renaissance-movie-lens_0
[2024-08-09T21:04:25.921Z] Running test renaissance-movie-lens_0 ...
[2024-08-09T21:04:25.921Z] ===============================================
[2024-08-09T21:04:25.921Z] renaissance-movie-lens_0 Start Time: Fri Aug 9 17:04:25 2024 Epoch Time (ms): 1723237465216
[2024-08-09T21:04:25.921Z] variation: NoOptions
[2024-08-09T21:04:25.921Z] JVM_OPTIONS:
[2024-08-09T21:04:25.921Z] { \
[2024-08-09T21:04:25.921Z] echo ""; echo "TEST SETUP:"; \
[2024-08-09T21:04:25.921Z] echo "Nothing to be done for setup."; \
[2024-08-09T21:04:25.921Z] mkdir -p "/Users/admin/workspace/workspace/Test_openjdk17_hs_extended.perf_aarch64_mac_testList_0/aqa-tests/TKG/../TKG/output_17232371846155/renaissance-movie-lens_0"; \
[2024-08-09T21:04:25.921Z] cd "/Users/admin/workspace/workspace/Test_openjdk17_hs_extended.perf_aarch64_mac_testList_0/aqa-tests/TKG/../TKG/output_17232371846155/renaissance-movie-lens_0"; \
[2024-08-09T21:04:25.921Z] echo ""; echo "TESTING:"; \
[2024-08-09T21:04:25.921Z] "/Users/admin/workspace/workspace/Test_openjdk17_hs_extended.perf_aarch64_mac_testList_0/jdkbinary/j2sdk-image/Contents/Home/bin/..//bin/java" --add-opens java.base/java.lang=ALL-UNNAMED --add-opens java.base/java.util=ALL-UNNAMED --add-opens java.base/java.util.concurrent=ALL-UNNAMED --add-opens java.base/java.nio=ALL-UNNAMED --add-opens java.base/sun.nio.ch=ALL-UNNAMED --add-opens java.base/java.lang.invoke=ALL-UNNAMED -jar "/Users/admin/workspace/workspace/Test_openjdk17_hs_extended.perf_aarch64_mac_testList_0/aqa-tests/TKG/../../jvmtest/perf/renaissance/renaissance.jar" --json ""/Users/admin/workspace/workspace/Test_openjdk17_hs_extended.perf_aarch64_mac_testList_0/aqa-tests/TKG/../TKG/output_17232371846155/renaissance-movie-lens_0"/movie-lens.json" movie-lens; \
[2024-08-09T21:04:25.921Z] if [ $? -eq 0 ]; then echo "-----------------------------------"; echo "renaissance-movie-lens_0""_PASSED"; echo "-----------------------------------"; cd /Users/admin/workspace/workspace/Test_openjdk17_hs_extended.perf_aarch64_mac_testList_0/aqa-tests/TKG/..; rm -f -r "/Users/admin/workspace/workspace/Test_openjdk17_hs_extended.perf_aarch64_mac_testList_0/aqa-tests/TKG/../TKG/output_17232371846155/renaissance-movie-lens_0"; else echo "-----------------------------------"; echo "renaissance-movie-lens_0""_FAILED"; echo "-----------------------------------"; fi; \
[2024-08-09T21:04:25.921Z] echo ""; echo "TEST TEARDOWN:"; \
[2024-08-09T21:04:25.921Z] echo "Nothing to be done for teardown."; \
[2024-08-09T21:04:25.921Z] } 2>&1 | tee -a "/Users/admin/workspace/workspace/Test_openjdk17_hs_extended.perf_aarch64_mac_testList_0/aqa-tests/TKG/../TKG/output_17232371846155/TestTargetResult";
[2024-08-09T21:04:25.921Z]
[2024-08-09T21:04:25.921Z] TEST SETUP:
[2024-08-09T21:04:25.921Z] Nothing to be done for setup.
[2024-08-09T21:04:25.921Z]
[2024-08-09T21:04:25.921Z] TESTING:
[2024-08-09T21:04:27.129Z] Using Spark's default log4j profile: org/apache/spark/log4j-defaults.properties
[2024-08-09T21:04:27.479Z] NOTE: 'movie-lens' benchmark uses Spark local executor with 3 (out of 3) threads.
[2024-08-09T21:04:28.686Z] Got 100004 ratings from 671 users on 9066 movies.
