renaissance-movie-lens_0
[2024-08-29T03:05:22.992Z] Running test renaissance-movie-lens_0 ...
[2024-08-29T03:05:22.992Z] ===============================================
[2024-08-29T03:05:22.992Z] renaissance-movie-lens_0 Start Time: Wed Aug 28 20:05:19 2024 Epoch Time (ms): 1724900719511
[2024-08-29T03:05:22.992Z] variation: NoOptions
[2024-08-29T03:05:22.992Z] JVM_OPTIONS:
[2024-08-29T03:05:22.992Z] { \
[2024-08-29T03:05:22.992Z] echo ""; echo "TEST SETUP:"; \
[2024-08-29T03:05:22.992Z] echo "Nothing to be done for setup."; \
[2024-08-29T03:05:22.992Z] mkdir -p "/Users/admin/workspace/workspace/Test_openjdk11_hs_extended.perf_x86-64_mac_testList_1/aqa-tests/TKG/../TKG/output_17248981116111/renaissance-movie-lens_0"; \
[2024-08-29T03:05:22.992Z] cd "/Users/admin/workspace/workspace/Test_openjdk11_hs_extended.perf_x86-64_mac_testList_1/aqa-tests/TKG/../TKG/output_17248981116111/renaissance-movie-lens_0"; \
[2024-08-29T03:05:22.992Z] echo ""; echo "TESTING:"; \
[2024-08-29T03:05:22.992Z] "/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_17248981116111/renaissance-movie-lens_0"/movie-lens.json" movie-lens; \
[2024-08-29T03:05:22.992Z] 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_17248981116111/renaissance-movie-lens_0"; else echo "-----------------------------------"; echo "renaissance-movie-lens_0""_FAILED"; echo "-----------------------------------"; fi; \
[2024-08-29T03:05:22.992Z] echo ""; echo "TEST TEARDOWN:"; \
[2024-08-29T03:05:22.992Z] echo "Nothing to be done for teardown."; \
[2024-08-29T03:05:22.992Z] } 2>&1 | tee -a "/Users/admin/workspace/workspace/Test_openjdk11_hs_extended.perf_x86-64_mac_testList_1/aqa-tests/TKG/../TKG/output_17248981116111/TestTargetResult";
[2024-08-29T03:05:22.992Z]
[2024-08-29T03:05:22.992Z] TEST SETUP:
[2024-08-29T03:05:22.992Z] Nothing to be done for setup.
[2024-08-29T03:05:22.992Z]
[2024-08-29T03:05:22.992Z] TESTING:
[2024-08-29T03:05:33.383Z] Using Spark's default log4j profile: org/apache/spark/log4j-defaults.properties
[2024-08-29T03:05:39.210Z] NOTE: 'movie-lens' benchmark uses Spark local executor with 3 (out of 3) threads.
[2024-08-29T03:05:52.342Z] Got 100004 ratings from 671 users on 9066 movies.
[2024-08-29T03:05:52.342Z] Training: 60056, validation: 20285, test: 19854
[2024-08-29T03:05:52.342Z] ====== movie-lens (apache-spark) [default], iteration 0 started ======
[2024-08-29T03:05:52.342Z] GC before operation: completed in 223.032 ms, heap usage 42.323 MB -> 36.620 MB.
[2024-08-29T03:06:18.761Z] RMSE (validation) = 3.62196895454876 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-08-29T03:06:34.363Z] RMSE (validation) = 2.1340923221100736 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-08-29T03:06:52.604Z] RMSE (validation) = 1.3105190428502682 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-08-29T03:07:11.599Z] RMSE (validation) = 0.9920028100571827 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-08-29T03:07:20.902Z] RMSE (validation) = 1.212631715629696 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-08-29T03:07:29.403Z] RMSE (validation) = 1.1025694034386657 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-08-29T03:07:36.880Z] RMSE (validation) = 0.9259652469249674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-08-29T03:07:56.164Z] RMSE (validation) = 0.8982232214592321 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-08-29T03:07:56.164Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9063003101263983.
[2024-08-29T03:07:56.164Z] The best model improves the baseline by 14.52%.
[2024-08-29T03:07:56.164Z] Movies recommended for you:
[2024-08-29T03:07:56.164Z] WARNING: This benchmark provides no result that can be validated.
