renaissance-movie-lens_0
[2024-09-25T20:32:55.628Z] Running test renaissance-movie-lens_0 ...
[2024-09-25T20:32:55.628Z] ===============================================
[2024-09-25T20:32:55.628Z] renaissance-movie-lens_0 Start Time: Wed Sep 25 16:32:55 2024 Epoch Time (ms): 1727296375275
[2024-09-25T20:32:55.628Z] variation: NoOptions
[2024-09-25T20:32:55.628Z] JVM_OPTIONS:
[2024-09-25T20:32:55.628Z] { \
[2024-09-25T20:32:55.628Z] echo ""; echo "TEST SETUP:"; \
[2024-09-25T20:32:55.628Z] echo "Nothing to be done for setup."; \
[2024-09-25T20:32:55.628Z] mkdir -p "/Users/admin/workspace/workspace/Test_openjdk17_hs_extended.perf_aarch64_mac/aqa-tests/TKG/../TKG/output_17272960926921/renaissance-movie-lens_0"; \
[2024-09-25T20:32:55.628Z] cd "/Users/admin/workspace/workspace/Test_openjdk17_hs_extended.perf_aarch64_mac/aqa-tests/TKG/../TKG/output_17272960926921/renaissance-movie-lens_0"; \
[2024-09-25T20:32:55.628Z] echo ""; echo "TESTING:"; \
[2024-09-25T20:32:55.629Z] "/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_17272960926921/renaissance-movie-lens_0"/movie-lens.json" movie-lens; \
[2024-09-25T20:32:55.629Z] 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_17272960926921/renaissance-movie-lens_0"; else echo "-----------------------------------"; echo "renaissance-movie-lens_0""_FAILED"; echo "-----------------------------------"; fi; \
[2024-09-25T20:32:55.629Z] echo ""; echo "TEST TEARDOWN:"; \
[2024-09-25T20:32:55.629Z] echo "Nothing to be done for teardown."; \
[2024-09-25T20:32:55.629Z] } 2>&1 | tee -a "/Users/admin/workspace/workspace/Test_openjdk17_hs_extended.perf_aarch64_mac/aqa-tests/TKG/../TKG/output_17272960926921/TestTargetResult";
[2024-09-25T20:32:55.629Z]
[2024-09-25T20:32:55.629Z] TEST SETUP:
[2024-09-25T20:32:55.629Z] Nothing to be done for setup.
[2024-09-25T20:32:55.629Z]
[2024-09-25T20:32:55.629Z] TESTING:
[2024-09-25T20:32:56.862Z] Using Spark's default log4j profile: org/apache/spark/log4j-defaults.properties
[2024-09-25T20:32:57.635Z] NOTE: 'movie-lens' benchmark uses Spark local executor with 3 (out of 3) threads.
[2024-09-25T20:32:58.913Z] Got 100004 ratings from 671 users on 9066 movies.
[2024-09-25T20:32:58.913Z] Training: 60056, validation: 20285, test: 19854
[2024-09-25T20:32:58.913Z] ====== movie-lens (apache-spark) [default], iteration 0 started ======
[2024-09-25T20:32:58.913Z] GC before operation: completed in 18.742 ms, heap usage 83.922 MB -> 37.314 MB.
[2024-09-25T20:33:01.341Z] RMSE (validation) = 3.6219689545487603 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-09-25T20:33:03.160Z] RMSE (validation) = 2.1340923221100736 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-09-25T20:33:04.414Z] RMSE (validation) = 1.3105190428502682 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-09-25T20:33:05.663Z] RMSE (validation) = 0.9920028100571827 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-09-25T20:33:06.438Z] RMSE (validation) = 1.212631715629696 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-09-25T20:33:07.225Z] RMSE (validation) = 1.1025694034386657 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-09-25T20:33:07.998Z] RMSE (validation) = 0.9259652469249674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-09-25T20:33:08.763Z] RMSE (validation) = 0.8982232214592321 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-09-25T20:33:08.763Z] 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.
[2024-09-25T20:33:08.763Z] The best model improves the baseline by 14.52%.
[2024-09-25T20:33:08.763Z] Movies recommended for you:
[2024-09-25T20:33:08.763Z] WARNING: This benchmark provides no result that can be validated.