[2024-08-09T21:04:29.042Z] Training: 60056, validation: 20285, test: 19854
[2024-08-09T21:04:29.042Z] ====== movie-lens (apache-spark) [default], iteration 0 started ======
[2024-08-09T21:04:29.042Z] GC before operation: completed in 18.373 ms, heap usage 95.347 MB -> 36.899 MB.
[2024-08-09T21:04:31.437Z] RMSE (validation) = 3.6219689545487603 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-08-09T21:04:32.646Z] RMSE (validation) = 2.1340923221100736 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-08-09T21:04:34.414Z] RMSE (validation) = 1.3105190428502682 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-08-09T21:04:35.623Z] RMSE (validation) = 0.9920028100571827 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-08-09T21:04:36.378Z] RMSE (validation) = 1.212631715629696 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-08-09T21:04:37.126Z] RMSE (validation) = 1.1025694034386657 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-08-09T21:04:37.884Z] RMSE (validation) = 0.9259652469249674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-08-09T21:04:38.648Z] RMSE (validation) = 0.8982232214592321 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-08-09T21:04:39.106Z] 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.
[2024-08-09T21:04:39.106Z] The best model improves the baseline by 14.52%.
[2024-08-09T21:04:39.106Z] Movies recommended for you:
[2024-08-09T21:04:39.106Z] WARNING: This benchmark provides no result that can be validated.
[2024-08-09T21:04:39.106Z] There is no way to check that no silent failure occurred.
[2024-08-09T21:04:39.106Z] ====== movie-lens (apache-spark) [default], iteration 0 completed (10062.952 ms) ======
[2024-08-09T21:04:39.106Z] ====== movie-lens (apache-spark) [default], iteration 1 started ======
[2024-08-09T21:04:39.106Z] GC before operation: completed in 30.870 ms, heap usage 190.634 MB -> 52.108 MB.
[2024-08-09T21:04:40.325Z] RMSE (validation) = 3.62196895454876 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-08-09T21:04:41.079Z] RMSE (validation) = 2.1340923221100736 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-08-09T21:04:42.306Z] RMSE (validation) = 1.3105190428502682 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-08-09T21:04:43.532Z] RMSE (validation) = 0.9920028100571827 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-08-09T21:04:44.290Z] RMSE (validation) = 1.212631715629696 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-08-09T21:04:45.091Z] RMSE (validation) = 1.1025694034386657 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-08-09T21:04:45.455Z] RMSE (validation) = 0.9259652469249674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-08-09T21:04:46.239Z] RMSE (validation) = 0.8982232214592321 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-08-09T21:04:46.239Z] 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.
[2024-08-09T21:04:46.239Z] The best model improves the baseline by 14.52%.
[2024-08-09T21:04:46.598Z] Movies recommended for you:
[2024-08-09T21:04:46.598Z] WARNING: This benchmark provides no result that can be validated.
[2024-08-09T21:04:46.598Z] There is no way to check that no silent failure occurred.
[2024-08-09T21:04:46.598Z] ====== movie-lens (apache-spark) [default], iteration 1 completed (7358.897 ms) ======
[2024-08-09T21:04:46.598Z] ====== movie-lens (apache-spark) [default], iteration 2 started ======
[2024-08-09T21:04:46.598Z] GC before operation: completed in 26.763 ms, heap usage 148.365 MB -> 49.174 MB.
[2024-08-09T21:04:47.823Z] RMSE (validation) = 3.62196895454876 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-08-09T21:04:48.596Z] RMSE (validation) = 2.1340923221100736 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-08-09T21:04:49.816Z] RMSE (validation) = 1.3105190428502682 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-08-09T21:04:50.569Z] RMSE (validation) = 0.9920028100571827 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-08-09T21:04:51.333Z] RMSE (validation) = 1.212631715629696 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-08-09T21:04:52.089Z] RMSE (validation) = 1.1025694034386657 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-08-09T21:04:52.854Z] RMSE (validation) = 0.9259652469249674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-08-09T21:04:53.219Z] RMSE (validation) = 0.8982232214592321 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-08-09T21:04:53.219Z] 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.
[2024-08-09T21:04:53.219Z] The best model improves the baseline by 14.52%.
[2024-08-09T21:04:53.575Z] Movies recommended for you:
[2024-08-09T21:04:53.575Z] WARNING: This benchmark provides no result that can be validated.
