renaissance-movie-lens_0
[2023-04-19T09:16:37.720Z] Running test renaissance-movie-lens_0 ...
[2023-04-19T09:16:37.720Z] ===============================================
[2023-04-19T09:16:37.720Z] renaissance-movie-lens_0 Start Time: Wed Apr 19 09:16:37 2023 Epoch Time (ms): 1681895797585
[2023-04-19T09:16:37.720Z] variation: NoOptions
[2023-04-19T09:16:37.720Z] JVM_OPTIONS:
[2023-04-19T09:16:37.720Z] { \
[2023-04-19T09:16:37.720Z] echo ""; echo "TEST SETUP:"; \
[2023-04-19T09:16:37.720Z] echo "Nothing to be done for setup."; \
[2023-04-19T09:16:37.720Z] mkdir -p "/home/jenkins/workspace/Test_openjdk8_hs_extended.perf_x86-64_linux/aqa-tests/TKG/../TKG/output_16818952196808/renaissance-movie-lens_0"; \
[2023-04-19T09:16:37.720Z] cd "/home/jenkins/workspace/Test_openjdk8_hs_extended.perf_x86-64_linux/aqa-tests/TKG/../TKG/output_16818952196808/renaissance-movie-lens_0"; \
[2023-04-19T09:16:37.720Z] echo ""; echo "TESTING:"; \
[2023-04-19T09:16:37.720Z] "/home/jenkins/workspace/Test_openjdk8_hs_extended.perf_x86-64_linux/openjdkbinary/j2sdk-image/jdk8u372-b07/bin/..//bin/java" -jar "/home/jenkins/workspace/Test_openjdk8_hs_extended.perf_x86-64_linux/aqa-tests/TKG/../../jvmtest/perf/renaissance/renaissance.jar" --json ""/home/jenkins/workspace/Test_openjdk8_hs_extended.perf_x86-64_linux/aqa-tests/TKG/../TKG/output_16818952196808/renaissance-movie-lens_0"/movie-lens.json" movie-lens; \
[2023-04-19T09:16:37.720Z] if [ $? -eq 0 ]; then echo "-----------------------------------"; echo "renaissance-movie-lens_0""_PASSED"; echo "-----------------------------------"; cd /home/jenkins/workspace/Test_openjdk8_hs_extended.perf_x86-64_linux/aqa-tests/TKG/..; rm -f -r "/home/jenkins/workspace/Test_openjdk8_hs_extended.perf_x86-64_linux/aqa-tests/TKG/../TKG/output_16818952196808/renaissance-movie-lens_0"; else echo "-----------------------------------"; echo "renaissance-movie-lens_0""_FAILED"; echo "-----------------------------------"; fi; \
[2023-04-19T09:16:37.720Z] echo ""; echo "TEST TEARDOWN:"; \
[2023-04-19T09:16:37.720Z] echo "Nothing to be done for teardown."; \
[2023-04-19T09:16:37.720Z] } 2>&1 | tee -a "/home/jenkins/workspace/Test_openjdk8_hs_extended.perf_x86-64_linux/aqa-tests/TKG/../TKG/output_16818952196808/TestTargetResult";
[2023-04-19T09:16:37.720Z]
[2023-04-19T09:16:37.720Z] TEST SETUP:
[2023-04-19T09:16:37.720Z] Nothing to be done for setup.
[2023-04-19T09:16:37.720Z]
[2023-04-19T09:16:37.720Z] TESTING:
[2023-04-19T09:16:40.804Z] Using Spark's default log4j profile: org/apache/spark/log4j-defaults.properties
[2023-04-19T09:16:41.995Z] NOTE: 'movie-lens' benchmark uses Spark local executor with 4 (out of 4) threads.
[2023-04-19T09:16:44.306Z] Got 100004 ratings from 671 users on 9066 movies.
[2023-04-19T09:16:44.306Z] Training: 60056, validation: 20285, test: 19854
[2023-04-19T09:16:44.306Z] ====== movie-lens (apache-spark) [default], iteration 0 started ======
[2023-04-19T09:16:44.306Z] GC before operation: completed in 134.304 ms, heap usage 115.688 MB -> 27.263 MB.
