renaissance-movie-lens_0
[2024-12-04T21:36:08.832Z] Running test renaissance-movie-lens_0 ...
[2024-12-04T21:36:08.832Z] ===============================================
[2024-12-04T21:36:08.832Z] renaissance-movie-lens_0 Start Time: Wed Dec 4 16:36:08 2024 Epoch Time (ms): 1733348168440
[2024-12-04T21:36:08.832Z] variation: NoOptions
[2024-12-04T21:36:08.832Z] JVM_OPTIONS:
[2024-12-04T21:36:08.832Z] { \
[2024-12-04T21:36:08.832Z] echo ""; echo "TEST SETUP:"; \
[2024-12-04T21:36:08.832Z] echo "Nothing to be done for setup."; \
[2024-12-04T21:36:08.832Z] mkdir -p "/Users/admin/workspace/workspace/Test_openjdk17_hs_extended.perf_aarch64_mac/aqa-tests/TKG/../TKG/output_17333478557119/renaissance-movie-lens_0"; \
[2024-12-04T21:36:08.832Z] cd "/Users/admin/workspace/workspace/Test_openjdk17_hs_extended.perf_aarch64_mac/aqa-tests/TKG/../TKG/output_17333478557119/renaissance-movie-lens_0"; \
[2024-12-04T21:36:08.832Z] echo ""; echo "TESTING:"; \
[2024-12-04T21:36:08.832Z] "/Users/admin/workspace/workspace/Test_openjdk17_hs_extended.perf_aarch64_mac/jdkbinary/j2sdk-image/Contents/Home/bin/..//bin/java" --add-opens java.base/java.lang=ALL-UNNAMED --add-opens java.base/java.util=ALL-UNNAMED --add-opens java.base/java.util.concurrent=ALL-UNNAMED --add-opens java.base/java.nio=ALL-UNNAMED --add-opens java.base/sun.nio.ch=ALL-UNNAMED --add-opens java.base/java.lang.invoke=ALL-UNNAMED -jar "/Users/admin/workspace/workspace/Test_openjdk17_hs_extended.perf_aarch64_mac/aqa-tests/TKG/../../jvmtest/perf/renaissance/renaissance.jar" --json ""/Users/admin/workspace/workspace/Test_openjdk17_hs_extended.perf_aarch64_mac/aqa-tests/TKG/../TKG/output_17333478557119/renaissance-movie-lens_0"/movie-lens.json" movie-lens; \
[2024-12-04T21:36:08.832Z] if [ $? -eq 0 ]; then echo "-----------------------------------"; echo "renaissance-movie-lens_0""_PASSED"; echo "-----------------------------------"; cd /Users/admin/workspace/workspace/Test_openjdk17_hs_extended.perf_aarch64_mac/aqa-tests/TKG/..; rm -f -r "/Users/admin/workspace/workspace/Test_openjdk17_hs_extended.perf_aarch64_mac/aqa-tests/TKG/../TKG/output_17333478557119/renaissance-movie-lens_0"; else echo "-----------------------------------"; echo "renaissance-movie-lens_0""_FAILED"; echo "-----------------------------------"; fi; \
[2024-12-04T21:36:08.832Z] echo ""; echo "TEST TEARDOWN:"; \
[2024-12-04T21:36:08.832Z] echo "Nothing to be done for teardown."; \
[2024-12-04T21:36:08.832Z] } 2>&1 | tee -a "/Users/admin/workspace/workspace/Test_openjdk17_hs_extended.perf_aarch64_mac/aqa-tests/TKG/../TKG/output_17333478557119/TestTargetResult";
[2024-12-04T21:36:08.832Z]
[2024-12-04T21:36:08.832Z] TEST SETUP:
[2024-12-04T21:36:08.832Z] Nothing to be done for setup.
[2024-12-04T21:36:08.832Z]
[2024-12-04T21:36:08.832Z] TESTING:
[2024-12-04T21:36:10.155Z] Using Spark's default log4j profile: org/apache/spark/log4j-defaults.properties
[2024-12-04T21:36:10.984Z] NOTE: 'movie-lens' benchmark uses Spark local executor with 3 (out of 3) threads.
[2024-12-04T21:36:12.373Z] Got 100004 ratings from 671 users on 9066 movies.