[2024-09-25T20:33:08.763Z] There is no way to check that no silent failure occurred.
[2024-09-25T20:33:08.763Z] ====== movie-lens (apache-spark) [default], iteration 0 completed (9913.781 ms) ======
[2024-09-25T20:33:08.763Z] ====== movie-lens (apache-spark) [default], iteration 1 started ======
[2024-09-25T20:33:08.763Z] GC before operation: completed in 30.541 ms, heap usage 89.955 MB -> 55.987 MB.
[2024-09-25T20:33:09.996Z] RMSE (validation) = 3.62196895454876 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-09-25T20:33:11.265Z] RMSE (validation) = 2.1340923221100736 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-09-25T20:33:12.056Z] RMSE (validation) = 1.3105190428502682 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-09-25T20:33:13.308Z] RMSE (validation) = 0.9920028100571827 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-09-25T20:33:13.697Z] RMSE (validation) = 1.212631715629696 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-09-25T20:33:14.469Z] RMSE (validation) = 1.1025694034386657 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-09-25T20:33:14.827Z] RMSE (validation) = 0.9259652469249674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-09-25T20:33:15.594Z] RMSE (validation) = 0.8982232214592321 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-09-25T20:33:15.594Z] 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.
[2024-09-25T20:33:15.594Z] The best model improves the baseline by 14.52%.
[2024-09-25T20:33:15.594Z] Movies recommended for you:
[2024-09-25T20:33:15.594Z] WARNING: This benchmark provides no result that can be validated.
[2024-09-25T20:33:15.594Z] There is no way to check that no silent failure occurred.
[2024-09-25T20:33:15.594Z] ====== movie-lens (apache-spark) [default], iteration 1 completed (6876.251 ms) ======
[2024-09-25T20:33:15.594Z] ====== movie-lens (apache-spark) [default], iteration 2 started ======
[2024-09-25T20:33:15.594Z] GC before operation: completed in 28.741 ms, heap usage 295.034 MB -> 49.738 MB.
[2024-09-25T20:33:16.850Z] RMSE (validation) = 3.6219689545487603 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-09-25T20:33:17.628Z] RMSE (validation) = 2.1340923221100736 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-09-25T20:33:18.871Z] RMSE (validation) = 1.3105190428502682 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-09-25T20:33:19.632Z] RMSE (validation) = 0.9920028100571827 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-09-25T20:33:20.412Z] RMSE (validation) = 1.212631715629696 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-09-25T20:33:20.768Z] RMSE (validation) = 1.1025694034386657 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-09-25T20:33:21.538Z] RMSE (validation) = 0.9259652469249674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-09-25T20:33:21.931Z] RMSE (validation) = 0.8982232214592321 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-09-25T20:33:21.931Z] 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.
[2024-09-25T20:33:21.931Z] The best model improves the baseline by 14.52%.
[2024-09-25T20:33:22.291Z] Movies recommended for you:
[2024-09-25T20:33:22.292Z] WARNING: This benchmark provides no result that can be validated.
[2024-09-25T20:33:22.292Z] There is no way to check that no silent failure occurred.
[2024-09-25T20:33:22.292Z] ====== movie-lens (apache-spark) [default], iteration 2 completed (6435.610 ms) ======
[2024-09-25T20:33:22.292Z] ====== movie-lens (apache-spark) [default], iteration 3 started ======
[2024-09-25T20:33:22.292Z] GC before operation: completed in 29.314 ms, heap usage 91.444 MB -> 49.895 MB.
[2024-09-25T20:33:23.060Z] RMSE (validation) = 3.6219689545487603 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-09-25T20:33:24.308Z] RMSE (validation) = 2.1340923221100736 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-09-25T20:33:25.081Z] RMSE (validation) = 1.3105190428502682 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-09-25T20:33:25.846Z] RMSE (validation) = 0.9920028100571827 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-09-25T20:33:26.612Z] RMSE (validation) = 1.212631715629696 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-09-25T20:33:26.968Z] RMSE (validation) = 1.1025694034386657 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-09-25T20:33:27.748Z] RMSE (validation) = 0.9259652469249674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-09-25T20:33:28.107Z] RMSE (validation) = 0.8982232214592321 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-09-25T20:33:28.107Z] 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.
