renaissance-movie-lens_0
[2025-03-05T23:27:28.577Z] Running test renaissance-movie-lens_0 ...
[2025-03-05T23:27:28.577Z] ===============================================
[2025-03-05T23:27:28.577Z] renaissance-movie-lens_0 Start Time: Wed Mar 5 15:27:28 2025 Epoch Time (ms): 1741217248068
[2025-03-05T23:27:28.577Z] variation: NoOptions
[2025-03-05T23:27:28.577Z] JVM_OPTIONS:
[2025-03-05T23:27:28.577Z] { \
[2025-03-05T23:27:28.577Z] echo ""; echo "TEST SETUP:"; \
[2025-03-05T23:27:28.577Z] echo "Nothing to be done for setup."; \
[2025-03-05T23:27:28.577Z] mkdir -p "/Users/admin/workspace/workspace/Test_openjdk11_hs_extended.perf_x86-64_mac_testList_1/aqa-tests/TKG/../TKG/output_17412147256252/renaissance-movie-lens_0"; \
[2025-03-05T23:27:28.577Z] cd "/Users/admin/workspace/workspace/Test_openjdk11_hs_extended.perf_x86-64_mac_testList_1/aqa-tests/TKG/../TKG/output_17412147256252/renaissance-movie-lens_0"; \
[2025-03-05T23:27:28.577Z] echo ""; echo "TESTING:"; \
[2025-03-05T23:27:28.577Z] "/Users/admin/workspace/workspace/Test_openjdk11_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_openjdk11_hs_extended.perf_x86-64_mac_testList_1/aqa-tests/TKG/../../jvmtest/perf/renaissance/renaissance.jar" --json ""/Users/admin/workspace/workspace/Test_openjdk11_hs_extended.perf_x86-64_mac_testList_1/aqa-tests/TKG/../TKG/output_17412147256252/renaissance-movie-lens_0"/movie-lens.json" movie-lens; \
[2025-03-05T23:27:28.577Z] if [ $? -eq 0 ]; then echo "-----------------------------------"; echo "renaissance-movie-lens_0""_PASSED"; echo "-----------------------------------"; cd /Users/admin/workspace/workspace/Test_openjdk11_hs_extended.perf_x86-64_mac_testList_1/aqa-tests/TKG/..; rm -f -r "/Users/admin/workspace/workspace/Test_openjdk11_hs_extended.perf_x86-64_mac_testList_1/aqa-tests/TKG/../TKG/output_17412147256252/renaissance-movie-lens_0"; else echo "-----------------------------------"; echo "renaissance-movie-lens_0""_FAILED"; echo "-----------------------------------"; fi; \
[2025-03-05T23:27:28.577Z] echo ""; echo "TEST TEARDOWN:"; \
[2025-03-05T23:27:28.577Z] echo "Nothing to be done for teardown."; \
[2025-03-05T23:27:28.577Z] } 2>&1 | tee -a "/Users/admin/workspace/workspace/Test_openjdk11_hs_extended.perf_x86-64_mac_testList_1/aqa-tests/TKG/../TKG/output_17412147256252/TestTargetResult";
[2025-03-05T23:27:28.577Z]
[2025-03-05T23:27:28.577Z] TEST SETUP:
[2025-03-05T23:27:28.577Z] Nothing to be done for setup.
[2025-03-05T23:27:28.577Z]
[2025-03-05T23:27:28.577Z] TESTING:
[2025-03-05T23:27:35.694Z] Using Spark's default log4j profile: org/apache/spark/log4j-defaults.properties
[2025-03-05T23:27:40.201Z] NOTE: 'movie-lens' benchmark uses Spark local executor with 3 (out of 3) threads.
[2025-03-05T23:27:49.153Z] Got 100004 ratings from 671 users on 9066 movies.
[2025-03-05T23:27:49.743Z] Training: 60056, validation: 20285, test: 19854
[2025-03-05T23:27:49.743Z] ====== movie-lens (apache-spark) [default], iteration 0 started ======
[2025-03-05T23:27:50.366Z] GC before operation: completed in 873.809 ms, heap usage 122.347 MB -> 36.579 MB.
