renaissance-movie-lens_0
[2024-11-17T22:40:21.544Z] Running test renaissance-movie-lens_0 ...
[2024-11-17T22:40:21.544Z] ===============================================
[2024-11-17T22:40:21.544Z] renaissance-movie-lens_0 Start Time: Sun Nov 17 22:40:20 2024 Epoch Time (ms): 1731883220802
[2024-11-17T22:40:21.544Z] variation: NoOptions
[2024-11-17T22:40:21.544Z] JVM_OPTIONS:
[2024-11-17T22:40:21.544Z] { \
[2024-11-17T22:40:21.544Z] echo ""; echo "TEST SETUP:"; \
[2024-11-17T22:40:21.544Z] echo "Nothing to be done for setup."; \
[2024-11-17T22:40:21.544Z] mkdir -p "/home/jenkins/workspace/Test_openjdk11_hs_extended.perf_ppc64_aix/aqa-tests/TKG/../TKG/output_17318821893441/renaissance-movie-lens_0"; \
[2024-11-17T22:40:21.544Z] cd "/home/jenkins/workspace/Test_openjdk11_hs_extended.perf_ppc64_aix/aqa-tests/TKG/../TKG/output_17318821893441/renaissance-movie-lens_0"; \
[2024-11-17T22:40:21.544Z] echo ""; echo "TESTING:"; \
[2024-11-17T22:40:21.544Z] "/home/jenkins/workspace/Test_openjdk11_hs_extended.perf_ppc64_aix/jdkbinary/j2sdk-image/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_openjdk11_hs_extended.perf_ppc64_aix/aqa-tests/TKG/../../jvmtest/perf/renaissance/renaissance.jar" --json ""/home/jenkins/workspace/Test_openjdk11_hs_extended.perf_ppc64_aix/aqa-tests/TKG/../TKG/output_17318821893441/renaissance-movie-lens_0"/movie-lens.json" movie-lens; \
[2024-11-17T22:40:21.544Z] if [ $? -eq 0 ]; then echo "-----------------------------------"; echo "renaissance-movie-lens_0""_PASSED"; echo "-----------------------------------"; cd /home/jenkins/workspace/Test_openjdk11_hs_extended.perf_ppc64_aix/aqa-tests/TKG/..; rm -f -r "/home/jenkins/workspace/Test_openjdk11_hs_extended.perf_ppc64_aix/aqa-tests/TKG/../TKG/output_17318821893441/renaissance-movie-lens_0"; else echo "-----------------------------------"; echo "renaissance-movie-lens_0""_FAILED"; echo "-----------------------------------"; fi; \
[2024-11-17T22:40:21.544Z] echo ""; echo "TEST TEARDOWN:"; \
[2024-11-17T22:40:21.544Z] echo "Nothing to be done for teardown."; \
[2024-11-17T22:40:21.544Z] } 2>&1 | tee -a "/home/jenkins/workspace/Test_openjdk11_hs_extended.perf_ppc64_aix/aqa-tests/TKG/../TKG/output_17318821893441/TestTargetResult";
[2024-11-17T22:40:21.544Z]
[2024-11-17T22:40:21.544Z] TEST SETUP:
[2024-11-17T22:40:21.544Z] Nothing to be done for setup.
[2024-11-17T22:40:21.544Z]
[2024-11-17T22:40:21.544Z] TESTING:
[2024-11-17T22:40:26.202Z] Using Spark's default log4j profile: org/apache/spark/log4j-defaults.properties
[2024-11-17T22:40:29.638Z] NOTE: 'movie-lens' benchmark uses Spark local executor with 8 (out of 24) threads.
[2024-11-17T22:40:33.062Z] Got 100004 ratings from 671 users on 9066 movies.
