renaissance-movie-lens_0
[2025-02-27T02:04:21.908Z] Running test renaissance-movie-lens_0 ...
[2025-02-27T02:04:21.908Z] ===============================================
[2025-02-27T02:04:21.908Z] renaissance-movie-lens_0 Start Time: Wed Feb 26 18:04:20 2025 Epoch Time (ms): 1740621860944
[2025-02-27T02:04:21.908Z] variation: NoOptions
[2025-02-27T02:04:21.908Z] JVM_OPTIONS:
[2025-02-27T02:04:21.908Z] { \
[2025-02-27T02:04:21.908Z] echo ""; echo "TEST SETUP:"; \
[2025-02-27T02:04:21.908Z] echo "Nothing to be done for setup."; \
[2025-02-27T02:04:21.908Z] mkdir -p "/Users/admin/workspace/workspace/Test_openjdk11_hs_extended.perf_x86-64_mac_testList_1/aqa-tests/TKG/../TKG/output_17406208911499/renaissance-movie-lens_0"; \
[2025-02-27T02:04:21.908Z] cd "/Users/admin/workspace/workspace/Test_openjdk11_hs_extended.perf_x86-64_mac_testList_1/aqa-tests/TKG/../TKG/output_17406208911499/renaissance-movie-lens_0"; \
[2025-02-27T02:04:21.908Z] echo ""; echo "TESTING:"; \
[2025-02-27T02:04:21.908Z] "/Users/admin/workspace/workspace/Test_openjdk11_hs_extended.perf_x86-64_mac_testList_1/jdkbinary/j2sdk-image/Contents/Home/bin/..//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 "/Users/admin/workspace/workspace/Test_openjdk11_hs_extended.perf_x86-64_mac_testList_1/aqa-tests/TKG/../../jvmtest/perf/renaissance/renaissance.jar" --json ""/Users/admin/workspace/workspace/Test_openjdk11_hs_extended.perf_x86-64_mac_testList_1/aqa-tests/TKG/../TKG/output_17406208911499/renaissance-movie-lens_0"/movie-lens.json" movie-lens; \
[2025-02-27T02:04:21.908Z] if [ $? -eq 0 ]; then echo "-----------------------------------"; echo "renaissance-movie-lens_0""_PASSED"; echo "-----------------------------------"; cd /Users/admin/workspace/workspace/Test_openjdk11_hs_extended.perf_x86-64_mac_testList_1/aqa-tests/TKG/..; rm -f -r "/Users/admin/workspace/workspace/Test_openjdk11_hs_extended.perf_x86-64_mac_testList_1/aqa-tests/TKG/../TKG/output_17406208911499/renaissance-movie-lens_0"; else echo "-----------------------------------"; echo "renaissance-movie-lens_0""_FAILED"; echo "-----------------------------------"; fi; \
[2025-02-27T02:04:21.908Z] echo ""; echo "TEST TEARDOWN:"; \
[2025-02-27T02:04:21.908Z] echo "Nothing to be done for teardown."; \
[2025-02-27T02:04:21.908Z] } 2>&1 | tee -a "/Users/admin/workspace/workspace/Test_openjdk11_hs_extended.perf_x86-64_mac_testList_1/aqa-tests/TKG/../TKG/output_17406208911499/TestTargetResult";
[2025-02-27T02:04:21.908Z]
[2025-02-27T02:04:21.908Z] TEST SETUP:
[2025-02-27T02:04:21.908Z] Nothing to be done for setup.
[2025-02-27T02:04:21.908Z]
[2025-02-27T02:04:21.908Z] TESTING:
[2025-02-27T02:04:27.344Z] Using Spark's default log4j profile: org/apache/spark/log4j-defaults.properties
[2025-02-27T02:04:30.714Z] NOTE: 'movie-lens' benchmark uses Spark local executor with 3 (out of 3) threads.
[2025-02-27T02:04:36.209Z] Got 100004 ratings from 671 users on 9066 movies.
[2025-02-27T02:04:36.209Z] Training: 60056, validation: 20285, test: 19854
[2025-02-27T02:04:36.209Z] ====== movie-lens (apache-spark) [default], iteration 0 started ======
[2025-02-27T02:04:36.209Z] GC before operation: completed in 166.018 ms, heap usage 168.651 MB -> 36.618 MB.