[2025-03-05T23:28:12.349Z] RMSE (validation) = 3.62196895454876 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2025-03-05T23:28:27.948Z] RMSE (validation) = 2.1340923221100736 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2025-03-05T23:28:38.420Z] RMSE (validation) = 1.3105190428502682 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2025-03-05T23:28:49.005Z] RMSE (validation) = 0.9920028100571827 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2025-03-05T23:28:53.915Z] RMSE (validation) = 1.212631715629696 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2025-03-05T23:29:01.224Z] RMSE (validation) = 1.1025694034386657 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2025-03-05T23:29:06.268Z] RMSE (validation) = 0.9259652469249674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2025-03-05T23:29:12.377Z] RMSE (validation) = 0.8982232214592321 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2025-03-05T23:29:13.994Z] 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.
[2025-03-05T23:29:14.434Z] The best model improves the baseline by 14.52%.
[2025-03-05T23:29:14.434Z] Movies recommended for you:
[2025-03-05T23:29:14.434Z] WARNING: This benchmark provides no result that can be validated.
[2025-03-05T23:29:14.434Z] There is no way to check that no silent failure occurred.
[2025-03-05T23:29:14.434Z] ====== movie-lens (apache-spark) [default], iteration 0 completed (84097.475 ms) ======
[2025-03-05T23:29:14.434Z] ====== movie-lens (apache-spark) [default], iteration 1 started ======
[2025-03-05T23:29:14.858Z] GC before operation: completed in 154.312 ms, heap usage 219.916 MB -> 46.890 MB.
[2025-03-05T23:29:25.533Z] RMSE (validation) = 3.62196895454876 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2025-03-05T23:29:37.749Z] RMSE (validation) = 2.1340923221100736 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2025-03-05T23:29:46.295Z] RMSE (validation) = 1.3105190428502682 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2025-03-05T23:29:56.259Z] RMSE (validation) = 0.9920028100571827 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2025-03-05T23:30:01.079Z] RMSE (validation) = 1.212631715629696 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2025-03-05T23:30:07.047Z] RMSE (validation) = 1.1025694034386657 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2025-03-05T23:30:13.060Z] RMSE (validation) = 0.9259652469249674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2025-03-05T23:30:20.003Z] RMSE (validation) = 0.8982232214592321 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2025-03-05T23:30:20.415Z] 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.
[2025-03-05T23:30:20.415Z] The best model improves the baseline by 14.52%.
[2025-03-05T23:30:20.811Z] Movies recommended for you:
[2025-03-05T23:30:20.811Z] WARNING: This benchmark provides no result that can be validated.
[2025-03-05T23:30:20.811Z] There is no way to check that no silent failure occurred.
[2025-03-05T23:30:20.811Z] ====== movie-lens (apache-spark) [default], iteration 1 completed (66060.553 ms) ======
[2025-03-05T23:30:20.811Z] ====== movie-lens (apache-spark) [default], iteration 2 started ======
[2025-03-05T23:30:20.811Z] GC before operation: completed in 157.865 ms, heap usage 244.403 MB -> 48.936 MB.
[2025-03-05T23:30:32.939Z] RMSE (validation) = 3.62196895454876 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2025-03-05T23:30:43.434Z] RMSE (validation) = 2.1340923221100736 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2025-03-05T23:30:55.971Z] RMSE (validation) = 1.3105190428502682 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2025-03-05T23:31:04.428Z] RMSE (validation) = 0.9920028100571827 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2025-03-05T23:31:08.147Z] RMSE (validation) = 1.212631715629696 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2025-03-05T23:31:12.612Z] RMSE (validation) = 1.1025694034386657 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2025-03-05T23:31:18.486Z] RMSE (validation) = 0.9259652469249674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2025-03-05T23:31:25.373Z] RMSE (validation) = 0.8982232214592321 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2025-03-05T23:31:28.248Z] 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.
[2025-03-05T23:31:28.248Z] The best model improves the baseline by 14.52%.
[2025-03-05T23:31:28.672Z] Movies recommended for you:
[2025-03-05T23:31:28.672Z] WARNING: This benchmark provides no result that can be validated.
[2025-03-05T23:31:28.672Z] There is no way to check that no silent failure occurred.
[2025-03-05T23:31:28.672Z] ====== movie-lens (apache-spark) [default], iteration 2 completed (67942.706 ms) ======
[2025-03-05T23:31:28.672Z] ====== movie-lens (apache-spark) [default], iteration 3 started ======
[2025-03-05T23:31:29.724Z] GC before operation: completed in 777.910 ms, heap usage 367.352 MB -> 52.579 MB.
