renaissance-movie-lens_0
[2024-06-27T02:02:05.774Z] Running test renaissance-movie-lens_0 ...
[2024-06-27T02:02:05.774Z] ===============================================
[2024-06-27T02:02:05.774Z] renaissance-movie-lens_0 Start Time: Thu Jun 27 02:02:05 2024 Epoch Time (ms): 1719453725226
[2024-06-27T02:02:05.774Z] variation: NoOptions
[2024-06-27T02:02:05.774Z] JVM_OPTIONS:
[2024-06-27T02:02:05.774Z] { \
[2024-06-27T02:02:05.774Z] echo ""; echo "TEST SETUP:"; \
[2024-06-27T02:02:05.774Z] echo "Nothing to be done for setup."; \
[2024-06-27T02:02:05.774Z] mkdir -p "/home/jenkins/workspace/Test_openjdk17_hs_extended.perf_ppc64_aix_testList_0/aqa-tests/TKG/../TKG/output_17194529154978/renaissance-movie-lens_0"; \
[2024-06-27T02:02:05.774Z] cd "/home/jenkins/workspace/Test_openjdk17_hs_extended.perf_ppc64_aix_testList_0/aqa-tests/TKG/../TKG/output_17194529154978/renaissance-movie-lens_0"; \
[2024-06-27T02:02:05.774Z] echo ""; echo "TESTING:"; \
[2024-06-27T02:02:05.774Z] "/home/jenkins/workspace/Test_openjdk17_hs_extended.perf_ppc64_aix_testList_0/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_testList_0/aqa-tests/TKG/../../jvmtest/perf/renaissance/renaissance.jar" --json ""/home/jenkins/workspace/Test_openjdk17_hs_extended.perf_ppc64_aix_testList_0/aqa-tests/TKG/../TKG/output_17194529154978/renaissance-movie-lens_0"/movie-lens.json" movie-lens; \
[2024-06-27T02:02:05.774Z] if [ $? -eq 0 ]; then echo "-----------------------------------"; echo "renaissance-movie-lens_0""_PASSED"; echo "-----------------------------------"; cd /home/jenkins/workspace/Test_openjdk17_hs_extended.perf_ppc64_aix_testList_0/aqa-tests/TKG/..; rm -f -r "/home/jenkins/workspace/Test_openjdk17_hs_extended.perf_ppc64_aix_testList_0/aqa-tests/TKG/../TKG/output_17194529154978/renaissance-movie-lens_0"; else echo "-----------------------------------"; echo "renaissance-movie-lens_0""_FAILED"; echo "-----------------------------------"; fi; \
[2024-06-27T02:02:05.774Z] echo ""; echo "TEST TEARDOWN:"; \
[2024-06-27T02:02:05.774Z] echo "Nothing to be done for teardown."; \
[2024-06-27T02:02:05.774Z] } 2>&1 | tee -a "/home/jenkins/workspace/Test_openjdk17_hs_extended.perf_ppc64_aix_testList_0/aqa-tests/TKG/../TKG/output_17194529154978/TestTargetResult";
[2024-06-27T02:02:05.774Z]
[2024-06-27T02:02:05.774Z] TEST SETUP:
[2024-06-27T02:02:05.774Z] Nothing to be done for setup.
[2024-06-27T02:02:05.774Z]
[2024-06-27T02:02:05.774Z] TESTING:
[2024-06-27T02:02:10.181Z] Using Spark's default log4j profile: org/apache/spark/log4j-defaults.properties
[2024-06-27T02:02:11.751Z] NOTE: 'movie-lens' benchmark uses Spark local executor with 8 (out of 24) threads.
[2024-06-27T02:02:15.135Z] Got 100004 ratings from 671 users on 9066 movies.
[2024-06-27T02:02:15.135Z] Training: 60056, validation: 20285, test: 19854
[2024-06-27T02:02:15.135Z] ====== movie-lens (apache-spark) [default], iteration 0 started ======
[2024-06-27T02:02:15.135Z] GC before operation: completed in 54.220 ms, heap usage 190.128 MB -> 37.560 MB.
