renaissance-movie-lens_0
[2023-04-18T22:04:20.357Z] Running test renaissance-movie-lens_0 ...
[2023-04-18T22:04:20.357Z] ===============================================
[2023-04-18T22:04:20.357Z] renaissance-movie-lens_0 Start Time: Tue Apr 18 22:04:20 2023 Epoch Time (ms): 1681855460042
[2023-04-18T22:04:20.357Z] variation: NoOptions
[2023-04-18T22:04:20.357Z] JVM_OPTIONS:
[2023-04-18T22:04:20.357Z] { \
[2023-04-18T22:04:20.357Z] echo ""; echo "TEST SETUP:"; \
[2023-04-18T22:04:20.357Z] echo "Nothing to be done for setup."; \
[2023-04-18T22:04:20.357Z] mkdir -p "/home/jenkins/workspace/Test_openjdk17_hs_extended.perf_x86-64_linux/aqa-tests/TKG/../TKG/output_16818550175499/renaissance-movie-lens_0"; \
[2023-04-18T22:04:20.357Z] cd "/home/jenkins/workspace/Test_openjdk17_hs_extended.perf_x86-64_linux/aqa-tests/TKG/../TKG/output_16818550175499/renaissance-movie-lens_0"; \
[2023-04-18T22:04:20.357Z] echo ""; echo "TESTING:"; \
[2023-04-18T22:04:20.357Z] "/home/jenkins/workspace/Test_openjdk17_hs_extended.perf_x86-64_linux/openjdkbinary/j2sdk-image/jdk-17.0.7+7/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 "/home/jenkins/workspace/Test_openjdk17_hs_extended.perf_x86-64_linux/aqa-tests/TKG/../../jvmtest/perf/renaissance/renaissance.jar" --json ""/home/jenkins/workspace/Test_openjdk17_hs_extended.perf_x86-64_linux/aqa-tests/TKG/../TKG/output_16818550175499/renaissance-movie-lens_0"/movie-lens.json" movie-lens; \
[2023-04-18T22:04:20.357Z] if [ $? -eq 0 ]; then echo "-----------------------------------"; echo "renaissance-movie-lens_0""_PASSED"; echo "-----------------------------------"; cd /home/jenkins/workspace/Test_openjdk17_hs_extended.perf_x86-64_linux/aqa-tests/TKG/..; rm -f -r "/home/jenkins/workspace/Test_openjdk17_hs_extended.perf_x86-64_linux/aqa-tests/TKG/../TKG/output_16818550175499/renaissance-movie-lens_0"; else echo "-----------------------------------"; echo "renaissance-movie-lens_0""_FAILED"; echo "-----------------------------------"; fi; \
[2023-04-18T22:04:20.357Z] echo ""; echo "TEST TEARDOWN:"; \
[2023-04-18T22:04:20.357Z] echo "Nothing to be done for teardown."; \
[2023-04-18T22:04:20.357Z] } 2>&1 | tee -a "/home/jenkins/workspace/Test_openjdk17_hs_extended.perf_x86-64_linux/aqa-tests/TKG/../TKG/output_16818550175499/TestTargetResult";
[2023-04-18T22:04:20.357Z]
[2023-04-18T22:04:20.357Z] TEST SETUP:
[2023-04-18T22:04:20.357Z] Nothing to be done for setup.
[2023-04-18T22:04:20.357Z]
[2023-04-18T22:04:20.357Z] TESTING:
[2023-04-18T22:04:22.672Z] Using Spark's default log4j profile: org/apache/spark/log4j-defaults.properties
[2023-04-18T22:04:24.382Z] NOTE: 'movie-lens' benchmark uses Spark local executor with 4 (out of 4) threads.
[2023-04-18T22:04:27.423Z] Got 100004 ratings from 671 users on 9066 movies.
[2023-04-18T22:04:27.423Z] Training: 60056, validation: 20285, test: 19854
[2023-04-18T22:04:27.423Z] ====== movie-lens (apache-spark) [default], iteration 0 started ======
[2023-04-18T22:04:27.423Z] GC before operation: completed in 52.648 ms, heap usage 93.849 MB -> 37.336 MB.