[2024-08-29T03:07:56.164Z] There is no way to check that no silent failure occurred.
[2024-08-29T03:07:56.164Z] ====== movie-lens (apache-spark) [default], iteration 0 completed (122902.427 ms) ======
[2024-08-29T03:07:56.164Z] ====== movie-lens (apache-spark) [default], iteration 1 started ======
[2024-08-29T03:07:56.164Z] GC before operation: completed in 298.350 ms, heap usage 184.296 MB -> 46.821 MB.
[2024-08-29T03:08:09.130Z] RMSE (validation) = 3.62196895454876 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-08-29T03:08:24.180Z] RMSE (validation) = 2.1340923221100736 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-08-29T03:08:35.124Z] RMSE (validation) = 1.3105190428502682 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-08-29T03:08:48.845Z] RMSE (validation) = 0.9920028100571827 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-08-29T03:08:56.539Z] RMSE (validation) = 1.212631715629696 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-08-29T03:09:07.937Z] RMSE (validation) = 1.1025694034386657 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-08-29T03:09:17.537Z] RMSE (validation) = 0.9259652469249674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-08-29T03:09:26.757Z] RMSE (validation) = 0.8982232214592321 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-08-29T03:09:26.757Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9063003101263983.
[2024-08-29T03:09:26.757Z] The best model improves the baseline by 14.52%.
[2024-08-29T03:09:26.757Z] Movies recommended for you:
[2024-08-29T03:09:26.757Z] WARNING: This benchmark provides no result that can be validated.
[2024-08-29T03:09:26.757Z] There is no way to check that no silent failure occurred.
[2024-08-29T03:09:26.757Z] ====== movie-lens (apache-spark) [default], iteration 1 completed (91215.948 ms) ======
[2024-08-29T03:09:26.757Z] ====== movie-lens (apache-spark) [default], iteration 2 started ======
[2024-08-29T03:09:27.824Z] GC before operation: completed in 1057.397 ms, heap usage 142.295 MB -> 48.847 MB.
[2024-08-29T03:09:40.605Z] RMSE (validation) = 3.6219689545487603 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-08-29T03:09:53.685Z] RMSE (validation) = 2.1340923221100736 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-08-29T03:10:12.866Z] RMSE (validation) = 1.3105190428502682 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-08-29T03:10:20.130Z] RMSE (validation) = 0.9920028100571827 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-08-29T03:10:26.675Z] RMSE (validation) = 1.212631715629696 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-08-29T03:10:33.127Z] RMSE (validation) = 1.1025694034386657 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-08-29T03:10:49.165Z] RMSE (validation) = 0.9259652469249674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-08-29T03:10:51.405Z] RMSE (validation) = 0.8982232214592321 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-08-29T03:10:52.346Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9063003101263983.
[2024-08-29T03:10:52.346Z] The best model improves the baseline by 14.52%.
[2024-08-29T03:10:52.346Z] Movies recommended for you:
[2024-08-29T03:10:52.346Z] WARNING: This benchmark provides no result that can be validated.
[2024-08-29T03:10:52.346Z] There is no way to check that no silent failure occurred.
[2024-08-29T03:10:52.346Z] ====== movie-lens (apache-spark) [default], iteration 2 completed (84738.357 ms) ======
[2024-08-29T03:10:52.346Z] ====== movie-lens (apache-spark) [default], iteration 3 started ======
[2024-08-29T03:10:52.346Z] GC before operation: completed in 182.337 ms, heap usage 234.468 MB -> 49.204 MB.
[2024-08-29T03:11:06.085Z] RMSE (validation) = 3.62196895454876 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-08-29T03:11:16.860Z] RMSE (validation) = 2.1340923221100736 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-08-29T03:11:25.661Z] RMSE (validation) = 1.3105190428502682 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-08-29T03:11:36.749Z] RMSE (validation) = 0.9920028100571827 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-08-29T03:11:46.085Z] RMSE (validation) = 1.212631715629696 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-08-29T03:12:00.163Z] RMSE (validation) = 1.1025694034386657 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-08-29T03:12:05.105Z] RMSE (validation) = 0.9259652469249674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-08-29T03:12:12.853Z] RMSE (validation) = 0.8982232214592321 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-08-29T03:12:12.853Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9063003101263983.