[2024-12-04T21:36:12.373Z] Training: 60056, validation: 20285, test: 19854
[2024-12-04T21:36:12.373Z] ====== movie-lens (apache-spark) [default], iteration 0 started ======
[2024-12-04T21:36:12.373Z] GC before operation: completed in 21.066 ms, heap usage 86.381 MB -> 37.386 MB.
[2024-12-04T21:36:15.704Z] RMSE (validation) = 3.62196895454876 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-12-04T21:36:17.066Z] RMSE (validation) = 2.1340923221100736 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-12-04T21:36:18.991Z] RMSE (validation) = 1.3105190428502682 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-12-04T21:36:20.333Z] RMSE (validation) = 0.9920028100571827 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-12-04T21:36:21.171Z] RMSE (validation) = 1.212631715629696 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-12-04T21:36:21.996Z] RMSE (validation) = 1.1025694034386657 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-12-04T21:36:22.820Z] RMSE (validation) = 0.9259652469249674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-12-04T21:36:23.656Z] RMSE (validation) = 0.8982232214592321 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-12-04T21:36:23.656Z] 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.
[2024-12-04T21:36:23.656Z] The best model improves the baseline by 14.52%.
[2024-12-04T21:36:24.043Z] Movies recommended for you:
[2024-12-04T21:36:24.043Z] WARNING: This benchmark provides no result that can be validated.
[2024-12-04T21:36:24.043Z] There is no way to check that no silent failure occurred.
[2024-12-04T21:36:24.043Z] ====== movie-lens (apache-spark) [default], iteration 0 completed (11365.863 ms) ======
[2024-12-04T21:36:24.043Z] ====== movie-lens (apache-spark) [default], iteration 1 started ======
[2024-12-04T21:36:24.043Z] GC before operation: completed in 38.571 ms, heap usage 402.554 MB -> 54.623 MB.
[2024-12-04T21:36:25.393Z] RMSE (validation) = 3.62196895454876 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-12-04T21:36:26.715Z] RMSE (validation) = 2.1340923221100736 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-12-04T21:36:28.054Z] RMSE (validation) = 1.3105190428502682 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-12-04T21:36:28.945Z] RMSE (validation) = 0.9920028100571827 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-12-04T21:36:29.780Z] RMSE (validation) = 1.212631715629696 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-12-04T21:36:30.611Z] RMSE (validation) = 1.1025694034386657 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-12-04T21:36:31.480Z] RMSE (validation) = 0.9259652469249674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-12-04T21:36:32.335Z] RMSE (validation) = 0.8982232214592321 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-12-04T21:36:32.335Z] 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.
[2024-12-04T21:36:32.335Z] The best model improves the baseline by 14.52%.
[2024-12-04T21:36:32.335Z] Movies recommended for you:
[2024-12-04T21:36:32.335Z] WARNING: This benchmark provides no result that can be validated.
[2024-12-04T21:36:32.335Z] There is no way to check that no silent failure occurred.
[2024-12-04T21:36:32.335Z] ====== movie-lens (apache-spark) [default], iteration 1 completed (8434.235 ms) ======
[2024-12-04T21:36:32.335Z] ====== movie-lens (apache-spark) [default], iteration 2 started ======
[2024-12-04T21:36:32.335Z] GC before operation: completed in 31.773 ms, heap usage 393.738 MB -> 53.068 MB.
[2024-12-04T21:36:33.736Z] RMSE (validation) = 3.62196895454876 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-12-04T21:36:35.087Z] RMSE (validation) = 2.1340923221100736 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-12-04T21:36:36.444Z] RMSE (validation) = 1.3105190428502682 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-12-04T21:36:37.768Z] RMSE (validation) = 0.9920028100571827 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-12-04T21:36:38.156Z] RMSE (validation) = 1.212631715629696 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-12-04T21:36:38.980Z] RMSE (validation) = 1.1025694034386657 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-12-04T21:36:39.825Z] RMSE (validation) = 0.9259652469249674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-12-04T21:36:40.660Z] RMSE (validation) = 0.8982232214592321 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-12-04T21:36:40.660Z] 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.
[2024-12-04T21:36:40.660Z] The best model improves the baseline by 14.52%.
[2024-12-04T21:36:40.660Z] Movies recommended for you:
[2024-12-04T21:36:40.660Z] WARNING: This benchmark provides no result that can be validated.
[2024-12-04T21:36:40.660Z] There is no way to check that no silent failure occurred.
