renaissance-movie-lens_0
[2023-04-18T23:28:54.164Z] Running test renaissance-movie-lens_0 ...
[2023-04-18T23:28:54.164Z] ===============================================
[2023-04-18T23:28:54.164Z] renaissance-movie-lens_0 Start Time: Tue Apr 18 23:39:41 2023 Epoch Time (ms): 1681861181484
[2023-04-18T23:28:54.164Z] variation: NoOptions
[2023-04-18T23:28:54.164Z] JVM_OPTIONS:
[2023-04-18T23:28:54.164Z] { \
[2023-04-18T23:28:54.164Z] echo ""; echo "TEST SETUP:"; \
[2023-04-18T23:28:54.164Z] echo "Nothing to be done for setup."; \
[2023-04-18T23:28:54.164Z] mkdir -p "/home/jenkins/workspace/Test_openjdk17_hs_extended.perf_ppc64_aix/aqa-tests/TKG/../TKG/output_16818603484140/renaissance-movie-lens_0"; \
[2023-04-18T23:28:54.164Z] cd "/home/jenkins/workspace/Test_openjdk17_hs_extended.perf_ppc64_aix/aqa-tests/TKG/../TKG/output_16818603484140/renaissance-movie-lens_0"; \
[2023-04-18T23:28:54.164Z] echo ""; echo "TESTING:"; \
[2023-04-18T23:28:54.164Z] "/home/jenkins/workspace/Test_openjdk17_hs_extended.perf_ppc64_aix/openjdkbinary/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_16818603484140/renaissance-movie-lens_0"/movie-lens.json" movie-lens; \
[2023-04-18T23:28:54.164Z] 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_16818603484140/renaissance-movie-lens_0"; else echo "-----------------------------------"; echo "renaissance-movie-lens_0""_FAILED"; echo "-----------------------------------"; fi; \
[2023-04-18T23:28:54.164Z] echo ""; echo "TEST TEARDOWN:"; \
[2023-04-18T23:28:54.164Z] echo "Nothing to be done for teardown."; \
[2023-04-18T23:28:54.164Z] } 2>&1 | tee -a "/home/jenkins/workspace/Test_openjdk17_hs_extended.perf_ppc64_aix/aqa-tests/TKG/../TKG/output_16818603484140/TestTargetResult";
[2023-04-18T23:28:54.164Z]
[2023-04-18T23:28:54.164Z] TEST SETUP:
[2023-04-18T23:28:54.164Z] Nothing to be done for setup.
[2023-04-18T23:28:54.164Z]
[2023-04-18T23:28:54.164Z] TESTING:
[2023-04-18T23:28:58.582Z] Using Spark's default log4j profile: org/apache/spark/log4j-defaults.properties
[2023-04-18T23:29:00.154Z] NOTE: 'movie-lens' benchmark uses Spark local executor with 8 (out of 24) threads.
[2023-04-18T23:29:03.550Z] Got 100004 ratings from 671 users on 9066 movies.
[2023-04-18T23:29:03.550Z] Training: 60056, validation: 20285, test: 19854
[2023-04-18T23:29:03.550Z] ====== movie-lens (apache-spark) [default], iteration 0 started ======
[2023-04-18T23:29:03.550Z] GC before operation: completed in 45.138 ms, heap usage 276.348 MB -> 37.647 MB.
[2023-04-18T23:29:07.996Z] RMSE (validation) = 3.621968954548751 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2023-04-18T23:29:11.399Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2023-04-18T23:29:14.784Z] RMSE (validation) = 1.3105189674610933 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2023-04-18T23:29:17.242Z] RMSE (validation) = 1.004540774800519 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2023-04-18T23:29:18.814Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2023-04-18T23:29:20.388Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2023-04-18T23:29:21.962Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2023-04-18T23:29:23.543Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2023-04-18T23:29:24.314Z] 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.
[2023-04-18T23:29:24.314Z] The best model improves the baseline by 14.43%.
[2023-04-18T23:29:24.314Z] Movies recommended for you:
[2023-04-18T23:29:24.314Z] WARNING: This benchmark provides no result that can be validated.
[2023-04-18T23:29:24.314Z] There is no way to check that no silent failure occurred.