[2024-06-27T02:02:20.687Z] RMSE (validation) = 3.621968954548751 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-06-27T02:02:23.145Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-06-27T02:02:26.546Z] RMSE (validation) = 1.3105189674610933 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-06-27T02:02:28.992Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-06-27T02:02:30.563Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-06-27T02:02:32.230Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-06-27T02:02:33.804Z] RMSE (validation) = 0.927571739133814 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-06-27T02:02:35.381Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-06-27T02:02:35.381Z] 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.
[2024-06-27T02:02:35.381Z] The best model improves the baseline by 14.43%.
[2024-06-27T02:02:35.381Z] Movies recommended for you:
[2024-06-27T02:02:35.381Z] WARNING: This benchmark provides no result that can be validated.
[2024-06-27T02:02:35.381Z] There is no way to check that no silent failure occurred.
[2024-06-27T02:02:35.381Z] ====== movie-lens (apache-spark) [default], iteration 0 completed (20251.586 ms) ======
[2024-06-27T02:02:35.381Z] ====== movie-lens (apache-spark) [default], iteration 1 started ======
[2024-06-27T02:02:36.142Z] GC before operation: completed in 76.882 ms, heap usage 663.872 MB -> 54.463 MB.
[2024-06-27T02:02:38.583Z] RMSE (validation) = 3.6219689545487506 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-06-27T02:02:41.034Z] RMSE (validation) = 2.1340923217118064 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-06-27T02:02:44.413Z] RMSE (validation) = 1.3105189674610935 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-06-27T02:02:46.856Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-06-27T02:02:48.432Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-06-27T02:02:49.199Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-06-27T02:02:51.657Z] RMSE (validation) = 0.927571739133814 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-06-27T02:02:52.451Z] RMSE (validation) = 0.9001440981626694 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-06-27T02:02:53.214Z] 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.
[2024-06-27T02:02:53.214Z] The best model improves the baseline by 14.43%.
[2024-06-27T02:02:53.214Z] Movies recommended for you:
[2024-06-27T02:02:53.214Z] WARNING: This benchmark provides no result that can be validated.
[2024-06-27T02:02:53.214Z] There is no way to check that no silent failure occurred.
[2024-06-27T02:02:53.214Z] ====== movie-lens (apache-spark) [default], iteration 1 completed (17334.199 ms) ======
[2024-06-27T02:02:53.214Z] ====== movie-lens (apache-spark) [default], iteration 2 started ======
[2024-06-27T02:02:53.214Z] GC before operation: completed in 81.941 ms, heap usage 101.762 MB -> 54.897 MB.
[2024-06-27T02:02:56.527Z] RMSE (validation) = 3.6219689545487506 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-06-27T02:02:59.282Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-06-27T02:03:01.528Z] RMSE (validation) = 1.3105189674610933 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-06-27T02:03:04.052Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-06-27T02:03:04.822Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-06-27T02:03:06.392Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-06-27T02:03:07.962Z] RMSE (validation) = 0.927571739133814 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-06-27T02:03:09.533Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-06-27T02:03:10.295Z] 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.
[2024-06-27T02:03:10.295Z] The best model improves the baseline by 14.43%.
[2024-06-27T02:03:10.295Z] Movies recommended for you:
[2024-06-27T02:03:10.295Z] WARNING: This benchmark provides no result that can be validated.
[2024-06-27T02:03:10.295Z] There is no way to check that no silent failure occurred.
[2024-06-27T02:03:10.295Z] ====== movie-lens (apache-spark) [default], iteration 2 completed (16854.747 ms) ======
[2024-06-27T02:03:10.295Z] ====== movie-lens (apache-spark) [default], iteration 3 started ======
[2024-06-27T02:03:10.295Z] GC before operation: completed in 72.277 ms, heap usage 103.014 MB -> 56.473 MB.
[2024-06-27T02:03:12.754Z] RMSE (validation) = 3.6219689545487506 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-06-27T02:03:15.212Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-06-27T02:03:17.810Z] RMSE (validation) = 1.3105189674610935 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-06-27T02:03:20.262Z] RMSE (validation) = 1.004540774800519 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-06-27T02:03:21.842Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-06-27T02:03:23.416Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-06-27T02:03:25.031Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-06-27T02:03:26.608Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-06-27T02:03:26.608Z] 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.
[2024-06-27T02:03:26.608Z] The best model improves the baseline by 14.43%.