[2024-09-25T20:33:28.107Z] The best model improves the baseline by 14.52%.
[2024-09-25T20:33:28.107Z] Movies recommended for you:
[2024-09-25T20:33:28.107Z] WARNING: This benchmark provides no result that can be validated.
[2024-09-25T20:33:28.107Z] There is no way to check that no silent failure occurred.
[2024-09-25T20:33:28.107Z] ====== movie-lens (apache-spark) [default], iteration 3 completed (6059.819 ms) ======
[2024-09-25T20:33:28.107Z] ====== movie-lens (apache-spark) [default], iteration 4 started ======
[2024-09-25T20:33:28.107Z] GC before operation: completed in 29.361 ms, heap usage 209.836 MB -> 50.217 MB.
[2024-09-25T20:33:29.337Z] RMSE (validation) = 3.62196895454876 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-09-25T20:33:30.122Z] RMSE (validation) = 2.1340923221100736 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-09-25T20:33:31.367Z] RMSE (validation) = 1.3105190428502682 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-09-25T20:33:32.134Z] RMSE (validation) = 0.9920028100571827 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-09-25T20:33:32.905Z] RMSE (validation) = 1.212631715629696 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-09-25T20:33:33.267Z] RMSE (validation) = 1.1025694034386657 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-09-25T20:33:33.640Z] RMSE (validation) = 0.9259652469249674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-09-25T20:33:34.506Z] RMSE (validation) = 0.8982232214592321 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-09-25T20:33:34.506Z] 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.
[2024-09-25T20:33:34.506Z] The best model improves the baseline by 14.52%.
[2024-09-25T20:33:34.506Z] Movies recommended for you:
[2024-09-25T20:33:34.506Z] WARNING: This benchmark provides no result that can be validated.
[2024-09-25T20:33:34.506Z] There is no way to check that no silent failure occurred.
[2024-09-25T20:33:34.506Z] ====== movie-lens (apache-spark) [default], iteration 4 completed (6187.619 ms) ======
[2024-09-25T20:33:34.506Z] ====== movie-lens (apache-spark) [default], iteration 5 started ======
[2024-09-25T20:33:34.507Z] GC before operation: completed in 29.645 ms, heap usage 302.069 MB -> 50.547 MB.
[2024-09-25T20:33:35.276Z] RMSE (validation) = 3.62196895454876 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-09-25T20:33:36.533Z] RMSE (validation) = 2.1340923221100736 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-09-25T20:33:37.302Z] RMSE (validation) = 1.3105190428502682 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-09-25T20:33:38.073Z] RMSE (validation) = 0.9920028100571827 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-09-25T20:33:38.450Z] RMSE (validation) = 1.212631715629696 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-09-25T20:33:39.220Z] RMSE (validation) = 1.1025694034386657 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-09-25T20:33:39.585Z] RMSE (validation) = 0.9259652469249674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-09-25T20:33:40.352Z] RMSE (validation) = 0.8982232214592321 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-09-25T20:33:40.352Z] 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.
[2024-09-25T20:33:40.352Z] The best model improves the baseline by 14.52%.
[2024-09-25T20:33:40.352Z] Movies recommended for you:
[2024-09-25T20:33:40.352Z] WARNING: This benchmark provides no result that can be validated.
[2024-09-25T20:33:40.352Z] There is no way to check that no silent failure occurred.
[2024-09-25T20:33:40.352Z] ====== movie-lens (apache-spark) [default], iteration 5 completed (5931.949 ms) ======
[2024-09-25T20:33:40.352Z] ====== movie-lens (apache-spark) [default], iteration 6 started ======
[2024-09-25T20:33:40.352Z] GC before operation: completed in 30.185 ms, heap usage 303.528 MB -> 50.589 MB.