[2025-03-05T23:31:44.924Z] RMSE (validation) = 3.62196895454876 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2025-03-05T23:31:53.513Z] RMSE (validation) = 2.1340923221100736 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2025-03-05T23:32:03.838Z] RMSE (validation) = 1.3105190428502682 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2025-03-05T23:32:24.869Z] RMSE (validation) = 0.9920028100571827 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2025-03-05T23:32:25.349Z] RMSE (validation) = 1.212631715629696 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2025-03-05T23:32:29.089Z] RMSE (validation) = 1.1025694034386657 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2025-03-05T23:32:34.837Z] RMSE (validation) = 0.9259652469249674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2025-03-05T23:32:39.257Z] RMSE (validation) = 0.8982232214592321 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2025-03-05T23:32:39.257Z] 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.
[2025-03-05T23:32:39.257Z] The best model improves the baseline by 14.52%.
[2025-03-05T23:32:40.202Z] Movies recommended for you:
[2025-03-05T23:32:40.202Z] WARNING: This benchmark provides no result that can be validated.
[2025-03-05T23:32:40.202Z] There is no way to check that no silent failure occurred.
[2025-03-05T23:32:40.202Z] ====== movie-lens (apache-spark) [default], iteration 3 completed (70378.311 ms) ======
[2025-03-05T23:32:40.202Z] ====== movie-lens (apache-spark) [default], iteration 4 started ======
[2025-03-05T23:32:40.610Z] GC before operation: completed in 561.102 ms, heap usage 321.265 MB -> 49.609 MB.
[2025-03-05T23:32:52.890Z] RMSE (validation) = 3.62196895454876 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2025-03-05T23:33:07.824Z] RMSE (validation) = 2.1340923221100736 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2025-03-05T23:33:19.956Z] RMSE (validation) = 1.3105190428502682 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2025-03-05T23:33:28.652Z] RMSE (validation) = 0.9920028100571827 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2025-03-05T23:33:35.737Z] RMSE (validation) = 1.212631715629696 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2025-03-05T23:33:40.318Z] RMSE (validation) = 1.1025694034386657 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2025-03-05T23:33:44.796Z] RMSE (validation) = 0.9259652469249674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2025-03-05T23:33:49.533Z] RMSE (validation) = 0.8982232214592321 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2025-03-05T23:33:50.016Z] 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.
[2025-03-05T23:33:50.016Z] The best model improves the baseline by 14.52%.
[2025-03-05T23:33:50.016Z] Movies recommended for you:
[2025-03-05T23:33:50.016Z] WARNING: This benchmark provides no result that can be validated.
[2025-03-05T23:33:50.016Z] There is no way to check that no silent failure occurred.
[2025-03-05T23:33:50.016Z] ====== movie-lens (apache-spark) [default], iteration 4 completed (69674.944 ms) ======
[2025-03-05T23:33:50.016Z] ====== movie-lens (apache-spark) [default], iteration 5 started ======
[2025-03-05T23:33:50.457Z] GC before operation: completed in 148.066 ms, heap usage 294.817 MB -> 49.769 MB.
[2025-03-05T23:34:00.848Z] RMSE (validation) = 3.62196895454876 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2025-03-05T23:34:08.218Z] RMSE (validation) = 2.1340923221100736 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2025-03-05T23:34:18.343Z] RMSE (validation) = 1.3105190428502682 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2025-03-05T23:34:26.854Z] RMSE (validation) = 0.9920028100571827 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2025-03-05T23:34:31.427Z] RMSE (validation) = 1.212631715629696 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2025-03-05T23:34:37.010Z] RMSE (validation) = 1.1025694034386657 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2025-03-05T23:34:43.977Z] RMSE (validation) = 0.9259652469249674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2025-03-05T23:34:46.946Z] RMSE (validation) = 0.8982232214592321 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2025-03-05T23:34:47.397Z] 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.
[2025-03-05T23:34:47.397Z] The best model improves the baseline by 14.52%.
[2025-03-05T23:34:47.816Z] Movies recommended for you:
[2025-03-05T23:34:47.816Z] WARNING: This benchmark provides no result that can be validated.
[2025-03-05T23:34:47.816Z] There is no way to check that no silent failure occurred.
[2025-03-05T23:34:47.816Z] ====== movie-lens (apache-spark) [default], iteration 5 completed (57397.453 ms) ======
[2025-03-05T23:34:47.816Z] ====== movie-lens (apache-spark) [default], iteration 6 started ======
[2025-03-05T23:34:47.816Z] GC before operation: completed in 152.465 ms, heap usage 144.207 MB -> 49.560 MB.
