923221100736 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-28T00:55:17.455Z] RMSE (validation) = 1.3105190428502682 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-28T00:55:34.701Z] RMSE (validation) = 0.9920028100571827 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-28T00:55:41.886Z] RMSE (validation) = 1.212631715629696 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-28T00:55:51.133Z] RMSE (validation) = 1.1025694034386657 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-28T00:55:59.636Z] RMSE (validation) = 0.9259652469249674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-28T00:56:09.413Z] RMSE (validation) = 0.8982232214592321 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-28T00:56:09.413Z] 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.
[2024-11-28T00:56:09.414Z] The best model improves the baseline by 14.52%.
[2024-11-28T00:56:09.414Z] Movies recommended for you:
[2024-11-28T00:56:09.414Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-28T00:56:09.414Z] There is no way to check that no silent failure occurred.
[2024-11-28T00:56:09.414Z] ====== movie-lens (apache-spark) [default], iteration 14 completed (98567.491 ms) ======
[2024-11-28T00:56:09.414Z] ====== movie-lens (apache-spark) [default], iteration 15 started ======
[2024-11-28T00:56:09.904Z] GC before operation: completed in 323.256 ms, heap usage 462.834 MB -> 54.459 MB.
[2024-11-28T00:56:26.049Z] RMSE (validation) = 3.62196895454876 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-28T00:56:37.713Z] RMSE (validation) = 2.1340923221100736 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-28T00:56:54.189Z] RMSE (validation) = 1.3105190428502682 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-28T00:57:05.891Z] RMSE (validation) = 0.9920028100571827 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-28T00:57:16.162Z] RMSE (validation) = 1.212631715629696 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-28T00:57:27.367Z] RMSE (validation) = 1.1025694034386657 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-28T00:57:37.246Z] RMSE (validation) = 0.9259652469249674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-28T00:57:47.440Z] RMSE (validation) = 0.8982232214592321 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-28T00:57:48.519Z] 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.
[2024-11-28T00:57:48.519Z] The best model improves the baseline by 14.52%.
[2024-11-28T00:57:49.060Z] Movies recommended for you:
[2024-11-28T00:57:49.060Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-28T00:57:49.060Z] There is no way to check that no silent failure occurred.
[2024-11-28T00:57:49.060Z] ====== movie-lens (apache-spark) [default], iteration 15 completed (99073.259 ms) ======
[2024-11-28T00:57:49.060Z] ====== movie-lens (apache-spark) [default], iteration 16 started ======
[2024-11-28T00:57:49.060Z] GC before operation: completed in 247.226 ms, heap usage 362.658 MB -> 54.830 MB.
[2024-11-28T00:58:09.006Z] RMSE (validation) = 3.62196895454876 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-28T00:58:22.830Z] RMSE (validation) = 2.1340923221100736 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-28T00:58:41.954Z] RMSE (validation) = 1.3105190428502682 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-28T00:58:58.497Z] RMSE (validation) = 0.9920028100571827 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-28T00:59:08.086Z] RMSE (validation) = 1.212631715629696 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-28T00:59:17.348Z] RMSE (validation) = 1.1025694034386657 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-28T00:59:32.225Z] RMSE (validation) = 0.9259652469249674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-28T00:59:43.197Z] RMSE (validation) = 0.8982232214592321 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-28T00:59:43.197Z] 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.
[2024-11-28T00:59:43.197Z] The best model improves the baseline by 14.52%.
[2024-11-28T00:59:43.197Z] Movies recommended for you:
[2024-11-28T00:59:43.197Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-28T00:59:43.197Z] There is no way to check that no silent failure occurred.
[2024-11-28T00:59:43.197Z] ====== movie-lens (apache-spark) [default], iteration 16 completed (114194.360 ms) ======
[2024-11-28T00:59:43.197Z] ====== movie-lens (apache-spark) [default], iteration 17 started ======
[2024-11-28T00:59:43.677Z] GC before operation: completed in 205.879 ms, heap usage 289.916 MB -> 50.951 MB.