[2024-11-17T22:40:33.832Z] Training: 60056, validation: 20285, test: 19854
[2024-11-17T22:40:33.832Z] ====== movie-lens (apache-spark) [default], iteration 0 started ======
[2024-11-17T22:40:33.832Z] GC before operation: completed in 60.501 ms, heap usage 51.843 MB -> 38.076 MB.
[2024-11-17T22:40:40.742Z] RMSE (validation) = 3.6219689545487506 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-17T22:40:44.179Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-17T22:40:48.657Z] RMSE (validation) = 1.3105189674610933 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-17T22:40:52.089Z] RMSE (validation) = 1.004540774800519 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-17T22:40:53.683Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-17T22:40:56.163Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-17T22:40:57.762Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-17T22:41:00.250Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-17T22:41:00.250Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9073522634082535.
[2024-11-17T22:41:00.250Z] The best model improves the baseline by 14.43%.
[2024-11-17T22:41:00.250Z] Movies recommended for you:
[2024-11-17T22:41:00.250Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-17T22:41:00.250Z] There is no way to check that no silent failure occurred.
[2024-11-17T22:41:00.250Z] ====== movie-lens (apache-spark) [default], iteration 0 completed (26725.560 ms) ======
[2024-11-17T22:41:00.250Z] ====== movie-lens (apache-spark) [default], iteration 1 started ======
[2024-11-17T22:41:00.250Z] GC before operation: completed in 109.578 ms, heap usage 646.115 MB -> 54.251 MB.
[2024-11-17T22:41:03.680Z] RMSE (validation) = 3.6219689545487506 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-17T22:41:07.122Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-17T22:41:10.553Z] RMSE (validation) = 1.3105189674610933 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-17T22:41:13.981Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-17T22:41:16.474Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-17T22:41:18.073Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-17T22:41:20.585Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-17T22:41:22.182Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-17T22:41:22.182Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9073522634082535.
[2024-11-17T22:41:22.966Z] The best model improves the baseline by 14.43%.
[2024-11-17T22:41:22.966Z] Movies recommended for you:
[2024-11-17T22:41:22.966Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-17T22:41:22.966Z] There is no way to check that no silent failure occurred.
[2024-11-17T22:41:22.966Z] ====== movie-lens (apache-spark) [default], iteration 1 completed (22113.718 ms) ======
[2024-11-17T22:41:22.966Z] ====== movie-lens (apache-spark) [default], iteration 2 started ======
[2024-11-17T22:41:22.966Z] GC before operation: completed in 127.922 ms, heap usage 559.886 MB -> 55.219 MB.
[2024-11-17T22:41:26.404Z] RMSE (validation) = 3.6219689545487506 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-17T22:41:28.879Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-17T22:41:32.310Z] RMSE (validation) = 1.3105189674610933 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-17T22:41:35.945Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-17T22:41:37.538Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-17T22:41:39.133Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-17T22:41:41.618Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-17T22:41:43.220Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-17T22:41:43.220Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9073522634082535.
[2024-11-17T22:41:44.003Z] The best model improves the baseline by 14.43%.
[2024-11-17T22:41:44.003Z] Movies recommended for you:
[2024-11-17T22:41:44.003Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-17T22:41:44.003Z] There is no way to check that no silent failure occurred.
[2024-11-17T22:41:44.003Z] ====== movie-lens (apache-spark) [default], iteration 2 completed (20911.362 ms) ======
[2024-11-17T22:41:44.003Z] ====== movie-lens (apache-spark) [default], iteration 3 started ======
[2024-11-17T22:41:44.003Z] GC before operation: completed in 113.304 ms, heap usage 245.571 MB -> 52.169 MB.
[2024-11-17T22:41:47.454Z] RMSE (validation) = 3.6219689545487506 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-17T22:41:49.925Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-17T22:41:53.355Z] RMSE (validation) = 1.3105189674610935 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-17T22:41:56.787Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-17T22:41:58.389Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-17T22:41:59.986Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-17T22:42:02.470Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-17T22:42:04.062Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-17T22:42:04.832Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9073522634082535.