[2023-04-18T23:29:24.314Z] ====== movie-lens (apache-spark) [default], iteration 0 completed (20746.667 ms) ======
[2023-04-18T23:29:24.314Z] ====== movie-lens (apache-spark) [default], iteration 1 started ======
[2023-04-18T23:29:24.314Z] GC before operation: completed in 89.998 ms, heap usage 218.440 MB -> 50.523 MB.
[2023-04-18T23:29:26.758Z] RMSE (validation) = 3.6219689545487506 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2023-04-18T23:29:30.155Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2023-04-18T23:29:32.598Z] RMSE (validation) = 1.3105189674610933 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2023-04-18T23:29:35.054Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2023-04-18T23:29:37.521Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2023-04-18T23:29:38.284Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2023-04-18T23:29:40.745Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2023-04-18T23:29:41.517Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2023-04-18T23:29:42.284Z] 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.
[2023-04-18T23:29:42.284Z] The best model improves the baseline by 14.43%.
[2023-04-18T23:29:42.284Z] Movies recommended for you:
[2023-04-18T23:29:42.284Z] WARNING: This benchmark provides no result that can be validated.
[2023-04-18T23:29:42.284Z] There is no way to check that no silent failure occurred.
[2023-04-18T23:29:42.284Z] ====== movie-lens (apache-spark) [default], iteration 1 completed (17764.965 ms) ======
[2023-04-18T23:29:42.284Z] ====== movie-lens (apache-spark) [default], iteration 2 started ======
[2023-04-18T23:29:42.284Z] GC before operation: completed in 85.063 ms, heap usage 2.612 GB -> 56.318 MB.
[2023-04-18T23:29:44.727Z] RMSE (validation) = 3.621968954548751 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2023-04-18T23:29:48.123Z] RMSE (validation) = 2.1340923217118064 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2023-04-18T23:29:50.572Z] RMSE (validation) = 1.3105189674610933 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2023-04-18T23:29:53.017Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2023-04-18T23:29:54.591Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2023-04-18T23:29:56.178Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2023-04-18T23:29:57.752Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2023-04-18T23:29:59.332Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2023-04-18T23:29:59.332Z] 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.
[2023-04-18T23:29:59.332Z] The best model improves the baseline by 14.43%.
[2023-04-18T23:29:59.332Z] Movies recommended for you:
[2023-04-18T23:29:59.332Z] WARNING: This benchmark provides no result that can be validated.
[2023-04-18T23:29:59.332Z] There is no way to check that no silent failure occurred.
[2023-04-18T23:29:59.332Z] ====== movie-lens (apache-spark) [default], iteration 2 completed (17132.293 ms) ======
[2023-04-18T23:29:59.332Z] ====== movie-lens (apache-spark) [default], iteration 3 started ======
[2023-04-18T23:29:59.332Z] GC before operation: completed in 77.810 ms, heap usage 859.068 MB -> 55.537 MB.
[2023-04-18T23:30:01.794Z] RMSE (validation) = 3.6219689545487506 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2023-04-18T23:30:04.411Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2023-04-18T23:30:07.802Z] RMSE (validation) = 1.3105189674610933 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2023-04-18T23:30:10.250Z] RMSE (validation) = 1.004540774800519 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2023-04-18T23:30:11.821Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2023-04-18T23:30:13.412Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2023-04-18T23:30:15.002Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2023-04-18T23:30:15.780Z] RMSE (validation) = 0.9001440981626694 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2023-04-18T23:30:16.549Z] 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.
[2023-04-18T23:30:16.549Z] The best model improves the baseline by 14.43%.
[2023-04-18T23:30:16.549Z] Movies recommended for you:
[2023-04-18T23:30:16.549Z] WARNING: This benchmark provides no result that can be validated.
[2023-04-18T23:30:16.549Z] There is no way to check that no silent failure occurred.
[2023-04-18T23:30:16.549Z] ====== movie-lens (apache-spark) [default], iteration 3 completed (16969.957 ms) ======
[2023-04-18T23:30:16.549Z] ====== movie-lens (apache-spark) [default], iteration 4 started ======
[2023-04-18T23:30:16.549Z] GC before operation: completed in 88.468 ms, heap usage 123.148 MB -> 61.412 MB.