[2024-09-25T20:33:41.596Z] RMSE (validation) = 3.62196895454876 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-09-25T20:33:42.378Z] RMSE (validation) = 2.1340923221100736 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-09-25T20:33:43.146Z] RMSE (validation) = 1.3105190428502682 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-09-25T20:33:44.374Z] RMSE (validation) = 0.9920028100571827 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-09-25T20:33:44.737Z] RMSE (validation) = 1.212631715629696 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-09-25T20:33:45.094Z] RMSE (validation) = 1.1025694034386657 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-09-25T20:33:45.872Z] RMSE (validation) = 0.9259652469249674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-09-25T20:33:46.235Z] RMSE (validation) = 0.8982232214592321 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-09-25T20:33:46.594Z] 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.
[2024-09-25T20:33:46.594Z] The best model improves the baseline by 14.52%.
[2024-09-25T20:33:46.594Z] Movies recommended for you:
[2024-09-25T20:33:46.594Z] WARNING: This benchmark provides no result that can be validated.
[2024-09-25T20:33:46.594Z] There is no way to check that no silent failure occurred.
[2024-09-25T20:33:46.594Z] ====== movie-lens (apache-spark) [default], iteration 6 completed (6036.803 ms) ======
[2024-09-25T20:33:46.594Z] ====== movie-lens (apache-spark) [default], iteration 7 started ======
[2024-09-25T20:33:46.594Z] GC before operation: completed in 30.514 ms, heap usage 302.100 MB -> 50.690 MB.
[2024-09-25T20:33:47.368Z] RMSE (validation) = 3.62196895454876 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-09-25T20:33:48.146Z] RMSE (validation) = 2.1340923221100736 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-09-25T20:33:49.397Z] RMSE (validation) = 1.3105190428502682 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-09-25T20:33:50.178Z] RMSE (validation) = 0.9920028100571827 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-09-25T20:33:50.535Z] RMSE (validation) = 1.212631715629696 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-09-25T20:33:51.323Z] RMSE (validation) = 1.1025694034386657 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-09-25T20:33:51.682Z] RMSE (validation) = 0.9259652469249674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-09-25T20:33:52.457Z] RMSE (validation) = 0.8982232214592321 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-09-25T20:33:52.457Z] 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.
[2024-09-25T20:33:52.457Z] The best model improves the baseline by 14.52%.
[2024-09-25T20:33:52.457Z] Movies recommended for you:
[2024-09-25T20:33:52.457Z] WARNING: This benchmark provides no result that can be validated.
[2024-09-25T20:33:52.457Z] There is no way to check that no silent failure occurred.
[2024-09-25T20:33:52.457Z] ====== movie-lens (apache-spark) [default], iteration 7 completed (5889.892 ms) ======
[2024-09-25T20:33:52.457Z] ====== movie-lens (apache-spark) [default], iteration 8 started ======
[2024-09-25T20:33:52.457Z] GC before operation: completed in 28.850 ms, heap usage 69.084 MB -> 51.084 MB.
[2024-09-25T20:33:53.223Z] RMSE (validation) = 3.62196895454876 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-09-25T20:33:54.477Z] RMSE (validation) = 2.1340923221100736 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-09-25T20:33:55.253Z] RMSE (validation) = 1.3105190428502682 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-09-25T20:33:56.494Z] RMSE (validation) = 0.9920028100571827 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-09-25T20:33:56.887Z] RMSE (validation) = 1.212631715629696 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-09-25T20:33:57.256Z] RMSE (validation) = 1.1025694034386657 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-09-25T20:33:58.025Z] RMSE (validation) = 0.9259652469249674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-09-25T20:33:58.434Z] RMSE (validation) = 0.8982232214592321 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-09-25T20:33:58.790Z] 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.
[2024-09-25T20:33:58.790Z] The best model improves the baseline by 14.52%.
[2024-09-25T20:33:58.790Z] Movies recommended for you:
[2024-09-25T20:33:58.790Z] WARNING: This benchmark provides no result that can be validated.
[2024-09-25T20:33:58.790Z] There is no way to check that no silent failure occurred.
[2024-09-25T20:33:58.790Z] ====== movie-lens (apache-spark) [default], iteration 8 completed (6276.347 ms) ======
[2024-09-25T20:33:58.790Z] ====== movie-lens (apache-spark) [default], iteration 9 started ======
[2024-09-25T20:33:58.790Z] GC before operation: completed in 30.178 ms, heap usage 95.682 MB -> 52.460 MB.