[2024-08-29T03:12:12.853Z] The best model improves the baseline by 14.52%.
[2024-08-29T03:12:13.280Z] Movies recommended for you:
[2024-08-29T03:12:13.280Z] WARNING: This benchmark provides no result that can be validated.
[2024-08-29T03:12:13.280Z] There is no way to check that no silent failure occurred.
[2024-08-29T03:12:13.280Z] ====== movie-lens (apache-spark) [default], iteration 3 completed (80558.027 ms) ======
[2024-08-29T03:12:13.280Z] ====== movie-lens (apache-spark) [default], iteration 4 started ======
[2024-08-29T03:12:13.280Z] GC before operation: completed in 230.868 ms, heap usage 259.758 MB -> 49.533 MB.
[2024-08-29T03:12:29.031Z] RMSE (validation) = 3.62196895454876 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-08-29T03:12:45.270Z] RMSE (validation) = 2.1340923221100736 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-08-29T03:12:58.401Z] RMSE (validation) = 1.3105190428502682 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-08-29T03:13:09.680Z] RMSE (validation) = 0.9920028100571827 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-08-29T03:13:17.081Z] RMSE (validation) = 1.212631715629696 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-08-29T03:13:24.503Z] RMSE (validation) = 1.1025694034386657 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-08-29T03:13:31.781Z] RMSE (validation) = 0.9259652469249674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-08-29T03:13:38.642Z] RMSE (validation) = 0.8982232214592321 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-08-29T03:13:39.685Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9063003101263983.
[2024-08-29T03:13:39.685Z] The best model improves the baseline by 14.52%.
[2024-08-29T03:13:39.685Z] Movies recommended for you:
[2024-08-29T03:13:39.685Z] WARNING: This benchmark provides no result that can be validated.
[2024-08-29T03:13:39.685Z] There is no way to check that no silent failure occurred.
[2024-08-29T03:13:39.685Z] ====== movie-lens (apache-spark) [default], iteration 4 completed (86386.073 ms) ======
[2024-08-29T03:13:39.685Z] ====== movie-lens (apache-spark) [default], iteration 5 started ======
[2024-08-29T03:13:41.056Z] GC before operation: completed in 935.873 ms, heap usage 412.647 MB -> 53.007 MB.
[2024-08-29T03:13:55.963Z] RMSE (validation) = 3.6219689545487603 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-08-29T03:14:08.796Z] RMSE (validation) = 2.1340923221100736 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-08-29T03:14:21.765Z] RMSE (validation) = 1.3105190428502682 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-08-29T03:14:37.069Z] RMSE (validation) = 0.9920028100571827 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-08-29T03:14:42.993Z] RMSE (validation) = 1.212631715629696 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-08-29T03:14:51.868Z] RMSE (validation) = 1.1025694034386657 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-08-29T03:14:58.163Z] RMSE (validation) = 0.9259652469249674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-08-29T03:15:14.624Z] RMSE (validation) = 0.8982232214592321 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-08-29T03:15:14.624Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9063003101263983.
[2024-08-29T03:15:14.624Z] The best model improves the baseline by 14.52%.
[2024-08-29T03:15:14.624Z] Movies recommended for you:
[2024-08-29T03:15:14.624Z] WARNING: This benchmark provides no result that can be validated.
[2024-08-29T03:15:14.624Z] There is no way to check that no silent failure occurred.
[2024-08-29T03:15:14.624Z] ====== movie-lens (apache-spark) [default], iteration 5 completed (94072.180 ms) ======
[2024-08-29T03:15:14.624Z] ====== movie-lens (apache-spark) [default], iteration 6 started ======
[2024-08-29T03:15:15.057Z] GC before operation: completed in 240.118 ms, heap usage 342.402 MB -> 49.750 MB.