[2024-11-28T01:00:00.949Z] RMSE (validation) = 3.62196895454876 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-28T01:00:17.377Z] RMSE (validation) = 2.1340923221100736 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-28T01:00:33.730Z] RMSE (validation) = 1.3105190428502682 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-28T01:00:50.215Z] RMSE (validation) = 0.9920028100571827 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-28T01:00:59.048Z] RMSE (validation) = 1.212631715629696 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-28T01:01:09.766Z] RMSE (validation) = 1.1025694034386657 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-28T01:01:19.560Z] RMSE (validation) = 0.9259652469249674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-28T01:01:26.406Z] RMSE (validation) = 0.8982232214592321 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-28T01:01:27.540Z] 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.
[2024-11-28T01:01:27.540Z] The best model improves the baseline by 14.52%.
[2024-11-28T01:01:28.101Z] Movies recommended for you:
[2024-11-28T01:01:28.101Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-28T01:01:28.101Z] There is no way to check that no silent failure occurred.
[2024-11-28T01:01:28.101Z] ====== movie-lens (apache-spark) [default], iteration 17 completed (104650.586 ms) ======
[2024-11-28T01:01:28.101Z] ====== movie-lens (apache-spark) [default], iteration 18 started ======
[2024-11-28T01:01:28.563Z] GC before operation: completed in 132.163 ms, heap usage 397.113 MB -> 54.384 MB.
[2024-11-28T01:01:48.110Z] RMSE (validation) = 3.6219689545487603 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-28T01:02:01.910Z] RMSE (validation) = 2.1340923221100736 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-28T01:02:15.996Z] RMSE (validation) = 1.3105190428502682 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-28T01:02:30.128Z] RMSE (validation) = 0.9920028100571827 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-28T01:02:38.705Z] RMSE (validation) = 1.212631715629696 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-28T01:02:50.304Z] RMSE (validation) = 1.1025694034386657 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-28T01:02:57.836Z] RMSE (validation) = 0.9259652469249674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-28T01:03:06.204Z] RMSE (validation) = 0.8982232214592321 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-28T01:03:07.842Z] 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.
[2024-11-28T01:03:07.842Z] The best model improves the baseline by 14.52%.
[2024-11-28T01:03:07.842Z] Movies recommended for you:
[2024-11-28T01:03:07.842Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-28T01:03:07.842Z] There is no way to check that no silent failure occurred.
[2024-11-28T01:03:07.842Z] ====== movie-lens (apache-spark) [default], iteration 18 completed (99317.681 ms) ======
[2024-11-28T01:03:07.842Z] ====== movie-lens (apache-spark) [default], iteration 19 started ======
[2024-11-28T01:03:07.842Z] GC before operation: completed in 167.593 ms, heap usage 287.124 MB -> 51.185 MB.
[2024-11-28T01:03:26.906Z] RMSE (validation) = 3.62196895454876 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-28T01:03:43.311Z] RMSE (validation) = 2.1340923221100736 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-28T01:04:00.769Z] RMSE (validation) = 1.3105190428502682 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-28T01:04:17.278Z] RMSE (validation) = 0.9920028100571827 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-28T01:04:27.050Z] RMSE (validation) = 1.212631715629696 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-28T01:04:35.539Z] RMSE (validation) = 1.1025694034386657 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-28T01:04:42.815Z] RMSE (validation) = 0.9259652469249674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-28T01:04:52.371Z] RMSE (validation) = 0.8982232214592321 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-28T01:04:53.481Z] 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.
[2024-11-28T01:04:53.481Z] The best model improves the baseline by 14.52%.
[2024-11-28T01:04:53.981Z] Movies recommended for you:
[2024-11-28T01:04:53.982Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-28T01:04:53.982Z] There is no way to check that no silent failure occurred.
[2024-11-28T01:04:53.982Z] ====== movie-lens (apache-spark) [default], iteration 19 completed (105949.372 ms) ======
[2024-11-28T01:04:59.366Z] -----------------------------------
[2024-11-28T01:04:59.366Z] renaissance-movie-lens_0_PASSED
[2024-11-28T01:04:59.366Z] -----------------------------------
[2024-11-28T01:04:59.366Z]
[2024-11-28T01:04:59.366Z] TEST TEARDOWN:
[2024-11-28T01:04:59.366Z] Nothing to be done for teardown.
[2024-11-28T01:04:59.366Z] renaissance-movie-lens_0 Finish Time: Wed Nov 27 17:04:58 2024 Epoch Time (ms): 1732755898556