[2024-09-25T20:33:59.554Z] RMSE (validation) = 3.62196895454876 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-09-25T20:34:00.826Z] RMSE (validation) = 2.1340923221100736 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-09-25T20:34:01.618Z] RMSE (validation) = 1.3105190428502682 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-09-25T20:34:02.390Z] RMSE (validation) = 0.9920028100571827 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-09-25T20:34:03.175Z] RMSE (validation) = 1.212631715629696 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-09-25T20:34:03.552Z] RMSE (validation) = 1.1025694034386657 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-09-25T20:34:04.338Z] RMSE (validation) = 0.9259652469249674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-09-25T20:34:04.696Z] RMSE (validation) = 0.8982232214592321 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-09-25T20:34:04.696Z] 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.
[2024-09-25T20:34:04.696Z] The best model improves the baseline by 14.52%.
[2024-09-25T20:34:04.696Z] Movies recommended for you:
[2024-09-25T20:34:04.696Z] WARNING: This benchmark provides no result that can be validated.
[2024-09-25T20:34:04.696Z] There is no way to check that no silent failure occurred.
[2024-09-25T20:34:04.696Z] ====== movie-lens (apache-spark) [default], iteration 9 completed (6026.733 ms) ======
[2024-09-25T20:34:04.696Z] ====== movie-lens (apache-spark) [default], iteration 10 started ======
[2024-09-25T20:34:04.696Z] GC before operation: completed in 31.087 ms, heap usage 211.695 MB -> 50.747 MB.
[2024-09-25T20:34:05.953Z] RMSE (validation) = 3.6219689545487603 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-09-25T20:34:06.726Z] RMSE (validation) = 2.1340923221100736 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-09-25T20:34:07.966Z] RMSE (validation) = 1.3105190428502682 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-09-25T20:34:08.757Z] RMSE (validation) = 0.9920028100571827 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-09-25T20:34:09.129Z] RMSE (validation) = 1.212631715629696 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-09-25T20:34:09.905Z] RMSE (validation) = 1.1025694034386657 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-09-25T20:34:10.283Z] RMSE (validation) = 0.9259652469249674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-09-25T20:34:11.057Z] RMSE (validation) = 0.8982232214592321 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-09-25T20:34:11.057Z] 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.
[2024-09-25T20:34:11.057Z] The best model improves the baseline by 14.52%.
[2024-09-25T20:34:11.057Z] Movies recommended for you:
[2024-09-25T20:34:11.057Z] WARNING: This benchmark provides no result that can be validated.
[2024-09-25T20:34:11.057Z] There is no way to check that no silent failure occurred.
[2024-09-25T20:34:11.057Z] ====== movie-lens (apache-spark) [default], iteration 10 completed (6231.933 ms) ======
[2024-09-25T20:34:11.057Z] ====== movie-lens (apache-spark) [default], iteration 11 started ======
[2024-09-25T20:34:11.057Z] GC before operation: completed in 29.692 ms, heap usage 283.696 MB -> 50.770 MB.
[2024-09-25T20:34:11.843Z] RMSE (validation) = 3.6219689545487603 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-09-25T20:34:13.086Z] RMSE (validation) = 2.1340923221100736 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-09-25T20:34:13.875Z] RMSE (validation) = 1.3105190428502682 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-09-25T20:34:15.119Z] RMSE (validation) = 0.9920028100571827 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-09-25T20:34:15.481Z] RMSE (validation) = 1.212631715629696 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-09-25T20:34:16.263Z] RMSE (validation) = 1.1025694034386657 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-09-25T20:34:16.626Z] RMSE (validation) = 0.9259652469249674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-09-25T20:34:16.986Z] RMSE (validation) = 0.8982232214592321 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-09-25T20:34:17.346Z] 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.
[2024-09-25T20:34:17.346Z] The best model improves the baseline by 14.52%.
[2024-09-25T20:34:17.346Z] Movies recommended for you:
[2024-09-25T20:34:17.346Z] WARNING: This benchmark provides no result that can be validated.
[2024-09-25T20:34:17.346Z] There is no way to check that no silent failure occurred.