[2025-02-27T02:04:50.527Z] RMSE (validation) = 3.62196895454876 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2025-02-27T02:04:57.283Z] RMSE (validation) = 2.1340923221100736 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2025-02-27T02:05:03.981Z] RMSE (validation) = 1.3105190428502682 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2025-02-27T02:05:10.725Z] RMSE (validation) = 0.9920028100571827 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2025-02-27T02:05:14.230Z] RMSE (validation) = 1.212631715629696 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2025-02-27T02:05:17.662Z] RMSE (validation) = 1.1025694034386657 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2025-02-27T02:05:22.145Z] RMSE (validation) = 0.9259652469249674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2025-02-27T02:05:25.653Z] RMSE (validation) = 0.8982232214592321 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2025-02-27T02:05:26.070Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9063003101263983.
[2025-02-27T02:05:26.070Z] The best model improves the baseline by 14.52%.
[2025-02-27T02:05:26.456Z] Movies recommended for you:
[2025-02-27T02:05:26.456Z] WARNING: This benchmark provides no result that can be validated.
[2025-02-27T02:05:26.456Z] There is no way to check that no silent failure occurred.
[2025-02-27T02:05:26.456Z] ====== movie-lens (apache-spark) [default], iteration 0 completed (49925.469 ms) ======
[2025-02-27T02:05:26.456Z] ====== movie-lens (apache-spark) [default], iteration 1 started ======
[2025-02-27T02:05:26.456Z] GC before operation: completed in 157.336 ms, heap usage 302.009 MB -> 46.923 MB.
[2025-02-27T02:05:33.105Z] RMSE (validation) = 3.6219689545487603 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2025-02-27T02:05:38.506Z] RMSE (validation) = 2.1340923221100736 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2025-02-27T02:05:45.101Z] RMSE (validation) = 1.3105190428502682 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2025-02-27T02:05:50.638Z] RMSE (validation) = 0.9920028100571827 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2025-02-27T02:05:53.300Z] RMSE (validation) = 1.212631715629696 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2025-02-27T02:05:57.659Z] RMSE (validation) = 1.1025694034386657 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2025-02-27T02:06:00.359Z] RMSE (validation) = 0.9259652469249674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2025-02-27T02:06:03.909Z] RMSE (validation) = 0.8982232214592321 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2025-02-27T02:06:03.909Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9063003101263983.
[2025-02-27T02:06:03.909Z] The best model improves the baseline by 14.52%.
[2025-02-27T02:06:04.312Z] Movies recommended for you:
[2025-02-27T02:06:04.312Z] WARNING: This benchmark provides no result that can be validated.
[2025-02-27T02:06:04.312Z] There is no way to check that no silent failure occurred.
[2025-02-27T02:06:04.312Z] ====== movie-lens (apache-spark) [default], iteration 1 completed (37812.409 ms) ======
[2025-02-27T02:06:04.312Z] ====== movie-lens (apache-spark) [default], iteration 2 started ======
[2025-02-27T02:06:04.312Z] GC before operation: completed in 198.407 ms, heap usage 121.464 MB -> 48.786 MB.
[2025-02-27T02:06:11.039Z] RMSE (validation) = 3.6219689545487603 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2025-02-27T02:06:17.617Z] RMSE (validation) = 2.1340923221100736 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2025-02-27T02:06:23.234Z] RMSE (validation) = 1.3105190428502682 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2025-02-27T02:06:28.691Z] RMSE (validation) = 0.9920028100571827 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2025-02-27T02:06:31.367Z] RMSE (validation) = 1.212631715629696 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2025-02-27T02:06:34.896Z] RMSE (validation) = 1.1025694034386657 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2025-02-27T02:06:38.405Z] RMSE (validation) = 0.9259652469249674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2025-02-27T02:06:41.091Z] RMSE (validation) = 0.8982232214592321 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2025-02-27T02:06:41.504Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9063003101263983.
[2025-02-27T02:06:41.504Z] The best model improves the baseline by 14.52%.
[2025-02-27T02:06:42.843Z] Movies recommended for you:
[2025-02-27T02:06:42.843Z] WARNING: This benchmark provides no result that can be validated.