[2024-12-04T21:36:40.660Z] ====== movie-lens (apache-spark) [default], iteration 2 completed (8330.215 ms) ======
[2024-12-04T21:36:40.660Z] ====== movie-lens (apache-spark) [default], iteration 3 started ======
[2024-12-04T21:36:40.660Z] GC before operation: completed in 40.539 ms, heap usage 94.426 MB -> 49.810 MB.
[2024-12-04T21:36:41.996Z] RMSE (validation) = 3.62196895454876 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-12-04T21:36:43.319Z] RMSE (validation) = 2.1340923221100736 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-12-04T21:36:44.654Z] RMSE (validation) = 1.3105190428502682 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-12-04T21:36:45.483Z] RMSE (validation) = 0.9920028100571827 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-12-04T21:36:46.318Z] RMSE (validation) = 1.212631715629696 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-12-04T21:36:47.197Z] RMSE (validation) = 1.1025694034386657 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-12-04T21:36:48.048Z] RMSE (validation) = 0.9259652469249674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-12-04T21:36:48.454Z] RMSE (validation) = 0.8982232214592321 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-12-04T21:36:48.454Z] 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.
[2024-12-04T21:36:48.454Z] The best model improves the baseline by 14.52%.
[2024-12-04T21:36:48.454Z] Movies recommended for you:
[2024-12-04T21:36:48.454Z] WARNING: This benchmark provides no result that can be validated.
[2024-12-04T21:36:48.454Z] There is no way to check that no silent failure occurred.
[2024-12-04T21:36:48.454Z] ====== movie-lens (apache-spark) [default], iteration 3 completed (7890.820 ms) ======
[2024-12-04T21:36:48.454Z] ====== movie-lens (apache-spark) [default], iteration 4 started ======
[2024-12-04T21:36:48.855Z] GC before operation: completed in 34.010 ms, heap usage 266.613 MB -> 50.294 MB.
[2024-12-04T21:36:49.691Z] RMSE (validation) = 3.62196895454876 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-12-04T21:36:51.028Z] RMSE (validation) = 2.1340923221100736 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-12-04T21:36:52.378Z] RMSE (validation) = 1.3105190428502682 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-12-04T21:36:53.714Z] RMSE (validation) = 0.9920028100571827 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-12-04T21:36:54.108Z] RMSE (validation) = 1.212631715629696 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-12-04T21:36:55.000Z] RMSE (validation) = 1.1025694034386657 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-12-04T21:36:55.879Z] RMSE (validation) = 0.9259652469249674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-12-04T21:36:56.288Z] RMSE (validation) = 0.8982232214592321 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-12-04T21:36:56.679Z] 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.
[2024-12-04T21:36:56.679Z] The best model improves the baseline by 14.52%.
[2024-12-04T21:36:56.679Z] Movies recommended for you:
[2024-12-04T21:36:56.679Z] WARNING: This benchmark provides no result that can be validated.
[2024-12-04T21:36:56.679Z] There is no way to check that no silent failure occurred.
[2024-12-04T21:36:56.679Z] ====== movie-lens (apache-spark) [default], iteration 4 completed (7880.042 ms) ======
[2024-12-04T21:36:56.679Z] ====== movie-lens (apache-spark) [default], iteration 5 started ======
[2024-12-04T21:36:56.679Z] GC before operation: completed in 29.940 ms, heap usage 146.434 MB -> 50.507 MB.
[2024-12-04T21:36:57.530Z] RMSE (validation) = 3.62196895454876 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-12-04T21:36:58.866Z] RMSE (validation) = 2.1340923221100736 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-12-04T21:37:00.226Z] RMSE (validation) = 1.3105190428502682 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-12-04T21:37:01.599Z] RMSE (validation) = 0.9920028100571827 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-12-04T21:37:02.007Z] RMSE (validation) = 1.212631715629696 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-12-04T21:37:02.862Z] RMSE (validation) = 1.1025694034386657 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-12-04T21:37:03.254Z] RMSE (validation) = 0.9259652469249674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-12-04T21:37:04.106Z] RMSE (validation) = 0.8982232214592321 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-12-04T21:37:04.106Z] 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.
[2024-12-04T21:37:04.106Z] The best model improves the baseline by 14.52%.
[2024-12-04T21:37:04.106Z] Movies recommended for you:
[2024-12-04T21:37:04.106Z] WARNING: This benchmark provides no result that can be validated.