[2024-09-25T20:34:17.346Z] ====== movie-lens (apache-spark) [default], iteration 11 completed (6234.827 ms) ======
[2024-09-25T20:34:17.346Z] ====== movie-lens (apache-spark) [default], iteration 12 started ======
[2024-09-25T20:34:17.346Z] GC before operation: completed in 30.194 ms, heap usage 302.740 MB -> 50.941 MB.
[2024-09-25T20:34:18.602Z] RMSE (validation) = 3.62196895454876 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-09-25T20:34:19.391Z] RMSE (validation) = 2.1340923221100736 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-09-25T20:34:20.169Z] RMSE (validation) = 1.3105190428502682 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-09-25T20:34:21.410Z] RMSE (validation) = 0.9920028100571827 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-09-25T20:34:21.766Z] RMSE (validation) = 1.212631715629696 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-09-25T20:34:22.201Z] RMSE (validation) = 1.1025694034386657 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-09-25T20:34:22.978Z] RMSE (validation) = 0.9259652469249674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-09-25T20:34:23.337Z] RMSE (validation) = 0.8982232214592321 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-09-25T20:34:23.337Z] 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.
[2024-09-25T20:34:23.337Z] The best model improves the baseline by 14.52%.
[2024-09-25T20:34:23.337Z] Movies recommended for you:
[2024-09-25T20:34:23.337Z] WARNING: This benchmark provides no result that can be validated.
[2024-09-25T20:34:23.337Z] There is no way to check that no silent failure occurred.
[2024-09-25T20:34:23.337Z] ====== movie-lens (apache-spark) [default], iteration 12 completed (6045.257 ms) ======
[2024-09-25T20:34:23.337Z] ====== movie-lens (apache-spark) [default], iteration 13 started ======
[2024-09-25T20:34:23.337Z] GC before operation: completed in 30.121 ms, heap usage 305.841 MB -> 50.964 MB.
[2024-09-25T20:34:24.295Z] RMSE (validation) = 3.62196895454876 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-09-25T20:34:25.542Z] RMSE (validation) = 2.1340923221100736 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-09-25T20:34:26.316Z] RMSE (validation) = 1.3105190428502682 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-09-25T20:34:27.081Z] RMSE (validation) = 0.9920028100571827 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-09-25T20:34:27.866Z] RMSE (validation) = 1.212631715629696 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-09-25T20:34:28.225Z] RMSE (validation) = 1.1025694034386657 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-09-25T20:34:28.992Z] RMSE (validation) = 0.9259652469249674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-09-25T20:34:29.775Z] RMSE (validation) = 0.8982232214592321 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-09-25T20:34:29.775Z] 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.
[2024-09-25T20:34:29.775Z] The best model improves the baseline by 14.52%.
[2024-09-25T20:34:29.775Z] Movies recommended for you:
[2024-09-25T20:34:29.775Z] WARNING: This benchmark provides no result that can be validated.
[2024-09-25T20:34:29.775Z] There is no way to check that no silent failure occurred.
[2024-09-25T20:34:29.775Z] ====== movie-lens (apache-spark) [default], iteration 13 completed (6333.116 ms) ======
[2024-09-25T20:34:29.775Z] ====== movie-lens (apache-spark) [default], iteration 14 started ======
[2024-09-25T20:34:29.775Z] GC before operation: completed in 30.927 ms, heap usage 296.655 MB -> 50.861 MB.
[2024-09-25T20:34:30.544Z] RMSE (validation) = 3.62196895454876 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-09-25T20:34:31.789Z] RMSE (validation) = 2.1340923221100736 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-09-25T20:34:32.562Z] RMSE (validation) = 1.3105190428502682 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-09-25T20:34:33.808Z] RMSE (validation) = 0.9920028100571827 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-09-25T20:34:34.164Z] RMSE (validation) = 1.212631715629696 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-09-25T20:34:34.943Z] RMSE (validation) = 1.1025694034386657 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-09-25T20:34:35.302Z] RMSE (validation) = 0.9259652469249674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-09-25T20:34:36.070Z] RMSE (validation) = 0.8982232214592321 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-09-25T20:34:36.070Z] 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.
[2024-09-25T20:34:36.070Z] The best model improves the baseline by 14.52%.
[2024-09-25T20:34:36.070Z] Movies recommended for you:
[2024-09-25T20:34:36.070Z] WARNING: This benchmark provides no result that can be validated.
