renaissance-movie-lens_0
[2024-10-31T02:39:32.966Z] Running test renaissance-movie-lens_0 ...
[2024-10-31T02:39:32.966Z] ===============================================
[2024-10-31T02:39:32.966Z] renaissance-movie-lens_0 Start Time: Thu Oct 31 02:39:32 2024 Epoch Time (ms): 1730342372688
[2024-10-31T02:39:32.966Z] variation: NoOptions
[2024-10-31T02:39:32.966Z] JVM_OPTIONS:
[2024-10-31T02:39:32.966Z] { \
[2024-10-31T02:39:32.966Z] echo ""; echo "TEST SETUP:"; \
[2024-10-31T02:39:32.966Z] echo "Nothing to be done for setup."; \
[2024-10-31T02:39:32.966Z] mkdir -p "/home/jenkins/workspace/Test_openjdk17_hs_extended.perf_ppc64_aix/aqa-tests/TKG/../TKG/output_17303415443030/renaissance-movie-lens_0"; \
[2024-10-31T02:39:32.966Z] cd "/home/jenkins/workspace/Test_openjdk17_hs_extended.perf_ppc64_aix/aqa-tests/TKG/../TKG/output_17303415443030/renaissance-movie-lens_0"; \
[2024-10-31T02:39:32.966Z] echo ""; echo "TESTING:"; \
[2024-10-31T02:39:32.966Z] "/home/jenkins/workspace/Test_openjdk17_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_openjdk17_hs_extended.perf_ppc64_aix/aqa-tests/TKG/../../jvmtest/perf/renaissance/renaissance.jar" --json ""/home/jenkins/workspace/Test_openjdk17_hs_extended.perf_ppc64_aix/aqa-tests/TKG/../TKG/output_17303415443030/renaissance-movie-lens_0"/movie-lens.json" movie-lens; \
[2024-10-31T02:39:32.966Z] if [ $? -eq 0 ]; then echo "-----------------------------------"; echo "renaissance-movie-lens_0""_PASSED"; echo "-----------------------------------"; cd /home/jenkins/workspace/Test_openjdk17_hs_extended.perf_ppc64_aix/aqa-tests/TKG/..; rm -f -r "/home/jenkins/workspace/Test_openjdk17_hs_extended.perf_ppc64_aix/aqa-tests/TKG/../TKG/output_17303415443030/renaissance-movie-lens_0"; else echo "-----------------------------------"; echo "renaissance-movie-lens_0""_FAILED"; echo "-----------------------------------"; fi; \
[2024-10-31T02:39:32.966Z] echo ""; echo "TEST TEARDOWN:"; \
[2024-10-31T02:39:32.966Z] echo "Nothing to be done for teardown."; \
[2024-10-31T02:39:32.966Z] } 2>&1 | tee -a "/home/jenkins/workspace/Test_openjdk17_hs_extended.perf_ppc64_aix/aqa-tests/TKG/../TKG/output_17303415443030/TestTargetResult";
[2024-10-31T02:39:32.966Z]
[2024-10-31T02:39:32.966Z] TEST SETUP:
[2024-10-31T02:39:32.966Z] Nothing to be done for setup.
[2024-10-31T02:39:32.966Z]
[2024-10-31T02:39:32.966Z] TESTING:
[2024-10-31T02:39:37.430Z] Using Spark's default log4j profile: org/apache/spark/log4j-defaults.properties
[2024-10-31T02:39:39.905Z] NOTE: 'movie-lens' benchmark uses Spark local executor with 8 (out of 24) threads.
[2024-10-31T02:39:43.331Z] Got 100004 ratings from 671 users on 9066 movies.
[2024-10-31T02:39:43.331Z] Training: 60056, validation: 20285, test: 19854
[2024-10-31T02:39:43.331Z] ====== movie-lens (apache-spark) [default], iteration 0 started ======
[2024-10-31T02:39:43.331Z] GC before operation: completed in 53.644 ms, heap usage 115.378 MB -> 37.814 MB.