[2025-02-27T02:06:42.843Z] There is no way to check that no silent failure occurred.
[2025-02-27T02:06:42.843Z] ====== movie-lens (apache-spark) [default], iteration 2 completed (38047.731 ms) ======
[2025-02-27T02:06:42.843Z] ====== movie-lens (apache-spark) [default], iteration 3 started ======
[2025-02-27T02:06:42.843Z] GC before operation: completed in 161.998 ms, heap usage 174.608 MB -> 49.111 MB.
[2025-02-27T02:06:48.201Z] RMSE (validation) = 3.6219689545487603 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2025-02-27T02:06:52.673Z] RMSE (validation) = 2.1340923221100736 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2025-02-27T02:06:58.133Z] RMSE (validation) = 1.3105190428502682 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2025-02-27T02:07:02.517Z] RMSE (validation) = 0.9920028100571827 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2025-02-27T02:07:06.001Z] RMSE (validation) = 1.212631715629696 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2025-02-27T02:07:09.492Z] RMSE (validation) = 1.1025694034386657 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2025-02-27T02:07:13.002Z] RMSE (validation) = 0.9259652469249674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2025-02-27T02:07:16.662Z] RMSE (validation) = 0.8982232214592321 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2025-02-27T02:07:16.662Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9063003101263983.
[2025-02-27T02:07:16.662Z] The best model improves the baseline by 14.52%.
[2025-02-27T02:07:17.127Z] Movies recommended for you:
[2025-02-27T02:07:17.127Z] WARNING: This benchmark provides no result that can be validated.
[2025-02-27T02:07:17.128Z] There is no way to check that no silent failure occurred.
[2025-02-27T02:07:17.128Z] ====== movie-lens (apache-spark) [default], iteration 3 completed (34176.412 ms) ======
[2025-02-27T02:07:17.128Z] ====== movie-lens (apache-spark) [default], iteration 4 started ======
[2025-02-27T02:07:17.128Z] GC before operation: completed in 176.746 ms, heap usage 146.918 MB -> 49.414 MB.
[2025-02-27T02:07:22.713Z] RMSE (validation) = 3.62196895454876 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2025-02-27T02:07:29.322Z] RMSE (validation) = 2.1340923221100736 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2025-02-27T02:07:33.704Z] RMSE (validation) = 1.3105190428502682 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2025-02-27T02:07:38.100Z] RMSE (validation) = 0.9920028100571827 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2025-02-27T02:07:40.113Z] RMSE (validation) = 1.212631715629696 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2025-02-27T02:07:44.430Z] RMSE (validation) = 1.1025694034386657 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2025-02-27T02:07:48.707Z] RMSE (validation) = 0.9259652469249674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2025-02-27T02:07:52.973Z] RMSE (validation) = 0.8982232214592321 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2025-02-27T02:07:52.973Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9063003101263983.
[2025-02-27T02:07:52.973Z] The best model improves the baseline by 14.52%.
[2025-02-27T02:07:53.373Z] Movies recommended for you:
[2025-02-27T02:07:53.373Z] WARNING: This benchmark provides no result that can be validated.
[2025-02-27T02:07:53.373Z] There is no way to check that no silent failure occurred.
[2025-02-27T02:07:53.373Z] ====== movie-lens (apache-spark) [default], iteration 4 completed (36267.097 ms) ======
[2025-02-27T02:07:53.373Z] ====== movie-lens (apache-spark) [default], iteration 5 started ======
[2025-02-27T02:07:53.373Z] GC before operation: completed in 184.887 ms, heap usage 417.695 MB -> 52.995 MB.
[2025-02-27T02:07:59.949Z] RMSE (validation) = 3.6219689545487603 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2025-02-27T02:08:07.934Z] RMSE (validation) = 2.1340923221100736 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2025-02-27T02:08:14.526Z] RMSE (validation) = 1.3105190428502682 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2025-02-27T02:08:20.007Z] RMSE (validation) = 0.9920028100571827 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2025-02-27T02:08:23.485Z] RMSE (validation) = 1.212631715629696 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2025-02-27T02:08:26.954Z] RMSE (validation) = 1.1025694034386657 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2025-02-27T02:08:31.245Z] RMSE (validation) = 0.9259652469249674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2025-02-27T02:08:33.913Z] RMSE (validation) = 0.8982232214592321 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2025-02-27T02:08:34.790Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9063003101263983.