[2024-06-27T02:03:26.608Z] Movies recommended for you:
[2024-06-27T02:03:26.608Z] WARNING: This benchmark provides no result that can be validated.
[2024-06-27T02:03:26.608Z] There is no way to check that no silent failure occurred.
[2024-06-27T02:03:26.608Z] ====== movie-lens (apache-spark) [default], iteration 3 completed (16656.294 ms) ======
[2024-06-27T02:03:26.608Z] ====== movie-lens (apache-spark) [default], iteration 4 started ======
[2024-06-27T02:03:26.608Z] GC before operation: completed in 80.953 ms, heap usage 2.328 GB -> 56.833 MB.
[2024-06-27T02:03:29.055Z] RMSE (validation) = 3.621968954548751 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-06-27T02:03:32.443Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-06-27T02:03:34.882Z] RMSE (validation) = 1.3105189674610933 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-06-27T02:03:37.516Z] RMSE (validation) = 1.004540774800519 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-06-27T02:03:38.281Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-06-27T02:03:39.856Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-06-27T02:03:41.434Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-06-27T02:03:43.007Z] RMSE (validation) = 0.9001440981626694 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-06-27T02:03:43.768Z] 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.
[2024-06-27T02:03:43.768Z] The best model improves the baseline by 14.43%.
[2024-06-27T02:03:43.768Z] Movies recommended for you:
[2024-06-27T02:03:43.768Z] WARNING: This benchmark provides no result that can be validated.
[2024-06-27T02:03:43.768Z] There is no way to check that no silent failure occurred.
[2024-06-27T02:03:43.768Z] ====== movie-lens (apache-spark) [default], iteration 4 completed (16628.043 ms) ======
[2024-06-27T02:03:43.768Z] ====== movie-lens (apache-spark) [default], iteration 5 started ======
[2024-06-27T02:03:43.768Z] GC before operation: completed in 78.116 ms, heap usage 178.640 MB -> 52.040 MB.
[2024-06-27T02:03:46.211Z] RMSE (validation) = 3.6219689545487506 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-06-27T02:03:48.660Z] RMSE (validation) = 2.1340923217118064 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-06-27T02:03:51.101Z] RMSE (validation) = 1.3105189674610935 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-06-27T02:03:53.544Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-06-27T02:03:55.112Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-06-27T02:03:56.698Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-06-27T02:03:58.269Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-06-27T02:03:59.850Z] RMSE (validation) = 0.9001440981626694 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-06-27T02:03:59.850Z] 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.
[2024-06-27T02:03:59.850Z] The best model improves the baseline by 14.43%.
[2024-06-27T02:03:59.850Z] Movies recommended for you:
[2024-06-27T02:03:59.850Z] WARNING: This benchmark provides no result that can be validated.
[2024-06-27T02:03:59.850Z] There is no way to check that no silent failure occurred.
[2024-06-27T02:03:59.850Z] ====== movie-lens (apache-spark) [default], iteration 5 completed (16551.285 ms) ======
[2024-06-27T02:03:59.850Z] ====== movie-lens (apache-spark) [default], iteration 6 started ======
[2024-06-27T02:04:00.611Z] GC before operation: completed in 91.928 ms, heap usage 2.618 GB -> 57.071 MB.
[2024-06-27T02:04:03.060Z] RMSE (validation) = 3.621968954548751 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-06-27T02:04:05.507Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-06-27T02:04:07.968Z] RMSE (validation) = 1.3105189674610935 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-06-27T02:04:10.502Z] RMSE (validation) = 1.004540774800519 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-06-27T02:04:12.074Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-06-27T02:04:13.650Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-06-27T02:04:15.225Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-06-27T02:04:16.122Z] RMSE (validation) = 0.9001440981626694 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-06-27T02:04:16.888Z] 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.
[2024-06-27T02:04:16.888Z] The best model improves the baseline by 14.43%.
[2024-06-27T02:04:16.888Z] Movies recommended for you:
[2024-06-27T02:04:16.888Z] WARNING: This benchmark provides no result that can be validated.
[2024-06-27T02:04:16.888Z] There is no way to check that no silent failure occurred.