[2024-10-31T02:39:47.781Z] RMSE (validation) = 3.6219689545487506 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-10-31T02:39:51.203Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-10-31T02:39:54.633Z] RMSE (validation) = 1.3105189674610935 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-10-31T02:39:57.106Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-10-31T02:39:58.709Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-10-31T02:40:00.329Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-10-31T02:40:01.922Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-10-31T02:40:03.504Z] RMSE (validation) = 0.9001440981626694 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-10-31T02:40:03.504Z] 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.
[2024-10-31T02:40:03.504Z] The best model improves the baseline by 14.43%.
[2024-10-31T02:40:03.504Z] Movies recommended for you:
[2024-10-31T02:40:03.504Z] WARNING: This benchmark provides no result that can be validated.
[2024-10-31T02:40:03.504Z] There is no way to check that no silent failure occurred.
[2024-10-31T02:40:03.504Z] ====== movie-lens (apache-spark) [default], iteration 0 completed (20526.532 ms) ======
[2024-10-31T02:40:03.504Z] ====== movie-lens (apache-spark) [default], iteration 1 started ======
[2024-10-31T02:40:04.271Z] GC before operation: completed in 86.330 ms, heap usage 2.390 GB -> 56.115 MB.
[2024-10-31T02:40:06.729Z] RMSE (validation) = 3.6219689545487506 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-10-31T02:40:09.204Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-10-31T02:40:12.621Z] RMSE (validation) = 1.3105189674610933 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-10-31T02:40:15.093Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-10-31T02:40:16.678Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-10-31T02:40:17.446Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-10-31T02:40:19.953Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-10-31T02:40:20.725Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-10-31T02:40:21.490Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9073522634082535.
[2024-10-31T02:40:21.490Z] The best model improves the baseline by 14.43%.
[2024-10-31T02:40:21.490Z] Movies recommended for you:
[2024-10-31T02:40:21.490Z] WARNING: This benchmark provides no result that can be validated.
[2024-10-31T02:40:21.490Z] There is no way to check that no silent failure occurred.
[2024-10-31T02:40:21.490Z] ====== movie-lens (apache-spark) [default], iteration 1 completed (17557.018 ms) ======
[2024-10-31T02:40:21.490Z] ====== movie-lens (apache-spark) [default], iteration 2 started ======
[2024-10-31T02:40:21.490Z] GC before operation: completed in 76.441 ms, heap usage 1.995 GB -> 56.550 MB.
[2024-10-31T02:40:23.953Z] RMSE (validation) = 3.6219689545487506 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-10-31T02:40:26.427Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-10-31T02:40:29.859Z] RMSE (validation) = 1.3105189674610935 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-10-31T02:40:32.318Z] RMSE (validation) = 1.004540774800519 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-10-31T02:40:33.907Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-10-31T02:40:35.488Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-10-31T02:40:37.077Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-10-31T02:40:38.666Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-10-31T02:40:38.666Z] 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.
[2024-10-31T02:40:38.666Z] The best model improves the baseline by 14.43%.
[2024-10-31T02:40:38.666Z] Movies recommended for you:
[2024-10-31T02:40:38.666Z] WARNING: This benchmark provides no result that can be validated.
[2024-10-31T02:40:38.666Z] There is no way to check that no silent failure occurred.
[2024-10-31T02:40:38.666Z] ====== movie-lens (apache-spark) [default], iteration 2 completed (17264.272 ms) ======
[2024-10-31T02:40:38.666Z] ====== movie-lens (apache-spark) [default], iteration 3 started ======
[2024-10-31T02:40:38.666Z] GC before operation: completed in 79.917 ms, heap usage 781.193 MB -> 55.424 MB.
[2024-10-31T02:40:41.145Z] RMSE (validation) = 3.6219689545487506 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-10-31T02:40:43.622Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-10-31T02:40:47.252Z] RMSE (validation) = 1.3105189674610935 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-10-31T02:40:48.842Z] RMSE (validation) = 1.004540774800519 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-10-31T02:40:50.429Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-10-31T02:40:52.021Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-10-31T02:40:53.609Z] RMSE (validation) = 0.927571739133814 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-10-31T02:40:55.198Z] RMSE (validation) = 0.9001440981626694 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-10-31T02:40:55.198Z] 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.