[2024-08-09T21:04:53.575Z] There is no way to check that no silent failure occurred.
[2024-08-09T21:04:53.575Z] ====== movie-lens (apache-spark) [default], iteration 2 completed (6980.878 ms) ======
[2024-08-09T21:04:53.576Z] ====== movie-lens (apache-spark) [default], iteration 3 started ======
[2024-08-09T21:04:53.576Z] GC before operation: completed in 29.758 ms, heap usage 68.525 MB -> 49.319 MB.
[2024-08-09T21:04:54.330Z] RMSE (validation) = 3.62196895454876 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-08-09T21:04:55.573Z] RMSE (validation) = 2.1340923221100736 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-08-09T21:04:56.794Z] RMSE (validation) = 1.3105190428502682 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-08-09T21:04:57.556Z] RMSE (validation) = 0.9920028100571827 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-08-09T21:04:58.335Z] RMSE (validation) = 1.212631715629696 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-08-09T21:04:58.685Z] RMSE (validation) = 1.1025694034386657 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-08-09T21:04:59.522Z] RMSE (validation) = 0.9259652469249674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-08-09T21:05:00.293Z] RMSE (validation) = 0.8982232214592321 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-08-09T21:05:00.293Z] 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.
[2024-08-09T21:05:00.293Z] The best model improves the baseline by 14.52%.
[2024-08-09T21:05:00.293Z] Movies recommended for you:
[2024-08-09T21:05:00.293Z] WARNING: This benchmark provides no result that can be validated.
[2024-08-09T21:05:00.293Z] There is no way to check that no silent failure occurred.
[2024-08-09T21:05:00.293Z] ====== movie-lens (apache-spark) [default], iteration 3 completed (6819.157 ms) ======
[2024-08-09T21:05:00.293Z] ====== movie-lens (apache-spark) [default], iteration 4 started ======
[2024-08-09T21:05:00.293Z] GC before operation: completed in 29.455 ms, heap usage 217.784 MB -> 49.816 MB.
[2024-08-09T21:05:01.544Z] RMSE (validation) = 3.62196895454876 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-08-09T21:05:02.305Z] RMSE (validation) = 2.1340923221100736 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-08-09T21:05:03.534Z] RMSE (validation) = 1.3105190428502682 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-08-09T21:05:04.779Z] RMSE (validation) = 0.9920028100571827 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-08-09T21:05:05.129Z] RMSE (validation) = 1.212631715629696 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-08-09T21:05:05.894Z] RMSE (validation) = 1.1025694034386657 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-08-09T21:05:06.252Z] RMSE (validation) = 0.9259652469249674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-08-09T21:05:07.010Z] RMSE (validation) = 0.8982232214592321 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-08-09T21:05:07.010Z] 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.
[2024-08-09T21:05:07.010Z] The best model improves the baseline by 14.52%.
[2024-08-09T21:05:07.361Z] Movies recommended for you:
[2024-08-09T21:05:07.361Z] WARNING: This benchmark provides no result that can be validated.
[2024-08-09T21:05:07.361Z] There is no way to check that no silent failure occurred.
[2024-08-09T21:05:07.361Z] ====== movie-lens (apache-spark) [default], iteration 4 completed (6897.853 ms) ======
[2024-08-09T21:05:07.361Z] ====== movie-lens (apache-spark) [default], iteration 5 started ======
[2024-08-09T21:05:07.361Z] GC before operation: completed in 28.681 ms, heap usage 199.808 MB -> 50.035 MB.
[2024-08-09T21:05:08.110Z] RMSE (validation) = 3.62196895454876 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-08-09T21:05:09.334Z] RMSE (validation) = 2.1340923221100736 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-08-09T21:05:10.544Z] RMSE (validation) = 1.3105190428502682 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-08-09T21:05:11.298Z] RMSE (validation) = 0.9920028100571827 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-08-09T21:05:12.067Z] RMSE (validation) = 1.212631715629696 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-08-09T21:05:12.417Z] RMSE (validation) = 1.1025694034386657 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-08-09T21:05:13.190Z] RMSE (validation) = 0.9259652469249674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-08-09T21:05:13.542Z] RMSE (validation) = 0.8982232214592321 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-08-09T21:05:13.896Z] 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.
[2024-08-09T21:05:13.896Z] The best model improves the baseline by 14.52%.
[2024-08-09T21:05:13.896Z] Movies recommended for you:
[2024-08-09T21:05:13.896Z] WARNING: This benchmark provides no result that can be validated.