[2025-03-05T23:34:58.436Z] RMSE (validation) = 3.62196895454876 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2025-03-05T23:35:13.653Z] RMSE (validation) = 2.1340923221100736 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2025-03-05T23:35:20.577Z] RMSE (validation) = 1.3105190428502682 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2025-03-05T23:35:29.114Z] RMSE (validation) = 0.9920028100571827 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2025-03-05T23:35:33.680Z] RMSE (validation) = 1.212631715629696 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2025-03-05T23:35:38.248Z] RMSE (validation) = 1.1025694034386657 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2025-03-05T23:35:43.673Z] RMSE (validation) = 0.9259652469249674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2025-03-05T23:35:47.217Z] RMSE (validation) = 0.8982232214592321 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2025-03-05T23:35:48.367Z] 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.
[2025-03-05T23:35:48.367Z] The best model improves the baseline by 14.52%.
[2025-03-05T23:35:49.297Z] Movies recommended for you:
[2025-03-05T23:35:49.297Z] WARNING: This benchmark provides no result that can be validated.
[2025-03-05T23:35:49.297Z] There is no way to check that no silent failure occurred.
[2025-03-05T23:35:49.297Z] ====== movie-lens (apache-spark) [default], iteration 6 completed (61087.469 ms) ======
[2025-03-05T23:35:49.297Z] ====== movie-lens (apache-spark) [default], iteration 7 started ======
[2025-03-05T23:35:49.297Z] GC before operation: completed in 128.898 ms, heap usage 132.538 MB -> 49.690 MB.
[2025-03-05T23:35:57.798Z] RMSE (validation) = 3.62196895454876 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2025-03-05T23:36:06.382Z] RMSE (validation) = 2.1340923221100736 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2025-03-05T23:36:14.574Z] RMSE (validation) = 1.3105190428502682 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2025-03-05T23:36:23.173Z] RMSE (validation) = 0.9920028100571827 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2025-03-05T23:36:27.801Z] RMSE (validation) = 1.212631715629696 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2025-03-05T23:36:33.376Z] RMSE (validation) = 1.1025694034386657 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2025-03-05T23:36:40.083Z] RMSE (validation) = 0.9259652469249674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2025-03-05T23:36:44.553Z] RMSE (validation) = 0.8982232214592321 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2025-03-05T23:36:45.521Z] 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.
[2025-03-05T23:36:45.521Z] The best model improves the baseline by 14.52%.
[2025-03-05T23:36:46.001Z] Movies recommended for you:
[2025-03-05T23:36:46.001Z] WARNING: This benchmark provides no result that can be validated.
[2025-03-05T23:36:46.001Z] There is no way to check that no silent failure occurred.
[2025-03-05T23:36:46.001Z] ====== movie-lens (apache-spark) [default], iteration 7 completed (56746.219 ms) ======
[2025-03-05T23:36:46.001Z] ====== movie-lens (apache-spark) [default], iteration 8 started ======
[2025-03-05T23:36:46.001Z] GC before operation: completed in 170.452 ms, heap usage 418.647 MB -> 53.389 MB.
[2025-03-05T23:36:58.494Z] RMSE (validation) = 3.6219689545487603 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2025-03-05T23:37:06.837Z] RMSE (validation) = 2.1340923221100736 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2025-03-05T23:37:15.191Z] RMSE (validation) = 1.3105190428502682 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2025-03-05T23:37:25.328Z] RMSE (validation) = 0.9920028100571827 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2025-03-05T23:37:31.010Z] RMSE (validation) = 1.212631715629696 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2025-03-05T23:37:42.036Z] RMSE (validation) = 1.1025694034386657 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2025-03-05T23:37:45.574Z] RMSE (validation) = 0.9259652469249674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2025-03-05T23:37:50.048Z] RMSE (validation) = 0.8982232214592321 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2025-03-05T23:37:50.048Z] 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.
[2025-03-05T23:37:50.048Z] The best model improves the baseline by 14.52%.
[2025-03-05T23:37:50.048Z] Movies recommended for you:
[2025-03-05T23:37:50.048Z] WARNING: This benchmark provides no result that can be validated.
[2025-03-05T23:37:50.048Z] There is no way to check that no silent failure occurred.
