renaissance-movie-lens_0
[2024-11-22T21:53:19.394Z] Running test renaissance-movie-lens_0 ...
[2024-11-22T21:53:19.394Z] ===============================================
[2024-11-22T21:53:19.394Z] renaissance-movie-lens_0 Start Time: Fri Nov 22 16:53:19 2024 Epoch Time (ms): 1732312399047
[2024-11-22T21:53:19.394Z] variation: NoOptions
[2024-11-22T21:53:19.394Z] JVM_OPTIONS:
[2024-11-22T21:53:19.394Z] { \
[2024-11-22T21:53:19.394Z] echo ""; echo "TEST SETUP:"; \
[2024-11-22T21:53:19.394Z] echo "Nothing to be done for setup."; \
[2024-11-22T21:53:19.394Z] mkdir -p "/Users/admin/workspace/workspace/Test_openjdk17_hs_extended.perf_aarch64_mac/aqa-tests/TKG/../TKG/output_17323120598852/renaissance-movie-lens_0"; \
[2024-11-22T21:53:19.394Z] cd "/Users/admin/workspace/workspace/Test_openjdk17_hs_extended.perf_aarch64_mac/aqa-tests/TKG/../TKG/output_17323120598852/renaissance-movie-lens_0"; \
[2024-11-22T21:53:19.394Z] echo ""; echo "TESTING:"; \
[2024-11-22T21:53:19.394Z] "/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_17323120598852/renaissance-movie-lens_0"/movie-lens.json" movie-lens; \
[2024-11-22T21:53:19.394Z] 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_17323120598852/renaissance-movie-lens_0"; else echo "-----------------------------------"; echo "renaissance-movie-lens_0""_FAILED"; echo "-----------------------------------"; fi; \
[2024-11-22T21:53:19.394Z] echo ""; echo "TEST TEARDOWN:"; \
[2024-11-22T21:53:19.394Z] echo "Nothing to be done for teardown."; \
[2024-11-22T21:53:19.394Z] } 2>&1 | tee -a "/Users/admin/workspace/workspace/Test_openjdk17_hs_extended.perf_aarch64_mac/aqa-tests/TKG/../TKG/output_17323120598852/TestTargetResult";
[2024-11-22T21:53:19.394Z]
[2024-11-22T21:53:19.394Z] TEST SETUP:
[2024-11-22T21:53:19.394Z] Nothing to be done for setup.
[2024-11-22T21:53:19.394Z]
[2024-11-22T21:53:19.394Z] TESTING:
[2024-11-22T21:53:21.173Z] Using Spark's default log4j profile: org/apache/spark/log4j-defaults.properties
[2024-11-22T21:53:21.951Z] NOTE: 'movie-lens' benchmark uses Spark local executor with 3 (out of 3) threads.
[2024-11-22T21:53:23.755Z] Got 100004 ratings from 671 users on 9066 movies.
[2024-11-22T21:53:23.755Z] Training: 60056, validation: 20285, test: 19854
[2024-11-22T21:53:23.755Z] ====== movie-lens (apache-spark) [default], iteration 0 started ======
[2024-11-22T21:53:23.755Z] GC before operation: completed in 17.950 ms, heap usage 129.115 MB -> 37.310 MB.
[2024-11-22T21:53:26.955Z] RMSE (validation) = 3.62196895454876 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-22T21:53:28.723Z] RMSE (validation) = 2.1340923221100736 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-22T21:53:30.526Z] RMSE (validation) = 1.3105190428502682 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-22T21:53:32.315Z] RMSE (validation) = 0.9920028100571827 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-22T21:53:33.092Z] RMSE (validation) = 1.212631715629696 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-22T21:53:33.896Z] RMSE (validation) = 1.1025694034386657 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-22T21:53:35.171Z] RMSE (validation) = 0.9259652469249674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-22T21:53:35.956Z] RMSE (validation) = 0.8982232214592321 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-22T21:53:35.956Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9063003101263983.