[2024-08-09T21:05:13.896Z] There is no way to check that no silent failure occurred.
[2024-08-09T21:05:13.896Z] ====== movie-lens (apache-spark) [default], iteration 5 completed (6585.761 ms) ======
[2024-08-09T21:05:13.896Z] ====== movie-lens (apache-spark) [default], iteration 6 started ======
[2024-08-09T21:05:13.896Z] GC before operation: completed in 28.959 ms, heap usage 176.692 MB -> 49.955 MB.
[2024-08-09T21:05:15.105Z] RMSE (validation) = 3.62196895454876 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-08-09T21:05:15.863Z] RMSE (validation) = 2.1340923221100736 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-08-09T21:05:16.615Z] RMSE (validation) = 1.3105190428502682 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-08-09T21:05:17.896Z] RMSE (validation) = 0.9920028100571827 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-08-09T21:05:18.249Z] RMSE (validation) = 1.212631715629696 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-08-09T21:05:19.014Z] RMSE (validation) = 1.1025694034386657 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-08-09T21:05:19.362Z] RMSE (validation) = 0.9259652469249674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-08-09T21:05:20.117Z] RMSE (validation) = 0.8982232214592321 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-08-09T21:05:20.117Z] 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.
[2024-08-09T21:05:20.117Z] The best model improves the baseline by 14.52%.
[2024-08-09T21:05:20.117Z] Movies recommended for you:
[2024-08-09T21:05:20.117Z] WARNING: This benchmark provides no result that can be validated.
[2024-08-09T21:05:20.117Z] There is no way to check that no silent failure occurred.
[2024-08-09T21:05:20.117Z] ====== movie-lens (apache-spark) [default], iteration 6 completed (6399.617 ms) ======
[2024-08-09T21:05:20.117Z] ====== movie-lens (apache-spark) [default], iteration 7 started ======
[2024-08-09T21:05:20.117Z] GC before operation: completed in 29.225 ms, heap usage 210.087 MB -> 50.135 MB.
[2024-08-09T21:05:21.331Z] RMSE (validation) = 3.62196895454876 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-08-09T21:05:22.555Z] RMSE (validation) = 2.1340923221100736 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-08-09T21:05:23.777Z] RMSE (validation) = 1.3105190428502682 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-08-09T21:05:24.538Z] RMSE (validation) = 0.9920028100571827 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-08-09T21:05:25.304Z] RMSE (validation) = 1.212631715629696 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-08-09T21:05:26.054Z] RMSE (validation) = 1.1025694034386657 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-08-09T21:05:26.806Z] RMSE (validation) = 0.9259652469249674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-08-09T21:05:27.571Z] RMSE (validation) = 0.8982232214592321 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-08-09T21:05:27.571Z] 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.
[2024-08-09T21:05:27.571Z] The best model improves the baseline by 14.52%.
[2024-08-09T21:05:27.571Z] Movies recommended for you:
[2024-08-09T21:05:27.571Z] WARNING: This benchmark provides no result that can be validated.
[2024-08-09T21:05:27.571Z] There is no way to check that no silent failure occurred.
[2024-08-09T21:05:27.571Z] ====== movie-lens (apache-spark) [default], iteration 7 completed (7213.002 ms) ======
[2024-08-09T21:05:27.571Z] ====== movie-lens (apache-spark) [default], iteration 8 started ======
[2024-08-09T21:05:27.571Z] GC before operation: completed in 28.852 ms, heap usage 267.092 MB -> 50.542 MB.
[2024-08-09T21:05:28.779Z] RMSE (validation) = 3.62196895454876 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-08-09T21:05:29.532Z] RMSE (validation) = 2.1340923221100736 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-08-09T21:05:30.763Z] RMSE (validation) = 1.3105190428502682 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-08-09T21:05:31.522Z] RMSE (validation) = 0.9920028100571827 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-08-09T21:05:32.282Z] RMSE (validation) = 1.212631715629696 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-08-09T21:05:33.041Z] RMSE (validation) = 1.1025694034386657 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-08-09T21:05:33.397Z] RMSE (validation) = 0.9259652469249674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-08-09T21:05:34.158Z] RMSE (validation) = 0.8982232214592321 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-08-09T21:05:34.158Z] 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.
[2024-08-09T21:05:34.158Z] The best model improves the baseline by 14.52%.