[2024-10-31T02:40:55.198Z] The best model improves the baseline by 14.43%.
[2024-10-31T02:40:55.198Z] Movies recommended for you:
[2024-10-31T02:40:55.198Z] WARNING: This benchmark provides no result that can be validated.
[2024-10-31T02:40:55.198Z] There is no way to check that no silent failure occurred.
[2024-10-31T02:40:55.198Z] ====== movie-lens (apache-spark) [default], iteration 3 completed (16669.358 ms) ======
[2024-10-31T02:40:55.198Z] ====== movie-lens (apache-spark) [default], iteration 4 started ======
[2024-10-31T02:40:55.967Z] GC before operation: completed in 77.037 ms, heap usage 1.841 GB -> 57.062 MB.
[2024-10-31T02:40:58.455Z] RMSE (validation) = 3.6219689545487506 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-10-31T02:41:00.917Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-10-31T02:41:03.397Z] RMSE (validation) = 1.3105189674610933 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-10-31T02:41:05.862Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-10-31T02:41:07.458Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-10-31T02:41:09.042Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-10-31T02:41:10.624Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-10-31T02:41:12.206Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-10-31T02:41:12.206Z] 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.
[2024-10-31T02:41:12.206Z] The best model improves the baseline by 14.43%.
[2024-10-31T02:41:12.206Z] Movies recommended for you:
[2024-10-31T02:41:12.206Z] WARNING: This benchmark provides no result that can be validated.
[2024-10-31T02:41:12.206Z] There is no way to check that no silent failure occurred.
[2024-10-31T02:41:12.206Z] ====== movie-lens (apache-spark) [default], iteration 4 completed (16733.130 ms) ======
[2024-10-31T02:41:12.206Z] ====== movie-lens (apache-spark) [default], iteration 5 started ======
[2024-10-31T02:41:12.206Z] GC before operation: completed in 83.531 ms, heap usage 1.794 GB -> 57.290 MB.
[2024-10-31T02:41:14.675Z] RMSE (validation) = 3.621968954548751 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-10-31T02:41:18.104Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-10-31T02:41:20.587Z] RMSE (validation) = 1.3105189674610933 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-10-31T02:41:23.060Z] RMSE (validation) = 1.004540774800519 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-10-31T02:41:24.655Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-10-31T02:41:26.245Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-10-31T02:41:27.843Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-10-31T02:41:29.427Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-10-31T02:41:29.427Z] 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.
[2024-10-31T02:41:29.427Z] The best model improves the baseline by 14.43%.
[2024-10-31T02:41:29.427Z] Movies recommended for you:
[2024-10-31T02:41:29.427Z] WARNING: This benchmark provides no result that can be validated.
[2024-10-31T02:41:29.427Z] There is no way to check that no silent failure occurred.
[2024-10-31T02:41:29.427Z] ====== movie-lens (apache-spark) [default], iteration 5 completed (17215.177 ms) ======
[2024-10-31T02:41:29.427Z] ====== movie-lens (apache-spark) [default], iteration 6 started ======
[2024-10-31T02:41:29.427Z] GC before operation: completed in 77.835 ms, heap usage 1.369 GB -> 57.088 MB.
[2024-10-31T02:41:31.897Z] RMSE (validation) = 3.6219689545487506 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-10-31T02:41:35.362Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-10-31T02:41:37.826Z] RMSE (validation) = 1.3105189674610935 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-10-31T02:41:40.288Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-10-31T02:41:41.878Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-10-31T02:41:43.514Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-10-31T02:41:45.100Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-10-31T02:41:46.693Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-10-31T02:41:46.693Z] 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.
[2024-10-31T02:41:46.693Z] The best model improves the baseline by 14.43%.
[2024-10-31T02:41:46.693Z] Movies recommended for you:
[2024-10-31T02:41:46.693Z] WARNING: This benchmark provides no result that can be validated.
[2024-10-31T02:41:46.693Z] There is no way to check that no silent failure occurred.
[2024-10-31T02:41:46.693Z] ====== movie-lens (apache-spark) [default], iteration 6 completed (17272.358 ms) ======
[2024-10-31T02:41:46.693Z] ====== movie-lens (apache-spark) [default], iteration 7 started ======
[2024-10-31T02:41:47.465Z] GC before operation: completed in 90.676 ms, heap usage 2.799 GB -> 57.400 MB.