[2024-09-25T20:34:36.070Z] There is no way to check that no silent failure occurred.
[2024-09-25T20:34:36.070Z] ====== movie-lens (apache-spark) [default], iteration 14 completed (6249.753 ms) ======
[2024-09-25T20:34:36.070Z] ====== movie-lens (apache-spark) [default], iteration 15 started ======
[2024-09-25T20:34:36.070Z] GC before operation: completed in 31.935 ms, heap usage 69.187 MB -> 50.909 MB.
[2024-09-25T20:34:36.841Z] RMSE (validation) = 3.62196895454876 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-09-25T20:34:38.072Z] RMSE (validation) = 2.1340923221100736 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-09-25T20:34:38.848Z] RMSE (validation) = 1.3105190428502682 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-09-25T20:34:39.612Z] RMSE (validation) = 0.9920028100571827 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-09-25T20:34:40.401Z] RMSE (validation) = 1.212631715629696 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-09-25T20:34:40.764Z] RMSE (validation) = 1.1025694034386657 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-09-25T20:34:41.544Z] RMSE (validation) = 0.9259652469249674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-09-25T20:34:41.909Z] RMSE (validation) = 0.8982232214592321 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-09-25T20:34:41.909Z] 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.
[2024-09-25T20:34:41.909Z] The best model improves the baseline by 14.52%.
[2024-09-25T20:34:42.266Z] Movies recommended for you:
[2024-09-25T20:34:42.266Z] WARNING: This benchmark provides no result that can be validated.
[2024-09-25T20:34:42.266Z] There is no way to check that no silent failure occurred.
[2024-09-25T20:34:42.266Z] ====== movie-lens (apache-spark) [default], iteration 15 completed (6076.035 ms) ======
[2024-09-25T20:34:42.266Z] ====== movie-lens (apache-spark) [default], iteration 16 started ======
[2024-09-25T20:34:42.266Z] GC before operation: completed in 30.123 ms, heap usage 274.199 MB -> 51.126 MB.
[2024-09-25T20:34:43.064Z] RMSE (validation) = 3.62196895454876 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-09-25T20:34:44.311Z] RMSE (validation) = 2.1340923221100736 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-09-25T20:34:45.198Z] RMSE (validation) = 1.3105190428502682 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-09-25T20:34:45.968Z] RMSE (validation) = 0.9920028100571827 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-09-25T20:34:46.327Z] RMSE (validation) = 1.212631715629696 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-09-25T20:34:47.120Z] RMSE (validation) = 1.1025694034386657 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-09-25T20:34:47.479Z] RMSE (validation) = 0.9259652469249674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-09-25T20:34:48.249Z] RMSE (validation) = 0.8982232214592321 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-09-25T20:34:48.249Z] 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.
[2024-09-25T20:34:48.249Z] The best model improves the baseline by 14.52%.
[2024-09-25T20:34:48.249Z] Movies recommended for you:
[2024-09-25T20:34:48.249Z] WARNING: This benchmark provides no result that can be validated.
[2024-09-25T20:34:48.249Z] There is no way to check that no silent failure occurred.
[2024-09-25T20:34:48.249Z] ====== movie-lens (apache-spark) [default], iteration 16 completed (6203.113 ms) ======
[2024-09-25T20:34:48.249Z] ====== movie-lens (apache-spark) [default], iteration 17 started ======
[2024-09-25T20:34:48.249Z] GC before operation: completed in 29.333 ms, heap usage 71.921 MB -> 50.734 MB.
[2024-09-25T20:34:49.493Z] RMSE (validation) = 3.6219689545487603 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-09-25T20:34:50.261Z] RMSE (validation) = 2.1340923221100736 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-09-25T20:34:51.052Z] RMSE (validation) = 1.3105190428502682 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-09-25T20:34:52.324Z] RMSE (validation) = 0.9920028100571827 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-09-25T20:34:52.690Z] RMSE (validation) = 1.212631715629696 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-09-25T20:34:53.049Z] RMSE (validation) = 1.1025694034386657 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-09-25T20:34:53.834Z] RMSE (validation) = 0.9259652469249674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-09-25T20:34:54.197Z] RMSE (validation) = 0.8982232214592321 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-09-25T20:34:54.197Z] 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.