[2025-03-05T23:37:50.048Z] ====== movie-lens (apache-spark) [default], iteration 8 completed (64087.060 ms) ======
[2025-03-05T23:37:50.048Z] ====== movie-lens (apache-spark) [default], iteration 9 started ======
[2025-03-05T23:37:50.621Z] GC before operation: completed in 191.613 ms, heap usage 236.090 MB -> 49.929 MB.
[2025-03-05T23:37:59.152Z] RMSE (validation) = 3.62196895454876 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2025-03-05T23:38:09.246Z] RMSE (validation) = 2.1340923221100736 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2025-03-05T23:38:19.330Z] RMSE (validation) = 1.3105190428502682 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2025-03-05T23:38:24.768Z] RMSE (validation) = 0.9920028100571827 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2025-03-05T23:38:31.691Z] RMSE (validation) = 1.212631715629696 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2025-03-05T23:38:35.390Z] RMSE (validation) = 1.1025694034386657 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2025-03-05T23:38:41.041Z] RMSE (validation) = 0.9259652469249674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2025-03-05T23:38:43.855Z] RMSE (validation) = 0.8982232214592321 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2025-03-05T23:38:44.267Z] 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.
[2025-03-05T23:38:44.686Z] The best model improves the baseline by 14.52%.
[2025-03-05T23:38:44.686Z] Movies recommended for you:
[2025-03-05T23:38:44.686Z] WARNING: This benchmark provides no result that can be validated.
[2025-03-05T23:38:44.686Z] There is no way to check that no silent failure occurred.
[2025-03-05T23:38:44.686Z] ====== movie-lens (apache-spark) [default], iteration 9 completed (54268.703 ms) ======
[2025-03-05T23:38:44.686Z] ====== movie-lens (apache-spark) [default], iteration 10 started ======
[2025-03-05T23:38:44.686Z] GC before operation: completed in 158.698 ms, heap usage 323.754 MB -> 50.217 MB.
[2025-03-05T23:38:54.752Z] RMSE (validation) = 3.62196895454876 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2025-03-05T23:39:04.607Z] RMSE (validation) = 2.1340923221100736 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2025-03-05T23:39:14.718Z] RMSE (validation) = 1.3105190428502682 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2025-03-05T23:39:21.780Z] RMSE (validation) = 0.9920028100571827 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2025-03-05T23:39:27.383Z] RMSE (validation) = 1.212631715629696 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2025-03-05T23:39:33.243Z] RMSE (validation) = 1.1025694034386657 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2025-03-05T23:39:39.067Z] RMSE (validation) = 0.9259652469249674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2025-03-05T23:39:42.603Z] RMSE (validation) = 0.8982232214592321 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2025-03-05T23:39:43.559Z] 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.
[2025-03-05T23:39:43.559Z] The best model improves the baseline by 14.52%.
[2025-03-05T23:39:43.559Z] Movies recommended for you:
[2025-03-05T23:39:43.559Z] WARNING: This benchmark provides no result that can be validated.
[2025-03-05T23:39:43.559Z] There is no way to check that no silent failure occurred.
[2025-03-05T23:39:43.559Z] ====== movie-lens (apache-spark) [default], iteration 10 completed (58836.556 ms) ======
[2025-03-05T23:39:43.559Z] ====== movie-lens (apache-spark) [default], iteration 11 started ======
[2025-03-05T23:39:44.063Z] GC before operation: completed in 199.668 ms, heap usage 63.855 MB -> 52.065 MB.
[2025-03-05T23:39:52.420Z] RMSE (validation) = 3.62196895454876 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2025-03-05T23:40:01.777Z] RMSE (validation) = 2.1340923221100736 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2025-03-05T23:40:08.815Z] RMSE (validation) = 1.3105190428502682 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2025-03-05T23:40:15.642Z] RMSE (validation) = 0.9920028100571827 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2025-03-05T23:40:20.094Z] RMSE (validation) = 1.212631715629696 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2025-03-05T23:40:25.681Z] RMSE (validation) = 1.1025694034386657 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2025-03-05T23:40:29.961Z] RMSE (validation) = 0.9259652469249674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2025-03-05T23:40:33.418Z] RMSE (validation) = 0.8982232214592321 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2025-03-05T23:40:33.418Z] 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.
[2025-03-05T23:40:33.418Z] The best model improves the baseline by 14.52%.
[2025-03-05T23:40:33.862Z] Movies recommended for you:
[2025-03-05T23:40:33.862Z] WARNING: This benchmark provides no result that can be validated.