[2024-10-31T02:41:49.932Z] RMSE (validation) = 3.6219689545487506 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-10-31T02:41:52.401Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-10-31T02:41:54.883Z] RMSE (validation) = 1.3105189674610935 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-10-31T02:41:58.322Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-10-31T02:41:59.103Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-10-31T02:42:00.704Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-10-31T02:42:03.171Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-10-31T02:42:03.944Z] RMSE (validation) = 0.9001440981626694 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-10-31T02:42:04.713Z] 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.
[2024-10-31T02:42:04.713Z] The best model improves the baseline by 14.43%.
[2024-10-31T02:42:04.713Z] Movies recommended for you:
[2024-10-31T02:42:04.713Z] WARNING: This benchmark provides no result that can be validated.
[2024-10-31T02:42:04.713Z] There is no way to check that no silent failure occurred.
[2024-10-31T02:42:04.713Z] ====== movie-lens (apache-spark) [default], iteration 7 completed (17578.725 ms) ======
[2024-10-31T02:42:04.713Z] ====== movie-lens (apache-spark) [default], iteration 8 started ======
[2024-10-31T02:42:04.713Z] GC before operation: completed in 79.532 ms, heap usage 427.527 MB -> 52.805 MB.
[2024-10-31T02:42:07.177Z] RMSE (validation) = 3.6219689545487506 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-10-31T02:42:10.593Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-10-31T02:42:13.054Z] RMSE (validation) = 1.3105189674610935 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-10-31T02:42:15.517Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-10-31T02:42:17.110Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-10-31T02:42:18.700Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-10-31T02:42:20.294Z] RMSE (validation) = 0.9275717391338142 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-10-31T02:42:21.887Z] RMSE (validation) = 0.9001440981626694 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-10-31T02:42:21.887Z] 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.
[2024-10-31T02:42:21.887Z] The best model improves the baseline by 14.43%.
[2024-10-31T02:42:22.655Z] Movies recommended for you:
[2024-10-31T02:42:22.655Z] WARNING: This benchmark provides no result that can be validated.
[2024-10-31T02:42:22.655Z] There is no way to check that no silent failure occurred.
[2024-10-31T02:42:22.655Z] ====== movie-lens (apache-spark) [default], iteration 8 completed (17554.818 ms) ======
[2024-10-31T02:42:22.655Z] ====== movie-lens (apache-spark) [default], iteration 9 started ======
[2024-10-31T02:42:22.655Z] GC before operation: completed in 91.368 ms, heap usage 1.904 GB -> 57.463 MB.
[2024-10-31T02:42:25.124Z] RMSE (validation) = 3.621968954548751 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-10-31T02:42:27.588Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-10-31T02:42:31.007Z] RMSE (validation) = 1.3105189674610933 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-10-31T02:42:33.480Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-10-31T02:42:34.254Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-10-31T02:42:35.845Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-10-31T02:42:38.318Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-10-31T02:42:39.089Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-10-31T02:42:39.856Z] 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.
[2024-10-31T02:42:39.856Z] The best model improves the baseline by 14.43%.
[2024-10-31T02:42:39.856Z] Movies recommended for you:
[2024-10-31T02:42:39.856Z] WARNING: This benchmark provides no result that can be validated.
[2024-10-31T02:42:39.856Z] There is no way to check that no silent failure occurred.
[2024-10-31T02:42:39.856Z] ====== movie-lens (apache-spark) [default], iteration 9 completed (17407.656 ms) ======
[2024-10-31T02:42:39.856Z] ====== movie-lens (apache-spark) [default], iteration 10 started ======
[2024-10-31T02:42:39.856Z] GC before operation: completed in 79.003 ms, heap usage 131.071 MB -> 54.462 MB.
[2024-10-31T02:42:42.324Z] RMSE (validation) = 3.621968954548751 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-10-31T02:42:44.802Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-10-31T02:42:48.217Z] RMSE (validation) = 1.3105189674610933 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-10-31T02:42:50.695Z] RMSE (validation) = 1.004540774800519 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-10-31T02:42:52.281Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-10-31T02:42:53.870Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-10-31T02:42:55.478Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-10-31T02:42:57.077Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-10-31T02:42:57.077Z] 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.