[2023-04-18T22:04:31.294Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2023-04-18T22:04:33.614Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2023-04-18T22:04:36.645Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2023-04-18T22:04:37.827Z] RMSE (validation) = 0.9919630846870685 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2023-04-18T22:04:39.015Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2023-04-18T22:04:40.201Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2023-04-18T22:04:41.519Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2023-04-18T22:04:42.252Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2023-04-18T22:04:42.594Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9063252168319611.
[2023-04-18T22:04:42.594Z] The best model improves the baseline by 14.52%.
[2023-04-18T22:04:42.594Z] Movies recommended for you:
[2023-04-18T22:04:42.594Z] WARNING: This benchmark provides no result that can be validated.
[2023-04-18T22:04:42.594Z] There is no way to check that no silent failure occurred.
[2023-04-18T22:04:42.594Z] ====== movie-lens (apache-spark) [default], iteration 0 completed (15424.407 ms) ======
[2023-04-18T22:04:42.594Z] ====== movie-lens (apache-spark) [default], iteration 1 started ======
[2023-04-18T22:04:42.594Z] GC before operation: completed in 45.191 ms, heap usage 170.753 MB -> 54.481 MB.
[2023-04-18T22:04:44.301Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2023-04-18T22:04:46.008Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2023-04-18T22:04:47.717Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2023-04-18T22:04:49.423Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2023-04-18T22:04:50.606Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2023-04-18T22:04:51.793Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2023-04-18T22:04:52.973Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2023-04-18T22:04:53.727Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2023-04-18T22:04:53.727Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9063252168319611.
[2023-04-18T22:04:53.727Z] The best model improves the baseline by 14.52%.
[2023-04-18T22:04:54.067Z] Movies recommended for you:
[2023-04-18T22:04:54.067Z] WARNING: This benchmark provides no result that can be validated.
[2023-04-18T22:04:54.067Z] There is no way to check that no silent failure occurred.
[2023-04-18T22:04:54.067Z] ====== movie-lens (apache-spark) [default], iteration 1 completed (11191.881 ms) ======
[2023-04-18T22:04:54.067Z] ====== movie-lens (apache-spark) [default], iteration 2 started ======
[2023-04-18T22:04:54.067Z] GC before operation: completed in 32.551 ms, heap usage 84.386 MB -> 49.773 MB.
[2023-04-18T22:04:55.773Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2023-04-18T22:04:57.485Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2023-04-18T22:04:59.192Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2023-04-18T22:05:00.899Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2023-04-18T22:05:02.081Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2023-04-18T22:05:03.269Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2023-04-18T22:05:04.465Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2023-04-18T22:05:05.646Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2023-04-18T22:05:05.646Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9063252168319611.
[2023-04-18T22:05:05.646Z] The best model improves the baseline by 14.52%.
[2023-04-18T22:05:05.646Z] Movies recommended for you:
[2023-04-18T22:05:05.646Z] WARNING: This benchmark provides no result that can be validated.
[2023-04-18T22:05:05.646Z] There is no way to check that no silent failure occurred.
[2023-04-18T22:05:05.646Z] ====== movie-lens (apache-spark) [default], iteration 2 completed (11788.975 ms) ======
[2023-04-18T22:05:05.646Z] ====== movie-lens (apache-spark) [default], iteration 3 started ======
[2023-04-18T22:05:05.646Z] GC before operation: completed in 38.016 ms, heap usage 317.503 MB -> 50.311 MB.
[2023-04-18T22:05:07.350Z] RMSE (validation) = 3.6219689545487617 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2023-04-18T22:05:08.659Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2023-04-18T22:05:10.422Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2023-04-18T22:05:12.139Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2023-04-18T22:05:12.871Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2023-04-18T22:05:14.058Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2023-04-18T22:05:14.789Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2023-04-18T22:05:15.974Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2023-04-18T22:05:15.974Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9063252168319611.