[2025-02-27T02:08:34.790Z] The best model improves the baseline by 14.52%.
[2025-02-27T02:08:34.790Z] Movies recommended for you:
[2025-02-27T02:08:34.790Z] WARNING: This benchmark provides no result that can be validated.
[2025-02-27T02:08:34.790Z] There is no way to check that no silent failure occurred.
[2025-02-27T02:08:34.790Z] ====== movie-lens (apache-spark) [default], iteration 5 completed (41414.656 ms) ======
[2025-02-27T02:08:34.790Z] ====== movie-lens (apache-spark) [default], iteration 6 started ======
[2025-02-27T02:08:35.211Z] GC before operation: completed in 202.066 ms, heap usage 199.427 MB -> 49.572 MB.
[2025-02-27T02:08:43.403Z] RMSE (validation) = 3.62196895454876 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2025-02-27T02:08:48.815Z] RMSE (validation) = 2.1340923221100736 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2025-02-27T02:08:56.884Z] RMSE (validation) = 1.3105190428502682 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2025-02-27T02:09:03.559Z] RMSE (validation) = 0.9920028100571827 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2025-02-27T02:09:06.468Z] RMSE (validation) = 1.212631715629696 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2025-02-27T02:09:10.991Z] RMSE (validation) = 1.1025694034386657 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2025-02-27T02:09:15.329Z] RMSE (validation) = 0.9259652469249674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2025-02-27T02:09:18.819Z] RMSE (validation) = 0.8982232214592321 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2025-02-27T02:09:18.820Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9063003101263983.
[2025-02-27T02:09:18.820Z] The best model improves the baseline by 14.52%.
[2025-02-27T02:09:19.209Z] Movies recommended for you:
[2025-02-27T02:09:19.209Z] WARNING: This benchmark provides no result that can be validated.
[2025-02-27T02:09:19.209Z] There is no way to check that no silent failure occurred.
[2025-02-27T02:09:19.209Z] ====== movie-lens (apache-spark) [default], iteration 6 completed (43970.027 ms) ======
[2025-02-27T02:09:19.209Z] ====== movie-lens (apache-spark) [default], iteration 7 started ======
[2025-02-27T02:09:19.209Z] GC before operation: completed in 166.161 ms, heap usage 79.372 MB -> 51.345 MB.
[2025-02-27T02:09:27.264Z] RMSE (validation) = 3.62196895454876 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2025-02-27T02:09:32.594Z] RMSE (validation) = 2.1340923221100736 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2025-02-27T02:09:39.127Z] RMSE (validation) = 1.3105190428502682 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2025-02-27T02:09:44.548Z] RMSE (validation) = 0.9920028100571827 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2025-02-27T02:09:47.270Z] RMSE (validation) = 1.212631715629696 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2025-02-27T02:09:49.848Z] RMSE (validation) = 1.1025694034386657 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2025-02-27T02:09:54.106Z] RMSE (validation) = 0.9259652469249674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2025-02-27T02:09:57.552Z] RMSE (validation) = 0.8982232214592321 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2025-02-27T02:09:57.552Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9063003101263983.
[2025-02-27T02:09:57.931Z] The best model improves the baseline by 14.52%.
[2025-02-27T02:09:57.931Z] Movies recommended for you:
[2025-02-27T02:09:57.931Z] WARNING: This benchmark provides no result that can be validated.
[2025-02-27T02:09:57.931Z] There is no way to check that no silent failure occurred.
[2025-02-27T02:09:57.931Z] ====== movie-lens (apache-spark) [default], iteration 7 completed (38691.129 ms) ======
[2025-02-27T02:09:57.931Z] ====== movie-lens (apache-spark) [default], iteration 8 started ======
[2025-02-27T02:09:58.341Z] GC before operation: completed in 188.580 ms, heap usage 296.892 MB -> 50.140 MB.