[2024-08-09T21:05:34.158Z] Movies recommended for you:
[2024-08-09T21:05:34.158Z] WARNING: This benchmark provides no result that can be validated.
[2024-08-09T21:05:34.158Z] There is no way to check that no silent failure occurred.
[2024-08-09T21:05:34.158Z] ====== movie-lens (apache-spark) [default], iteration 8 completed (6769.828 ms) ======
[2024-08-09T21:05:34.158Z] ====== movie-lens (apache-spark) [default], iteration 9 started ======
[2024-08-09T21:05:34.158Z] GC before operation: completed in 29.179 ms, heap usage 202.242 MB -> 50.299 MB.
[2024-08-09T21:05:35.375Z] RMSE (validation) = 3.62196895454876 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-08-09T21:05:36.604Z] RMSE (validation) = 2.1340923221100736 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-08-09T21:05:37.392Z] RMSE (validation) = 1.3105190428502682 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-08-09T21:05:38.610Z] RMSE (validation) = 0.9920028100571827 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-08-09T21:05:38.963Z] RMSE (validation) = 1.212631715629696 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-08-09T21:05:39.747Z] RMSE (validation) = 1.1025694034386657 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-08-09T21:05:40.504Z] RMSE (validation) = 0.9259652469249674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-08-09T21:05:40.879Z] RMSE (validation) = 0.8982232214592321 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-08-09T21:05:41.234Z] 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.
[2024-08-09T21:05:41.234Z] The best model improves the baseline by 14.52%.
[2024-08-09T21:05:41.234Z] Movies recommended for you:
[2024-08-09T21:05:41.234Z] WARNING: This benchmark provides no result that can be validated.
[2024-08-09T21:05:41.234Z] There is no way to check that no silent failure occurred.
[2024-08-09T21:05:41.234Z] ====== movie-lens (apache-spark) [default], iteration 9 completed (6805.315 ms) ======
[2024-08-09T21:05:41.234Z] ====== movie-lens (apache-spark) [default], iteration 10 started ======
[2024-08-09T21:05:41.234Z] GC before operation: completed in 29.965 ms, heap usage 200.755 MB -> 50.319 MB.
[2024-08-09T21:05:41.990Z] RMSE (validation) = 3.6219689545487603 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-08-09T21:05:43.219Z] RMSE (validation) = 2.1340923221100736 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-08-09T21:05:43.992Z] RMSE (validation) = 1.3105190428502682 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-08-09T21:05:45.223Z] RMSE (validation) = 0.9920028100571827 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-08-09T21:05:45.574Z] RMSE (validation) = 1.212631715629696 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-08-09T21:05:46.341Z] RMSE (validation) = 1.1025694034386657 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-08-09T21:05:46.711Z] RMSE (validation) = 0.9259652469249674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-08-09T21:05:47.484Z] RMSE (validation) = 0.8982232214592321 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-08-09T21:05:47.484Z] 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.
[2024-08-09T21:05:47.484Z] The best model improves the baseline by 14.52%.
[2024-08-09T21:05:47.484Z] Movies recommended for you:
[2024-08-09T21:05:47.484Z] WARNING: This benchmark provides no result that can be validated.
[2024-08-09T21:05:47.484Z] There is no way to check that no silent failure occurred.
[2024-08-09T21:05:47.484Z] ====== movie-lens (apache-spark) [default], iteration 10 completed (6276.218 ms) ======
[2024-08-09T21:05:47.484Z] ====== movie-lens (apache-spark) [default], iteration 11 started ======
[2024-08-09T21:05:47.484Z] GC before operation: completed in 27.922 ms, heap usage 61.783 MB -> 50.040 MB.
[2024-08-09T21:05:48.254Z] RMSE (validation) = 3.62196895454876 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-08-09T21:05:49.473Z] RMSE (validation) = 2.1340923221100736 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-08-09T21:05:50.688Z] RMSE (validation) = 1.3105190428502682 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-08-09T21:05:51.443Z] RMSE (validation) = 0.9920028100571827 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-08-09T21:05:52.231Z] RMSE (validation) = 1.212631715629696 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-08-09T21:05:52.580Z] RMSE (validation) = 1.1025694034386657 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-08-09T21:05:53.335Z] RMSE (validation) = 0.9259652469249674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-08-09T21:05:53.696Z] RMSE (validation) = 0.8982232214592321 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-08-09T21:05:54.048Z] 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.