[2023-04-18T22:05:15.974Z] The best model improves the baseline by 14.52%.
[2023-04-18T22:05:15.974Z] Movies recommended for you:
[2023-04-18T22:05:15.974Z] WARNING: This benchmark provides no result that can be validated.
[2023-04-18T22:05:15.974Z] There is no way to check that no silent failure occurred.
[2023-04-18T22:05:15.974Z] ====== movie-lens (apache-spark) [default], iteration 3 completed (10286.336 ms) ======
[2023-04-18T22:05:15.974Z] ====== movie-lens (apache-spark) [default], iteration 4 started ======
[2023-04-18T22:05:15.974Z] GC before operation: completed in 41.405 ms, heap usage 384.996 MB -> 50.740 MB.
[2023-04-18T22:05:17.677Z] RMSE (validation) = 3.6219689545487617 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2023-04-18T22:05:19.385Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2023-04-18T22:05:21.093Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2023-04-18T22:05:22.275Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2023-04-18T22:05:23.475Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2023-04-18T22:05:24.205Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2023-04-18T22:05:24.936Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2023-04-18T22:05:25.666Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2023-04-18T22:05:26.008Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9063252168319611.
[2023-04-18T22:05:26.008Z] The best model improves the baseline by 14.52%.
[2023-04-18T22:05:26.008Z] Movies recommended for you:
[2023-04-18T22:05:26.008Z] WARNING: This benchmark provides no result that can be validated.
[2023-04-18T22:05:26.008Z] There is no way to check that no silent failure occurred.
[2023-04-18T22:05:26.008Z] ====== movie-lens (apache-spark) [default], iteration 4 completed (9964.378 ms) ======
[2023-04-18T22:05:26.008Z] ====== movie-lens (apache-spark) [default], iteration 5 started ======
[2023-04-18T22:05:26.008Z] GC before operation: completed in 32.604 ms, heap usage 76.537 MB -> 50.603 MB.
[2023-04-18T22:05:27.187Z] RMSE (validation) = 3.6219689545487617 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2023-04-18T22:05:28.896Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2023-04-18T22:05:30.605Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2023-04-18T22:05:31.788Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2023-04-18T22:05:32.521Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2023-04-18T22:05:33.250Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2023-04-18T22:05:34.436Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2023-04-18T22:05:35.166Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2023-04-18T22:05:35.166Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9063252168319611.
[2023-04-18T22:05:35.166Z] The best model improves the baseline by 14.52%.
[2023-04-18T22:05:35.166Z] Movies recommended for you:
[2023-04-18T22:05:35.166Z] WARNING: This benchmark provides no result that can be validated.
[2023-04-18T22:05:35.166Z] There is no way to check that no silent failure occurred.
[2023-04-18T22:05:35.166Z] ====== movie-lens (apache-spark) [default], iteration 5 completed (9078.361 ms) ======
[2023-04-18T22:05:35.166Z] ====== movie-lens (apache-spark) [default], iteration 6 started ======
[2023-04-18T22:05:35.166Z] GC before operation: completed in 29.590 ms, heap usage 75.994 MB -> 50.858 MB.
[2023-04-18T22:05:36.346Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2023-04-18T22:05:37.526Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2023-04-18T22:05:38.886Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2023-04-18T22:05:40.065Z] RMSE (validation) = 0.9919630846870685 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2023-04-18T22:05:40.406Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2023-04-18T22:05:41.137Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2023-04-18T22:05:41.869Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2023-04-18T22:05:42.598Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2023-04-18T22:05:42.938Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9063252168319611.
[2023-04-18T22:05:42.938Z] The best model improves the baseline by 14.52%.
[2023-04-18T22:05:42.938Z] Movies recommended for you:
[2023-04-18T22:05:42.938Z] WARNING: This benchmark provides no result that can be validated.
[2023-04-18T22:05:42.938Z] There is no way to check that no silent failure occurred.