[2024-12-04T21:37:04.106Z] There is no way to check that no silent failure occurred.
[2024-12-04T21:37:04.106Z] ====== movie-lens (apache-spark) [default], iteration 5 completed (7671.311 ms) ======
[2024-12-04T21:37:04.106Z] ====== movie-lens (apache-spark) [default], iteration 6 started ======
[2024-12-04T21:37:04.106Z] GC before operation: completed in 31.038 ms, heap usage 266.995 MB -> 50.559 MB.
[2024-12-04T21:37:05.441Z] RMSE (validation) = 3.62196895454876 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-12-04T21:37:06.781Z] RMSE (validation) = 2.1340923221100736 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-12-04T21:37:08.750Z] RMSE (validation) = 1.3105190428502682 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-12-04T21:37:10.082Z] RMSE (validation) = 0.9920028100571827 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-12-04T21:37:10.489Z] RMSE (validation) = 1.212631715629696 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-12-04T21:37:11.321Z] RMSE (validation) = 1.1025694034386657 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-12-04T21:37:12.149Z] RMSE (validation) = 0.9259652469249674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-12-04T21:37:12.551Z] RMSE (validation) = 0.8982232214592321 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-12-04T21:37:12.942Z] 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.
[2024-12-04T21:37:12.942Z] The best model improves the baseline by 14.52%.
[2024-12-04T21:37:12.942Z] Movies recommended for you:
[2024-12-04T21:37:12.943Z] WARNING: This benchmark provides no result that can be validated.
[2024-12-04T21:37:12.943Z] There is no way to check that no silent failure occurred.
[2024-12-04T21:37:12.943Z] ====== movie-lens (apache-spark) [default], iteration 6 completed (8557.647 ms) ======
[2024-12-04T21:37:12.943Z] ====== movie-lens (apache-spark) [default], iteration 7 started ======
[2024-12-04T21:37:12.943Z] GC before operation: completed in 30.167 ms, heap usage 159.221 MB -> 50.662 MB.
[2024-12-04T21:37:13.774Z] RMSE (validation) = 3.62196895454876 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-12-04T21:37:15.108Z] RMSE (validation) = 2.1340923221100736 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-12-04T21:37:15.944Z] RMSE (validation) = 1.3105190428502682 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-12-04T21:37:16.786Z] RMSE (validation) = 0.9920028100571827 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-12-04T21:37:17.623Z] RMSE (validation) = 1.212631715629696 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-12-04T21:37:18.047Z] RMSE (validation) = 1.1025694034386657 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-12-04T21:37:18.875Z] RMSE (validation) = 0.9259652469249674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-12-04T21:37:19.739Z] RMSE (validation) = 0.8982232214592321 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-12-04T21:37:20.134Z] 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.
[2024-12-04T21:37:20.134Z] The best model improves the baseline by 14.52%.
[2024-12-04T21:37:20.134Z] Movies recommended for you:
[2024-12-04T21:37:20.134Z] WARNING: This benchmark provides no result that can be validated.
[2024-12-04T21:37:20.134Z] There is no way to check that no silent failure occurred.
[2024-12-04T21:37:20.134Z] ====== movie-lens (apache-spark) [default], iteration 7 completed (7173.676 ms) ======
[2024-12-04T21:37:20.134Z] ====== movie-lens (apache-spark) [default], iteration 8 started ======
[2024-12-04T21:37:20.134Z] GC before operation: completed in 36.002 ms, heap usage 60.411 MB -> 50.816 MB.
[2024-12-04T21:37:21.473Z] RMSE (validation) = 3.62196895454876 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-12-04T21:37:22.304Z] RMSE (validation) = 2.1340923221100736 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-12-04T21:37:23.126Z] RMSE (validation) = 1.3105190428502682 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-12-04T21:37:24.523Z] RMSE (validation) = 0.9920028100571827 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-12-04T21:37:24.915Z] RMSE (validation) = 1.212631715629696 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-12-04T21:37:25.776Z] RMSE (validation) = 1.1025694034386657 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-12-04T21:37:26.172Z] RMSE (validation) = 0.9259652469249674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-12-04T21:37:27.009Z] RMSE (validation) = 0.8982232214592321 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-12-04T21:37:27.009Z] 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.
[2024-12-04T21:37:27.009Z] The best model improves the baseline by 14.52%.