[2024-11-22T21:53:35.956Z] The best model improves the baseline by 14.52%.
[2024-11-22T21:53:35.956Z] Movies recommended for you:
[2024-11-22T21:53:35.956Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-22T21:53:35.956Z] There is no way to check that no silent failure occurred.
[2024-11-22T21:53:35.956Z] ====== movie-lens (apache-spark) [default], iteration 0 completed (12328.439 ms) ======
[2024-11-22T21:53:35.956Z] ====== movie-lens (apache-spark) [default], iteration 1 started ======
[2024-11-22T21:53:35.956Z] GC before operation: completed in 47.791 ms, heap usage 140.901 MB -> 54.203 MB.
[2024-11-22T21:53:37.765Z] RMSE (validation) = 3.62196895454876 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-22T21:53:39.006Z] RMSE (validation) = 2.1340923221100736 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-22T21:53:40.276Z] RMSE (validation) = 1.3105190428502682 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-22T21:53:41.545Z] RMSE (validation) = 0.9920028100571827 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-22T21:53:42.333Z] RMSE (validation) = 1.212631715629696 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-22T21:53:43.132Z] RMSE (validation) = 1.1025694034386657 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-22T21:53:44.386Z] RMSE (validation) = 0.9259652469249674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-22T21:53:45.171Z] RMSE (validation) = 0.8982232214592321 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-22T21:53:45.171Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9063003101263983.
[2024-11-22T21:53:45.171Z] The best model improves the baseline by 14.52%.
[2024-11-22T21:53:45.171Z] Movies recommended for you:
[2024-11-22T21:53:45.171Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-22T21:53:45.171Z] There is no way to check that no silent failure occurred.
[2024-11-22T21:53:45.171Z] ====== movie-lens (apache-spark) [default], iteration 1 completed (9026.871 ms) ======
[2024-11-22T21:53:45.171Z] ====== movie-lens (apache-spark) [default], iteration 2 started ======
[2024-11-22T21:53:45.171Z] GC before operation: completed in 28.109 ms, heap usage 238.321 MB -> 49.774 MB.
[2024-11-22T21:53:46.415Z] RMSE (validation) = 3.62196895454876 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-22T21:53:48.234Z] RMSE (validation) = 2.1340923221100736 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-22T21:53:49.482Z] RMSE (validation) = 1.3105190428502682 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-22T21:53:50.748Z] RMSE (validation) = 0.9920028100571827 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-22T21:53:51.531Z] RMSE (validation) = 1.212631715629696 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-22T21:53:52.329Z] RMSE (validation) = 1.1025694034386657 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-22T21:53:53.104Z] RMSE (validation) = 0.9259652469249674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-22T21:53:53.875Z] RMSE (validation) = 0.8982232214592321 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-22T21:53:54.232Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9063003101263983.
[2024-11-22T21:53:54.232Z] The best model improves the baseline by 14.52%.
[2024-11-22T21:53:54.232Z] Movies recommended for you:
[2024-11-22T21:53:54.232Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-22T21:53:54.232Z] There is no way to check that no silent failure occurred.
[2024-11-22T21:53:54.232Z] ====== movie-lens (apache-spark) [default], iteration 2 completed (9074.587 ms) ======
[2024-11-22T21:53:54.232Z] ====== movie-lens (apache-spark) [default], iteration 3 started ======
[2024-11-22T21:53:54.232Z] GC before operation: completed in 35.934 ms, heap usage 170.073 MB -> 50.052 MB.
[2024-11-22T21:53:55.499Z] RMSE (validation) = 3.62196895454876 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-22T21:53:56.769Z] RMSE (validation) = 2.1340923221100736 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-22T21:53:58.560Z] RMSE (validation) = 1.3105190428502682 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-22T21:53:59.806Z] RMSE (validation) = 0.9920028100571827 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-22T21:54:00.600Z] RMSE (validation) = 1.212631715629696 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-22T21:54:01.382Z] RMSE (validation) = 1.1025694034386657 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-22T21:54:02.184Z] RMSE (validation) = 0.9259652469249674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-22T21:54:02.984Z] RMSE (validation) = 0.8982232214592321 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-22T21:54:02.984Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9063003101263983.