[2023-04-18T22:05:42.938Z] ====== movie-lens (apache-spark) [default], iteration 6 completed (7719.960 ms) ======
[2023-04-18T22:05:42.938Z] ====== movie-lens (apache-spark) [default], iteration 7 started ======
[2023-04-18T22:05:42.938Z] GC before operation: completed in 32.601 ms, heap usage 75.825 MB -> 50.870 MB.
[2023-04-18T22:05:44.120Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2023-04-18T22:05:45.303Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2023-04-18T22:05:46.483Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2023-04-18T22:05:47.662Z] RMSE (validation) = 0.9919630846870685 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2023-04-18T22:05:48.006Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2023-04-18T22:05:48.736Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2023-04-18T22:05:49.479Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2023-04-18T22:05:50.215Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2023-04-18T22:05:50.216Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9063252168319611.
[2023-04-18T22:05:50.216Z] The best model improves the baseline by 14.52%.
[2023-04-18T22:05:50.216Z] Movies recommended for you:
[2023-04-18T22:05:50.216Z] WARNING: This benchmark provides no result that can be validated.
[2023-04-18T22:05:50.216Z] There is no way to check that no silent failure occurred.
[2023-04-18T22:05:50.216Z] ====== movie-lens (apache-spark) [default], iteration 7 completed (7255.865 ms) ======
[2023-04-18T22:05:50.216Z] ====== movie-lens (apache-spark) [default], iteration 8 started ======
[2023-04-18T22:05:50.216Z] GC before operation: completed in 30.610 ms, heap usage 102.302 MB -> 51.178 MB.
[2023-04-18T22:05:51.397Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2023-04-18T22:05:52.576Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2023-04-18T22:05:53.305Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2023-04-18T22:05:54.499Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2023-04-18T22:05:55.228Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2023-04-18T22:05:55.569Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2023-04-18T22:05:56.301Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2023-04-18T22:05:57.034Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2023-04-18T22:05:57.035Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9063252168319611.
[2023-04-18T22:05:57.035Z] The best model improves the baseline by 14.52%.
[2023-04-18T22:05:57.035Z] Movies recommended for you:
[2023-04-18T22:05:57.035Z] WARNING: This benchmark provides no result that can be validated.
[2023-04-18T22:05:57.035Z] There is no way to check that no silent failure occurred.
[2023-04-18T22:05:57.035Z] ====== movie-lens (apache-spark) [default], iteration 8 completed (6842.472 ms) ======
[2023-04-18T22:05:57.035Z] ====== movie-lens (apache-spark) [default], iteration 9 started ======
[2023-04-18T22:05:57.035Z] GC before operation: completed in 28.929 ms, heap usage 74.450 MB -> 50.868 MB.
[2023-04-18T22:05:58.213Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2023-04-18T22:05:58.948Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2023-04-18T22:06:00.653Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2023-04-18T22:06:01.387Z] RMSE (validation) = 0.9919630846870685 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2023-04-18T22:06:02.121Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2023-04-18T22:06:02.856Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2023-04-18T22:06:03.714Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2023-04-18T22:06:04.453Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2023-04-18T22:06:04.453Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9063252168319611.
[2023-04-18T22:06:04.453Z] The best model improves the baseline by 14.52%.
[2023-04-18T22:06:04.453Z] Movies recommended for you:
[2023-04-18T22:06:04.453Z] WARNING: This benchmark provides no result that can be validated.
[2023-04-18T22:06:04.453Z] There is no way to check that no silent failure occurred.
[2023-04-18T22:06:04.453Z] ====== movie-lens (apache-spark) [default], iteration 9 completed (7490.441 ms) ======
[2023-04-18T22:06:04.453Z] ====== movie-lens (apache-spark) [default], iteration 10 started ======
[2023-04-18T22:06:04.453Z] GC before operation: completed in 34.188 ms, heap usage 102.574 MB -> 51.118 MB.
[2023-04-18T22:06:05.635Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2023-04-18T22:06:06.821Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2023-04-18T22:06:08.007Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2023-04-18T22:06:09.190Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2023-04-18T22:06:09.532Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2023-04-18T22:06:10.263Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2023-04-18T22:06:10.994Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2023-04-18T22:06:11.335Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2023-04-18T22:06:11.676Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9063252168319611.