[2024-12-04T21:37:27.009Z] Movies recommended for you:
[2024-12-04T21:37:27.009Z] WARNING: This benchmark provides no result that can be validated.
[2024-12-04T21:37:27.009Z] There is no way to check that no silent failure occurred.
[2024-12-04T21:37:27.009Z] ====== movie-lens (apache-spark) [default], iteration 8 completed (6899.338 ms) ======
[2024-12-04T21:37:27.009Z] ====== movie-lens (apache-spark) [default], iteration 9 started ======
[2024-12-04T21:37:27.009Z] GC before operation: completed in 28.906 ms, heap usage 248.357 MB -> 50.998 MB.
[2024-12-04T21:37:27.845Z] RMSE (validation) = 3.62196895454876 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-12-04T21:37:29.180Z] RMSE (validation) = 2.1340923221100736 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-12-04T21:37:30.008Z] RMSE (validation) = 1.3105190428502682 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-12-04T21:37:31.348Z] RMSE (validation) = 0.9920028100571827 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-12-04T21:37:32.197Z] RMSE (validation) = 1.212631715629696 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-12-04T21:37:32.585Z] RMSE (validation) = 1.1025694034386657 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-12-04T21:37:33.429Z] RMSE (validation) = 0.9259652469249674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-12-04T21:37:33.819Z] RMSE (validation) = 0.8982232214592321 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-12-04T21:37:33.819Z] 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.
[2024-12-04T21:37:33.819Z] The best model improves the baseline by 14.52%.
[2024-12-04T21:37:33.819Z] Movies recommended for you:
[2024-12-04T21:37:33.819Z] WARNING: This benchmark provides no result that can be validated.
[2024-12-04T21:37:33.819Z] There is no way to check that no silent failure occurred.
[2024-12-04T21:37:33.819Z] ====== movie-lens (apache-spark) [default], iteration 9 completed (6956.893 ms) ======
[2024-12-04T21:37:33.819Z] ====== movie-lens (apache-spark) [default], iteration 10 started ======
[2024-12-04T21:37:33.819Z] GC before operation: completed in 30.749 ms, heap usage 123.603 MB -> 50.831 MB.
[2024-12-04T21:37:35.155Z] RMSE (validation) = 3.62196895454876 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-12-04T21:37:35.979Z] RMSE (validation) = 2.1340923221100736 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-12-04T21:37:37.319Z] RMSE (validation) = 1.3105190428502682 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-12-04T21:37:38.151Z] RMSE (validation) = 0.9920028100571827 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-12-04T21:37:38.976Z] RMSE (validation) = 1.212631715629696 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-12-04T21:37:39.821Z] RMSE (validation) = 1.1025694034386657 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-12-04T21:37:40.212Z] RMSE (validation) = 0.9259652469249674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-12-04T21:37:41.046Z] RMSE (validation) = 0.8982232214592321 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-12-04T21:37:41.046Z] 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.
[2024-12-04T21:37:41.046Z] The best model improves the baseline by 14.52%.
[2024-12-04T21:37:41.439Z] Movies recommended for you:
[2024-12-04T21:37:41.440Z] WARNING: This benchmark provides no result that can be validated.
[2024-12-04T21:37:41.440Z] There is no way to check that no silent failure occurred.
[2024-12-04T21:37:41.440Z] ====== movie-lens (apache-spark) [default], iteration 10 completed (7254.852 ms) ======
[2024-12-04T21:37:41.440Z] ====== movie-lens (apache-spark) [default], iteration 11 started ======
[2024-12-04T21:37:41.440Z] GC before operation: completed in 34.449 ms, heap usage 72.872 MB -> 50.449 MB.
[2024-12-04T21:37:42.266Z] RMSE (validation) = 3.62196895454876 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-12-04T21:37:43.598Z] RMSE (validation) = 2.1340923221100736 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-12-04T21:37:44.931Z] RMSE (validation) = 1.3105190428502682 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-12-04T21:37:45.796Z] RMSE (validation) = 0.9920028100571827 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-12-04T21:37:46.634Z] RMSE (validation) = 1.212631715629696 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-12-04T21:37:47.462Z] RMSE (validation) = 1.1025694034386657 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-12-04T21:37:48.309Z] RMSE (validation) = 0.9259652469249674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-12-04T21:37:48.817Z] RMSE (validation) = 0.8982232214592321 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-12-04T21:37:48.817Z] 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.