[2025-02-27T02:10:04.895Z] RMSE (validation) = 3.6219689545487603 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2025-02-27T02:10:10.306Z] RMSE (validation) = 2.1340923221100736 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2025-02-27T02:10:13.493Z] RMSE (validation) = 1.3105190428502682 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2025-02-27T02:10:16.054Z] RMSE (validation) = 0.9920028100571827 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2025-02-27T02:10:17.334Z] RMSE (validation) = 1.212631715629696 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2025-02-27T02:10:18.612Z] RMSE (validation) = 1.1025694034386657 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2025-02-27T02:10:20.538Z] RMSE (validation) = 0.9259652469249674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2025-02-27T02:10:22.429Z] RMSE (validation) = 0.8982232214592321 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2025-02-27T02:10:23.292Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9063003101263983.
[2025-02-27T02:10:23.292Z] The best model improves the baseline by 14.52%.
[2025-02-27T02:10:23.688Z] Movies recommended for you:
[2025-02-27T02:10:23.688Z] WARNING: This benchmark provides no result that can be validated.
[2025-02-27T02:10:23.688Z] There is no way to check that no silent failure occurred.
[2025-02-27T02:10:23.688Z] ====== movie-lens (apache-spark) [default], iteration 8 completed (25381.581 ms) ======
[2025-02-27T02:10:23.688Z] ====== movie-lens (apache-spark) [default], iteration 9 started ======
[2025-02-27T02:10:23.688Z] GC before operation: completed in 147.575 ms, heap usage 81.574 MB -> 52.848 MB.
[2025-02-27T02:10:31.686Z] RMSE (validation) = 3.62196895454876 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2025-02-27T02:10:38.285Z] RMSE (validation) = 2.1340923221100736 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2025-02-27T02:10:44.865Z] RMSE (validation) = 1.3105190428502682 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2025-02-27T02:10:51.545Z] RMSE (validation) = 0.9920028100571827 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2025-02-27T02:10:54.130Z] RMSE (validation) = 1.212631715629696 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2025-02-27T02:10:56.796Z] RMSE (validation) = 1.1025694034386657 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2025-02-27T02:10:59.345Z] RMSE (validation) = 0.9259652469249674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2025-02-27T02:11:01.945Z] RMSE (validation) = 0.8982232214592321 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2025-02-27T02:11:02.327Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9063003101263983.
[2025-02-27T02:11:02.327Z] The best model improves the baseline by 14.52%.
[2025-02-27T02:11:02.777Z] Movies recommended for you:
[2025-02-27T02:11:02.777Z] WARNING: This benchmark provides no result that can be validated.
[2025-02-27T02:11:02.777Z] There is no way to check that no silent failure occurred.
[2025-02-27T02:11:02.777Z] ====== movie-lens (apache-spark) [default], iteration 9 completed (38874.937 ms) ======
[2025-02-27T02:11:02.777Z] ====== movie-lens (apache-spark) [default], iteration 10 started ======
[2025-02-27T02:11:02.777Z] GC before operation: completed in 128.845 ms, heap usage 457.434 MB -> 53.361 MB.
[2025-02-27T02:11:07.027Z] RMSE (validation) = 3.6219689545487603 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2025-02-27T02:11:11.171Z] RMSE (validation) = 2.1340923221100736 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2025-02-27T02:11:14.531Z] RMSE (validation) = 1.3105190428502682 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2025-02-27T02:11:17.029Z] RMSE (validation) = 0.9920028100571827 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2025-02-27T02:11:18.885Z] RMSE (validation) = 1.212631715629696 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2025-02-27T02:11:21.409Z] RMSE (validation) = 1.1025694034386657 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2025-02-27T02:11:22.737Z] RMSE (validation) = 0.9259652469249674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2025-02-27T02:11:24.624Z] RMSE (validation) = 0.8982232214592321 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2025-02-27T02:11:24.995Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9063003101263983.
[2025-02-27T02:11:24.995Z] The best model improves the baseline by 14.52%.
[2025-02-27T02:11:24.995Z] Movies recommended for you:
[2025-02-27T02:11:24.995Z] WARNING: This benchmark provides no result that can be validated.
[2025-02-27T02:11:24.995Z] There is no way to check that no silent failure occurred.