[2024-08-29T03:15:27.900Z] RMSE (validation) = 3.6219689545487603 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-08-29T03:15:43.264Z] RMSE (validation) = 2.1340923221100736 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-08-29T03:15:56.432Z] RMSE (validation) = 1.3105190428502682 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-08-29T03:16:09.315Z] RMSE (validation) = 0.9920028100571827 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-08-29T03:16:27.697Z] RMSE (validation) = 1.212631715629696 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-08-29T03:16:30.545Z] RMSE (validation) = 1.1025694034386657 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-08-29T03:16:38.041Z] RMSE (validation) = 0.9259652469249674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-08-29T03:16:48.962Z] RMSE (validation) = 0.8982232214592321 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-08-29T03:16:49.391Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9063003101263983.
[2024-08-29T03:16:49.391Z] The best model improves the baseline by 14.52%.
[2024-08-29T03:16:49.391Z] Movies recommended for you:
[2024-08-29T03:16:49.391Z] WARNING: This benchmark provides no result that can be validated.
[2024-08-29T03:16:49.391Z] There is no way to check that no silent failure occurred.
[2024-08-29T03:16:49.391Z] ====== movie-lens (apache-spark) [default], iteration 6 completed (94457.158 ms) ======
[2024-08-29T03:16:49.391Z] ====== movie-lens (apache-spark) [default], iteration 7 started ======
[2024-08-29T03:16:49.995Z] GC before operation: completed in 244.264 ms, heap usage 206.373 MB -> 49.783 MB.
[2024-08-29T03:17:05.408Z] RMSE (validation) = 3.62196895454876 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-08-29T03:17:18.347Z] RMSE (validation) = 2.1340923221100736 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-08-29T03:17:40.620Z] RMSE (validation) = 1.3105190428502682 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-08-29T03:17:53.754Z] RMSE (validation) = 0.9920028100571827 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-08-29T03:17:59.682Z] RMSE (validation) = 1.212631715629696 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-08-29T03:18:07.568Z] RMSE (validation) = 1.1025694034386657 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-08-29T03:18:16.777Z] RMSE (validation) = 0.9259652469249674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-08-29T03:18:25.377Z] RMSE (validation) = 0.8982232214592321 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-08-29T03:18:25.909Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9063003101263983.
[2024-08-29T03:18:25.909Z] The best model improves the baseline by 14.52%.
[2024-08-29T03:18:26.499Z] Movies recommended for you:
[2024-08-29T03:18:26.499Z] WARNING: This benchmark provides no result that can be validated.
[2024-08-29T03:18:26.499Z] There is no way to check that no silent failure occurred.
[2024-08-29T03:18:26.499Z] ====== movie-lens (apache-spark) [default], iteration 7 completed (96465.098 ms) ======
[2024-08-29T03:18:26.499Z] ====== movie-lens (apache-spark) [default], iteration 8 started ======
[2024-08-29T03:18:26.499Z] GC before operation: completed in 294.999 ms, heap usage 96.153 MB -> 52.127 MB.
[2024-08-29T03:18:42.042Z] RMSE (validation) = 3.62196895454876 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-08-29T03:18:54.883Z] RMSE (validation) = 2.1340923221100736 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-08-29T03:19:10.384Z] RMSE (validation) = 1.3105190428502682 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-08-29T03:19:23.478Z] RMSE (validation) = 0.9920028100571827 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-08-29T03:19:40.271Z] RMSE (validation) = 1.212631715629696 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-08-29T03:19:47.261Z] RMSE (validation) = 1.1025694034386657 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-08-29T03:19:54.902Z] RMSE (validation) = 0.9259652469249674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-08-29T03:20:02.078Z] RMSE (validation) = 0.8982232214592321 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-08-29T03:20:02.521Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9063003101263983.
[2024-08-29T03:20:02.521Z] The best model improves the baseline by 14.52%.
[2024-08-29T03:20:03.048Z] Movies recommended for you:
[2024-08-29T03:20:03.048Z] WARNING: This benchmark provides no result that can be validated.
[2024-08-29T03:20:03.048Z] There is no way to check that no silent failure occurred.
[2024-08-29T03:20:03.048Z] ====== movie-lens (apache-spark) [default], iteration 8 completed (96275.858 ms) ======
[2024-08-29T03:20:03.048Z] ====== movie-lens (apache-spark) [default], iteration 9 started ======
[2024-08-29T03:20:03.048Z] GC before operation: completed in 224.562 ms, heap usage 434.326 MB -> 53.289 MB.