[2024-08-09T21:05:54.048Z] The best model improves the baseline by 14.52%.
[2024-08-09T21:05:54.048Z] Movies recommended for you:
[2024-08-09T21:05:54.048Z] WARNING: This benchmark provides no result that can be validated.
[2024-08-09T21:05:54.048Z] There is no way to check that no silent failure occurred.
[2024-08-09T21:05:54.048Z] ====== movie-lens (apache-spark) [default], iteration 11 completed (6482.583 ms) ======
[2024-08-09T21:05:54.048Z] ====== movie-lens (apache-spark) [default], iteration 12 started ======
[2024-08-09T21:05:54.048Z] GC before operation: completed in 32.836 ms, heap usage 361.081 MB -> 50.468 MB.
[2024-08-09T21:05:55.281Z] RMSE (validation) = 3.62196895454876 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-08-09T21:05:56.503Z] RMSE (validation) = 2.1340923221100736 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-08-09T21:05:57.728Z] RMSE (validation) = 1.3105190428502682 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-08-09T21:05:59.030Z] RMSE (validation) = 0.9920028100571827 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-08-09T21:05:59.378Z] RMSE (validation) = 1.212631715629696 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-08-09T21:06:00.154Z] RMSE (validation) = 1.1025694034386657 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-08-09T21:06:00.510Z] RMSE (validation) = 0.9259652469249674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-08-09T21:06:01.298Z] RMSE (validation) = 0.8982232214592321 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-08-09T21:06:01.298Z] 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.
[2024-08-09T21:06:01.299Z] The best model improves the baseline by 14.52%.
[2024-08-09T21:06:01.299Z] Movies recommended for you:
[2024-08-09T21:06:01.299Z] WARNING: This benchmark provides no result that can be validated.
[2024-08-09T21:06:01.299Z] There is no way to check that no silent failure occurred.
[2024-08-09T21:06:01.299Z] ====== movie-lens (apache-spark) [default], iteration 12 completed (7278.491 ms) ======
[2024-08-09T21:06:01.299Z] ====== movie-lens (apache-spark) [default], iteration 13 started ======
[2024-08-09T21:06:01.299Z] GC before operation: completed in 29.302 ms, heap usage 201.037 MB -> 50.394 MB.
[2024-08-09T21:06:02.072Z] RMSE (validation) = 3.62196895454876 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-08-09T21:06:03.310Z] RMSE (validation) = 2.1340923221100736 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-08-09T21:06:04.542Z] RMSE (validation) = 1.3105190428502682 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-08-09T21:06:05.297Z] RMSE (validation) = 0.9920028100571827 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-08-09T21:06:05.650Z] RMSE (validation) = 1.212631715629696 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-08-09T21:06:06.414Z] RMSE (validation) = 1.1025694034386657 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-08-09T21:06:07.182Z] RMSE (validation) = 0.9259652469249674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-08-09T21:06:07.943Z] RMSE (validation) = 0.8982232214592321 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-08-09T21:06:07.943Z] 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.
[2024-08-09T21:06:07.943Z] The best model improves the baseline by 14.52%.
[2024-08-09T21:06:08.300Z] Movies recommended for you:
[2024-08-09T21:06:08.300Z] WARNING: This benchmark provides no result that can be validated.
[2024-08-09T21:06:08.300Z] There is no way to check that no silent failure occurred.
[2024-08-09T21:06:08.300Z] ====== movie-lens (apache-spark) [default], iteration 13 completed (6849.993 ms) ======
[2024-08-09T21:06:08.300Z] ====== movie-lens (apache-spark) [default], iteration 14 started ======
[2024-08-09T21:06:08.300Z] GC before operation: completed in 29.240 ms, heap usage 170.010 MB -> 50.314 MB.
[2024-08-09T21:06:09.053Z] RMSE (validation) = 3.62196895454876 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-08-09T21:06:10.286Z] RMSE (validation) = 2.1340923221100736 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-08-09T21:06:11.050Z] RMSE (validation) = 1.3105190428502682 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-08-09T21:06:12.291Z] RMSE (validation) = 0.9920028100571827 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-08-09T21:06:12.643Z] RMSE (validation) = 1.212631715629696 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-08-09T21:06:13.416Z] RMSE (validation) = 1.1025694034386657 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-08-09T21:06:13.769Z] RMSE (validation) = 0.9259652469249674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-08-09T21:06:14.520Z] RMSE (validation) = 0.8982232214592321 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-08-09T21:06:14.521Z] 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.