[2024-11-22T21:54:02.984Z] The best model improves the baseline by 14.52%.
[2024-11-22T21:54:02.984Z] Movies recommended for you:
[2024-11-22T21:54:02.984Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-22T21:54:02.984Z] There is no way to check that no silent failure occurred.
[2024-11-22T21:54:02.984Z] ====== movie-lens (apache-spark) [default], iteration 3 completed (8796.882 ms) ======
[2024-11-22T21:54:02.984Z] ====== movie-lens (apache-spark) [default], iteration 4 started ======
[2024-11-22T21:54:02.984Z] GC before operation: completed in 33.993 ms, heap usage 176.398 MB -> 50.373 MB.
[2024-11-22T21:54:04.821Z] RMSE (validation) = 3.6219689545487603 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-22T21:54:06.085Z] RMSE (validation) = 2.1340923221100736 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-22T21:54:07.381Z] RMSE (validation) = 1.3105190428502682 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-22T21:54:08.627Z] RMSE (validation) = 0.9920028100571827 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-22T21:54:09.417Z] RMSE (validation) = 1.212631715629696 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-22T21:54:10.206Z] RMSE (validation) = 1.1025694034386657 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-22T21:54:16.133Z] RMSE (validation) = 0.9259652469249674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-22T21:54:16.133Z] RMSE (validation) = 0.8982232214592321 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-22T21:54:16.133Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9063003101263983.
[2024-11-22T21:54:16.133Z] The best model improves the baseline by 14.52%.
[2024-11-22T21:54:16.133Z] Movies recommended for you:
[2024-11-22T21:54:16.133Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-22T21:54:16.133Z] There is no way to check that no silent failure occurred.
[2024-11-22T21:54:16.133Z] ====== movie-lens (apache-spark) [default], iteration 4 completed (8639.024 ms) ======
[2024-11-22T21:54:16.133Z] ====== movie-lens (apache-spark) [default], iteration 5 started ======
[2024-11-22T21:54:16.133Z] GC before operation: completed in 41.472 ms, heap usage 236.536 MB -> 50.624 MB.
[2024-11-22T21:54:16.133Z] RMSE (validation) = 3.6219689545487603 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-22T21:54:16.133Z] RMSE (validation) = 2.1340923221100736 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-22T21:54:16.133Z] RMSE (validation) = 1.3105190428502682 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-22T21:54:16.898Z] RMSE (validation) = 0.9920028100571827 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-22T21:54:17.675Z] RMSE (validation) = 1.212631715629696 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-22T21:54:18.458Z] RMSE (validation) = 1.1025694034386657 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-22T21:54:19.234Z] RMSE (validation) = 0.9259652469249674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-22T21:54:20.115Z] RMSE (validation) = 0.8982232214592321 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-22T21:54:20.115Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9063003101263983.
[2024-11-22T21:54:20.115Z] The best model improves the baseline by 14.52%.
[2024-11-22T21:54:20.115Z] Movies recommended for you:
[2024-11-22T21:54:20.115Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-22T21:54:20.115Z] There is no way to check that no silent failure occurred.
[2024-11-22T21:54:20.115Z] ====== movie-lens (apache-spark) [default], iteration 5 completed (8263.305 ms) ======
[2024-11-22T21:54:20.115Z] ====== movie-lens (apache-spark) [default], iteration 6 started ======
[2024-11-22T21:54:20.115Z] GC before operation: completed in 32.867 ms, heap usage 137.381 MB -> 50.392 MB.