[2024-06-27T02:04:16.888Z] ====== movie-lens (apache-spark) [default], iteration 6 completed (16443.926 ms) ======
[2024-06-27T02:04:16.888Z] ====== movie-lens (apache-spark) [default], iteration 7 started ======
[2024-06-27T02:04:16.888Z] GC before operation: completed in 89.946 ms, heap usage 332.012 MB -> 52.288 MB.
[2024-06-27T02:04:19.328Z] RMSE (validation) = 3.621968954548751 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-06-27T02:04:21.779Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-06-27T02:04:24.228Z] RMSE (validation) = 1.3105189674610933 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-06-27T02:04:26.670Z] RMSE (validation) = 1.004540774800519 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-06-27T02:04:28.238Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-06-27T02:04:29.818Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-06-27T02:04:31.389Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-06-27T02:04:32.978Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-06-27T02:04:32.978Z] 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.
[2024-06-27T02:04:32.978Z] The best model improves the baseline by 14.43%.
[2024-06-27T02:04:32.978Z] Movies recommended for you:
[2024-06-27T02:04:32.978Z] WARNING: This benchmark provides no result that can be validated.
[2024-06-27T02:04:32.978Z] There is no way to check that no silent failure occurred.
[2024-06-27T02:04:32.978Z] ====== movie-lens (apache-spark) [default], iteration 7 completed (16544.566 ms) ======
[2024-06-27T02:04:32.978Z] ====== movie-lens (apache-spark) [default], iteration 8 started ======
[2024-06-27T02:04:33.738Z] GC before operation: completed in 90.132 ms, heap usage 1.769 GB -> 57.346 MB.
[2024-06-27T02:04:36.182Z] RMSE (validation) = 3.6219689545487506 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-06-27T02:04:38.640Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-06-27T02:04:41.097Z] RMSE (validation) = 1.3105189674610933 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-06-27T02:04:43.540Z] RMSE (validation) = 1.004540774800519 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-06-27T02:04:45.110Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-06-27T02:04:46.686Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-06-27T02:04:48.258Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-06-27T02:04:49.017Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-06-27T02:04:49.786Z] 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.
[2024-06-27T02:04:49.786Z] The best model improves the baseline by 14.43%.
[2024-06-27T02:04:49.786Z] Movies recommended for you:
[2024-06-27T02:04:49.786Z] WARNING: This benchmark provides no result that can be validated.
[2024-06-27T02:04:49.786Z] There is no way to check that no silent failure occurred.
[2024-06-27T02:04:49.786Z] ====== movie-lens (apache-spark) [default], iteration 8 completed (16329.820 ms) ======
[2024-06-27T02:04:49.786Z] ====== movie-lens (apache-spark) [default], iteration 9 started ======
[2024-06-27T02:04:49.786Z] GC before operation: completed in 82.050 ms, heap usage 1.535 GB -> 57.080 MB.
[2024-06-27T02:04:52.232Z] RMSE (validation) = 3.6219689545487506 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-06-27T02:04:54.676Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-06-27T02:04:57.116Z] RMSE (validation) = 1.3105189674610933 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-06-27T02:04:59.609Z] RMSE (validation) = 1.004540774800519 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-06-27T02:05:01.182Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-06-27T02:05:02.756Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-06-27T02:05:04.327Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-06-27T02:05:05.904Z] RMSE (validation) = 0.9001440981626696 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-06-27T02:05:05.904Z] 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.
[2024-06-27T02:05:05.904Z] The best model improves the baseline by 14.43%.
[2024-06-27T02:05:05.904Z] Movies recommended for you:
[2024-06-27T02:05:05.904Z] WARNING: This benchmark provides no result that can be validated.
[2024-06-27T02:05:05.904Z] There is no way to check that no silent failure occurred.
[2024-06-27T02:05:05.904Z] ====== movie-lens (apache-spark) [default], iteration 9 completed (16352.396 ms) ======
[2024-06-27T02:05:05.904Z] ====== movie-lens (apache-spark) [default], iteration 10 started ======
[2024-06-27T02:05:05.904Z] GC before operation: completed in 77.400 ms, heap usage 101.441 MB -> 55.794 MB.
[2024-06-27T02:05:08.370Z] RMSE (validation) = 3.621968954548751 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-06-27T02:05:10.826Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-06-27T02:05:14.220Z] RMSE (validation) = 1.3105189674610933 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-06-27T02:05:15.816Z] RMSE (validation) = 1.004540774800519 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-06-27T02:05:17.384Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-06-27T02:05:18.965Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-06-27T02:05:20.557Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-06-27T02:05:22.151Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-06-27T02:05:22.151Z] 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.