[2024-09-25T20:34:54.197Z] The best model improves the baseline by 14.52%.
[2024-09-25T20:34:54.197Z] Movies recommended for you:
[2024-09-25T20:34:54.197Z] WARNING: This benchmark provides no result that can be validated.
[2024-09-25T20:34:54.197Z] There is no way to check that no silent failure occurred.
[2024-09-25T20:34:54.197Z] ====== movie-lens (apache-spark) [default], iteration 17 completed (5893.404 ms) ======
[2024-09-25T20:34:54.197Z] ====== movie-lens (apache-spark) [default], iteration 18 started ======
[2024-09-25T20:34:54.197Z] GC before operation: completed in 30.731 ms, heap usage 380.579 MB -> 54.100 MB.
[2024-09-25T20:34:55.434Z] RMSE (validation) = 3.62196895454876 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-09-25T20:34:56.213Z] RMSE (validation) = 2.1340923221100736 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-09-25T20:34:56.980Z] RMSE (validation) = 1.3105190428502682 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-09-25T20:34:58.262Z] RMSE (validation) = 0.9920028100571827 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-09-25T20:34:58.624Z] RMSE (validation) = 1.212631715629696 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-09-25T20:34:59.399Z] RMSE (validation) = 1.1025694034386657 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-09-25T20:34:59.766Z] RMSE (validation) = 0.9259652469249674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-09-25T20:35:00.561Z] RMSE (validation) = 0.8982232214592321 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-09-25T20:35:00.561Z] 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.
[2024-09-25T20:35:00.561Z] The best model improves the baseline by 14.52%.
[2024-09-25T20:35:00.561Z] Movies recommended for you:
[2024-09-25T20:35:00.561Z] WARNING: This benchmark provides no result that can be validated.
[2024-09-25T20:35:00.561Z] There is no way to check that no silent failure occurred.
[2024-09-25T20:35:00.561Z] ====== movie-lens (apache-spark) [default], iteration 18 completed (6185.204 ms) ======
[2024-09-25T20:35:00.561Z] ====== movie-lens (apache-spark) [default], iteration 19 started ======
[2024-09-25T20:35:00.561Z] GC before operation: completed in 31.012 ms, heap usage 316.127 MB -> 51.236 MB.
[2024-09-25T20:35:01.360Z] RMSE (validation) = 3.62196895454876 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-09-25T20:35:02.131Z] RMSE (validation) = 2.1340923221100736 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-09-25T20:35:03.393Z] RMSE (validation) = 1.3105190428502682 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-09-25T20:35:04.167Z] RMSE (validation) = 0.9920028100571827 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-09-25T20:35:04.537Z] RMSE (validation) = 1.212631715629696 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-09-25T20:35:05.312Z] RMSE (validation) = 1.1025694034386657 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-09-25T20:35:06.083Z] RMSE (validation) = 0.9259652469249674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-09-25T20:35:06.476Z] RMSE (validation) = 0.8982232214592321 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-09-25T20:35:06.476Z] 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.
[2024-09-25T20:35:06.476Z] The best model improves the baseline by 14.52%.
[2024-09-25T20:35:06.476Z] Movies recommended for you:
[2024-09-25T20:35:06.476Z] WARNING: This benchmark provides no result that can be validated.
[2024-09-25T20:35:06.476Z] There is no way to check that no silent failure occurred.
[2024-09-25T20:35:06.476Z] ====== movie-lens (apache-spark) [default], iteration 19 completed (6088.171 ms) ======
[2024-09-25T20:35:06.832Z] -----------------------------------
[2024-09-25T20:35:06.832Z] renaissance-movie-lens_0_PASSED
[2024-09-25T20:35:06.832Z] -----------------------------------
[2024-09-25T20:35:06.832Z]
[2024-09-25T20:35:06.832Z] TEST TEARDOWN:
[2024-09-25T20:35:06.832Z] Nothing to be done for teardown.
[2024-09-25T20:35:06.832Z] renaissance-movie-lens_0 Finish Time: Wed Sep 25 16:35:06 2024 Epoch Time (ms): 1727296506580