[2024-12-04T21:37:48.817Z] The best model improves the baseline by 14.52%.
[2024-12-04T21:37:48.817Z] Movies recommended for you:
[2024-12-04T21:37:48.817Z] WARNING: This benchmark provides no result that can be validated.
[2024-12-04T21:37:48.817Z] There is no way to check that no silent failure occurred.
[2024-12-04T21:37:48.817Z] ====== movie-lens (apache-spark) [default], iteration 11 completed (7672.195 ms) ======
[2024-12-04T21:37:48.817Z] ====== movie-lens (apache-spark) [default], iteration 12 started ======
[2024-12-04T21:37:48.817Z] GC before operation: completed in 42.996 ms, heap usage 322.489 MB -> 51.036 MB.
[2024-12-04T21:37:50.734Z] RMSE (validation) = 3.6219689545487603 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-12-04T21:37:51.600Z] RMSE (validation) = 2.1340923221100736 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-12-04T21:37:52.950Z] RMSE (validation) = 1.3105190428502682 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-12-04T21:37:54.296Z] RMSE (validation) = 0.9920028100571827 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-12-04T21:37:55.151Z] RMSE (validation) = 1.212631715629696 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-12-04T21:37:55.997Z] RMSE (validation) = 1.1025694034386657 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-12-04T21:37:56.826Z] RMSE (validation) = 0.9259652469249674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-12-04T21:37:57.664Z] RMSE (validation) = 0.8982232214592321 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-12-04T21:37:58.052Z] 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.
[2024-12-04T21:37:58.052Z] The best model improves the baseline by 14.52%.
[2024-12-04T21:37:58.052Z] Movies recommended for you:
[2024-12-04T21:37:58.052Z] WARNING: This benchmark provides no result that can be validated.
[2024-12-04T21:37:58.052Z] There is no way to check that no silent failure occurred.
[2024-12-04T21:37:58.052Z] ====== movie-lens (apache-spark) [default], iteration 12 completed (8917.269 ms) ======
[2024-12-04T21:37:58.052Z] ====== movie-lens (apache-spark) [default], iteration 13 started ======
[2024-12-04T21:37:58.052Z] GC before operation: completed in 37.800 ms, heap usage 152.076 MB -> 51.248 MB.
[2024-12-04T21:37:59.387Z] RMSE (validation) = 3.62196895454876 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-12-04T21:38:00.836Z] RMSE (validation) = 2.1340923221100736 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-12-04T21:38:02.203Z] RMSE (validation) = 1.3105190428502682 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-12-04T21:38:03.055Z] RMSE (validation) = 0.9920028100571827 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-12-04T21:38:03.898Z] RMSE (validation) = 1.212631715629696 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-12-04T21:38:04.756Z] RMSE (validation) = 1.1025694034386657 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-12-04T21:38:05.594Z] RMSE (validation) = 0.9259652469249674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-12-04T21:38:06.432Z] RMSE (validation) = 0.8982232214592321 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-12-04T21:38:06.432Z] 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.
[2024-12-04T21:38:06.432Z] The best model improves the baseline by 14.52%.
[2024-12-04T21:38:06.820Z] Movies recommended for you:
[2024-12-04T21:38:06.820Z] WARNING: This benchmark provides no result that can be validated.
[2024-12-04T21:38:06.820Z] There is no way to check that no silent failure occurred.
[2024-12-04T21:38:06.820Z] ====== movie-lens (apache-spark) [default], iteration 13 completed (8702.148 ms) ======
[2024-12-04T21:38:06.820Z] ====== movie-lens (apache-spark) [default], iteration 14 started ======
[2024-12-04T21:38:06.820Z] GC before operation: completed in 40.505 ms, heap usage 306.395 MB -> 51.026 MB.
[2024-12-04T21:38:08.163Z] RMSE (validation) = 3.62196895454876 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-12-04T21:38:09.493Z] RMSE (validation) = 2.1340923221100736 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-12-04T21:38:10.828Z] RMSE (validation) = 1.3105190428502682 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-12-04T21:38:12.170Z] RMSE (validation) = 0.9920028100571827 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-12-04T21:38:13.005Z] RMSE (validation) = 1.212631715629696 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-12-04T21:38:13.836Z] RMSE (validation) = 1.1025694034386657 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-12-04T21:38:14.672Z] RMSE (validation) = 0.9259652469249674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-12-04T21:38:15.512Z] RMSE (validation) = 0.8982232214592321 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-12-04T21:38:15.512Z] 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.