[2023-04-18T22:06:11.676Z] The best model improves the baseline by 14.52%.
[2023-04-18T22:06:11.676Z] Movies recommended for you:
[2023-04-18T22:06:11.676Z] WARNING: This benchmark provides no result that can be validated.
[2023-04-18T22:06:11.676Z] There is no way to check that no silent failure occurred.
[2023-04-18T22:06:11.676Z] ====== movie-lens (apache-spark) [default], iteration 10 completed (6992.749 ms) ======
[2023-04-18T22:06:11.676Z] ====== movie-lens (apache-spark) [default], iteration 11 started ======
[2023-04-18T22:06:11.676Z] GC before operation: completed in 35.173 ms, heap usage 432.766 MB -> 54.291 MB.
[2023-04-18T22:06:12.856Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2023-04-18T22:06:13.587Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2023-04-18T22:06:14.780Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2023-04-18T22:06:15.513Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2023-04-18T22:06:16.253Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2023-04-18T22:06:16.984Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2023-04-18T22:06:17.713Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2023-04-18T22:06:18.911Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2023-04-18T22:06:18.911Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9063252168319611.
[2023-04-18T22:06:18.911Z] The best model improves the baseline by 14.52%.
[2023-04-18T22:06:18.911Z] Movies recommended for you:
[2023-04-18T22:06:18.911Z] WARNING: This benchmark provides no result that can be validated.
[2023-04-18T22:06:18.911Z] There is no way to check that no silent failure occurred.
[2023-04-18T22:06:18.911Z] ====== movie-lens (apache-spark) [default], iteration 11 completed (7203.746 ms) ======
[2023-04-18T22:06:18.911Z] ====== movie-lens (apache-spark) [default], iteration 12 started ======
[2023-04-18T22:06:18.911Z] GC before operation: completed in 44.671 ms, heap usage 200.366 MB -> 51.086 MB.
[2023-04-18T22:06:20.095Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2023-04-18T22:06:21.279Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2023-04-18T22:06:22.467Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2023-04-18T22:06:23.651Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2023-04-18T22:06:24.384Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2023-04-18T22:06:25.113Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2023-04-18T22:06:25.843Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2023-04-18T22:06:26.573Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2023-04-18T22:06:26.573Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9063252168319611.
[2023-04-18T22:06:26.573Z] The best model improves the baseline by 14.52%.
[2023-04-18T22:06:26.923Z] Movies recommended for you:
[2023-04-18T22:06:26.924Z] WARNING: This benchmark provides no result that can be validated.
[2023-04-18T22:06:26.924Z] There is no way to check that no silent failure occurred.
[2023-04-18T22:06:26.924Z] ====== movie-lens (apache-spark) [default], iteration 12 completed (7864.746 ms) ======
[2023-04-18T22:06:26.924Z] ====== movie-lens (apache-spark) [default], iteration 13 started ======
[2023-04-18T22:06:26.924Z] GC before operation: completed in 40.236 ms, heap usage 75.222 MB -> 51.047 MB.
[2023-04-18T22:06:27.656Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2023-04-18T22:06:28.845Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2023-04-18T22:06:30.029Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2023-04-18T22:06:31.209Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2023-04-18T22:06:32.109Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2023-04-18T22:06:32.451Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2023-04-18T22:06:33.180Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2023-04-18T22:06:33.919Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2023-04-18T22:06:33.919Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9063252168319611.
[2023-04-18T22:06:33.919Z] The best model improves the baseline by 14.52%.
[2023-04-18T22:06:33.919Z] Movies recommended for you:
[2023-04-18T22:06:33.919Z] WARNING: This benchmark provides no result that can be validated.
[2023-04-18T22:06:33.919Z] There is no way to check that no silent failure occurred.
[2023-04-18T22:06:33.919Z] ====== movie-lens (apache-spark) [default], iteration 13 completed (7058.414 ms) ======
[2023-04-18T22:06:33.919Z] ====== movie-lens (apache-spark) [default], iteration 14 started ======
[2023-04-18T22:06:33.919Z] GC before operation: completed in 39.097 ms, heap usage 103.102 MB -> 50.996 MB.