[2024-06-27T02:05:22.151Z] The best model improves the baseline by 14.43%.
[2024-06-27T02:05:22.151Z] Movies recommended for you:
[2024-06-27T02:05:22.151Z] WARNING: This benchmark provides no result that can be validated.
[2024-06-27T02:05:22.151Z] There is no way to check that no silent failure occurred.
[2024-06-27T02:05:22.151Z] ====== movie-lens (apache-spark) [default], iteration 10 completed (16239.792 ms) ======
[2024-06-27T02:05:22.151Z] ====== movie-lens (apache-spark) [default], iteration 11 started ======
[2024-06-27T02:05:22.917Z] GC before operation: completed in 89.390 ms, heap usage 95.521 MB -> 55.525 MB.
[2024-06-27T02:05:25.366Z] RMSE (validation) = 3.6219689545487506 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-06-27T02:05:27.836Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-06-27T02:05:30.286Z] RMSE (validation) = 1.3105189674610933 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-06-27T02:05:32.724Z] RMSE (validation) = 1.004540774800519 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-06-27T02:05:34.295Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-06-27T02:05:35.874Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-06-27T02:05:37.455Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-06-27T02:05:38.216Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-06-27T02:05:38.977Z] 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.
[2024-06-27T02:05:38.977Z] The best model improves the baseline by 14.43%.
[2024-06-27T02:05:38.977Z] Movies recommended for you:
[2024-06-27T02:05:38.977Z] WARNING: This benchmark provides no result that can be validated.
[2024-06-27T02:05:38.977Z] There is no way to check that no silent failure occurred.
[2024-06-27T02:05:38.977Z] ====== movie-lens (apache-spark) [default], iteration 11 completed (16407.919 ms) ======
[2024-06-27T02:05:38.977Z] ====== movie-lens (apache-spark) [default], iteration 12 started ======
[2024-06-27T02:05:38.977Z] GC before operation: completed in 82.334 ms, heap usage 808.043 MB -> 55.964 MB.
[2024-06-27T02:05:41.427Z] RMSE (validation) = 3.6219689545487506 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-06-27T02:05:43.868Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-06-27T02:05:46.325Z] RMSE (validation) = 1.3105189674610935 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-06-27T02:05:48.767Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-06-27T02:05:50.351Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-06-27T02:05:51.926Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-06-27T02:05:53.503Z] RMSE (validation) = 0.927571739133814 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-06-27T02:05:55.085Z] RMSE (validation) = 0.9001440981626694 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-06-27T02:05:55.085Z] 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.
[2024-06-27T02:05:55.085Z] The best model improves the baseline by 14.43%.
[2024-06-27T02:05:55.085Z] Movies recommended for you:
[2024-06-27T02:05:55.085Z] WARNING: This benchmark provides no result that can be validated.
[2024-06-27T02:05:55.085Z] There is no way to check that no silent failure occurred.
[2024-06-27T02:05:55.085Z] ====== movie-lens (apache-spark) [default], iteration 12 completed (16285.545 ms) ======
[2024-06-27T02:05:55.085Z] ====== movie-lens (apache-spark) [default], iteration 13 started ======
[2024-06-27T02:05:55.085Z] GC before operation: completed in 73.117 ms, heap usage 181.093 MB -> 52.460 MB.
[2024-06-27T02:05:57.554Z] RMSE (validation) = 3.6219689545487506 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-06-27T02:06:00.015Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-06-27T02:06:03.406Z] RMSE (validation) = 1.3105189674610935 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-06-27T02:06:05.844Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-06-27T02:06:07.428Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-06-27T02:06:08.202Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-06-27T02:06:09.784Z] RMSE (validation) = 0.927571739133814 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-06-27T02:06:11.357Z] RMSE (validation) = 0.9001440981626694 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-06-27T02:06:11.357Z] 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.
[2024-06-27T02:06:12.122Z] The best model improves the baseline by 14.43%.
[2024-06-27T02:06:12.122Z] Movies recommended for you:
[2024-06-27T02:06:12.122Z] WARNING: This benchmark provides no result that can be validated.