[2024-11-22T21:54:21.361Z] RMSE (validation) = 3.6219689545487603 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-22T21:54:23.189Z] RMSE (validation) = 2.1340923221100736 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-22T21:54:24.453Z] RMSE (validation) = 1.3105190428502682 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-22T21:54:25.692Z] RMSE (validation) = 0.9920028100571827 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-22T21:54:26.484Z] RMSE (validation) = 1.212631715629696 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-22T21:54:27.275Z] RMSE (validation) = 1.1025694034386657 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-22T21:54:28.061Z] RMSE (validation) = 0.9259652469249674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-22T21:54:28.844Z] RMSE (validation) = 0.8982232214592321 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-22T21:54:28.844Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9063003101263983.
[2024-11-22T21:54:28.844Z] The best model improves the baseline by 14.52%.
[2024-11-22T21:54:28.844Z] Movies recommended for you:
[2024-11-22T21:54:28.844Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-22T21:54:28.844Z] There is no way to check that no silent failure occurred.
[2024-11-22T21:54:28.844Z] ====== movie-lens (apache-spark) [default], iteration 6 completed (8940.058 ms) ======
[2024-11-22T21:54:28.844Z] ====== movie-lens (apache-spark) [default], iteration 7 started ======
[2024-11-22T21:54:29.206Z] GC before operation: completed in 40.515 ms, heap usage 136.600 MB -> 50.630 MB.
[2024-11-22T21:54:30.549Z] RMSE (validation) = 3.62196895454876 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-22T21:54:31.841Z] RMSE (validation) = 2.1340923221100736 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-22T21:54:33.107Z] RMSE (validation) = 1.3105190428502682 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-22T21:54:34.372Z] RMSE (validation) = 0.9920028100571827 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-22T21:54:35.203Z] RMSE (validation) = 1.212631715629696 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-22T21:54:35.989Z] RMSE (validation) = 1.1025694034386657 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-22T21:54:36.784Z] RMSE (validation) = 0.9259652469249674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-22T21:54:37.559Z] RMSE (validation) = 0.8982232214592321 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-22T21:54:37.559Z] 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.
[2024-11-22T21:54:37.559Z] The best model improves the baseline by 14.52%.
[2024-11-22T21:54:37.924Z] Movies recommended for you:
[2024-11-22T21:54:37.924Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-22T21:54:37.924Z] There is no way to check that no silent failure occurred.
[2024-11-22T21:54:37.924Z] ====== movie-lens (apache-spark) [default], iteration 7 completed (8686.227 ms) ======
[2024-11-22T21:54:37.924Z] ====== movie-lens (apache-spark) [default], iteration 8 started ======
[2024-11-22T21:54:37.924Z] GC before operation: completed in 32.927 ms, heap usage 91.206 MB -> 53.594 MB.
[2024-11-22T21:54:39.165Z] RMSE (validation) = 3.62196895454876 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-22T21:54:40.421Z] RMSE (validation) = 2.1340923221100736 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-22T21:54:42.217Z] RMSE (validation) = 1.3105190428502682 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-22T21:54:42.994Z] RMSE (validation) = 0.9920028100571827 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-22T21:54:44.240Z] RMSE (validation) = 1.212631715629696 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-22T21:54:45.027Z] RMSE (validation) = 1.1025694034386657 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-22T21:54:45.824Z] RMSE (validation) = 0.9259652469249674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-22T21:54:46.229Z] RMSE (validation) = 0.8982232214592321 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-22T21:54:46.586Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9063003101263983.
[2024-11-22T21:54:46.586Z] The best model improves the baseline by 14.52%.
[2024-11-22T21:54:46.586Z] Movies recommended for you:
[2024-11-22T21:54:46.586Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-22T21:54:46.586Z] There is no way to check that no silent failure occurred.
[2024-11-22T21:54:46.586Z] ====== movie-lens (apache-spark) [default], iteration 8 completed (8794.226 ms) ======
[2024-11-22T21:54:46.586Z] ====== movie-lens (apache-spark) [default], iteration 9 started ======
[2024-11-22T21:54:46.586Z] GC before operation: completed in 34.989 ms, heap usage 90.568 MB -> 51.620 MB.