[2023-04-19T09:16:49.158Z] RMSE (validation) = 3.6219689545487617 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2023-04-19T09:16:51.491Z] RMSE (validation) = 2.1340923220285766 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2023-04-19T09:16:54.547Z] RMSE (validation) = 1.3105186462138083 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2023-04-19T09:16:56.868Z] RMSE (validation) = 0.9919630851507418 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2023-04-19T09:16:58.059Z] RMSE (validation) = 1.2070175276098325 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2023-04-19T09:16:59.248Z] RMSE (validation) = 1.1146801722401463 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2023-04-19T09:17:00.466Z] RMSE (validation) = 0.9227419497655963 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2023-04-19T09:17:01.656Z] RMSE (validation) = 0.8980643995666189 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2023-04-19T09:17:01.656Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9063252187379536.
[2023-04-19T09:17:01.996Z] The best model improves the baseline by 14.52%.
[2023-04-19T09:17:01.996Z] Movies recommended for you:
[2023-04-19T09:17:01.996Z] WARNING: This benchmark provides no result that can be validated.
[2023-04-19T09:17:01.996Z] There is no way to check that no silent failure occurred.
[2023-04-19T09:17:01.996Z] ====== movie-lens (apache-spark) [default], iteration 0 completed (17529.268 ms) ======
[2023-04-19T09:17:01.996Z] ====== movie-lens (apache-spark) [default], iteration 1 started ======
[2023-04-19T09:17:02.341Z] GC before operation: completed in 177.135 ms, heap usage 114.196 MB -> 40.881 MB.
[2023-04-19T09:17:04.054Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2023-04-19T09:17:06.380Z] RMSE (validation) = 2.1340923220285766 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2023-04-19T09:17:08.087Z] RMSE (validation) = 1.3105186462138083 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2023-04-19T09:17:10.500Z] RMSE (validation) = 0.9919630851507418 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2023-04-19T09:17:11.697Z] RMSE (validation) = 1.2070175276098325 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2023-04-19T09:17:13.411Z] RMSE (validation) = 1.1146801722401463 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2023-04-19T09:17:14.603Z] RMSE (validation) = 0.9227419497655963 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2023-04-19T09:17:15.793Z] RMSE (validation) = 0.8980643995666189 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2023-04-19T09:17:15.793Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9063252187379536.
[2023-04-19T09:17:15.793Z] The best model improves the baseline by 14.52%.
[2023-04-19T09:17:15.793Z] Movies recommended for you:
[2023-04-19T09:17:15.793Z] WARNING: This benchmark provides no result that can be validated.
[2023-04-19T09:17:15.793Z] There is no way to check that no silent failure occurred.
[2023-04-19T09:17:15.793Z] ====== movie-lens (apache-spark) [default], iteration 1 completed (13696.236 ms) ======
[2023-04-19T09:17:15.793Z] ====== movie-lens (apache-spark) [default], iteration 2 started ======
[2023-04-19T09:17:16.136Z] GC before operation: completed in 132.888 ms, heap usage 463.100 MB -> 46.272 MB.
[2023-04-19T09:17:18.466Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2023-04-19T09:17:20.178Z] RMSE (validation) = 2.1340923220285766 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2023-04-19T09:17:21.897Z] RMSE (validation) = 1.3105186462138083 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2023-04-19T09:17:24.222Z] RMSE (validation) = 0.9919630851507418 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2023-04-19T09:17:24.972Z] RMSE (validation) = 1.2070175276098327 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2023-04-19T09:17:26.165Z] RMSE (validation) = 1.1146801722401463 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2023-04-19T09:17:27.350Z] RMSE (validation) = 0.9227419497655963 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2023-04-19T09:17:28.085Z] RMSE (validation) = 0.8980643995666189 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2023-04-19T09:17:28.086Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9063252187379536.
[2023-04-19T09:17:28.086Z] The best model improves the baseline by 14.52%.
[2023-04-19T09:17:28.427Z] Movies recommended for you:
[2023-04-19T09:17:28.427Z] WARNING: This benchmark provides no result that can be validated.
[2023-04-19T09:17:28.427Z] There is no way to check that no silent failure occurred.
[2023-04-19T09:17:28.427Z] ====== movie-lens (apache-spark) [default], iteration 2 completed (12322.906 ms) ======
[2023-04-19T09:17:28.427Z] ====== movie-lens (apache-spark) [default], iteration 3 started ======
[2023-04-19T09:17:28.427Z] GC before operation: completed in 120.602 ms, heap usage 300.412 MB -> 42.949 MB.