[2024-11-17T22:42:04.832Z] The best model improves the baseline by 14.43%.
[2024-11-17T22:42:04.832Z] Movies recommended for you:
[2024-11-17T22:42:04.832Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-17T22:42:04.832Z] There is no way to check that no silent failure occurred.
[2024-11-17T22:42:04.832Z] ====== movie-lens (apache-spark) [default], iteration 3 completed (20839.311 ms) ======
[2024-11-17T22:42:04.832Z] ====== movie-lens (apache-spark) [default], iteration 4 started ======
[2024-11-17T22:42:04.832Z] GC before operation: completed in 111.107 ms, heap usage 411.129 MB -> 52.642 MB.
[2024-11-17T22:42:08.288Z] RMSE (validation) = 3.6219689545487506 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-17T22:42:10.760Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-17T22:42:14.189Z] RMSE (validation) = 1.3105189674610935 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-17T22:42:17.620Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-17T22:42:19.217Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-17T22:42:21.691Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-17T22:42:23.288Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-17T22:42:24.900Z] RMSE (validation) = 0.9001440981626694 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-17T22:42:25.671Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9073522634082535.
[2024-11-17T22:42:25.671Z] The best model improves the baseline by 14.43%.
[2024-11-17T22:42:25.671Z] Movies recommended for you:
[2024-11-17T22:42:25.671Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-17T22:42:25.671Z] There is no way to check that no silent failure occurred.
[2024-11-17T22:42:25.671Z] ====== movie-lens (apache-spark) [default], iteration 4 completed (20744.160 ms) ======
[2024-11-17T22:42:25.671Z] ====== movie-lens (apache-spark) [default], iteration 5 started ======
[2024-11-17T22:42:25.671Z] GC before operation: completed in 114.189 ms, heap usage 474.679 MB -> 56.014 MB.
[2024-11-17T22:42:29.129Z] RMSE (validation) = 3.6219689545487506 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-17T22:42:31.611Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-17T22:42:35.040Z] RMSE (validation) = 1.3105189674610935 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-17T22:42:38.478Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-17T22:42:40.078Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-17T22:42:41.672Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-17T22:42:44.153Z] RMSE (validation) = 0.927571739133814 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-17T22:42:45.761Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-17T22:42:45.761Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9073522634082535.
[2024-11-17T22:42:46.532Z] The best model improves the baseline by 14.43%.
[2024-11-17T22:42:46.532Z] Movies recommended for you:
[2024-11-17T22:42:46.532Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-17T22:42:46.532Z] There is no way to check that no silent failure occurred.
[2024-11-17T22:42:46.532Z] ====== movie-lens (apache-spark) [default], iteration 5 completed (20617.947 ms) ======
[2024-11-17T22:42:46.532Z] ====== movie-lens (apache-spark) [default], iteration 6 started ======
[2024-11-17T22:42:46.532Z] GC before operation: completed in 118.183 ms, heap usage 100.912 MB -> 54.115 MB.
[2024-11-17T22:42:49.981Z] RMSE (validation) = 3.621968954548751 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-17T22:42:52.465Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-17T22:42:55.899Z] RMSE (validation) = 1.3105189674610935 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-17T22:42:58.370Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-17T22:43:00.843Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-17T22:43:02.442Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-17T22:43:04.032Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-17T22:43:06.506Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-17T22:43:06.506Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9073522634082535.
[2024-11-17T22:43:06.506Z] The best model improves the baseline by 14.43%.
[2024-11-17T22:43:06.506Z] Movies recommended for you:
[2024-11-17T22:43:06.506Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-17T22:43:06.506Z] There is no way to check that no silent failure occurred.
[2024-11-17T22:43:06.506Z] ====== movie-lens (apache-spark) [default], iteration 6 completed (20374.287 ms) ======
[2024-11-17T22:43:06.506Z] ====== movie-lens (apache-spark) [default], iteration 7 started ======
[2024-11-17T22:43:07.281Z] GC before operation: completed in 130.490 ms, heap usage 385.593 MB -> 52.714 MB.