[2025-02-27T02:11:24.995Z] ====== movie-lens (apache-spark) [default], iteration 10 completed (22284.245 ms) ======
[2025-02-27T02:11:24.995Z] ====== movie-lens (apache-spark) [default], iteration 11 started ======
[2025-02-27T02:11:24.995Z] GC before operation: completed in 81.863 ms, heap usage 396.600 MB -> 53.073 MB.
[2025-02-27T02:11:32.991Z] RMSE (validation) = 3.62196895454876 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2025-02-27T02:11:38.394Z] RMSE (validation) = 2.1340923221100736 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2025-02-27T02:11:45.142Z] RMSE (validation) = 1.3105190428502682 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2025-02-27T02:11:53.179Z] RMSE (validation) = 0.9920028100571827 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2025-02-27T02:11:57.469Z] RMSE (validation) = 1.212631715629696 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2025-02-27T02:12:00.237Z] RMSE (validation) = 1.1025694034386657 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2025-02-27T02:12:04.470Z] RMSE (validation) = 0.9259652469249674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2025-02-27T02:12:07.144Z] RMSE (validation) = 0.8982232214592321 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2025-02-27T02:12:08.002Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9063003101263983.
[2025-02-27T02:12:08.002Z] The best model improves the baseline by 14.52%.
[2025-02-27T02:12:08.002Z] Movies recommended for you:
[2025-02-27T02:12:08.002Z] WARNING: This benchmark provides no result that can be validated.
[2025-02-27T02:12:08.002Z] There is no way to check that no silent failure occurred.
[2025-02-27T02:12:08.469Z] ====== movie-lens (apache-spark) [default], iteration 11 completed (43125.756 ms) ======
[2025-02-27T02:12:08.469Z] ====== movie-lens (apache-spark) [default], iteration 12 started ======
[2025-02-27T02:12:08.469Z] GC before operation: completed in 154.319 ms, heap usage 254.123 MB -> 49.944 MB.
[2025-02-27T02:12:13.758Z] RMSE (validation) = 3.62196895454876 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2025-02-27T02:12:17.012Z] RMSE (validation) = 2.1340923221100736 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2025-02-27T02:12:21.216Z] RMSE (validation) = 1.3105190428502682 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2025-02-27T02:12:25.395Z] RMSE (validation) = 0.9920028100571827 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2025-02-27T02:12:27.311Z] RMSE (validation) = 1.212631715629696 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2025-02-27T02:12:29.908Z] RMSE (validation) = 1.1025694034386657 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2025-02-27T02:12:32.467Z] RMSE (validation) = 0.9259652469249674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2025-02-27T02:12:35.196Z] RMSE (validation) = 0.8982232214592321 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2025-02-27T02:12:35.597Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9063003101263983.
[2025-02-27T02:12:35.597Z] The best model improves the baseline by 14.52%.
[2025-02-27T02:12:36.014Z] Movies recommended for you:
[2025-02-27T02:12:36.014Z] WARNING: This benchmark provides no result that can be validated.
[2025-02-27T02:12:36.014Z] There is no way to check that no silent failure occurred.
[2025-02-27T02:12:36.014Z] ====== movie-lens (apache-spark) [default], iteration 12 completed (27511.172 ms) ======
[2025-02-27T02:12:36.014Z] ====== movie-lens (apache-spark) [default], iteration 13 started ======
[2025-02-27T02:12:36.014Z] GC before operation: completed in 124.327 ms, heap usage 270.819 MB -> 50.101 MB.
[2025-02-27T02:12:45.793Z] RMSE (validation) = 3.62196895454876 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2025-02-27T02:12:51.198Z] RMSE (validation) = 2.1340923221100736 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2025-02-27T02:12:59.201Z] RMSE (validation) = 1.3105190428502682 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2025-02-27T02:13:07.215Z] RMSE (validation) = 0.9920028100571827 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2025-02-27T02:13:10.637Z] RMSE (validation) = 1.212631715629696 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2025-02-27T02:13:16.030Z] RMSE (validation) = 1.1025694034386657 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2025-02-27T02:13:20.362Z] RMSE (validation) = 0.9259652469249674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2025-02-27T02:13:23.856Z] RMSE (validation) = 0.8982232214592321 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2025-02-27T02:13:23.856Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9063003101263983.