[2023-04-19T09:17:30.133Z] RMSE (validation) = 3.6219689545487617 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2023-04-19T09:17:31.839Z] RMSE (validation) = 2.1340923220285766 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2023-04-19T09:17:33.558Z] RMSE (validation) = 1.3105186462138083 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2023-04-19T09:17:35.281Z] RMSE (validation) = 0.9919630851507418 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2023-04-19T09:17:36.469Z] RMSE (validation) = 1.2070175276098327 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2023-04-19T09:17:37.206Z] RMSE (validation) = 1.1146801722401463 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2023-04-19T09:17:38.398Z] RMSE (validation) = 0.9227419497655963 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2023-04-19T09:17:39.154Z] RMSE (validation) = 0.8980643995666189 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2023-04-19T09:17:39.154Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9063252187379536.
[2023-04-19T09:17:39.154Z] The best model improves the baseline by 14.52%.
[2023-04-19T09:17:39.154Z] Movies recommended for you:
[2023-04-19T09:17:39.154Z] WARNING: This benchmark provides no result that can be validated.
[2023-04-19T09:17:39.154Z] There is no way to check that no silent failure occurred.
[2023-04-19T09:17:39.154Z] ====== movie-lens (apache-spark) [default], iteration 3 completed (10856.752 ms) ======
[2023-04-19T09:17:39.154Z] ====== movie-lens (apache-spark) [default], iteration 4 started ======
[2023-04-19T09:17:39.494Z] GC before operation: completed in 100.857 ms, heap usage 255.908 MB -> 43.339 MB.
[2023-04-19T09:17:40.685Z] RMSE (validation) = 3.6219689545487617 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2023-04-19T09:17:42.398Z] RMSE (validation) = 2.1340923220285766 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2023-04-19T09:17:44.115Z] RMSE (validation) = 1.3105186462138083 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2023-04-19T09:17:45.825Z] RMSE (validation) = 0.9919630851507418 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2023-04-19T09:17:46.561Z] RMSE (validation) = 1.2070175276098325 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2023-04-19T09:17:47.301Z] RMSE (validation) = 1.1146801722401463 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2023-04-19T09:17:48.096Z] RMSE (validation) = 0.9227419497655963 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2023-04-19T09:17:49.287Z] RMSE (validation) = 0.8980643995666189 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2023-04-19T09:17:49.287Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9063252187379536.
[2023-04-19T09:17:49.287Z] The best model improves the baseline by 14.52%.
[2023-04-19T09:17:49.287Z] Movies recommended for you:
[2023-04-19T09:17:49.287Z] WARNING: This benchmark provides no result that can be validated.
[2023-04-19T09:17:49.287Z] There is no way to check that no silent failure occurred.
[2023-04-19T09:17:49.287Z] ====== movie-lens (apache-spark) [default], iteration 4 completed (9998.875 ms) ======
[2023-04-19T09:17:49.287Z] ====== movie-lens (apache-spark) [default], iteration 5 started ======
[2023-04-19T09:17:49.631Z] GC before operation: completed in 121.434 ms, heap usage 288.241 MB -> 43.567 MB.
[2023-04-19T09:17:51.345Z] RMSE (validation) = 3.6219689545487617 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2023-04-19T09:17:52.528Z] RMSE (validation) = 2.1340923220285766 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2023-04-19T09:17:53.710Z] RMSE (validation) = 1.3105186462138083 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2023-04-19T09:17:55.411Z] RMSE (validation) = 0.9919630851507418 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2023-04-19T09:17:56.142Z] RMSE (validation) = 1.2070175276098325 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2023-04-19T09:17:56.873Z] RMSE (validation) = 1.1146801722401463 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2023-04-19T09:17:58.062Z] RMSE (validation) = 0.9227419497655963 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2023-04-19T09:17:58.794Z] RMSE (validation) = 0.8980643995666189 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2023-04-19T09:17:59.135Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9063252187379536.
[2023-04-19T09:17:59.135Z] The best model improves the baseline by 14.52%.
[2023-04-19T09:17:59.135Z] Movies recommended for you:
[2023-04-19T09:17:59.135Z] WARNING: This benchmark provides no result that can be validated.
[2023-04-19T09:17:59.135Z] There is no way to check that no silent failure occurred.