[2024-11-17T22:43:09.759Z] RMSE (validation) = 3.621968954548751 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-17T22:43:13.378Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-17T22:43:16.823Z] RMSE (validation) = 1.3105189674610935 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-17T22:43:19.302Z] RMSE (validation) = 1.004540774800519 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-17T22:43:20.892Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-17T22:43:23.366Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-17T22:43:24.964Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-17T22:43:27.449Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-17T22:43:27.449Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9073522634082535.
[2024-11-17T22:43:27.449Z] The best model improves the baseline by 14.43%.
[2024-11-17T22:43:27.449Z] Movies recommended for you:
[2024-11-17T22:43:27.449Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-17T22:43:27.449Z] There is no way to check that no silent failure occurred.
[2024-11-17T22:43:27.449Z] ====== movie-lens (apache-spark) [default], iteration 7 completed (20627.460 ms) ======
[2024-11-17T22:43:27.449Z] ====== movie-lens (apache-spark) [default], iteration 8 started ======
[2024-11-17T22:43:27.449Z] GC before operation: completed in 118.720 ms, heap usage 257.895 MB -> 52.995 MB.
[2024-11-17T22:43:30.892Z] RMSE (validation) = 3.6219689545487506 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-17T22:43:34.327Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-17T22:43:36.827Z] RMSE (validation) = 1.3105189674610935 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-17T22:43:40.259Z] RMSE (validation) = 1.004540774800519 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-17T22:43:41.865Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-17T22:43:44.348Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-17T22:43:46.018Z] RMSE (validation) = 0.927571739133814 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-17T22:43:47.616Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-17T22:43:48.404Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9073522634082535.
[2024-11-17T22:43:48.404Z] The best model improves the baseline by 14.43%.
[2024-11-17T22:43:48.404Z] Movies recommended for you:
[2024-11-17T22:43:48.404Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-17T22:43:48.404Z] There is no way to check that no silent failure occurred.
[2024-11-17T22:43:48.404Z] ====== movie-lens (apache-spark) [default], iteration 8 completed (20493.873 ms) ======
[2024-11-17T22:43:48.404Z] ====== movie-lens (apache-spark) [default], iteration 9 started ======
[2024-11-17T22:43:48.404Z] GC before operation: completed in 117.152 ms, heap usage 226.073 MB -> 52.811 MB.
[2024-11-17T22:43:51.848Z] RMSE (validation) = 3.6219689545487506 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-17T22:43:54.335Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-17T22:43:57.767Z] RMSE (validation) = 1.3105189674610935 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-17T22:44:01.192Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-17T22:44:02.786Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-17T22:44:04.384Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-17T22:44:05.980Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-17T22:44:08.464Z] RMSE (validation) = 0.9001440981626694 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-17T22:44:08.464Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9073522634082535.
[2024-11-17T22:44:08.464Z] The best model improves the baseline by 14.43%.
[2024-11-17T22:44:08.464Z] Movies recommended for you:
[2024-11-17T22:44:08.464Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-17T22:44:08.464Z] There is no way to check that no silent failure occurred.
[2024-11-17T22:44:08.464Z] ====== movie-lens (apache-spark) [default], iteration 9 completed (20431.838 ms) ======
[2024-11-17T22:44:08.464Z] ====== movie-lens (apache-spark) [default], iteration 10 started ======
[2024-11-17T22:44:08.464Z] GC before operation: completed in 120.705 ms, heap usage 436.002 MB -> 53.056 MB.
[2024-11-17T22:44:11.899Z] RMSE (validation) = 3.6219689545487506 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-17T22:44:15.338Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-17T22:44:17.819Z] RMSE (validation) = 1.3105189674610933 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-17T22:44:21.242Z] RMSE (validation) = 1.004540774800519 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-17T22:44:22.832Z] RMSE (validation) = 1.2218330581874073 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-17T22:44:24.423Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-17T22:44:26.901Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-17T22:44:28.510Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-17T22:44:28.510Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9073522634082535.