[2024-08-29T03:20:21.808Z] RMSE (validation) = 3.6219689545487603 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-08-29T03:20:41.239Z] RMSE (validation) = 2.1340923221100736 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-08-29T03:20:59.540Z] RMSE (validation) = 1.3105190428502682 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-08-29T03:21:10.009Z] RMSE (validation) = 0.9920028100571827 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-08-29T03:21:20.560Z] RMSE (validation) = 1.212631715629696 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-08-29T03:21:27.526Z] RMSE (validation) = 1.1025694034386657 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-08-29T03:21:43.340Z] RMSE (validation) = 0.9259652469249674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-08-29T03:21:49.454Z] RMSE (validation) = 0.8982232214592321 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-08-29T03:21:50.490Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9063003101263983.
[2024-08-29T03:21:50.490Z] The best model improves the baseline by 14.52%.
[2024-08-29T03:21:50.490Z] Movies recommended for you:
[2024-08-29T03:21:50.490Z] WARNING: This benchmark provides no result that can be validated.
[2024-08-29T03:21:50.490Z] There is no way to check that no silent failure occurred.
[2024-08-29T03:21:50.490Z] ====== movie-lens (apache-spark) [default], iteration 9 completed (107527.547 ms) ======
[2024-08-29T03:21:50.490Z] ====== movie-lens (apache-spark) [default], iteration 10 started ======
[2024-08-29T03:21:50.867Z] GC before operation: completed in 287.665 ms, heap usage 384.875 MB -> 53.326 MB.
[2024-08-29T03:22:06.272Z] RMSE (validation) = 3.6219689545487603 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-08-29T03:22:18.990Z] RMSE (validation) = 2.1340923221100736 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-08-29T03:22:35.237Z] RMSE (validation) = 1.3105190428502682 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-08-29T03:22:50.643Z] RMSE (validation) = 0.9920028100571827 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-08-29T03:22:56.519Z] RMSE (validation) = 1.212631715629696 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-08-29T03:23:15.194Z] RMSE (validation) = 1.1025694034386657 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-08-29T03:23:20.008Z] RMSE (validation) = 0.9259652469249674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-08-29T03:23:29.108Z] RMSE (validation) = 0.8982232214592321 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-08-29T03:23:29.108Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9063003101263983.
[2024-08-29T03:23:29.108Z] The best model improves the baseline by 14.52%.
[2024-08-29T03:23:29.856Z] Movies recommended for you:
[2024-08-29T03:23:29.856Z] WARNING: This benchmark provides no result that can be validated.
[2024-08-29T03:23:29.856Z] There is no way to check that no silent failure occurred.
[2024-08-29T03:23:29.856Z] ====== movie-lens (apache-spark) [default], iteration 10 completed (98565.152 ms) ======
[2024-08-29T03:23:29.856Z] ====== movie-lens (apache-spark) [default], iteration 11 started ======
[2024-08-29T03:23:30.858Z] GC before operation: completed in 1347.348 ms, heap usage 100.335 MB -> 50.428 MB.
[2024-08-29T03:23:48.941Z] RMSE (validation) = 3.62196895454876 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-08-29T03:24:04.576Z] RMSE (validation) = 2.1340923221100736 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-08-29T03:24:23.650Z] RMSE (validation) = 1.3105190428502682 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-08-29T03:24:37.065Z] RMSE (validation) = 0.9920028100571827 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-08-29T03:24:46.761Z] RMSE (validation) = 1.212631715629696 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-08-29T03:24:54.595Z] RMSE (validation) = 1.1025694034386657 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-08-29T03:25:04.009Z] RMSE (validation) = 0.9259652469249674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-08-29T03:25:08.541Z] RMSE (validation) = 0.8982232214592321 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-08-29T03:25:09.521Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9063003101263983.
[2024-08-29T03:25:09.521Z] The best model improves the baseline by 14.52%.
[2024-08-29T03:25:10.036Z] Movies recommended for you:
[2024-08-29T03:25:10.036Z] WARNING: This benchmark provides no result that can be validated.
[2024-08-29T03:25:10.036Z] There is no way to check that no silent failure occurred.