[2025-03-05T23:40:33.862Z] There is no way to check that no silent failure occurred.
[2025-03-05T23:40:33.862Z] ====== movie-lens (apache-spark) [default], iteration 11 completed (49832.452 ms) ======
[2025-03-05T23:40:33.862Z] ====== movie-lens (apache-spark) [default], iteration 12 started ======
[2025-03-05T23:40:33.862Z] GC before operation: completed in 122.259 ms, heap usage 71.709 MB -> 52.760 MB.
[2025-03-05T23:40:44.510Z] RMSE (validation) = 3.62196895454876 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2025-03-05T23:41:02.401Z] RMSE (validation) = 2.1340923221100736 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2025-03-05T23:41:10.947Z] RMSE (validation) = 1.3105190428502682 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2025-03-05T23:41:17.879Z] RMSE (validation) = 0.9920028100571827 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2025-03-05T23:41:21.402Z] RMSE (validation) = 1.212631715629696 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2025-03-05T23:41:25.022Z] RMSE (validation) = 1.1025694034386657 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2025-03-05T23:41:29.597Z] RMSE (validation) = 0.9259652469249674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2025-03-05T23:41:35.119Z] RMSE (validation) = 0.8982232214592321 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2025-03-05T23:41:35.119Z] 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.
[2025-03-05T23:41:35.119Z] The best model improves the baseline by 14.52%.
[2025-03-05T23:41:35.554Z] Movies recommended for you:
[2025-03-05T23:41:35.554Z] WARNING: This benchmark provides no result that can be validated.
[2025-03-05T23:41:35.554Z] There is no way to check that no silent failure occurred.
[2025-03-05T23:41:35.554Z] ====== movie-lens (apache-spark) [default], iteration 12 completed (61591.011 ms) ======
[2025-03-05T23:41:35.554Z] ====== movie-lens (apache-spark) [default], iteration 13 started ======
[2025-03-05T23:41:35.554Z] GC before operation: completed in 77.459 ms, heap usage 213.151 MB -> 50.060 MB.
[2025-03-05T23:41:43.879Z] RMSE (validation) = 3.6219689545487603 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2025-03-05T23:41:50.942Z] RMSE (validation) = 2.1340923221100736 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2025-03-05T23:42:04.162Z] RMSE (validation) = 1.3105190428502682 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2025-03-05T23:42:14.686Z] RMSE (validation) = 0.9920028100571827 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2025-03-05T23:42:17.317Z] RMSE (validation) = 1.212631715629696 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2025-03-05T23:42:21.109Z] RMSE (validation) = 1.1025694034386657 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2025-03-05T23:42:25.519Z] RMSE (validation) = 0.9259652469249674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2025-03-05T23:42:28.998Z] RMSE (validation) = 0.8982232214592321 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2025-03-05T23:42:29.437Z] 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.
[2025-03-05T23:42:29.437Z] The best model improves the baseline by 14.52%.
[2025-03-05T23:42:29.437Z] Movies recommended for you:
[2025-03-05T23:42:29.437Z] WARNING: This benchmark provides no result that can be validated.
[2025-03-05T23:42:29.437Z] There is no way to check that no silent failure occurred.
[2025-03-05T23:42:29.437Z] ====== movie-lens (apache-spark) [default], iteration 13 completed (54163.540 ms) ======
[2025-03-05T23:42:29.437Z] ====== movie-lens (apache-spark) [default], iteration 14 started ======
[2025-03-05T23:42:29.927Z] GC before operation: completed in 133.545 ms, heap usage 213.887 MB -> 49.888 MB.
[2025-03-05T23:42:40.086Z] RMSE (validation) = 3.62196895454876 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2025-03-05T23:42:47.102Z] RMSE (validation) = 2.1340923221100736 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2025-03-05T23:42:55.702Z] RMSE (validation) = 1.3105190428502682 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2025-03-05T23:43:10.835Z] RMSE (validation) = 0.9920028100571827 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2025-03-05T23:43:17.972Z] RMSE (validation) = 1.212631715629696 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2025-03-05T23:43:19.866Z] RMSE (validation) = 1.1025694034386657 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2025-03-05T23:43:21.773Z] RMSE (validation) = 0.9259652469249674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2025-03-05T23:43:23.700Z] RMSE (validation) = 0.8982232214592321 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2025-03-05T23:43:23.700Z] 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.