[2023-04-18T22:06:35.099Z] RMSE (validation) = 3.6219689545487617 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2023-04-18T22:06:35.834Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2023-04-18T22:06:37.016Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2023-04-18T22:06:38.201Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2023-04-18T22:06:38.934Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2023-04-18T22:06:39.275Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2023-04-18T22:06:40.006Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2023-04-18T22:06:40.735Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2023-04-18T22:06:40.735Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9063252168319611.
[2023-04-18T22:06:40.735Z] The best model improves the baseline by 14.52%.
[2023-04-18T22:06:40.735Z] Movies recommended for you:
[2023-04-18T22:06:40.735Z] WARNING: This benchmark provides no result that can be validated.
[2023-04-18T22:06:40.735Z] There is no way to check that no silent failure occurred.
[2023-04-18T22:06:40.735Z] ====== movie-lens (apache-spark) [default], iteration 14 completed (6931.450 ms) ======
[2023-04-18T22:06:40.735Z] ====== movie-lens (apache-spark) [default], iteration 15 started ======
[2023-04-18T22:06:40.735Z] GC before operation: completed in 40.716 ms, heap usage 74.164 MB -> 51.044 MB.
[2023-04-18T22:06:41.919Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2023-04-18T22:06:43.098Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2023-04-18T22:06:44.283Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2023-04-18T22:06:45.464Z] RMSE (validation) = 0.9919630846870685 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2023-04-18T22:06:45.804Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2023-04-18T22:06:46.537Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2023-04-18T22:06:47.268Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2023-04-18T22:06:48.001Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2023-04-18T22:06:48.001Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9063252168319611.
[2023-04-18T22:06:48.341Z] The best model improves the baseline by 14.52%.
[2023-04-18T22:06:48.341Z] Movies recommended for you:
[2023-04-18T22:06:48.341Z] WARNING: This benchmark provides no result that can be validated.
[2023-04-18T22:06:48.341Z] There is no way to check that no silent failure occurred.
[2023-04-18T22:06:48.341Z] ====== movie-lens (apache-spark) [default], iteration 15 completed (7357.078 ms) ======
[2023-04-18T22:06:48.342Z] ====== movie-lens (apache-spark) [default], iteration 16 started ======
[2023-04-18T22:06:48.342Z] GC before operation: completed in 38.480 ms, heap usage 74.610 MB -> 51.186 MB.
[2023-04-18T22:06:49.526Z] RMSE (validation) = 3.6219689545487617 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2023-04-18T22:06:50.261Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2023-04-18T22:06:51.457Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2023-04-18T22:06:52.642Z] RMSE (validation) = 0.9919630846870685 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2023-04-18T22:06:52.984Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2023-04-18T22:06:53.716Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2023-04-18T22:06:54.448Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2023-04-18T22:06:55.181Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2023-04-18T22:06:55.181Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9063252168319611.
[2023-04-18T22:06:55.181Z] The best model improves the baseline by 14.52%.
[2023-04-18T22:06:55.181Z] Movies recommended for you:
[2023-04-18T22:06:55.181Z] WARNING: This benchmark provides no result that can be validated.
[2023-04-18T22:06:55.181Z] There is no way to check that no silent failure occurred.
[2023-04-18T22:06:55.181Z] ====== movie-lens (apache-spark) [default], iteration 16 completed (7067.452 ms) ======
[2023-04-18T22:06:55.181Z] ====== movie-lens (apache-spark) [default], iteration 17 started ======
[2023-04-18T22:06:55.521Z] GC before operation: completed in 38.194 ms, heap usage 76.011 MB -> 50.931 MB.