[2023-04-19T09:17:59.135Z] ====== movie-lens (apache-spark) [default], iteration 5 completed (9631.391 ms) ======
[2023-04-19T09:17:59.135Z] ====== movie-lens (apache-spark) [default], iteration 6 started ======
[2023-04-19T09:17:59.135Z] GC before operation: completed in 116.001 ms, heap usage 231.443 MB -> 43.405 MB.
[2023-04-19T09:18:00.841Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2023-04-19T09:18:02.025Z] RMSE (validation) = 2.1340923220285766 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2023-04-19T09:18:03.728Z] RMSE (validation) = 1.3105186462138083 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2023-04-19T09:18:04.908Z] RMSE (validation) = 0.9919630851507418 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2023-04-19T09:18:05.643Z] RMSE (validation) = 1.2070175276098327 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2023-04-19T09:18:06.376Z] RMSE (validation) = 1.1146801722401463 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2023-04-19T09:18:07.115Z] RMSE (validation) = 0.9227419497655963 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2023-04-19T09:18:08.300Z] RMSE (validation) = 0.8980643995666189 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2023-04-19T09:18:08.300Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9063252187379536.
[2023-04-19T09:18:08.300Z] The best model improves the baseline by 14.52%.
[2023-04-19T09:18:08.300Z] Movies recommended for you:
[2023-04-19T09:18:08.300Z] WARNING: This benchmark provides no result that can be validated.
[2023-04-19T09:18:08.300Z] There is no way to check that no silent failure occurred.
[2023-04-19T09:18:08.300Z] ====== movie-lens (apache-spark) [default], iteration 6 completed (9000.839 ms) ======
[2023-04-19T09:18:08.300Z] ====== movie-lens (apache-spark) [default], iteration 7 started ======
[2023-04-19T09:18:08.300Z] GC before operation: completed in 92.270 ms, heap usage 222.018 MB -> 43.559 MB.
[2023-04-19T09:18:09.485Z] RMSE (validation) = 3.6219689545487617 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2023-04-19T09:18:11.220Z] RMSE (validation) = 2.1340923220285766 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2023-04-19T09:18:12.409Z] RMSE (validation) = 1.3105186462138083 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2023-04-19T09:18:13.609Z] RMSE (validation) = 0.9919630851507418 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2023-04-19T09:18:14.435Z] RMSE (validation) = 1.2070175276098327 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2023-04-19T09:18:15.173Z] RMSE (validation) = 1.1146801722401463 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2023-04-19T09:18:15.937Z] RMSE (validation) = 0.9227419497655963 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2023-04-19T09:18:17.121Z] RMSE (validation) = 0.8980643995666189 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2023-04-19T09:18:17.121Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9063252187379536.
[2023-04-19T09:18:17.121Z] The best model improves the baseline by 14.52%.
[2023-04-19T09:18:17.121Z] Movies recommended for you:
[2023-04-19T09:18:17.121Z] WARNING: This benchmark provides no result that can be validated.
[2023-04-19T09:18:17.121Z] There is no way to check that no silent failure occurred.
[2023-04-19T09:18:17.121Z] ====== movie-lens (apache-spark) [default], iteration 7 completed (8757.370 ms) ======
[2023-04-19T09:18:17.121Z] ====== movie-lens (apache-spark) [default], iteration 8 started ======
[2023-04-19T09:18:17.121Z] GC before operation: completed in 100.000 ms, heap usage 265.979 MB -> 43.953 MB.
[2023-04-19T09:18:18.302Z] RMSE (validation) = 3.6219689545487617 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2023-04-19T09:18:19.489Z] RMSE (validation) = 2.1340923220285766 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2023-04-19T09:18:21.193Z] RMSE (validation) = 1.3105186462138083 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2023-04-19T09:18:22.371Z] RMSE (validation) = 0.9919630851507418 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2023-04-19T09:18:22.712Z] RMSE (validation) = 1.2070175276098325 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2023-04-19T09:18:23.441Z] RMSE (validation) = 1.1146801722401463 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2023-04-19T09:18:24.626Z] RMSE (validation) = 0.9227419497655963 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2023-04-19T09:18:25.356Z] RMSE (validation) = 0.8980643995666189 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2023-04-19T09:18:25.356Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9063252187379536.
[2023-04-19T09:18:25.356Z] The best model improves the baseline by 14.52%.
[2023-04-19T09:18:25.356Z] Movies recommended for you:
[2023-04-19T09:18:25.356Z] WARNING: This benchmark provides no result that can be validated.