[2024-11-22T21:54:47.836Z] RMSE (validation) = 3.62196895454876 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-22T21:54:49.083Z] RMSE (validation) = 2.1340923221100736 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-22T21:54:50.347Z] RMSE (validation) = 1.3105190428502682 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-22T21:54:51.589Z] RMSE (validation) = 0.9920028100571827 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-22T21:54:52.369Z] RMSE (validation) = 1.212631715629696 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-22T21:54:53.150Z] RMSE (validation) = 1.1025694034386657 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-22T21:54:53.923Z] RMSE (validation) = 0.9259652469249674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-22T21:54:54.723Z] RMSE (validation) = 0.8982232214592321 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-22T21:54:54.723Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9063003101263983.
[2024-11-22T21:54:54.723Z] The best model improves the baseline by 14.52%.
[2024-11-22T21:54:55.095Z] Movies recommended for you:
[2024-11-22T21:54:55.095Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-22T21:54:55.095Z] There is no way to check that no silent failure occurred.
[2024-11-22T21:54:55.095Z] ====== movie-lens (apache-spark) [default], iteration 9 completed (8313.991 ms) ======
[2024-11-22T21:54:55.095Z] ====== movie-lens (apache-spark) [default], iteration 10 started ======
[2024-11-22T21:54:55.095Z] GC before operation: completed in 41.272 ms, heap usage 413.585 MB -> 54.281 MB.
[2024-11-22T21:54:56.340Z] RMSE (validation) = 3.62196895454876 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-22T21:54:57.594Z] RMSE (validation) = 2.1340923221100736 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-22T21:54:59.416Z] RMSE (validation) = 1.3105190428502682 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-22T21:55:00.673Z] RMSE (validation) = 0.9920028100571827 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-22T21:55:01.037Z] RMSE (validation) = 1.212631715629696 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-22T21:55:02.307Z] RMSE (validation) = 1.1025694034386657 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-22T21:55:02.692Z] RMSE (validation) = 0.9259652469249674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-22T21:55:03.488Z] RMSE (validation) = 0.8982232214592321 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-22T21:55:03.488Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9063003101263983.
[2024-11-22T21:55:03.488Z] The best model improves the baseline by 14.52%.
[2024-11-22T21:55:03.857Z] Movies recommended for you:
[2024-11-22T21:55:03.857Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-22T21:55:03.857Z] There is no way to check that no silent failure occurred.
[2024-11-22T21:55:03.857Z] ====== movie-lens (apache-spark) [default], iteration 10 completed (8726.649 ms) ======
[2024-11-22T21:55:03.857Z] ====== movie-lens (apache-spark) [default], iteration 11 started ======
[2024-11-22T21:55:03.857Z] GC before operation: completed in 28.564 ms, heap usage 82.332 MB -> 50.707 MB.
[2024-11-22T21:55:04.623Z] RMSE (validation) = 3.62196895454876 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-22T21:55:06.424Z] RMSE (validation) = 2.1340923221100736 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-22T21:55:07.659Z] RMSE (validation) = 1.3105190428502682 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-22T21:55:08.901Z] RMSE (validation) = 0.9920028100571827 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-22T21:55:09.673Z] RMSE (validation) = 1.212631715629696 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-22T21:55:10.439Z] RMSE (validation) = 1.1025694034386657 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-22T21:55:11.362Z] RMSE (validation) = 0.9259652469249674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-22T21:55:12.168Z] RMSE (validation) = 0.8982232214592321 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-22T21:55:12.526Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9063003101263983.
[2024-11-22T21:55:12.526Z] The best model improves the baseline by 14.52%.
[2024-11-22T21:55:12.526Z] Movies recommended for you:
[2024-11-22T21:55:12.526Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-22T21:55:12.526Z] There is no way to check that no silent failure occurred.