[2024-12-04T21:38:15.512Z] The best model improves the baseline by 14.52%.
[2024-12-04T21:38:15.512Z] Movies recommended for you:
[2024-12-04T21:38:15.512Z] WARNING: This benchmark provides no result that can be validated.
[2024-12-04T21:38:15.512Z] There is no way to check that no silent failure occurred.
[2024-12-04T21:38:15.512Z] ====== movie-lens (apache-spark) [default], iteration 14 completed (8964.926 ms) ======
[2024-12-04T21:38:15.512Z] ====== movie-lens (apache-spark) [default], iteration 15 started ======
[2024-12-04T21:38:15.909Z] GC before operation: completed in 39.577 ms, heap usage 152.214 MB -> 51.000 MB.
[2024-12-04T21:38:17.352Z] RMSE (validation) = 3.62196895454876 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-12-04T21:38:18.179Z] RMSE (validation) = 2.1340923221100736 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-12-04T21:38:19.509Z] RMSE (validation) = 1.3105190428502682 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-12-04T21:38:20.862Z] RMSE (validation) = 0.9920028100571827 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-12-04T21:38:21.694Z] RMSE (validation) = 1.212631715629696 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-12-04T21:38:22.522Z] RMSE (validation) = 1.1025694034386657 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-12-04T21:38:23.346Z] RMSE (validation) = 0.9259652469249674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-12-04T21:38:23.792Z] RMSE (validation) = 0.8982232214592321 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-12-04T21:38:24.195Z] 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.
[2024-12-04T21:38:24.195Z] The best model improves the baseline by 14.52%.
[2024-12-04T21:38:24.195Z] Movies recommended for you:
[2024-12-04T21:38:24.195Z] WARNING: This benchmark provides no result that can be validated.
[2024-12-04T21:38:24.195Z] There is no way to check that no silent failure occurred.
[2024-12-04T21:38:24.195Z] ====== movie-lens (apache-spark) [default], iteration 15 completed (8386.910 ms) ======
[2024-12-04T21:38:24.195Z] ====== movie-lens (apache-spark) [default], iteration 16 started ======
[2024-12-04T21:38:24.195Z] GC before operation: completed in 29.406 ms, heap usage 266.551 MB -> 51.153 MB.
[2024-12-04T21:38:25.036Z] RMSE (validation) = 3.62196895454876 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-12-04T21:38:26.369Z] RMSE (validation) = 2.1340923221100736 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-12-04T21:38:27.726Z] RMSE (validation) = 1.3105190428502682 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-12-04T21:38:28.575Z] RMSE (validation) = 0.9920028100571827 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-12-04T21:38:29.423Z] RMSE (validation) = 1.212631715629696 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-12-04T21:38:29.814Z] RMSE (validation) = 1.1025694034386657 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-12-04T21:38:30.658Z] RMSE (validation) = 0.9259652469249674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-12-04T21:38:31.509Z] RMSE (validation) = 0.8982232214592321 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-12-04T21:38:31.509Z] 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.
[2024-12-04T21:38:31.509Z] The best model improves the baseline by 14.52%.
[2024-12-04T21:38:31.509Z] Movies recommended for you:
[2024-12-04T21:38:31.509Z] WARNING: This benchmark provides no result that can be validated.
[2024-12-04T21:38:31.509Z] There is no way to check that no silent failure occurred.
[2024-12-04T21:38:31.509Z] ====== movie-lens (apache-spark) [default], iteration 16 completed (7548.042 ms) ======
[2024-12-04T21:38:31.509Z] ====== movie-lens (apache-spark) [default], iteration 17 started ======
[2024-12-04T21:38:31.898Z] GC before operation: completed in 31.190 ms, heap usage 151.250 MB -> 50.928 MB.
[2024-12-04T21:38:33.266Z] RMSE (validation) = 3.62196895454876 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-12-04T21:38:34.091Z] RMSE (validation) = 2.1340923221100736 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-12-04T21:38:35.420Z] RMSE (validation) = 1.3105190428502682 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-12-04T21:38:36.744Z] RMSE (validation) = 0.9920028100571827 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-12-04T21:38:37.573Z] RMSE (validation) = 1.212631715629696 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-12-04T21:38:37.963Z] RMSE (validation) = 1.1025694034386657 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-12-04T21:38:38.790Z] RMSE (validation) = 0.9259652469249674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-12-04T21:38:39.614Z] RMSE (validation) = 0.8982232214592321 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-12-04T21:38:39.614Z] 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.