[2023-04-19T09:18:25.356Z] There is no way to check that no silent failure occurred.
[2023-04-19T09:18:25.356Z] ====== movie-lens (apache-spark) [default], iteration 8 completed (8190.558 ms) ======
[2023-04-19T09:18:25.356Z] ====== movie-lens (apache-spark) [default], iteration 9 started ======
[2023-04-19T09:18:25.356Z] GC before operation: completed in 94.328 ms, heap usage 242.814 MB -> 43.686 MB.
[2023-04-19T09:18:26.533Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2023-04-19T09:18:28.241Z] RMSE (validation) = 2.1340923220285766 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2023-04-19T09:18:29.418Z] RMSE (validation) = 1.3105186462138083 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2023-04-19T09:18:30.595Z] RMSE (validation) = 0.9919630851507418 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2023-04-19T09:18:31.328Z] RMSE (validation) = 1.2070175276098327 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2023-04-19T09:18:32.062Z] RMSE (validation) = 1.1146801722401463 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2023-04-19T09:18:32.790Z] RMSE (validation) = 0.9227419497655963 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2023-04-19T09:18:33.519Z] RMSE (validation) = 0.8980643995666189 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2023-04-19T09:18:33.962Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9063252187379536.
[2023-04-19T09:18:33.962Z] The best model improves the baseline by 14.52%.
[2023-04-19T09:18:33.962Z] Movies recommended for you:
[2023-04-19T09:18:33.963Z] WARNING: This benchmark provides no result that can be validated.
[2023-04-19T09:18:33.963Z] There is no way to check that no silent failure occurred.
[2023-04-19T09:18:33.963Z] ====== movie-lens (apache-spark) [default], iteration 9 completed (8432.359 ms) ======
[2023-04-19T09:18:33.963Z] ====== movie-lens (apache-spark) [default], iteration 10 started ======
[2023-04-19T09:18:33.963Z] GC before operation: completed in 129.863 ms, heap usage 244.127 MB -> 43.809 MB.
[2023-04-19T09:18:35.159Z] RMSE (validation) = 3.6219689545487617 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2023-04-19T09:18:36.864Z] RMSE (validation) = 2.1340923220285766 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2023-04-19T09:18:38.042Z] RMSE (validation) = 1.3105186462138083 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2023-04-19T09:18:39.302Z] RMSE (validation) = 0.9919630851507418 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2023-04-19T09:18:40.034Z] RMSE (validation) = 1.2070175276098327 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2023-04-19T09:18:40.377Z] RMSE (validation) = 1.1146801722401463 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2023-04-19T09:18:41.114Z] RMSE (validation) = 0.9227419497655963 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2023-04-19T09:18:41.843Z] RMSE (validation) = 0.8980643995666189 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2023-04-19T09:18:41.843Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9063252187379536.
[2023-04-19T09:18:41.843Z] The best model improves the baseline by 14.52%.
[2023-04-19T09:18:41.843Z] Movies recommended for you:
[2023-04-19T09:18:41.843Z] WARNING: This benchmark provides no result that can be validated.
[2023-04-19T09:18:41.843Z] There is no way to check that no silent failure occurred.
[2023-04-19T09:18:41.843Z] ====== movie-lens (apache-spark) [default], iteration 10 completed (7899.794 ms) ======
[2023-04-19T09:18:41.843Z] ====== movie-lens (apache-spark) [default], iteration 11 started ======
[2023-04-19T09:18:42.182Z] GC before operation: completed in 81.143 ms, heap usage 234.932 MB -> 43.453 MB.
[2023-04-19T09:18:42.926Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2023-04-19T09:18:44.103Z] RMSE (validation) = 2.1340923220285766 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2023-04-19T09:18:45.283Z] RMSE (validation) = 1.3105186462138083 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2023-04-19T09:18:46.461Z] RMSE (validation) = 0.9919630851507418 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2023-04-19T09:18:47.190Z] RMSE (validation) = 1.2070175276098325 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2023-04-19T09:18:47.919Z] RMSE (validation) = 1.1146801722401463 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2023-04-19T09:18:48.652Z] RMSE (validation) = 0.9227419497655963 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2023-04-19T09:18:49.386Z] RMSE (validation) = 0.8980643995666189 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2023-04-19T09:18:49.727Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9063252187379536.
[2023-04-19T09:18:49.727Z] The best model improves the baseline by 14.52%.