[2024-11-22T21:55:12.526Z] ====== movie-lens (apache-spark) [default], iteration 11 completed (8769.865 ms) ======
[2024-11-22T21:55:12.526Z] ====== movie-lens (apache-spark) [default], iteration 12 started ======
[2024-11-22T21:55:12.526Z] GC before operation: completed in 34.177 ms, heap usage 200.441 MB -> 50.868 MB.
[2024-11-22T21:55:13.778Z] RMSE (validation) = 3.62196895454876 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-22T21:55:15.035Z] RMSE (validation) = 2.1340923221100736 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-22T21:55:16.290Z] RMSE (validation) = 1.3105190428502682 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-22T21:55:18.069Z] RMSE (validation) = 0.9920028100571827 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-22T21:55:18.452Z] RMSE (validation) = 1.212631715629696 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-22T21:55:19.254Z] RMSE (validation) = 1.1025694034386657 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-22T21:55:20.533Z] RMSE (validation) = 0.9259652469249674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-22T21:55:20.949Z] RMSE (validation) = 0.8982232214592321 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-22T21:55:21.320Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9063003101263983.
[2024-11-22T21:55:21.320Z] The best model improves the baseline by 14.52%.
[2024-11-22T21:55:21.320Z] Movies recommended for you:
[2024-11-22T21:55:21.320Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-22T21:55:21.320Z] There is no way to check that no silent failure occurred.
[2024-11-22T21:55:21.320Z] ====== movie-lens (apache-spark) [default], iteration 12 completed (8713.012 ms) ======
[2024-11-22T21:55:21.320Z] ====== movie-lens (apache-spark) [default], iteration 13 started ======
[2024-11-22T21:55:21.320Z] GC before operation: completed in 50.356 ms, heap usage 120.863 MB -> 50.879 MB.
[2024-11-22T21:55:22.584Z] RMSE (validation) = 3.62196895454876 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-22T21:55:24.390Z] RMSE (validation) = 2.1340923221100736 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-22T21:55:25.649Z] RMSE (validation) = 1.3105190428502682 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-22T21:55:26.922Z] RMSE (validation) = 0.9920028100571827 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-22T21:55:27.700Z] RMSE (validation) = 1.212631715629696 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-22T21:55:28.481Z] RMSE (validation) = 1.1025694034386657 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-22T21:55:29.789Z] RMSE (validation) = 0.9259652469249674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-22T21:55:30.153Z] RMSE (validation) = 0.8982232214592321 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-22T21:55:30.516Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9063003101263983.
[2024-11-22T21:55:30.516Z] The best model improves the baseline by 14.52%.
[2024-11-22T21:55:30.516Z] Movies recommended for you:
[2024-11-22T21:55:30.516Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-22T21:55:30.516Z] There is no way to check that no silent failure occurred.
[2024-11-22T21:55:30.516Z] ====== movie-lens (apache-spark) [default], iteration 13 completed (9219.656 ms) ======
[2024-11-22T21:55:30.516Z] ====== movie-lens (apache-spark) [default], iteration 14 started ======
[2024-11-22T21:55:30.516Z] GC before operation: completed in 37.784 ms, heap usage 149.098 MB -> 50.621 MB.
[2024-11-22T21:55:31.779Z] RMSE (validation) = 3.62196895454876 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-22T21:55:33.025Z] RMSE (validation) = 2.1340923221100736 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-22T21:55:34.881Z] RMSE (validation) = 1.3105190428502682 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-22T21:55:36.148Z] RMSE (validation) = 0.9920028100571827 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-22T21:55:36.920Z] RMSE (validation) = 1.212631715629696 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-22T21:55:37.690Z] RMSE (validation) = 1.1025694034386657 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-22T21:55:38.473Z] RMSE (validation) = 0.9259652469249674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-22T21:55:39.253Z] RMSE (validation) = 0.8982232214592321 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-22T21:55:39.253Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9063003101263983.
[2024-11-22T21:55:39.253Z] The best model improves the baseline by 14.52%.