[2024-10-31T02:42:57.077Z] The best model improves the baseline by 14.43%.
[2024-10-31T02:42:57.077Z] Movies recommended for you:
[2024-10-31T02:42:57.077Z] WARNING: This benchmark provides no result that can be validated.
[2024-10-31T02:42:57.077Z] There is no way to check that no silent failure occurred.
[2024-10-31T02:42:57.077Z] ====== movie-lens (apache-spark) [default], iteration 10 completed (17491.649 ms) ======
[2024-10-31T02:42:57.077Z] ====== movie-lens (apache-spark) [default], iteration 11 started ======
[2024-10-31T02:42:57.845Z] GC before operation: completed in 88.171 ms, heap usage 125.812 MB -> 54.872 MB.
[2024-10-31T02:43:00.310Z] RMSE (validation) = 3.6219689545487506 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-10-31T02:43:02.979Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-10-31T02:43:05.984Z] RMSE (validation) = 1.3105189674610933 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-10-31T02:43:07.739Z] RMSE (validation) = 1.004540774800519 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-10-31T02:43:09.325Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-10-31T02:43:10.920Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-10-31T02:43:13.392Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-10-31T02:43:14.979Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-10-31T02:43:14.979Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9073522634082535.
[2024-10-31T02:43:14.979Z] The best model improves the baseline by 14.43%.
[2024-10-31T02:43:14.979Z] Movies recommended for you:
[2024-10-31T02:43:14.979Z] WARNING: This benchmark provides no result that can be validated.
[2024-10-31T02:43:14.979Z] There is no way to check that no silent failure occurred.
[2024-10-31T02:43:14.979Z] ====== movie-lens (apache-spark) [default], iteration 11 completed (17573.625 ms) ======
[2024-10-31T02:43:14.979Z] ====== movie-lens (apache-spark) [default], iteration 12 started ======
[2024-10-31T02:43:14.979Z] GC before operation: completed in 89.109 ms, heap usage 2.640 GB -> 57.504 MB.
[2024-10-31T02:43:17.440Z] RMSE (validation) = 3.6219689545487506 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-10-31T02:43:20.874Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-10-31T02:43:23.385Z] RMSE (validation) = 1.3105189674610933 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-10-31T02:43:25.849Z] RMSE (validation) = 1.004540774800519 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-10-31T02:43:27.439Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-10-31T02:43:29.025Z] RMSE (validation) = 1.117495376637101 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-10-31T02:43:30.622Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-10-31T02:43:32.216Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-10-31T02:43:32.986Z] 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.
[2024-10-31T02:43:32.986Z] The best model improves the baseline by 14.43%.
[2024-10-31T02:43:32.986Z] Movies recommended for you:
[2024-10-31T02:43:32.986Z] WARNING: This benchmark provides no result that can be validated.
[2024-10-31T02:43:32.986Z] There is no way to check that no silent failure occurred.
[2024-10-31T02:43:32.986Z] ====== movie-lens (apache-spark) [default], iteration 12 completed (17646.505 ms) ======
[2024-10-31T02:43:32.986Z] ====== movie-lens (apache-spark) [default], iteration 13 started ======
[2024-10-31T02:43:32.986Z] GC before operation: completed in 89.823 ms, heap usage 2.434 GB -> 57.680 MB.
[2024-10-31T02:43:35.454Z] RMSE (validation) = 3.621968954548751 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-10-31T02:43:37.930Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-10-31T02:43:41.362Z] RMSE (validation) = 1.3105189674610933 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-10-31T02:43:43.843Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-10-31T02:43:45.454Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-10-31T02:43:46.238Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-10-31T02:43:48.716Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-10-31T02:43:49.485Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-10-31T02:43:50.254Z] 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.
[2024-10-31T02:43:50.254Z] The best model improves the baseline by 14.43%.
[2024-10-31T02:43:50.254Z] Movies recommended for you:
[2024-10-31T02:43:50.254Z] WARNING: This benchmark provides no result that can be validated.