[2023-04-19T09:18:49.727Z] Movies recommended for you:
[2023-04-19T09:18:49.727Z] WARNING: This benchmark provides no result that can be validated.
[2023-04-19T09:18:49.727Z] There is no way to check that no silent failure occurred.
[2023-04-19T09:18:49.727Z] ====== movie-lens (apache-spark) [default], iteration 11 completed (7630.871 ms) ======
[2023-04-19T09:18:49.727Z] ====== movie-lens (apache-spark) [default], iteration 12 started ======
[2023-04-19T09:18:49.727Z] GC before operation: completed in 73.103 ms, heap usage 221.525 MB -> 43.717 MB.
[2023-04-19T09:18:50.904Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2023-04-19T09:18:52.089Z] RMSE (validation) = 2.1340923220285766 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2023-04-19T09:18:53.273Z] RMSE (validation) = 1.3105186462138083 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2023-04-19T09:18:54.451Z] RMSE (validation) = 0.9919630851507418 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2023-04-19T09:18:55.179Z] RMSE (validation) = 1.2070175276098325 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2023-04-19T09:18:55.909Z] RMSE (validation) = 1.1146801722401463 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2023-04-19T09:18:56.249Z] RMSE (validation) = 0.9227419497655963 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2023-04-19T09:18:56.979Z] RMSE (validation) = 0.8980643995666189 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2023-04-19T09:18:56.979Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9063252187379536.
[2023-04-19T09:18:56.979Z] The best model improves the baseline by 14.52%.
[2023-04-19T09:18:57.320Z] Movies recommended for you:
[2023-04-19T09:18:57.320Z] WARNING: This benchmark provides no result that can be validated.
[2023-04-19T09:18:57.320Z] There is no way to check that no silent failure occurred.
[2023-04-19T09:18:57.320Z] ====== movie-lens (apache-spark) [default], iteration 12 completed (7409.646 ms) ======
[2023-04-19T09:18:57.320Z] ====== movie-lens (apache-spark) [default], iteration 13 started ======
[2023-04-19T09:18:57.320Z] GC before operation: completed in 73.946 ms, heap usage 248.205 MB -> 43.981 MB.
[2023-04-19T09:18:58.498Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2023-04-19T09:18:59.232Z] RMSE (validation) = 2.1340923220285766 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2023-04-19T09:19:00.512Z] RMSE (validation) = 1.3105186462138083 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2023-04-19T09:19:01.693Z] RMSE (validation) = 0.9919630851507418 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2023-04-19T09:19:02.425Z] RMSE (validation) = 1.2070175276098327 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2023-04-19T09:19:02.766Z] RMSE (validation) = 1.1146801722401463 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2023-04-19T09:19:03.497Z] RMSE (validation) = 0.9227419497655963 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2023-04-19T09:19:04.683Z] RMSE (validation) = 0.8980643995666189 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2023-04-19T09:19:04.683Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9063252187379536.
[2023-04-19T09:19:04.683Z] The best model improves the baseline by 14.52%.
[2023-04-19T09:19:04.683Z] Movies recommended for you:
[2023-04-19T09:19:04.683Z] WARNING: This benchmark provides no result that can be validated.
[2023-04-19T09:19:04.683Z] There is no way to check that no silent failure occurred.
[2023-04-19T09:19:04.683Z] ====== movie-lens (apache-spark) [default], iteration 13 completed (7462.354 ms) ======
[2023-04-19T09:19:04.683Z] ====== movie-lens (apache-spark) [default], iteration 14 started ======
[2023-04-19T09:19:04.683Z] GC before operation: completed in 81.979 ms, heap usage 222.280 MB -> 43.524 MB.
[2023-04-19T09:19:06.383Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2023-04-19T09:19:07.564Z] RMSE (validation) = 2.1340923220285766 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2023-04-19T09:19:08.742Z] RMSE (validation) = 1.3105186462138083 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2023-04-19T09:19:09.922Z] RMSE (validation) = 0.9919630851507418 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2023-04-19T09:19:11.110Z] RMSE (validation) = 1.2070175276098327 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2023-04-19T09:19:11.461Z] RMSE (validation) = 1.1146801722401463 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2023-04-19T09:19:12.195Z] RMSE (validation) = 0.9227419497655963 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2023-04-19T09:19:12.925Z] RMSE (validation) = 0.8980643995666189 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2023-04-19T09:19:13.264Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9063252187379536.