[2023-04-18T23:30:19.000Z] RMSE (validation) = 3.621968954548751 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2023-04-18T23:30:21.442Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2023-04-18T23:30:24.850Z] RMSE (validation) = 1.3105189674610933 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2023-04-18T23:30:27.324Z] RMSE (validation) = 1.004540774800519 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2023-04-18T23:30:28.088Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2023-04-18T23:30:29.671Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2023-04-18T23:30:31.264Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2023-04-18T23:30:32.850Z] RMSE (validation) = 0.9001440981626694 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2023-04-18T23:30:33.610Z] 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.
[2023-04-18T23:30:33.610Z] The best model improves the baseline by 14.43%.
[2023-04-18T23:30:33.610Z] Movies recommended for you:
[2023-04-18T23:30:33.610Z] WARNING: This benchmark provides no result that can be validated.
[2023-04-18T23:30:33.610Z] There is no way to check that no silent failure occurred.
[2023-04-18T23:30:33.610Z] ====== movie-lens (apache-spark) [default], iteration 4 completed (16860.251 ms) ======
[2023-04-18T23:30:33.610Z] ====== movie-lens (apache-spark) [default], iteration 5 started ======
[2023-04-18T23:30:33.610Z] GC before operation: completed in 80.423 ms, heap usage 960.802 MB -> 56.220 MB.
[2023-04-18T23:30:36.054Z] RMSE (validation) = 3.621968954548751 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2023-04-18T23:30:38.514Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2023-04-18T23:30:41.915Z] RMSE (validation) = 1.3105189674610935 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2023-04-18T23:30:43.489Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2023-04-18T23:30:45.080Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2023-04-18T23:30:46.665Z] RMSE (validation) = 1.117495376637101 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2023-04-18T23:30:48.249Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2023-04-18T23:30:49.830Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2023-04-18T23:30:50.592Z] 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.
[2023-04-18T23:30:50.592Z] The best model improves the baseline by 14.43%.
[2023-04-18T23:30:50.592Z] Movies recommended for you:
[2023-04-18T23:30:50.592Z] WARNING: This benchmark provides no result that can be validated.
[2023-04-18T23:30:50.592Z] There is no way to check that no silent failure occurred.
[2023-04-18T23:30:50.592Z] ====== movie-lens (apache-spark) [default], iteration 5 completed (16936.147 ms) ======
[2023-04-18T23:30:50.592Z] ====== movie-lens (apache-spark) [default], iteration 6 started ======
[2023-04-18T23:30:50.592Z] GC before operation: completed in 82.117 ms, heap usage 108.681 MB -> 55.906 MB.
[2023-04-18T23:30:53.041Z] RMSE (validation) = 3.621968954548751 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2023-04-18T23:30:55.486Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2023-04-18T23:30:58.893Z] RMSE (validation) = 1.3105189674610935 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2023-04-18T23:31:00.467Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2023-04-18T23:31:02.040Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2023-04-18T23:31:03.623Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2023-04-18T23:31:05.210Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2023-04-18T23:31:06.799Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2023-04-18T23:31:07.570Z] 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.
[2023-04-18T23:31:07.570Z] The best model improves the baseline by 14.43%.
[2023-04-18T23:31:07.570Z] Movies recommended for you:
[2023-04-18T23:31:07.570Z] WARNING: This benchmark provides no result that can be validated.
[2023-04-18T23:31:07.570Z] There is no way to check that no silent failure occurred.
[2023-04-18T23:31:07.570Z] ====== movie-lens (apache-spark) [default], iteration 6 completed (16765.372 ms) ======
[2023-04-18T23:31:07.570Z] ====== movie-lens (apache-spark) [default], iteration 7 started ======
[2023-04-18T23:31:07.570Z] GC before operation: completed in 82.345 ms, heap usage 1.097 GB -> 56.654 MB.
[2023-04-18T23:31:10.038Z] RMSE (validation) = 3.6219689545487506 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2023-04-18T23:31:12.482Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2023-04-18T23:31:14.928Z] RMSE (validation) = 1.3105189674610935 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2023-04-18T23:31:17.381Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2023-04-18T23:31:18.953Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2023-04-18T23:31:20.546Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2023-04-18T23:31:22.129Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2023-04-18T23:31:23.810Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2023-04-18T23:31:24.867Z] 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.