[2024-11-17T22:44:29.282Z] The best model improves the baseline by 14.43%.
[2024-11-17T22:44:29.282Z] Movies recommended for you:
[2024-11-17T22:44:29.282Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-17T22:44:29.282Z] There is no way to check that no silent failure occurred.
[2024-11-17T22:44:29.282Z] ====== movie-lens (apache-spark) [default], iteration 10 completed (20201.574 ms) ======
[2024-11-17T22:44:29.282Z] ====== movie-lens (apache-spark) [default], iteration 11 started ======
[2024-11-17T22:44:29.282Z] GC before operation: completed in 150.273 ms, heap usage 305.039 MB -> 52.704 MB.
[2024-11-17T22:44:32.706Z] RMSE (validation) = 3.621968954548751 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-17T22:44:35.195Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-17T22:44:38.632Z] RMSE (validation) = 1.3105189674610935 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-17T22:44:42.072Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-17T22:44:43.666Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-17T22:44:45.256Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-17T22:44:47.735Z] RMSE (validation) = 0.927571739133814 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-17T22:44:49.333Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-17T22:44:50.112Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9073522634082535.
[2024-11-17T22:44:50.112Z] The best model improves the baseline by 14.43%.
[2024-11-17T22:44:50.112Z] Movies recommended for you:
[2024-11-17T22:44:50.112Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-17T22:44:50.112Z] There is no way to check that no silent failure occurred.
[2024-11-17T22:44:50.112Z] ====== movie-lens (apache-spark) [default], iteration 11 completed (20691.890 ms) ======
[2024-11-17T22:44:50.112Z] ====== movie-lens (apache-spark) [default], iteration 12 started ======
[2024-11-17T22:44:50.112Z] GC before operation: completed in 115.307 ms, heap usage 482.800 MB -> 53.015 MB.
[2024-11-17T22:44:52.770Z] RMSE (validation) = 3.621968954548751 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-17T22:44:56.204Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-17T22:44:59.661Z] RMSE (validation) = 1.3105189674610933 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-17T22:45:02.133Z] RMSE (validation) = 1.004540774800519 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-17T22:45:04.608Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-17T22:45:06.202Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-17T22:45:07.796Z] RMSE (validation) = 0.9275717391338142 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-17T22:45:10.267Z] RMSE (validation) = 0.9001440981626694 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-17T22:45:10.267Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9073522634082535.
[2024-11-17T22:45:10.267Z] The best model improves the baseline by 14.43%.
[2024-11-17T22:45:10.267Z] Movies recommended for you:
[2024-11-17T22:45:10.267Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-17T22:45:10.267Z] There is no way to check that no silent failure occurred.
[2024-11-17T22:45:10.267Z] ====== movie-lens (apache-spark) [default], iteration 12 completed (20449.581 ms) ======
[2024-11-17T22:45:10.267Z] ====== movie-lens (apache-spark) [default], iteration 13 started ======
[2024-11-17T22:45:10.267Z] GC before operation: completed in 119.417 ms, heap usage 265.505 MB -> 53.043 MB.
[2024-11-17T22:45:13.693Z] RMSE (validation) = 3.6219689545487506 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-17T22:45:17.118Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-17T22:45:19.607Z] RMSE (validation) = 1.3105189674610933 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-17T22:45:23.042Z] RMSE (validation) = 1.004540774800519 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-17T22:45:24.641Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-17T22:45:26.244Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-17T22:45:28.730Z] RMSE (validation) = 0.927571739133814 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-17T22:45:30.324Z] RMSE (validation) = 0.9001440981626694 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-17T22:45:31.095Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9073522634082535.
[2024-11-17T22:45:31.095Z] The best model improves the baseline by 14.43%.