[2024-10-31T02:43:50.254Z] There is no way to check that no silent failure occurred.
[2024-10-31T02:43:50.254Z] ====== movie-lens (apache-spark) [default], iteration 13 completed (17352.389 ms) ======
[2024-10-31T02:43:50.254Z] ====== movie-lens (apache-spark) [default], iteration 14 started ======
[2024-10-31T02:43:50.254Z] GC before operation: completed in 87.119 ms, heap usage 1.250 GB -> 56.965 MB.
[2024-10-31T02:43:52.718Z] RMSE (validation) = 3.6219689545487506 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-10-31T02:43:55.188Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-10-31T02:43:58.621Z] RMSE (validation) = 1.3105189674610935 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-10-31T02:44:01.083Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-10-31T02:44:02.674Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-10-31T02:44:04.259Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-10-31T02:44:05.877Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-10-31T02:44:07.671Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-10-31T02:44:07.671Z] 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.
[2024-10-31T02:44:07.671Z] The best model improves the baseline by 14.43%.
[2024-10-31T02:44:07.671Z] Movies recommended for you:
[2024-10-31T02:44:07.671Z] WARNING: This benchmark provides no result that can be validated.
[2024-10-31T02:44:07.671Z] There is no way to check that no silent failure occurred.
[2024-10-31T02:44:07.671Z] ====== movie-lens (apache-spark) [default], iteration 14 completed (17367.144 ms) ======
[2024-10-31T02:44:07.671Z] ====== movie-lens (apache-spark) [default], iteration 15 started ======
[2024-10-31T02:44:07.671Z] GC before operation: completed in 83.334 ms, heap usage 1.854 GB -> 57.573 MB.
[2024-10-31T02:44:10.144Z] RMSE (validation) = 3.621968954548751 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-10-31T02:44:13.568Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-10-31T02:44:16.031Z] RMSE (validation) = 1.3105189674610933 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-10-31T02:44:18.502Z] RMSE (validation) = 1.004540774800519 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-10-31T02:44:20.095Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-10-31T02:44:21.686Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-10-31T02:44:23.271Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-10-31T02:44:24.871Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-10-31T02:44:24.871Z] 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.
[2024-10-31T02:44:24.871Z] The best model improves the baseline by 14.43%.
[2024-10-31T02:44:24.871Z] Movies recommended for you:
[2024-10-31T02:44:24.871Z] WARNING: This benchmark provides no result that can be validated.
[2024-10-31T02:44:24.871Z] There is no way to check that no silent failure occurred.
[2024-10-31T02:44:24.871Z] ====== movie-lens (apache-spark) [default], iteration 15 completed (17287.543 ms) ======
[2024-10-31T02:44:24.871Z] ====== movie-lens (apache-spark) [default], iteration 16 started ======
[2024-10-31T02:44:24.871Z] GC before operation: completed in 82.726 ms, heap usage 1.995 GB -> 57.629 MB.
[2024-10-31T02:44:27.332Z] RMSE (validation) = 3.6219689545487506 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-10-31T02:44:30.760Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-10-31T02:44:33.227Z] RMSE (validation) = 1.3105189674610935 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-10-31T02:44:35.695Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-10-31T02:44:37.280Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-10-31T02:44:38.865Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-10-31T02:44:40.458Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-10-31T02:44:42.045Z] RMSE (validation) = 0.9001440981626694 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-10-31T02:44:42.822Z] 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.
[2024-10-31T02:44:42.822Z] The best model improves the baseline by 14.43%.
[2024-10-31T02:44:42.822Z] Movies recommended for you:
[2024-10-31T02:44:42.822Z] WARNING: This benchmark provides no result that can be validated.
[2024-10-31T02:44:42.822Z] There is no way to check that no silent failure occurred.
[2024-10-31T02:44:42.822Z] ====== movie-lens (apache-spark) [default], iteration 16 completed (17499.824 ms) ======
[2024-10-31T02:44:42.822Z] ====== movie-lens (apache-spark) [default], iteration 17 started ======
[2024-10-31T02:44:42.822Z] GC before operation: completed in 81.271 ms, heap usage 144.753 MB -> 52.436 MB.