[2023-04-18T23:31:24.867Z] The best model improves the baseline by 14.43%.
[2023-04-18T23:31:24.867Z] Movies recommended for you:
[2023-04-18T23:31:24.867Z] WARNING: This benchmark provides no result that can be validated.
[2023-04-18T23:31:24.867Z] There is no way to check that no silent failure occurred.
[2023-04-18T23:31:24.867Z] ====== movie-lens (apache-spark) [default], iteration 7 completed (16838.813 ms) ======
[2023-04-18T23:31:24.867Z] ====== movie-lens (apache-spark) [default], iteration 8 started ======
[2023-04-18T23:31:24.867Z] GC before operation: completed in 88.792 ms, heap usage 1.733 GB -> 57.457 MB.
[2023-04-18T23:31:27.469Z] RMSE (validation) = 3.6219689545487506 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2023-04-18T23:31:29.933Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2023-04-18T23:31:32.408Z] RMSE (validation) = 1.3105189674610935 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2023-04-18T23:31:34.858Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2023-04-18T23:31:36.439Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2023-04-18T23:31:38.033Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2023-04-18T23:31:39.617Z] RMSE (validation) = 0.927571739133814 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2023-04-18T23:31:40.402Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2023-04-18T23:31:41.167Z] 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.
[2023-04-18T23:31:41.167Z] The best model improves the baseline by 14.43%.
[2023-04-18T23:31:41.167Z] Movies recommended for you:
[2023-04-18T23:31:41.167Z] WARNING: This benchmark provides no result that can be validated.
[2023-04-18T23:31:41.167Z] There is no way to check that no silent failure occurred.
[2023-04-18T23:31:41.167Z] ====== movie-lens (apache-spark) [default], iteration 8 completed (16816.738 ms) ======
[2023-04-18T23:31:41.167Z] ====== movie-lens (apache-spark) [default], iteration 9 started ======
[2023-04-18T23:31:41.167Z] GC before operation: completed in 81.346 ms, heap usage 1.761 GB -> 57.301 MB.
[2023-04-18T23:31:43.615Z] RMSE (validation) = 3.6219689545487506 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2023-04-18T23:31:46.057Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2023-04-18T23:31:49.460Z] RMSE (validation) = 1.3105189674610935 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2023-04-18T23:31:51.918Z] RMSE (validation) = 1.004540774800519 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2023-04-18T23:31:53.518Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2023-04-18T23:31:54.283Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2023-04-18T23:31:55.881Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2023-04-18T23:31:57.468Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2023-04-18T23:31:58.229Z] 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.
[2023-04-18T23:31:58.229Z] The best model improves the baseline by 14.43%.
[2023-04-18T23:31:58.229Z] Movies recommended for you:
[2023-04-18T23:31:58.229Z] WARNING: This benchmark provides no result that can be validated.
[2023-04-18T23:31:58.229Z] There is no way to check that no silent failure occurred.
[2023-04-18T23:31:58.229Z] ====== movie-lens (apache-spark) [default], iteration 9 completed (16960.248 ms) ======
[2023-04-18T23:31:58.229Z] ====== movie-lens (apache-spark) [default], iteration 10 started ======
[2023-04-18T23:31:58.229Z] GC before operation: completed in 94.504 ms, heap usage 1.718 GB -> 57.387 MB.
[2023-04-18T23:32:00.675Z] RMSE (validation) = 3.6219689545487506 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2023-04-18T23:32:03.129Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2023-04-18T23:32:06.529Z] RMSE (validation) = 1.3105189674610935 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2023-04-18T23:32:08.980Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2023-04-18T23:32:10.559Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2023-04-18T23:32:11.327Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2023-04-18T23:32:12.901Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2023-04-18T23:32:14.479Z] RMSE (validation) = 0.9001440981626694 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2023-04-18T23:32:15.242Z] 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.
[2023-04-18T23:32:15.243Z] The best model improves the baseline by 14.43%.
[2023-04-18T23:32:15.243Z] Movies recommended for you:
[2023-04-18T23:32:15.243Z] WARNING: This benchmark provides no result that can be validated.