[2023-04-19T09:19:13.265Z] The best model improves the baseline by 14.52%.
[2023-04-19T09:19:13.265Z] Movies recommended for you:
[2023-04-19T09:19:13.265Z] WARNING: This benchmark provides no result that can be validated.
[2023-04-19T09:19:13.265Z] There is no way to check that no silent failure occurred.
[2023-04-19T09:19:13.265Z] ====== movie-lens (apache-spark) [default], iteration 14 completed (8484.565 ms) ======
[2023-04-19T09:19:13.265Z] ====== movie-lens (apache-spark) [default], iteration 15 started ======
[2023-04-19T09:19:13.265Z] GC before operation: completed in 81.914 ms, heap usage 222.993 MB -> 43.719 MB.
[2023-04-19T09:19:14.445Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2023-04-19T09:19:15.625Z] RMSE (validation) = 2.1340923220285766 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2023-04-19T09:19:16.805Z] RMSE (validation) = 1.3105186462138083 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2023-04-19T09:19:17.985Z] RMSE (validation) = 0.9919630851507418 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2023-04-19T09:19:18.717Z] RMSE (validation) = 1.2070175276098325 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2023-04-19T09:19:19.446Z] RMSE (validation) = 1.1146801722401463 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2023-04-19T09:19:20.632Z] RMSE (validation) = 0.9227419497655963 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2023-04-19T09:19:21.364Z] RMSE (validation) = 0.8980643995666189 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2023-04-19T09:19:21.364Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9063252187379536.
[2023-04-19T09:19:21.364Z] The best model improves the baseline by 14.52%.
[2023-04-19T09:19:21.364Z] Movies recommended for you:
[2023-04-19T09:19:21.364Z] WARNING: This benchmark provides no result that can be validated.
[2023-04-19T09:19:21.364Z] There is no way to check that no silent failure occurred.
[2023-04-19T09:19:21.364Z] ====== movie-lens (apache-spark) [default], iteration 15 completed (8117.561 ms) ======
[2023-04-19T09:19:21.364Z] ====== movie-lens (apache-spark) [default], iteration 16 started ======
[2023-04-19T09:19:21.708Z] GC before operation: completed in 92.045 ms, heap usage 234.450 MB -> 43.856 MB.
[2023-04-19T09:19:22.900Z] RMSE (validation) = 3.6219689545487617 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2023-04-19T09:19:24.086Z] RMSE (validation) = 2.1340923220285766 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2023-04-19T09:19:25.272Z] RMSE (validation) = 1.3105186462138083 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2023-04-19T09:19:26.466Z] RMSE (validation) = 0.9919630851507418 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2023-04-19T09:19:27.200Z] RMSE (validation) = 1.2070175276098325 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2023-04-19T09:19:27.982Z] RMSE (validation) = 1.1146801722401463 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2023-04-19T09:19:28.716Z] RMSE (validation) = 0.9227419497655963 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2023-04-19T09:19:29.449Z] RMSE (validation) = 0.8980643995666189 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2023-04-19T09:19:29.449Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9063252187379536.
[2023-04-19T09:19:29.792Z] The best model improves the baseline by 14.52%.
[2023-04-19T09:19:29.792Z] Movies recommended for you:
[2023-04-19T09:19:29.792Z] WARNING: This benchmark provides no result that can be validated.
[2023-04-19T09:19:29.792Z] There is no way to check that no silent failure occurred.
[2023-04-19T09:19:29.792Z] ====== movie-lens (apache-spark) [default], iteration 16 completed (8121.684 ms) ======
[2023-04-19T09:19:29.792Z] ====== movie-lens (apache-spark) [default], iteration 17 started ======
[2023-04-19T09:19:29.792Z] GC before operation: completed in 79.459 ms, heap usage 233.974 MB -> 43.840 MB.
[2023-04-19T09:19:30.981Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2023-04-19T09:19:32.691Z] RMSE (validation) = 2.1340923220285766 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2023-04-19T09:19:33.874Z] RMSE (validation) = 1.3105186462138083 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2023-04-19T09:19:35.062Z] RMSE (validation) = 0.9919630851507418 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2023-04-19T09:19:35.796Z] RMSE (validation) = 1.2070175276098327 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2023-04-19T09:19:36.530Z] RMSE (validation) = 1.1146801722401463 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2023-04-19T09:19:37.263Z] RMSE (validation) = 0.9227419497655963 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2023-04-19T09:19:37.993Z] RMSE (validation) = 0.8980643995666189 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2023-04-19T09:19:37.993Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9063252187379536.