[2024-10-31T02:44:46.235Z] RMSE (validation) = 3.621968954548751 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-10-31T02:44:48.698Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-10-31T02:44:52.119Z] RMSE (validation) = 1.3105189674610935 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-10-31T02:44:55.537Z] RMSE (validation) = 1.004540774800519 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-10-31T02:44:56.304Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-10-31T02:44:57.896Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-10-31T02:45:00.368Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-10-31T02:45:01.973Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-10-31T02:45:01.973Z] 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.
[2024-10-31T02:45:01.973Z] The best model improves the baseline by 14.43%.
[2024-10-31T02:45:01.973Z] Movies recommended for you:
[2024-10-31T02:45:01.973Z] WARNING: This benchmark provides no result that can be validated.
[2024-10-31T02:45:01.973Z] There is no way to check that no silent failure occurred.
[2024-10-31T02:45:01.973Z] ====== movie-lens (apache-spark) [default], iteration 17 completed (19312.432 ms) ======
[2024-10-31T02:45:01.973Z] ====== movie-lens (apache-spark) [default], iteration 18 started ======
[2024-10-31T02:45:01.973Z] GC before operation: completed in 91.438 ms, heap usage 891.810 MB -> 56.445 MB.
[2024-10-31T02:45:05.392Z] RMSE (validation) = 3.621968954548751 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-10-31T02:45:07.858Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-10-31T02:45:11.269Z] RMSE (validation) = 1.3105189674610935 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-10-31T02:45:13.738Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-10-31T02:45:15.322Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-10-31T02:45:16.916Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-10-31T02:45:18.503Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-10-31T02:45:20.098Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-10-31T02:45:20.098Z] 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.
[2024-10-31T02:45:20.098Z] The best model improves the baseline by 14.43%.
[2024-10-31T02:45:20.865Z] Movies recommended for you:
[2024-10-31T02:45:20.865Z] WARNING: This benchmark provides no result that can be validated.
[2024-10-31T02:45:20.865Z] There is no way to check that no silent failure occurred.
[2024-10-31T02:45:20.865Z] ====== movie-lens (apache-spark) [default], iteration 18 completed (18385.161 ms) ======
[2024-10-31T02:45:20.865Z] ====== movie-lens (apache-spark) [default], iteration 19 started ======
[2024-10-31T02:45:20.865Z] GC before operation: completed in 96.999 ms, heap usage 2.108 GB -> 59.582 MB.
[2024-10-31T02:45:23.331Z] RMSE (validation) = 3.621968954548751 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-10-31T02:45:25.794Z] RMSE (validation) = 2.1340923217118064 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-10-31T02:45:29.393Z] RMSE (validation) = 1.3105189674610933 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-10-31T02:45:31.859Z] RMSE (validation) = 1.004540774800519 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-10-31T02:45:33.446Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-10-31T02:45:35.033Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-10-31T02:45:36.625Z] RMSE (validation) = 0.927571739133814 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-10-31T02:45:38.216Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-10-31T02:45:38.984Z] 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.
[2024-10-31T02:45:38.984Z] The best model improves the baseline by 14.43%.
[2024-10-31T02:45:38.984Z] Movies recommended for you:
[2024-10-31T02:45:38.984Z] WARNING: This benchmark provides no result that can be validated.
[2024-10-31T02:45:38.984Z] There is no way to check that no silent failure occurred.
[2024-10-31T02:45:38.984Z] ====== movie-lens (apache-spark) [default], iteration 19 completed (18387.381 ms) ======
[2024-10-31T02:45:39.753Z] -----------------------------------
[2024-10-31T02:45:39.753Z] renaissance-movie-lens_0_PASSED
[2024-10-31T02:45:39.753Z] -----------------------------------
[2024-10-31T02:45:39.753Z]
[2024-10-31T02:45:39.753Z] TEST TEARDOWN:
[2024-10-31T02:45:39.753Z] Nothing to be done for teardown.
[2024-10-31T02:45:39.753Z] renaissance-movie-lens_0 Finish Time: Thu Oct 31 02:45:39 2024 Epoch Time (ms): 1730342739303