[2023-04-18T23:32:15.243Z] There is no way to check that no silent failure occurred.
[2023-04-18T23:32:15.243Z] ====== movie-lens (apache-spark) [default], iteration 10 completed (16871.769 ms) ======
[2023-04-18T23:32:15.243Z] ====== movie-lens (apache-spark) [default], iteration 11 started ======
[2023-04-18T23:32:15.243Z] GC before operation: completed in 96.773 ms, heap usage 1.396 GB -> 56.890 MB.
[2023-04-18T23:32:17.700Z] RMSE (validation) = 3.6219689545487506 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2023-04-18T23:32:20.149Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2023-04-18T23:32:23.547Z] RMSE (validation) = 1.3105189674610933 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2023-04-18T23:32:25.116Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2023-04-18T23:32:27.566Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2023-04-18T23:32:28.333Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2023-04-18T23:32:29.911Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2023-04-18T23:32:31.486Z] RMSE (validation) = 0.9001440981626694 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2023-04-18T23:32:32.248Z] 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.
[2023-04-18T23:32:32.248Z] The best model improves the baseline by 14.43%.
[2023-04-18T23:32:32.248Z] Movies recommended for you:
[2023-04-18T23:32:32.248Z] WARNING: This benchmark provides no result that can be validated.
[2023-04-18T23:32:32.248Z] There is no way to check that no silent failure occurred.
[2023-04-18T23:32:32.248Z] ====== movie-lens (apache-spark) [default], iteration 11 completed (16888.000 ms) ======
[2023-04-18T23:32:32.248Z] ====== movie-lens (apache-spark) [default], iteration 12 started ======
[2023-04-18T23:32:32.248Z] GC before operation: completed in 80.264 ms, heap usage 831.691 MB -> 56.082 MB.
[2023-04-18T23:32:34.698Z] RMSE (validation) = 3.6219689545487506 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2023-04-18T23:32:37.157Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2023-04-18T23:32:40.547Z] RMSE (validation) = 1.3105189674610935 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2023-04-18T23:32:43.017Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2023-04-18T23:32:43.778Z] RMSE (validation) = 1.2218330581874073 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2023-04-18T23:32:45.358Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2023-04-18T23:32:46.932Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2023-04-18T23:32:48.505Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2023-04-18T23:32:49.266Z] 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.
[2023-04-18T23:32:49.266Z] The best model improves the baseline by 14.43%.
[2023-04-18T23:32:49.266Z] Movies recommended for you:
[2023-04-18T23:32:49.266Z] WARNING: This benchmark provides no result that can be validated.
[2023-04-18T23:32:49.267Z] There is no way to check that no silent failure occurred.
[2023-04-18T23:32:49.267Z] ====== movie-lens (apache-spark) [default], iteration 12 completed (16899.247 ms) ======
[2023-04-18T23:32:49.267Z] ====== movie-lens (apache-spark) [default], iteration 13 started ======
[2023-04-18T23:32:49.267Z] GC before operation: completed in 84.843 ms, heap usage 782.797 MB -> 56.284 MB.
[2023-04-18T23:32:51.711Z] RMSE (validation) = 3.621968954548751 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2023-04-18T23:32:54.157Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2023-04-18T23:32:57.574Z] RMSE (validation) = 1.3105189674610933 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2023-04-18T23:32:59.151Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2023-04-18T23:33:00.721Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2023-04-18T23:33:02.300Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2023-04-18T23:33:03.876Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2023-04-18T23:33:05.453Z] RMSE (validation) = 0.9001440981626694 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2023-04-18T23:33:06.213Z] 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.
[2023-04-18T23:33:06.213Z] The best model improves the baseline by 14.43%.
[2023-04-18T23:33:06.213Z] Movies recommended for you:
[2023-04-18T23:33:06.213Z] WARNING: This benchmark provides no result that can be validated.
[2023-04-18T23:33:06.213Z] There is no way to check that no silent failure occurred.
[2023-04-18T23:33:06.213Z] ====== movie-lens (apache-spark) [default], iteration 13 completed (16888.183 ms) ======
[2023-04-18T23:33:06.213Z] ====== movie-lens (apache-spark) [default], iteration 14 started ======
[2023-04-18T23:33:06.213Z] GC before operation: completed in 96.556 ms, heap usage 2.938 GB -> 57.376 MB.