[2024-06-27T02:06:12.122Z] There is no way to check that no silent failure occurred.
[2024-06-27T02:06:12.122Z] ====== movie-lens (apache-spark) [default], iteration 13 completed (16400.057 ms) ======
[2024-06-27T02:06:12.122Z] ====== movie-lens (apache-spark) [default], iteration 14 started ======
[2024-06-27T02:06:12.122Z] GC before operation: completed in 93.431 ms, heap usage 3.654 GB -> 61.721 MB.
[2024-06-27T02:06:14.574Z] RMSE (validation) = 3.6219689545487506 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-06-27T02:06:17.034Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-06-27T02:06:19.479Z] RMSE (validation) = 1.3105189674610935 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-06-27T02:06:21.920Z] RMSE (validation) = 1.004540774800519 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-06-27T02:06:23.495Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-06-27T02:06:25.083Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-06-27T02:06:26.669Z] RMSE (validation) = 0.9275717391338142 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-06-27T02:06:27.443Z] RMSE (validation) = 0.9001440981626694 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-06-27T02:06:28.203Z] 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.
[2024-06-27T02:06:28.203Z] The best model improves the baseline by 14.43%.
[2024-06-27T02:06:28.203Z] Movies recommended for you:
[2024-06-27T02:06:28.203Z] WARNING: This benchmark provides no result that can be validated.
[2024-06-27T02:06:28.203Z] There is no way to check that no silent failure occurred.
[2024-06-27T02:06:28.203Z] ====== movie-lens (apache-spark) [default], iteration 14 completed (16209.348 ms) ======
[2024-06-27T02:06:28.203Z] ====== movie-lens (apache-spark) [default], iteration 15 started ======
[2024-06-27T02:06:28.203Z] GC before operation: completed in 73.462 ms, heap usage 172.360 MB -> 52.389 MB.
[2024-06-27T02:06:30.647Z] RMSE (validation) = 3.6219689545487506 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-06-27T02:06:33.092Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-06-27T02:06:36.499Z] RMSE (validation) = 1.3105189674610933 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-06-27T02:06:38.074Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-06-27T02:06:39.644Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-06-27T02:06:41.227Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-06-27T02:06:42.804Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-06-27T02:06:44.378Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-06-27T02:06:44.378Z] 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.
[2024-06-27T02:06:44.378Z] The best model improves the baseline by 14.43%.
[2024-06-27T02:06:44.378Z] Movies recommended for you:
[2024-06-27T02:06:44.378Z] WARNING: This benchmark provides no result that can be validated.
[2024-06-27T02:06:44.378Z] There is no way to check that no silent failure occurred.
[2024-06-27T02:06:44.378Z] ====== movie-lens (apache-spark) [default], iteration 15 completed (16347.196 ms) ======
[2024-06-27T02:06:44.378Z] ====== movie-lens (apache-spark) [default], iteration 16 started ======
[2024-06-27T02:06:44.378Z] GC before operation: completed in 89.975 ms, heap usage 425.055 MB -> 52.634 MB.
[2024-06-27T02:06:46.835Z] RMSE (validation) = 3.6219689545487506 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-06-27T02:06:49.284Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-06-27T02:06:52.755Z] RMSE (validation) = 1.3105189674610935 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-06-27T02:06:54.338Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-06-27T02:06:55.922Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-06-27T02:06:57.492Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-06-27T02:06:59.062Z] RMSE (validation) = 0.9275717391338142 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-06-27T02:07:00.635Z] RMSE (validation) = 0.9001440981626694 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-06-27T02:07:00.635Z] 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.
[2024-06-27T02:07:00.635Z] The best model improves the baseline by 14.43%.
[2024-06-27T02:07:00.635Z] Movies recommended for you:
[2024-06-27T02:07:00.635Z] WARNING: This benchmark provides no result that can be validated.
[2024-06-27T02:07:00.635Z] There is no way to check that no silent failure occurred.
[2024-06-27T02:07:00.635Z] ====== movie-lens (apache-spark) [default], iteration 16 completed (16263.095 ms) ======
[2024-06-27T02:07:00.635Z] ====== movie-lens (apache-spark) [default], iteration 17 started ======
[2024-06-27T02:07:00.635Z] GC before operation: completed in 83.953 ms, heap usage 1.326 GB -> 56.929 MB.