[2024-08-29T03:25:10.036Z] ====== movie-lens (apache-spark) [default], iteration 11 completed (99131.458 ms) ======
[2024-08-29T03:25:10.036Z] ====== movie-lens (apache-spark) [default], iteration 12 started ======
[2024-08-29T03:25:10.036Z] GC before operation: completed in 248.834 ms, heap usage 345.053 MB -> 50.053 MB.
[2024-08-29T03:25:25.313Z] RMSE (validation) = 3.62196895454876 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-08-29T03:25:38.295Z] RMSE (validation) = 2.1340923221100736 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-08-29T03:25:51.025Z] RMSE (validation) = 1.3105190428502682 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-08-29T03:26:03.988Z] RMSE (validation) = 0.9920028100571827 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-08-29T03:26:14.791Z] RMSE (validation) = 1.212631715629696 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-08-29T03:26:22.281Z] RMSE (validation) = 1.1025694034386657 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-08-29T03:26:30.141Z] RMSE (validation) = 0.9259652469249674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-08-29T03:26:38.912Z] RMSE (validation) = 0.8982232214592321 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-08-29T03:26:38.912Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9063003101263983.
[2024-08-29T03:26:39.449Z] The best model improves the baseline by 14.52%.
[2024-08-29T03:26:39.449Z] Movies recommended for you:
[2024-08-29T03:26:39.449Z] WARNING: This benchmark provides no result that can be validated.
[2024-08-29T03:26:39.449Z] There is no way to check that no silent failure occurred.
[2024-08-29T03:26:39.449Z] ====== movie-lens (apache-spark) [default], iteration 12 completed (89412.499 ms) ======
[2024-08-29T03:26:39.449Z] ====== movie-lens (apache-spark) [default], iteration 13 started ======
[2024-08-29T03:26:40.010Z] GC before operation: completed in 225.458 ms, heap usage 120.693 MB -> 49.973 MB.
[2024-08-29T03:26:55.379Z] RMSE (validation) = 3.62196895454876 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-08-29T03:27:10.445Z] RMSE (validation) = 2.1340923221100736 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-08-29T03:27:28.944Z] RMSE (validation) = 1.3105190428502682 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-08-29T03:27:41.769Z] RMSE (validation) = 0.9920028100571827 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-08-29T03:27:54.595Z] RMSE (validation) = 1.212631715629696 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-08-29T03:28:00.875Z] RMSE (validation) = 1.1025694034386657 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-08-29T03:28:08.329Z] RMSE (validation) = 0.9259652469249674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-08-29T03:28:15.452Z] RMSE (validation) = 0.8982232214592321 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-08-29T03:28:15.876Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9063003101263983.
[2024-08-29T03:28:15.877Z] The best model improves the baseline by 14.52%.
[2024-08-29T03:28:16.330Z] Movies recommended for you:
[2024-08-29T03:28:16.330Z] WARNING: This benchmark provides no result that can be validated.
[2024-08-29T03:28:16.330Z] There is no way to check that no silent failure occurred.
[2024-08-29T03:28:16.330Z] ====== movie-lens (apache-spark) [default], iteration 13 completed (96427.725 ms) ======
[2024-08-29T03:28:16.330Z] ====== movie-lens (apache-spark) [default], iteration 14 started ======
[2024-08-29T03:28:17.080Z] GC before operation: completed in 202.071 ms, heap usage 435.476 MB -> 53.237 MB.
[2024-08-29T03:28:32.492Z] RMSE (validation) = 3.62196895454876 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-08-29T03:28:48.209Z] RMSE (validation) = 2.1340923221100736 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-08-29T03:29:01.181Z] RMSE (validation) = 1.3105190428502682 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-08-29T03:29:17.110Z] RMSE (validation) = 0.9920028100571827 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-08-29T03:29:21.374Z] RMSE (validation) = 1.212631715629696 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-08-29T03:29:30.623Z] RMSE (validation) = 1.1025694034386657 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-08-29T03:29:36.890Z] RMSE (validation) = 0.9259652469249674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-08-29T03:29:45.702Z] RMSE (validation) = 0.8982232214592321 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-08-29T03:29:46.760Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9063003101263983.
[2024-08-29T03:29:46.760Z] The best model improves the baseline by 14.52%.
[2024-08-29T03:29:47.982Z] Movies recommended for you:
[2024-08-29T03:29:47.982Z] WARNING: This benchmark provides no result that can be validated.