[2024-12-04T21:38:39.614Z] The best model improves the baseline by 14.52%.
[2024-12-04T21:38:39.614Z] Movies recommended for you:
[2024-12-04T21:38:39.614Z] WARNING: This benchmark provides no result that can be validated.
[2024-12-04T21:38:39.614Z] There is no way to check that no silent failure occurred.
[2024-12-04T21:38:39.614Z] ====== movie-lens (apache-spark) [default], iteration 17 completed (7975.466 ms) ======
[2024-12-04T21:38:39.614Z] ====== movie-lens (apache-spark) [default], iteration 18 started ======
[2024-12-04T21:38:39.614Z] GC before operation: completed in 35.297 ms, heap usage 248.984 MB -> 51.046 MB.
[2024-12-04T21:38:40.945Z] RMSE (validation) = 3.62196895454876 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-12-04T21:38:42.279Z] RMSE (validation) = 2.1340923221100736 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-12-04T21:38:43.117Z] RMSE (validation) = 1.3105190428502682 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-12-04T21:38:43.942Z] RMSE (validation) = 0.9920028100571827 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-12-04T21:38:44.768Z] RMSE (validation) = 1.212631715629696 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-12-04T21:38:45.593Z] RMSE (validation) = 1.1025694034386657 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-12-04T21:38:45.996Z] RMSE (validation) = 0.9259652469249674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-12-04T21:38:46.826Z] RMSE (validation) = 0.8982232214592321 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-12-04T21:38:46.826Z] 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.
[2024-12-04T21:38:46.826Z] The best model improves the baseline by 14.52%.
[2024-12-04T21:38:46.826Z] Movies recommended for you:
[2024-12-04T21:38:46.826Z] WARNING: This benchmark provides no result that can be validated.
[2024-12-04T21:38:46.826Z] There is no way to check that no silent failure occurred.
[2024-12-04T21:38:46.826Z] ====== movie-lens (apache-spark) [default], iteration 18 completed (7165.458 ms) ======
[2024-12-04T21:38:46.826Z] ====== movie-lens (apache-spark) [default], iteration 19 started ======
[2024-12-04T21:38:46.826Z] GC before operation: completed in 32.282 ms, heap usage 177.929 MB -> 51.256 MB.
[2024-12-04T21:38:48.142Z] RMSE (validation) = 3.6219689545487603 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-12-04T21:38:49.476Z] RMSE (validation) = 2.1340923221100736 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-12-04T21:38:50.345Z] RMSE (validation) = 1.3105190428502682 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-12-04T21:38:51.743Z] RMSE (validation) = 0.9920028100571827 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-12-04T21:38:52.151Z] RMSE (validation) = 1.212631715629696 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-12-04T21:38:52.987Z] RMSE (validation) = 1.1025694034386657 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-12-04T21:38:53.818Z] RMSE (validation) = 0.9259652469249674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-12-04T21:38:54.232Z] RMSE (validation) = 0.8982232214592321 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-12-04T21:38:54.619Z] 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.
[2024-12-04T21:38:54.619Z] The best model improves the baseline by 14.52%.
[2024-12-04T21:38:54.619Z] Movies recommended for you:
[2024-12-04T21:38:54.619Z] WARNING: This benchmark provides no result that can be validated.
[2024-12-04T21:38:54.619Z] There is no way to check that no silent failure occurred.
[2024-12-04T21:38:54.619Z] ====== movie-lens (apache-spark) [default], iteration 19 completed (7596.161 ms) ======
[2024-12-04T21:38:54.619Z] -----------------------------------
[2024-12-04T21:38:54.619Z] renaissance-movie-lens_0_PASSED
[2024-12-04T21:38:54.619Z] -----------------------------------
[2024-12-04T21:38:54.619Z]
[2024-12-04T21:38:54.619Z] TEST TEARDOWN:
[2024-12-04T21:38:54.619Z] Nothing to be done for teardown.
[2024-12-04T21:38:54.619Z] renaissance-movie-lens_0 Finish Time: Wed Dec 4 16:38:54 2024 Epoch Time (ms): 1733348334462