[2024-08-09T21:06:14.521Z] The best model improves the baseline by 14.52%.
[2024-08-09T21:06:14.521Z] Movies recommended for you:
[2024-08-09T21:06:14.521Z] WARNING: This benchmark provides no result that can be validated.
[2024-08-09T21:06:14.521Z] There is no way to check that no silent failure occurred.
[2024-08-09T21:06:14.521Z] ====== movie-lens (apache-spark) [default], iteration 14 completed (6399.278 ms) ======
[2024-08-09T21:06:14.521Z] ====== movie-lens (apache-spark) [default], iteration 15 started ======
[2024-08-09T21:06:14.521Z] GC before operation: completed in 30.258 ms, heap usage 193.404 MB -> 50.417 MB.
[2024-08-09T21:06:15.750Z] RMSE (validation) = 3.62196895454876 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-08-09T21:06:16.505Z] RMSE (validation) = 2.1340923221100736 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-08-09T21:06:17.736Z] RMSE (validation) = 1.3105190428502682 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-08-09T21:06:18.489Z] RMSE (validation) = 0.9920028100571827 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-08-09T21:06:19.246Z] RMSE (validation) = 1.212631715629696 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-08-09T21:06:19.597Z] RMSE (validation) = 1.1025694034386657 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-08-09T21:06:20.371Z] RMSE (validation) = 0.9259652469249674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-08-09T21:06:20.732Z] RMSE (validation) = 0.8982232214592321 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-08-09T21:06:20.732Z] 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.
[2024-08-09T21:06:20.732Z] The best model improves the baseline by 14.52%.
[2024-08-09T21:06:20.732Z] Movies recommended for you:
[2024-08-09T21:06:20.732Z] WARNING: This benchmark provides no result that can be validated.
[2024-08-09T21:06:20.732Z] There is no way to check that no silent failure occurred.
[2024-08-09T21:06:20.732Z] ====== movie-lens (apache-spark) [default], iteration 15 completed (6257.371 ms) ======
[2024-08-09T21:06:20.732Z] ====== movie-lens (apache-spark) [default], iteration 16 started ======
[2024-08-09T21:06:20.732Z] GC before operation: completed in 28.671 ms, heap usage 166.013 MB -> 50.442 MB.
[2024-08-09T21:06:21.492Z] RMSE (validation) = 3.6219689545487603 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-08-09T21:06:22.730Z] RMSE (validation) = 2.1340923221100736 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-08-09T21:06:23.499Z] RMSE (validation) = 1.3105190428502682 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-08-09T21:06:24.257Z] RMSE (validation) = 0.9920028100571827 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-08-09T21:06:25.026Z] RMSE (validation) = 1.212631715629696 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-08-09T21:06:25.384Z] RMSE (validation) = 1.1025694034386657 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-08-09T21:06:25.749Z] RMSE (validation) = 0.9259652469249674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-08-09T21:06:26.513Z] RMSE (validation) = 0.8982232214592321 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-08-09T21:06:26.513Z] 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.
[2024-08-09T21:06:26.513Z] The best model improves the baseline by 14.52%.
[2024-08-09T21:06:26.513Z] Movies recommended for you:
[2024-08-09T21:06:26.513Z] WARNING: This benchmark provides no result that can be validated.
[2024-08-09T21:06:26.513Z] There is no way to check that no silent failure occurred.
[2024-08-09T21:06:26.513Z] ====== movie-lens (apache-spark) [default], iteration 16 completed (5588.911 ms) ======
[2024-08-09T21:06:26.513Z] ====== movie-lens (apache-spark) [default], iteration 17 started ======
[2024-08-09T21:06:26.513Z] GC before operation: completed in 29.914 ms, heap usage 130.128 MB -> 50.316 MB.
[2024-08-09T21:06:27.266Z] RMSE (validation) = 3.62196895454876 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-08-09T21:06:28.483Z] RMSE (validation) = 2.1340923221100736 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-08-09T21:06:29.229Z] RMSE (validation) = 1.3105190428502682 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-08-09T21:06:30.452Z] RMSE (validation) = 0.9920028100571827 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-08-09T21:06:30.804Z] RMSE (validation) = 1.212631715629696 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-08-09T21:06:31.560Z] RMSE (validation) = 1.1025694034386657 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-08-09T21:06:32.005Z] RMSE (validation) = 0.9259652469249674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-08-09T21:06:32.789Z] RMSE (validation) = 0.8982232214592321 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-08-09T21:06:32.789Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9063003101263983.