[2024-06-27T02:07:03.102Z] RMSE (validation) = 3.621968954548751 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-06-27T02:07:05.723Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-06-27T02:07:08.176Z] RMSE (validation) = 1.3105189674610935 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-06-27T02:07:10.623Z] RMSE (validation) = 1.004540774800519 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-06-27T02:07:12.197Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-06-27T02:07:13.765Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-06-27T02:07:15.337Z] RMSE (validation) = 0.927571739133814 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-06-27T02:07:16.929Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-06-27T02:07:16.929Z] 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.
[2024-06-27T02:07:16.929Z] The best model improves the baseline by 14.43%.
[2024-06-27T02:07:16.929Z] Movies recommended for you:
[2024-06-27T02:07:16.929Z] WARNING: This benchmark provides no result that can be validated.
[2024-06-27T02:07:16.929Z] There is no way to check that no silent failure occurred.
[2024-06-27T02:07:16.929Z] ====== movie-lens (apache-spark) [default], iteration 17 completed (16243.825 ms) ======
[2024-06-27T02:07:16.929Z] ====== movie-lens (apache-spark) [default], iteration 18 started ======
[2024-06-27T02:07:17.691Z] GC before operation: completed in 83.960 ms, heap usage 2.423 GB -> 57.405 MB.
[2024-06-27T02:07:20.143Z] RMSE (validation) = 3.621968954548751 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-06-27T02:07:22.605Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-06-27T02:07:25.077Z] RMSE (validation) = 1.3105189674610935 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-06-27T02:07:27.541Z] RMSE (validation) = 1.004540774800519 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-06-27T02:07:28.300Z] RMSE (validation) = 1.2218330581874073 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-06-27T02:07:29.874Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-06-27T02:07:31.451Z] RMSE (validation) = 0.927571739133814 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-06-27T02:07:33.019Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-06-27T02:07:33.019Z] 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.
[2024-06-27T02:07:33.019Z] The best model improves the baseline by 14.43%.
[2024-06-27T02:07:33.779Z] Movies recommended for you:
[2024-06-27T02:07:33.779Z] WARNING: This benchmark provides no result that can be validated.
[2024-06-27T02:07:33.779Z] There is no way to check that no silent failure occurred.
[2024-06-27T02:07:33.779Z] ====== movie-lens (apache-spark) [default], iteration 18 completed (16110.919 ms) ======
[2024-06-27T02:07:33.779Z] ====== movie-lens (apache-spark) [default], iteration 19 started ======
[2024-06-27T02:07:33.779Z] GC before operation: completed in 78.174 ms, heap usage 170.394 MB -> 52.521 MB.
[2024-06-27T02:07:36.242Z] RMSE (validation) = 3.6219689545487506 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-06-27T02:07:38.694Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-06-27T02:07:41.133Z] RMSE (validation) = 1.3105189674610935 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-06-27T02:07:43.580Z] RMSE (validation) = 1.004540774800519 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-06-27T02:07:45.162Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-06-27T02:07:46.741Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-06-27T02:07:48.320Z] RMSE (validation) = 0.927571739133814 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-06-27T02:07:49.894Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-06-27T02:07:49.894Z] 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.
[2024-06-27T02:07:49.894Z] The best model improves the baseline by 14.43%.
[2024-06-27T02:07:49.894Z] Movies recommended for you:
[2024-06-27T02:07:49.894Z] WARNING: This benchmark provides no result that can be validated.
[2024-06-27T02:07:49.894Z] There is no way to check that no silent failure occurred.
[2024-06-27T02:07:49.894Z] ====== movie-lens (apache-spark) [default], iteration 19 completed (16431.817 ms) ======
[2024-06-27T02:07:50.656Z] -----------------------------------
[2024-06-27T02:07:50.656Z] renaissance-movie-lens_0_PASSED
[2024-06-27T02:07:50.656Z] -----------------------------------
[2024-06-27T02:07:50.656Z]
[2024-06-27T02:07:50.656Z] TEST TEARDOWN:
[2024-06-27T02:07:50.656Z] Nothing to be done for teardown.
[2024-06-27T02:07:50.656Z] renaissance-movie-lens_0 Finish Time: Thu Jun 27 02:07:50 2024 Epoch Time (ms): 1719454070373