[2024-11-22T21:55:39.618Z] Movies recommended for you:
[2024-11-22T21:55:39.618Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-22T21:55:39.618Z] There is no way to check that no silent failure occurred.
[2024-11-22T21:55:39.618Z] ====== movie-lens (apache-spark) [default], iteration 14 completed (8936.616 ms) ======
[2024-11-22T21:55:39.618Z] ====== movie-lens (apache-spark) [default], iteration 15 started ======
[2024-11-22T21:55:39.618Z] GC before operation: completed in 38.629 ms, heap usage 253.393 MB -> 50.960 MB.
[2024-11-22T21:55:40.881Z] RMSE (validation) = 3.62196895454876 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-22T21:55:42.159Z] RMSE (validation) = 2.1340923221100736 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-22T21:55:43.955Z] RMSE (validation) = 1.3105190428502682 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-22T21:55:45.202Z] RMSE (validation) = 0.9920028100571827 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-22T21:55:46.099Z] RMSE (validation) = 1.212631715629696 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-22T21:55:46.924Z] RMSE (validation) = 1.1025694034386657 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-22T21:55:47.699Z] RMSE (validation) = 0.9259652469249674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-22T21:55:48.471Z] RMSE (validation) = 0.8982232214592321 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-22T21:55:48.471Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9063003101263983.
[2024-11-22T21:55:48.471Z] The best model improves the baseline by 14.52%.
[2024-11-22T21:55:48.471Z] Movies recommended for you:
[2024-11-22T21:55:48.471Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-22T21:55:48.471Z] There is no way to check that no silent failure occurred.
[2024-11-22T21:55:48.471Z] ====== movie-lens (apache-spark) [default], iteration 15 completed (9102.421 ms) ======
[2024-11-22T21:55:48.471Z] ====== movie-lens (apache-spark) [default], iteration 16 started ======
[2024-11-22T21:55:48.828Z] GC before operation: completed in 47.715 ms, heap usage 236.886 MB -> 50.977 MB.
[2024-11-22T21:55:50.081Z] RMSE (validation) = 3.6219689545487603 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-22T21:55:51.339Z] RMSE (validation) = 2.1340923221100736 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-22T21:55:53.159Z] RMSE (validation) = 1.3105190428502682 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-22T21:55:54.401Z] RMSE (validation) = 0.9920028100571827 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-22T21:55:55.170Z] RMSE (validation) = 1.212631715629696 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-22T21:55:56.067Z] RMSE (validation) = 1.1025694034386657 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-22T21:55:56.867Z] RMSE (validation) = 0.9259652469249674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-22T21:55:57.642Z] RMSE (validation) = 0.8982232214592321 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-22T21:55:57.642Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9063003101263983.
[2024-11-22T21:55:57.642Z] The best model improves the baseline by 14.52%.
[2024-11-22T21:55:57.642Z] Movies recommended for you:
[2024-11-22T21:55:57.642Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-22T21:55:57.642Z] There is no way to check that no silent failure occurred.
[2024-11-22T21:55:57.642Z] ====== movie-lens (apache-spark) [default], iteration 16 completed (9021.931 ms) ======
[2024-11-22T21:55:57.642Z] ====== movie-lens (apache-spark) [default], iteration 17 started ======
[2024-11-22T21:55:57.642Z] GC before operation: completed in 37.919 ms, heap usage 65.910 MB -> 50.678 MB.
[2024-11-22T21:55:58.897Z] RMSE (validation) = 3.6219689545487603 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-22T21:56:00.149Z] RMSE (validation) = 2.1340923221100736 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-22T21:56:01.427Z] RMSE (validation) = 1.3105190428502682 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-22T21:56:02.691Z] RMSE (validation) = 0.9920028100571827 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-22T21:56:03.476Z] RMSE (validation) = 1.212631715629696 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-22T21:56:04.252Z] RMSE (validation) = 1.1025694034386657 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-22T21:56:05.024Z] RMSE (validation) = 0.9259652469249674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-22T21:56:05.807Z] RMSE (validation) = 0.8982232214592321 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-22T21:56:06.173Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9063003101263983.