[2024-11-17T22:45:31.095Z] Movies recommended for you:
[2024-11-17T22:45:31.095Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-17T22:45:31.095Z] There is no way to check that no silent failure occurred.
[2024-11-17T22:45:31.095Z] ====== movie-lens (apache-spark) [default], iteration 13 completed (20425.663 ms) ======
[2024-11-17T22:45:31.095Z] ====== movie-lens (apache-spark) [default], iteration 14 started ======
[2024-11-17T22:45:31.095Z] GC before operation: completed in 114.490 ms, heap usage 604.886 MB -> 56.273 MB.
[2024-11-17T22:45:34.532Z] RMSE (validation) = 3.621968954548751 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-17T22:45:37.010Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-17T22:45:40.441Z] RMSE (validation) = 1.3105189674610935 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-17T22:45:43.895Z] RMSE (validation) = 1.004540774800519 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-17T22:45:45.493Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-17T22:45:47.085Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-17T22:45:48.686Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-17T22:45:51.170Z] RMSE (validation) = 0.9001440981626694 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-17T22:45:51.170Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9073522634082535.
[2024-11-17T22:45:51.170Z] The best model improves the baseline by 14.43%.
[2024-11-17T22:45:51.170Z] Movies recommended for you:
[2024-11-17T22:45:51.170Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-17T22:45:51.170Z] There is no way to check that no silent failure occurred.
[2024-11-17T22:45:51.170Z] ====== movie-lens (apache-spark) [default], iteration 14 completed (20322.579 ms) ======
[2024-11-17T22:45:51.170Z] ====== movie-lens (apache-spark) [default], iteration 15 started ======
[2024-11-17T22:45:51.170Z] GC before operation: completed in 120.794 ms, heap usage 415.316 MB -> 53.088 MB.
[2024-11-17T22:45:54.611Z] RMSE (validation) = 3.621968954548751 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-17T22:45:58.044Z] RMSE (validation) = 2.1340923217118064 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-17T22:46:00.699Z] RMSE (validation) = 1.3105189674610935 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-17T22:46:04.128Z] RMSE (validation) = 1.004540774800519 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-17T22:46:05.731Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-17T22:46:07.331Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-17T22:46:09.822Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-17T22:46:11.434Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-17T22:46:11.434Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9073522634082535.
[2024-11-17T22:46:11.434Z] The best model improves the baseline by 14.43%.
[2024-11-17T22:46:12.205Z] Movies recommended for you:
[2024-11-17T22:46:12.205Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-17T22:46:12.205Z] There is no way to check that no silent failure occurred.
[2024-11-17T22:46:12.205Z] ====== movie-lens (apache-spark) [default], iteration 15 completed (20322.960 ms) ======
[2024-11-17T22:46:12.205Z] ====== movie-lens (apache-spark) [default], iteration 16 started ======
[2024-11-17T22:46:12.205Z] GC before operation: completed in 125.903 ms, heap usage 241.870 MB -> 53.043 MB.
[2024-11-17T22:46:14.751Z] RMSE (validation) = 3.6219689545487506 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-17T22:46:18.197Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-17T22:46:21.628Z] RMSE (validation) = 1.3105189674610933 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-17T22:46:24.099Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-17T22:46:26.590Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-17T22:46:28.181Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-17T22:46:29.774Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-17T22:46:31.420Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-17T22:46:32.190Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9073522634082535.
[2024-11-17T22:46:32.190Z] The best model improves the baseline by 14.43%.
[2024-11-17T22:46:32.190Z] Movies recommended for you:
[2024-11-17T22:46:32.190Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-17T22:46:32.190Z] There is no way to check that no silent failure occurred.
[2024-11-17T22:46:32.190Z] ====== movie-lens (apache-spark) [default], iteration 16 completed (20182.931 ms) ======
[2024-11-17T22:46:32.190Z] ====== movie-lens (apache-spark) [default], iteration 17 started ======
[2024-11-17T22:46:32.190Z] GC before operation: completed in 114.886 ms, heap usage 357.392 MB -> 52.947 MB.