[2023-04-18T23:33:08.654Z] RMSE (validation) = 3.6219689545487506 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2023-04-18T23:33:11.093Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2023-04-18T23:33:14.474Z] RMSE (validation) = 1.3105189674610933 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2023-04-18T23:33:16.054Z] RMSE (validation) = 1.004540774800519 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2023-04-18T23:33:17.645Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2023-04-18T23:33:19.229Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2023-04-18T23:33:20.810Z] RMSE (validation) = 0.927571739133814 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2023-04-18T23:33:22.382Z] RMSE (validation) = 0.9001440981626694 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2023-04-18T23:33:23.149Z] 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.
[2023-04-18T23:33:23.149Z] The best model improves the baseline by 14.43%.
[2023-04-18T23:33:23.149Z] Movies recommended for you:
[2023-04-18T23:33:23.149Z] WARNING: This benchmark provides no result that can be validated.
[2023-04-18T23:33:23.149Z] There is no way to check that no silent failure occurred.
[2023-04-18T23:33:23.149Z] ====== movie-lens (apache-spark) [default], iteration 14 completed (16787.501 ms) ======
[2023-04-18T23:33:23.149Z] ====== movie-lens (apache-spark) [default], iteration 15 started ======
[2023-04-18T23:33:23.149Z] GC before operation: completed in 82.602 ms, heap usage 108.233 MB -> 57.053 MB.
[2023-04-18T23:33:25.600Z] RMSE (validation) = 3.6219689545487506 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2023-04-18T23:33:28.043Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2023-04-18T23:33:31.440Z] RMSE (validation) = 1.3105189674610933 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2023-04-18T23:33:33.893Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2023-04-18T23:33:34.659Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2023-04-18T23:33:36.243Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2023-04-18T23:33:37.827Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2023-04-18T23:33:39.406Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2023-04-18T23:33:40.173Z] 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.
[2023-04-18T23:33:40.174Z] The best model improves the baseline by 14.43%.
[2023-04-18T23:33:40.174Z] Movies recommended for you:
[2023-04-18T23:33:40.174Z] WARNING: This benchmark provides no result that can be validated.
[2023-04-18T23:33:40.174Z] There is no way to check that no silent failure occurred.
[2023-04-18T23:33:40.174Z] ====== movie-lens (apache-spark) [default], iteration 15 completed (16966.660 ms) ======
[2023-04-18T23:33:40.174Z] ====== movie-lens (apache-spark) [default], iteration 16 started ======
[2023-04-18T23:33:40.174Z] GC before operation: completed in 84.161 ms, heap usage 109.280 MB -> 56.384 MB.
[2023-04-18T23:33:42.618Z] RMSE (validation) = 3.6219689545487506 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2023-04-18T23:33:45.064Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2023-04-18T23:33:48.456Z] RMSE (validation) = 1.3105189674610935 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2023-04-18T23:33:50.912Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2023-04-18T23:33:52.492Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2023-04-18T23:33:54.085Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2023-04-18T23:33:55.683Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2023-04-18T23:33:56.444Z] RMSE (validation) = 0.9001440981626694 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2023-04-18T23:33:57.205Z] 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.
[2023-04-18T23:33:57.205Z] The best model improves the baseline by 14.43%.
[2023-04-18T23:33:57.205Z] Movies recommended for you:
[2023-04-18T23:33:57.205Z] WARNING: This benchmark provides no result that can be validated.
[2023-04-18T23:33:57.205Z] There is no way to check that no silent failure occurred.
[2023-04-18T23:33:57.205Z] ====== movie-lens (apache-spark) [default], iteration 16 completed (17019.323 ms) ======
[2023-04-18T23:33:57.205Z] ====== movie-lens (apache-spark) [default], iteration 17 started ======
[2023-04-18T23:33:57.205Z] GC before operation: completed in 98.563 ms, heap usage 1.011 GB -> 56.590 MB.