[2025-03-05T23:43:23.700Z] The best model improves the baseline by 14.52%.
[2025-03-05T23:43:23.700Z] Movies recommended for you:
[2025-03-05T23:43:23.700Z] WARNING: This benchmark provides no result that can be validated.
[2025-03-05T23:43:23.700Z] There is no way to check that no silent failure occurred.
[2025-03-05T23:43:23.700Z] ====== movie-lens (apache-spark) [default], iteration 14 completed (53971.429 ms) ======
[2025-03-05T23:43:23.700Z] ====== movie-lens (apache-spark) [default], iteration 15 started ======
[2025-03-05T23:43:23.700Z] GC before operation: completed in 77.732 ms, heap usage 435.106 MB -> 53.415 MB.
[2025-03-05T23:43:26.993Z] RMSE (validation) = 3.6219689545487603 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2025-03-05T23:43:29.511Z] RMSE (validation) = 2.1340923221100736 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2025-03-05T23:43:35.094Z] RMSE (validation) = 1.3105190428502682 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2025-03-05T23:43:43.285Z] RMSE (validation) = 0.9920028100571827 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2025-03-05T23:43:47.711Z] RMSE (validation) = 1.212631715629696 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2025-03-05T23:43:50.478Z] RMSE (validation) = 1.1025694034386657 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2025-03-05T23:43:53.077Z] RMSE (validation) = 0.9259652469249674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2025-03-05T23:43:57.640Z] RMSE (validation) = 0.8982232214592321 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2025-03-05T23:43:58.025Z] 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.
[2025-03-05T23:43:58.025Z] The best model improves the baseline by 14.52%.
[2025-03-05T23:43:59.604Z] Movies recommended for you:
[2025-03-05T23:43:59.604Z] WARNING: This benchmark provides no result that can be validated.
[2025-03-05T23:43:59.604Z] There is no way to check that no silent failure occurred.
[2025-03-05T23:43:59.604Z] ====== movie-lens (apache-spark) [default], iteration 15 completed (34742.865 ms) ======
[2025-03-05T23:43:59.604Z] ====== movie-lens (apache-spark) [default], iteration 16 started ======
[2025-03-05T23:43:59.604Z] GC before operation: completed in 626.865 ms, heap usage 312.428 MB -> 50.188 MB.
[2025-03-05T23:44:08.009Z] RMSE (validation) = 3.62196895454876 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2025-03-05T23:44:25.933Z] RMSE (validation) = 2.1340923221100736 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2025-03-05T23:44:36.086Z] RMSE (validation) = 1.3105190428502682 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2025-03-05T23:44:42.947Z] RMSE (validation) = 0.9920028100571827 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2025-03-05T23:44:48.496Z] RMSE (validation) = 1.212631715629696 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2025-03-05T23:44:51.177Z] RMSE (validation) = 1.1025694034386657 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2025-03-05T23:44:56.741Z] RMSE (validation) = 0.9259652469249674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2025-03-05T23:44:59.503Z] RMSE (validation) = 0.8982232214592321 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2025-03-05T23:44:59.918Z] 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.
[2025-03-05T23:44:59.918Z] The best model improves the baseline by 14.52%.
[2025-03-05T23:44:59.918Z] Movies recommended for you:
[2025-03-05T23:44:59.918Z] WARNING: This benchmark provides no result that can be validated.
[2025-03-05T23:44:59.918Z] There is no way to check that no silent failure occurred.
[2025-03-05T23:44:59.918Z] ====== movie-lens (apache-spark) [default], iteration 16 completed (60942.350 ms) ======
[2025-03-05T23:44:59.918Z] ====== movie-lens (apache-spark) [default], iteration 17 started ======
[2025-03-05T23:45:00.383Z] GC before operation: completed in 102.207 ms, heap usage 394.331 MB -> 53.256 MB.
[2025-03-05T23:45:07.508Z] RMSE (validation) = 3.62196895454876 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2025-03-05T23:45:15.822Z] RMSE (validation) = 2.1340923221100736 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2025-03-05T23:45:25.964Z] RMSE (validation) = 1.3105190428502682 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2025-03-05T23:45:34.531Z] RMSE (validation) = 0.9920028100571827 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2025-03-05T23:45:38.247Z] RMSE (validation) = 1.212631715629696 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2025-03-05T23:45:42.678Z] RMSE (validation) = 1.1025694034386657 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2025-03-05T23:45:46.163Z] RMSE (validation) = 0.9259652469249674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2025-03-05T23:45:49.111Z] RMSE (validation) = 0.8982232214592321 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2025-03-05T23:45:49.111Z] 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.