[2024-08-29T03:29:47.982Z] There is no way to check that no silent failure occurred.
[2024-08-29T03:29:47.982Z] ====== movie-lens (apache-spark) [default], iteration 14 completed (91594.106 ms) ======
[2024-08-29T03:29:47.982Z] ====== movie-lens (apache-spark) [default], iteration 15 started ======
[2024-08-29T03:29:48.652Z] GC before operation: completed in 276.117 ms, heap usage 222.279 MB -> 50.074 MB.
[2024-08-29T03:30:04.153Z] RMSE (validation) = 3.62196895454876 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-08-29T03:30:19.509Z] RMSE (validation) = 2.1340923221100736 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-08-29T03:30:42.002Z] RMSE (validation) = 1.3105190428502682 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-08-29T03:30:54.505Z] RMSE (validation) = 0.9920028100571827 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-08-29T03:31:00.259Z] RMSE (validation) = 1.212631715629696 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-08-29T03:31:14.613Z] RMSE (validation) = 1.1025694034386657 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-08-29T03:31:23.837Z] RMSE (validation) = 0.9259652469249674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-08-29T03:31:33.395Z] RMSE (validation) = 0.8982232214592321 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-08-29T03:31:33.395Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9063003101263983.
[2024-08-29T03:31:34.427Z] The best model improves the baseline by 14.52%.
[2024-08-29T03:31:35.598Z] Movies recommended for you:
[2024-08-29T03:31:35.598Z] WARNING: This benchmark provides no result that can be validated.
[2024-08-29T03:31:35.598Z] There is no way to check that no silent failure occurred.
[2024-08-29T03:31:35.598Z] ====== movie-lens (apache-spark) [default], iteration 15 completed (106791.769 ms) ======
[2024-08-29T03:31:35.598Z] ====== movie-lens (apache-spark) [default], iteration 16 started ======
[2024-08-29T03:31:35.599Z] GC before operation: completed in 421.206 ms, heap usage 357.855 MB -> 53.427 MB.
[2024-08-29T03:31:51.357Z] RMSE (validation) = 3.6219689545487603 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-08-29T03:32:10.092Z] RMSE (validation) = 2.1340923221100736 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-08-29T03:32:23.697Z] RMSE (validation) = 1.3105190428502682 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-08-29T03:32:36.519Z] RMSE (validation) = 0.9920028100571827 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-08-29T03:32:46.953Z] RMSE (validation) = 1.212631715629696 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-08-29T03:32:54.160Z] RMSE (validation) = 1.1025694034386657 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-08-29T03:33:09.867Z] RMSE (validation) = 0.9259652469249674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-08-29T03:33:12.896Z] RMSE (validation) = 0.8982232214592321 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-08-29T03:33:16.033Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9063003101263983.
[2024-08-29T03:33:16.033Z] The best model improves the baseline by 14.52%.
[2024-08-29T03:33:16.033Z] Movies recommended for you:
[2024-08-29T03:33:16.033Z] WARNING: This benchmark provides no result that can be validated.
[2024-08-29T03:33:16.033Z] There is no way to check that no silent failure occurred.
[2024-08-29T03:33:16.033Z] ====== movie-lens (apache-spark) [default], iteration 16 completed (100345.687 ms) ======
[2024-08-29T03:33:16.033Z] ====== movie-lens (apache-spark) [default], iteration 17 started ======
[2024-08-29T03:33:16.033Z] GC before operation: completed in 231.975 ms, heap usage 342.381 MB -> 50.058 MB.
[2024-08-29T03:33:31.504Z] RMSE (validation) = 3.6219689545487603 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-08-29T03:33:46.528Z] RMSE (validation) = 2.1340923221100736 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-08-29T03:33:59.428Z] RMSE (validation) = 1.3105190428502682 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-08-29T03:34:12.472Z] RMSE (validation) = 0.9920028100571827 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-08-29T03:34:18.352Z] RMSE (validation) = 1.212631715629696 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-08-29T03:34:27.773Z] RMSE (validation) = 1.1025694034386657 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-08-29T03:34:33.799Z] RMSE (validation) = 0.9259652469249674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-08-29T03:34:49.687Z] RMSE (validation) = 0.8982232214592321 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-08-29T03:34:49.687Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9063003101263983.