[2023-04-18T22:06:56.735Z] RMSE (validation) = 3.6219689545487617 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2023-04-18T22:06:57.940Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2023-04-18T22:06:58.674Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2023-04-18T22:06:59.856Z] RMSE (validation) = 0.9919630846870685 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2023-04-18T22:07:00.586Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2023-04-18T22:07:01.317Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2023-04-18T22:07:01.660Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2023-04-18T22:07:02.390Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2023-04-18T22:07:02.390Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9063252168319611.
[2023-04-18T22:07:02.390Z] The best model improves the baseline by 14.52%.
[2023-04-18T22:07:02.390Z] Movies recommended for you:
[2023-04-18T22:07:02.390Z] WARNING: This benchmark provides no result that can be validated.
[2023-04-18T22:07:02.391Z] There is no way to check that no silent failure occurred.
[2023-04-18T22:07:02.391Z] ====== movie-lens (apache-spark) [default], iteration 17 completed (7155.442 ms) ======
[2023-04-18T22:07:02.391Z] ====== movie-lens (apache-spark) [default], iteration 18 started ======
[2023-04-18T22:07:02.732Z] GC before operation: completed in 39.184 ms, heap usage 431.144 MB -> 54.614 MB.
[2023-04-18T22:07:03.464Z] RMSE (validation) = 3.6219689545487617 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2023-04-18T22:07:04.646Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2023-04-18T22:07:05.839Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2023-04-18T22:07:06.569Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2023-04-18T22:07:07.298Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2023-04-18T22:07:07.638Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2023-04-18T22:07:08.371Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2023-04-18T22:07:09.102Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2023-04-18T22:07:09.102Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9063252168319611.
[2023-04-18T22:07:09.102Z] The best model improves the baseline by 14.52%.
[2023-04-18T22:07:09.102Z] Movies recommended for you:
[2023-04-18T22:07:09.102Z] WARNING: This benchmark provides no result that can be validated.
[2023-04-18T22:07:09.102Z] There is no way to check that no silent failure occurred.
[2023-04-18T22:07:09.102Z] ====== movie-lens (apache-spark) [default], iteration 18 completed (6609.068 ms) ======
[2023-04-18T22:07:09.102Z] ====== movie-lens (apache-spark) [default], iteration 19 started ======
[2023-04-18T22:07:09.102Z] GC before operation: completed in 39.730 ms, heap usage 324.840 MB -> 51.510 MB.
[2023-04-18T22:07:10.284Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2023-04-18T22:07:11.018Z] RMSE (validation) = 2.1340923218923 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2023-04-18T22:07:12.196Z] RMSE (validation) = 1.310518647704681 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2023-04-18T22:07:12.927Z] RMSE (validation) = 0.9919630846870686 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2023-04-18T22:07:13.655Z] RMSE (validation) = 1.2070175349451324 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2023-04-18T22:07:14.386Z] RMSE (validation) = 1.114680167277025 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2023-04-18T22:07:15.116Z] RMSE (validation) = 0.922741950338674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2023-04-18T22:07:15.458Z] RMSE (validation) = 0.898064398059034 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2023-04-18T22:07:15.800Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9063252168319611.
[2023-04-18T22:07:15.800Z] The best model improves the baseline by 14.52%.
[2023-04-18T22:07:15.800Z] Movies recommended for you:
[2023-04-18T22:07:15.800Z] WARNING: This benchmark provides no result that can be validated.
[2023-04-18T22:07:15.800Z] There is no way to check that no silent failure occurred.
[2023-04-18T22:07:15.800Z] ====== movie-lens (apache-spark) [default], iteration 19 completed (6502.041 ms) ======
[2023-04-18T22:07:16.141Z] -----------------------------------
[2023-04-18T22:07:16.141Z] renaissance-movie-lens_0_PASSED
[2023-04-18T22:07:16.141Z] -----------------------------------
[2023-04-18T22:07:16.141Z]
[2023-04-18T22:07:16.141Z] TEST TEARDOWN:
[2023-04-18T22:07:16.141Z] Nothing to be done for teardown.
[2023-04-18T22:07:16.141Z] renaissance-movie-lens_0 Finish Time: Tue Apr 18 22:07:15 2023 Epoch Time (ms): 1681855635872