[2025-03-05T23:45:49.111Z] The best model improves the baseline by 14.52%.
[2025-03-05T23:45:49.111Z] Movies recommended for you:
[2025-03-05T23:45:49.111Z] WARNING: This benchmark provides no result that can be validated.
[2025-03-05T23:45:49.111Z] There is no way to check that no silent failure occurred.
[2025-03-05T23:45:49.111Z] ====== movie-lens (apache-spark) [default], iteration 17 completed (48917.867 ms) ======
[2025-03-05T23:45:49.111Z] ====== movie-lens (apache-spark) [default], iteration 18 started ======
[2025-03-05T23:45:49.111Z] GC before operation: completed in 75.083 ms, heap usage 81.299 MB -> 49.822 MB.
[2025-03-05T23:45:58.217Z] RMSE (validation) = 3.62196895454876 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2025-03-05T23:46:02.919Z] RMSE (validation) = 2.1340923221100736 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2025-03-05T23:46:10.564Z] RMSE (validation) = 1.3105190428502682 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2025-03-05T23:46:21.418Z] RMSE (validation) = 0.9920028100571827 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2025-03-05T23:46:26.979Z] RMSE (validation) = 1.212631715629696 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2025-03-05T23:46:26.979Z] RMSE (validation) = 1.1025694034386657 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2025-03-05T23:46:26.979Z] RMSE (validation) = 0.9259652469249674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2025-03-05T23:46:29.655Z] RMSE (validation) = 0.8982232214592321 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2025-03-05T23:46:29.655Z] 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.
[2025-03-05T23:46:29.655Z] The best model improves the baseline by 14.52%.
[2025-03-05T23:46:29.655Z] Movies recommended for you:
[2025-03-05T23:46:29.655Z] WARNING: This benchmark provides no result that can be validated.
[2025-03-05T23:46:29.655Z] There is no way to check that no silent failure occurred.
[2025-03-05T23:46:29.655Z] ====== movie-lens (apache-spark) [default], iteration 18 completed (40468.800 ms) ======
[2025-03-05T23:46:29.655Z] ====== movie-lens (apache-spark) [default], iteration 19 started ======
[2025-03-05T23:46:29.655Z] GC before operation: completed in 86.558 ms, heap usage 250.924 MB -> 50.203 MB.
[2025-03-05T23:46:34.049Z] RMSE (validation) = 3.6219689545487603 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2025-03-05T23:46:37.346Z] RMSE (validation) = 2.1340923221100736 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2025-03-05T23:46:41.732Z] RMSE (validation) = 1.3105190428502682 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2025-03-05T23:46:45.271Z] RMSE (validation) = 0.9920028100571827 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2025-03-05T23:46:49.823Z] RMSE (validation) = 1.212631715629696 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2025-03-05T23:46:53.500Z] RMSE (validation) = 1.1025694034386657 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2025-03-05T23:46:57.021Z] RMSE (validation) = 0.9259652469249674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2025-03-05T23:47:00.736Z] RMSE (validation) = 0.8982232214592321 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2025-03-05T23:47:01.166Z] 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.
[2025-03-05T23:47:01.166Z] The best model improves the baseline by 14.52%.
[2025-03-05T23:47:01.166Z] Movies recommended for you:
[2025-03-05T23:47:01.166Z] WARNING: This benchmark provides no result that can be validated.
[2025-03-05T23:47:01.166Z] There is no way to check that no silent failure occurred.
[2025-03-05T23:47:01.166Z] ====== movie-lens (apache-spark) [default], iteration 19 completed (31465.221 ms) ======
[2025-03-05T23:47:03.180Z] -----------------------------------
[2025-03-05T23:47:03.180Z] renaissance-movie-lens_0_PASSED
[2025-03-05T23:47:03.180Z] -----------------------------------
[2025-03-05T23:47:03.180Z]
[2025-03-05T23:47:03.180Z] TEST TEARDOWN:
[2025-03-05T23:47:03.180Z] Nothing to be done for teardown.
[2025-03-05T23:47:03.180Z] renaissance-movie-lens_0 Finish Time: Wed Mar 5 15:47:02 2025 Epoch Time (ms): 1741218422767