[2024-08-29T03:34:49.687Z] The best model improves the baseline by 14.52%.
[2024-08-29T03:34:50.156Z] Movies recommended for you:
[2024-08-29T03:34:50.156Z] WARNING: This benchmark provides no result that can be validated.
[2024-08-29T03:34:50.156Z] There is no way to check that no silent failure occurred.
[2024-08-29T03:34:50.156Z] ====== movie-lens (apache-spark) [default], iteration 17 completed (94091.568 ms) ======
[2024-08-29T03:34:50.156Z] ====== movie-lens (apache-spark) [default], iteration 18 started ======
[2024-08-29T03:34:50.623Z] GC before operation: completed in 270.353 ms, heap usage 462.853 MB -> 53.391 MB.
[2024-08-29T03:35:02.892Z] RMSE (validation) = 3.6219689545487603 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-08-29T03:35:15.863Z] RMSE (validation) = 2.1340923221100736 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-08-29T03:35:31.417Z] RMSE (validation) = 1.3105190428502682 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-08-29T03:35:44.007Z] RMSE (validation) = 0.9920028100571827 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-08-29T03:35:52.827Z] RMSE (validation) = 1.212631715629696 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-08-29T03:36:00.287Z] RMSE (validation) = 1.1025694034386657 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-08-29T03:36:06.631Z] RMSE (validation) = 0.9259652469249674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-08-29T03:36:14.430Z] RMSE (validation) = 0.8982232214592321 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-08-29T03:36:14.891Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9063003101263983.
[2024-08-29T03:36:14.892Z] The best model improves the baseline by 14.52%.
[2024-08-29T03:36:15.369Z] Movies recommended for you:
[2024-08-29T03:36:15.369Z] WARNING: This benchmark provides no result that can be validated.
[2024-08-29T03:36:15.369Z] There is no way to check that no silent failure occurred.
[2024-08-29T03:36:15.369Z] ====== movie-lens (apache-spark) [default], iteration 18 completed (84745.281 ms) ======
[2024-08-29T03:36:15.369Z] ====== movie-lens (apache-spark) [default], iteration 19 started ======
[2024-08-29T03:36:15.369Z] GC before operation: completed in 307.529 ms, heap usage 194.689 MB -> 50.153 MB.
[2024-08-29T03:36:33.709Z] RMSE (validation) = 3.6219689545487603 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-08-29T03:36:46.647Z] RMSE (validation) = 2.1340923221100736 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-08-29T03:36:56.873Z] RMSE (validation) = 1.3105190428502682 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-08-29T03:37:09.828Z] RMSE (validation) = 0.9920028100571827 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-08-29T03:37:17.138Z] RMSE (validation) = 1.212631715629696 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-08-29T03:37:23.151Z] RMSE (validation) = 1.1025694034386657 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-08-29T03:37:28.683Z] RMSE (validation) = 0.9259652469249674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-08-29T03:37:37.791Z] RMSE (validation) = 0.8982232214592321 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-08-29T03:37:38.741Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9063003101263983.
[2024-08-29T03:37:38.741Z] The best model improves the baseline by 14.52%.
[2024-08-29T03:37:38.741Z] Movies recommended for you:
[2024-08-29T03:37:38.741Z] WARNING: This benchmark provides no result that can be validated.
[2024-08-29T03:37:38.741Z] There is no way to check that no silent failure occurred.
[2024-08-29T03:37:38.741Z] ====== movie-lens (apache-spark) [default], iteration 19 completed (83419.694 ms) ======
[2024-08-29T03:37:44.564Z] -----------------------------------
[2024-08-29T03:37:44.564Z] renaissance-movie-lens_0_PASSED
[2024-08-29T03:37:44.564Z] -----------------------------------
[2024-08-29T03:37:44.564Z]
[2024-08-29T03:37:44.564Z] TEST TEARDOWN:
[2024-08-29T03:37:44.564Z] Nothing to be done for teardown.
[2024-08-29T03:37:44.564Z] renaissance-movie-lens_0 Finish Time: Wed Aug 28 20:37:43 2024 Epoch Time (ms): 1724902663273