[2024-11-22T21:56:06.173Z] The best model improves the baseline by 14.52%.
[2024-11-22T21:56:06.173Z] Movies recommended for you:
[2024-11-22T21:56:06.173Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-22T21:56:06.173Z] There is no way to check that no silent failure occurred.
[2024-11-22T21:56:06.173Z] ====== movie-lens (apache-spark) [default], iteration 17 completed (8407.744 ms) ======
[2024-11-22T21:56:06.173Z] ====== movie-lens (apache-spark) [default], iteration 18 started ======
[2024-11-22T21:56:06.173Z] GC before operation: completed in 40.108 ms, heap usage 209.284 MB -> 50.925 MB.
[2024-11-22T21:56:07.422Z] RMSE (validation) = 3.6219689545487603 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-22T21:56:08.696Z] RMSE (validation) = 2.1340923221100736 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-22T21:56:09.957Z] RMSE (validation) = 1.3105190428502682 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-22T21:56:11.211Z] RMSE (validation) = 0.9920028100571827 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-22T21:56:12.007Z] RMSE (validation) = 1.212631715629696 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-22T21:56:12.785Z] RMSE (validation) = 1.1025694034386657 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-22T21:56:13.568Z] RMSE (validation) = 0.9259652469249674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-22T21:56:14.387Z] RMSE (validation) = 0.8982232214592321 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-22T21:56:14.744Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9063003101263983.
[2024-11-22T21:56:14.744Z] The best model improves the baseline by 14.52%.
[2024-11-22T21:56:14.744Z] Movies recommended for you:
[2024-11-22T21:56:14.744Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-22T21:56:14.744Z] There is no way to check that no silent failure occurred.
[2024-11-22T21:56:14.744Z] ====== movie-lens (apache-spark) [default], iteration 18 completed (8439.931 ms) ======
[2024-11-22T21:56:14.744Z] ====== movie-lens (apache-spark) [default], iteration 19 started ======
[2024-11-22T21:56:14.744Z] GC before operation: completed in 36.064 ms, heap usage 210.168 MB -> 51.083 MB.
[2024-11-22T21:56:15.981Z] RMSE (validation) = 3.62196895454876 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-22T21:56:17.223Z] RMSE (validation) = 2.1340923221100736 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-22T21:56:19.019Z] RMSE (validation) = 1.3105190428502682 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-22T21:56:20.291Z] RMSE (validation) = 0.9920028100571827 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-22T21:56:21.118Z] RMSE (validation) = 1.212631715629696 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-22T21:56:21.487Z] RMSE (validation) = 1.1025694034386657 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-22T21:56:22.760Z] RMSE (validation) = 0.9259652469249674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-22T21:56:23.607Z] RMSE (validation) = 0.8982232214592321 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-22T21:56:23.607Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9063003101263983.
[2024-11-22T21:56:23.607Z] The best model improves the baseline by 14.52%.
[2024-11-22T21:56:23.607Z] Movies recommended for you:
[2024-11-22T21:56:23.607Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-22T21:56:23.607Z] There is no way to check that no silent failure occurred.
[2024-11-22T21:56:23.607Z] ====== movie-lens (apache-spark) [default], iteration 19 completed (8992.662 ms) ======
[2024-11-22T21:56:23.967Z] -----------------------------------
[2024-11-22T21:56:23.967Z] renaissance-movie-lens_0_PASSED
[2024-11-22T21:56:23.967Z] -----------------------------------
[2024-11-22T21:56:23.967Z]
[2024-11-22T21:56:23.967Z] TEST TEARDOWN:
[2024-11-22T21:56:23.967Z] Nothing to be done for teardown.
[2024-11-22T21:56:23.967Z] renaissance-movie-lens_0 Finish Time: Fri Nov 22 16:56:23 2024 Epoch Time (ms): 1732312583683