[2024-08-09T21:06:32.789Z] The best model improves the baseline by 14.52%.
[2024-08-09T21:06:32.789Z] Movies recommended for you:
[2024-08-09T21:06:32.789Z] WARNING: This benchmark provides no result that can be validated.
[2024-08-09T21:06:32.789Z] There is no way to check that no silent failure occurred.
[2024-08-09T21:06:32.789Z] ====== movie-lens (apache-spark) [default], iteration 17 completed (6243.793 ms) ======
[2024-08-09T21:06:32.789Z] ====== movie-lens (apache-spark) [default], iteration 18 started ======
[2024-08-09T21:06:32.789Z] GC before operation: completed in 32.470 ms, heap usage 316.443 MB -> 50.494 MB.
[2024-08-09T21:06:34.010Z] RMSE (validation) = 3.6219689545487603 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-08-09T21:06:34.764Z] RMSE (validation) = 2.1340923221100736 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-08-09T21:06:35.997Z] RMSE (validation) = 1.3105190428502682 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-08-09T21:06:36.753Z] RMSE (validation) = 0.9920028100571827 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-08-09T21:06:37.522Z] RMSE (validation) = 1.212631715629696 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-08-09T21:06:37.882Z] RMSE (validation) = 1.1025694034386657 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-08-09T21:06:38.678Z] RMSE (validation) = 0.9259652469249674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-08-09T21:06:39.440Z] RMSE (validation) = 0.8982232214592321 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-08-09T21:06:39.440Z] 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.
[2024-08-09T21:06:39.440Z] The best model improves the baseline by 14.52%.
[2024-08-09T21:06:39.440Z] Movies recommended for you:
[2024-08-09T21:06:39.440Z] WARNING: This benchmark provides no result that can be validated.
[2024-08-09T21:06:39.440Z] There is no way to check that no silent failure occurred.
[2024-08-09T21:06:39.440Z] ====== movie-lens (apache-spark) [default], iteration 18 completed (6649.808 ms) ======
[2024-08-09T21:06:39.440Z] ====== movie-lens (apache-spark) [default], iteration 19 started ======
[2024-08-09T21:06:39.440Z] GC before operation: completed in 29.732 ms, heap usage 166.488 MB -> 50.672 MB.
[2024-08-09T21:06:40.664Z] RMSE (validation) = 3.62196895454876 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-08-09T21:06:41.426Z] RMSE (validation) = 2.1340923221100736 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-08-09T21:06:42.663Z] RMSE (validation) = 1.3105190428502682 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-08-09T21:06:43.420Z] RMSE (validation) = 0.9920028100571827 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-08-09T21:06:44.184Z] RMSE (validation) = 1.212631715629696 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-08-09T21:06:44.541Z] RMSE (validation) = 1.1025694034386657 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-08-09T21:06:45.300Z] RMSE (validation) = 0.9259652469249674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-08-09T21:06:46.054Z] RMSE (validation) = 0.8982232214592321 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-08-09T21:06:46.054Z] 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.
[2024-08-09T21:06:46.054Z] The best model improves the baseline by 14.52%.
[2024-08-09T21:06:46.054Z] Movies recommended for you:
[2024-08-09T21:06:46.054Z] WARNING: This benchmark provides no result that can be validated.
[2024-08-09T21:06:46.054Z] There is no way to check that no silent failure occurred.
[2024-08-09T21:06:46.054Z] ====== movie-lens (apache-spark) [default], iteration 19 completed (6597.076 ms) ======
[2024-08-09T21:06:46.405Z] -----------------------------------
[2024-08-09T21:06:46.405Z] renaissance-movie-lens_0_PASSED
[2024-08-09T21:06:46.405Z] -----------------------------------
[2024-08-09T21:06:46.405Z]
[2024-08-09T21:06:46.405Z] TEST TEARDOWN:
[2024-08-09T21:06:46.405Z] Nothing to be done for teardown.
[2024-08-09T21:06:46.405Z] renaissance-movie-lens_0 Finish Time: Fri Aug 9 17:06:45 2024 Epoch Time (ms): 1723237605931