[2023-04-19T09:19:38.333Z] The best model improves the baseline by 14.52%.
[2023-04-19T09:19:38.333Z] Movies recommended for you:
[2023-04-19T09:19:38.333Z] WARNING: This benchmark provides no result that can be validated.
[2023-04-19T09:19:38.333Z] There is no way to check that no silent failure occurred.
[2023-04-19T09:19:38.333Z] ====== movie-lens (apache-spark) [default], iteration 17 completed (8464.967 ms) ======
[2023-04-19T09:19:38.333Z] ====== movie-lens (apache-spark) [default], iteration 18 started ======
[2023-04-19T09:19:38.333Z] GC before operation: completed in 87.506 ms, heap usage 223.822 MB -> 43.773 MB.
[2023-04-19T09:19:39.516Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2023-04-19T09:19:40.699Z] RMSE (validation) = 2.1340923220285766 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2023-04-19T09:19:41.880Z] RMSE (validation) = 1.3105186462138083 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2023-04-19T09:19:43.060Z] RMSE (validation) = 0.9919630851507418 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2023-04-19T09:19:44.246Z] RMSE (validation) = 1.2070175276098327 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2023-04-19T09:19:44.978Z] RMSE (validation) = 1.1146801722401463 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2023-04-19T09:19:45.711Z] RMSE (validation) = 0.9227419497655963 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2023-04-19T09:19:46.450Z] RMSE (validation) = 0.8980643995666189 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2023-04-19T09:19:46.450Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9063252187379536.
[2023-04-19T09:19:46.450Z] The best model improves the baseline by 14.52%.
[2023-04-19T09:19:46.450Z] Movies recommended for you:
[2023-04-19T09:19:46.450Z] WARNING: This benchmark provides no result that can be validated.
[2023-04-19T09:19:46.450Z] There is no way to check that no silent failure occurred.
[2023-04-19T09:19:46.450Z] ====== movie-lens (apache-spark) [default], iteration 18 completed (8251.566 ms) ======
[2023-04-19T09:19:46.450Z] ====== movie-lens (apache-spark) [default], iteration 19 started ======
[2023-04-19T09:19:46.788Z] GC before operation: completed in 82.007 ms, heap usage 249.760 MB -> 44.061 MB.
[2023-04-19T09:19:47.967Z] RMSE (validation) = 3.621968954548761 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2023-04-19T09:19:48.695Z] RMSE (validation) = 2.1340923220285766 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2023-04-19T09:19:49.870Z] RMSE (validation) = 1.3105186462138083 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2023-04-19T09:19:51.046Z] RMSE (validation) = 0.9919630851507418 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2023-04-19T09:19:51.775Z] RMSE (validation) = 1.2070175276098325 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2023-04-19T09:19:52.506Z] RMSE (validation) = 1.1146801722401463 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2023-04-19T09:19:53.234Z] RMSE (validation) = 0.9227419497655963 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2023-04-19T09:19:54.062Z] RMSE (validation) = 0.8980643995666189 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2023-04-19T09:19:54.062Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9063252187379536.
[2023-04-19T09:19:54.062Z] The best model improves the baseline by 14.52%.
[2023-04-19T09:19:54.062Z] Movies recommended for you:
[2023-04-19T09:19:54.062Z] WARNING: This benchmark provides no result that can be validated.
[2023-04-19T09:19:54.062Z] There is no way to check that no silent failure occurred.
[2023-04-19T09:19:54.062Z] ====== movie-lens (apache-spark) [default], iteration 19 completed (7423.321 ms) ======
[2023-04-19T09:19:54.402Z] -----------------------------------
[2023-04-19T09:19:54.402Z] renaissance-movie-lens_0_PASSED
[2023-04-19T09:19:54.402Z] -----------------------------------
[2023-04-19T09:19:54.402Z]
[2023-04-19T09:19:54.402Z] TEST TEARDOWN:
[2023-04-19T09:19:54.402Z] Nothing to be done for teardown.
[2023-04-19T09:19:54.402Z] renaissance-movie-lens_0 Finish Time: Wed Apr 19 09:19:54 2023 Epoch Time (ms): 1681895994236