[2023-04-18T23:33:59.647Z] RMSE (validation) = 3.621968954548751 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2023-04-18T23:34:02.093Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2023-04-18T23:34:05.476Z] RMSE (validation) = 1.3105189674610933 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2023-04-18T23:34:07.923Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2023-04-18T23:34:09.512Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2023-04-18T23:34:10.273Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2023-04-18T23:34:11.858Z] RMSE (validation) = 0.9275717391338142 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2023-04-18T23:34:13.430Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2023-04-18T23:34:14.192Z] 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.
[2023-04-18T23:34:14.192Z] The best model improves the baseline by 14.43%.
[2023-04-18T23:34:14.192Z] Movies recommended for you:
[2023-04-18T23:34:14.192Z] WARNING: This benchmark provides no result that can be validated.
[2023-04-18T23:34:14.192Z] There is no way to check that no silent failure occurred.
[2023-04-18T23:34:14.192Z] ====== movie-lens (apache-spark) [default], iteration 17 completed (16910.422 ms) ======
[2023-04-18T23:34:14.192Z] ====== movie-lens (apache-spark) [default], iteration 18 started ======
[2023-04-18T23:34:14.192Z] GC before operation: completed in 90.877 ms, heap usage 1.538 GB -> 57.302 MB.
[2023-04-18T23:34:16.642Z] RMSE (validation) = 3.621968954548751 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2023-04-18T23:34:19.102Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2023-04-18T23:34:22.514Z] RMSE (validation) = 1.3105189674610933 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2023-04-18T23:34:24.098Z] RMSE (validation) = 1.004540774800519 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2023-04-18T23:34:25.675Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2023-04-18T23:34:27.253Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2023-04-18T23:34:28.836Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2023-04-18T23:34:30.412Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2023-04-18T23:34:31.174Z] 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.
[2023-04-18T23:34:31.174Z] The best model improves the baseline by 14.43%.
[2023-04-18T23:34:31.174Z] Movies recommended for you:
[2023-04-18T23:34:31.174Z] WARNING: This benchmark provides no result that can be validated.
[2023-04-18T23:34:31.174Z] There is no way to check that no silent failure occurred.
[2023-04-18T23:34:31.174Z] ====== movie-lens (apache-spark) [default], iteration 18 completed (16845.955 ms) ======
[2023-04-18T23:34:31.174Z] ====== movie-lens (apache-spark) [default], iteration 19 started ======
[2023-04-18T23:34:31.174Z] GC before operation: completed in 94.677 ms, heap usage 500.801 MB -> 52.890 MB.
[2023-04-18T23:34:33.626Z] RMSE (validation) = 3.621968954548751 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2023-04-18T23:34:36.071Z] RMSE (validation) = 2.1340923217118064 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2023-04-18T23:34:39.471Z] RMSE (validation) = 1.3105189674610933 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2023-04-18T23:34:41.940Z] RMSE (validation) = 1.004540774800519 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2023-04-18T23:34:42.702Z] RMSE (validation) = 1.2218330581874073 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2023-04-18T23:34:44.280Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2023-04-18T23:34:45.855Z] RMSE (validation) = 0.927571739133814 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2023-04-18T23:34:47.427Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2023-04-18T23:34:48.189Z] 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.
[2023-04-18T23:34:48.189Z] The best model improves the baseline by 14.43%.
[2023-04-18T23:34:48.189Z] Movies recommended for you:
[2023-04-18T23:34:48.189Z] WARNING: This benchmark provides no result that can be validated.
[2023-04-18T23:34:48.189Z] There is no way to check that no silent failure occurred.
[2023-04-18T23:34:48.189Z] ====== movie-lens (apache-spark) [default], iteration 19 completed (16783.489 ms) ======
[2023-04-18T23:34:48.951Z] -----------------------------------
[2023-04-18T23:34:48.951Z] renaissance-movie-lens_0_PASSED
[2023-04-18T23:34:48.951Z] -----------------------------------
[2023-04-18T23:34:48.951Z]
[2023-04-18T23:34:48.951Z] TEST TEARDOWN:
[2023-04-18T23:34:48.951Z] Nothing to be done for teardown.
[2023-04-18T23:34:48.951Z] renaissance-movie-lens_0 Finish Time: Tue Apr 18 23:45:36 2023 Epoch Time (ms): 1681861536578