[2024-11-17T22:46:35.630Z] RMSE (validation) = 3.6219689545487506 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-17T22:46:38.104Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-17T22:46:41.531Z] RMSE (validation) = 1.3105189674610935 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-17T22:46:44.962Z] RMSE (validation) = 1.004540774800519 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-17T22:46:46.557Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-17T22:46:48.151Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-17T22:46:50.626Z] RMSE (validation) = 0.9275717391338142 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-17T22:46:52.221Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-17T22:46:52.991Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9073522634082535.
[2024-11-17T22:46:52.991Z] The best model improves the baseline by 14.43%.
[2024-11-17T22:46:52.991Z] Movies recommended for you:
[2024-11-17T22:46:52.991Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-17T22:46:52.991Z] There is no way to check that no silent failure occurred.
[2024-11-17T22:46:52.991Z] ====== movie-lens (apache-spark) [default], iteration 17 completed (20528.381 ms) ======
[2024-11-17T22:46:52.991Z] ====== movie-lens (apache-spark) [default], iteration 18 started ======
[2024-11-17T22:46:52.991Z] GC before operation: completed in 127.129 ms, heap usage 493.693 MB -> 56.382 MB.
[2024-11-17T22:46:56.434Z] RMSE (validation) = 3.6219689545487506 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-17T22:46:59.084Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-17T22:47:02.531Z] RMSE (validation) = 1.3105189674610935 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-17T22:47:05.010Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-17T22:47:07.489Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-17T22:47:09.090Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-17T22:47:10.688Z] RMSE (validation) = 0.927571739133814 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-17T22:47:13.170Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-17T22:47:13.170Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9073522634082535.
[2024-11-17T22:47:13.170Z] The best model improves the baseline by 14.43%.
[2024-11-17T22:47:13.170Z] Movies recommended for you:
[2024-11-17T22:47:13.170Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-17T22:47:13.170Z] There is no way to check that no silent failure occurred.
[2024-11-17T22:47:13.170Z] ====== movie-lens (apache-spark) [default], iteration 18 completed (20270.751 ms) ======
[2024-11-17T22:47:13.170Z] ====== movie-lens (apache-spark) [default], iteration 19 started ======
[2024-11-17T22:47:13.170Z] GC before operation: completed in 117.579 ms, heap usage 315.666 MB -> 53.200 MB.
[2024-11-17T22:47:16.607Z] RMSE (validation) = 3.621968954548751 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-11-17T22:47:20.040Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-11-17T22:47:22.511Z] RMSE (validation) = 1.3105189674610935 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-11-17T22:47:25.950Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-11-17T22:47:27.548Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-11-17T22:47:29.139Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-11-17T22:47:31.612Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-11-17T22:47:33.203Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-11-17T22:47:33.203Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9073522634082535.
[2024-11-17T22:47:33.203Z] The best model improves the baseline by 14.43%.
[2024-11-17T22:47:33.203Z] Movies recommended for you:
[2024-11-17T22:47:33.203Z] WARNING: This benchmark provides no result that can be validated.
[2024-11-17T22:47:33.203Z] There is no way to check that no silent failure occurred.
[2024-11-17T22:47:33.203Z] ====== movie-lens (apache-spark) [default], iteration 19 completed (20202.575 ms) ======
[2024-11-17T22:47:33.974Z] -----------------------------------
[2024-11-17T22:47:33.974Z] renaissance-movie-lens_0_PASSED
[2024-11-17T22:47:33.974Z] -----------------------------------
[2024-11-17T22:47:33.974Z]
[2024-11-17T22:47:33.974Z] TEST TEARDOWN:
[2024-11-17T22:47:33.974Z] Nothing to be done for teardown.
[2024-11-17T22:47:33.974Z] renaissance-movie-lens_0 Finish Time: Sun Nov 17 22:47:33 2024 Epoch Time (ms): 1731883653705