[2025-02-27T02:13:23.856Z] The best model improves the baseline by 14.52%.
[2025-02-27T02:13:24.237Z] Movies recommended for you:
[2025-02-27T02:13:24.237Z] WARNING: This benchmark provides no result that can be validated.
[2025-02-27T02:13:24.237Z] There is no way to check that no silent failure occurred.
[2025-02-27T02:13:24.237Z] ====== movie-lens (apache-spark) [default], iteration 13 completed (48167.100 ms) ======
[2025-02-27T02:13:24.237Z] ====== movie-lens (apache-spark) [default], iteration 14 started ======
[2025-02-27T02:13:24.237Z] GC before operation: completed in 193.445 ms, heap usage 184.551 MB -> 49.855 MB.
[2025-02-27T02:13:32.304Z] RMSE (validation) = 3.6219689545487603 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2025-02-27T02:13:38.870Z] RMSE (validation) = 2.1340923221100736 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2025-02-27T02:13:46.782Z] RMSE (validation) = 1.3105190428502682 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2025-02-27T02:13:52.246Z] RMSE (validation) = 0.9920028100571827 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2025-02-27T02:13:55.594Z] RMSE (validation) = 1.212631715629696 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2025-02-27T02:13:59.940Z] RMSE (validation) = 1.1025694034386657 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2025-02-27T02:14:03.365Z] RMSE (validation) = 0.9259652469249674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2025-02-27T02:14:06.798Z] RMSE (validation) = 0.8982232214592321 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2025-02-27T02:14:07.622Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9063003101263983.
[2025-02-27T02:14:07.622Z] The best model improves the baseline by 14.52%.
[2025-02-27T02:14:07.622Z] Movies recommended for you:
[2025-02-27T02:14:07.622Z] WARNING: This benchmark provides no result that can be validated.
[2025-02-27T02:14:07.622Z] There is no way to check that no silent failure occurred.
[2025-02-27T02:14:07.622Z] ====== movie-lens (apache-spark) [default], iteration 14 completed (43236.355 ms) ======
[2025-02-27T02:14:07.622Z] ====== movie-lens (apache-spark) [default], iteration 15 started ======
[2025-02-27T02:14:07.997Z] GC before operation: completed in 218.265 ms, heap usage 273.720 MB -> 50.109 MB.
[2025-02-27T02:14:15.966Z] RMSE (validation) = 3.62196895454876 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2025-02-27T02:14:22.734Z] RMSE (validation) = 2.1340923221100736 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2025-02-27T02:14:29.224Z] RMSE (validation) = 1.3105190428502682 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2025-02-27T02:14:35.862Z] RMSE (validation) = 0.9920028100571827 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2025-02-27T02:14:38.446Z] RMSE (validation) = 1.212631715629696 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2025-02-27T02:14:42.677Z] RMSE (validation) = 1.1025694034386657 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2025-02-27T02:14:48.001Z] RMSE (validation) = 0.9259652469249674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2025-02-27T02:14:52.351Z] RMSE (validation) = 0.8982232214592321 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2025-02-27T02:14:52.769Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9063003101263983.
[2025-02-27T02:14:52.769Z] The best model improves the baseline by 14.52%.
[2025-02-27T02:14:53.189Z] Movies recommended for you:
[2025-02-27T02:14:53.189Z] WARNING: This benchmark provides no result that can be validated.
[2025-02-27T02:14:53.189Z] There is no way to check that no silent failure occurred.
[2025-02-27T02:14:53.189Z] ====== movie-lens (apache-spark) [default], iteration 15 completed (45142.712 ms) ======
[2025-02-27T02:14:53.189Z] ====== movie-lens (apache-spark) [default], iteration 16 started ======
[2025-02-27T02:14:53.189Z] GC before operation: completed in 186.819 ms, heap usage 339.129 MB -> 50.185 MB.
[2025-02-27T02:15:01.297Z] RMSE (validation) = 3.62196895454876 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2025-02-27T02:15:08.063Z] RMSE (validation) = 2.1340923221100736 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2025-02-27T02:15:16.084Z] RMSE (validation) = 1.3105190428502682 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2025-02-27T02:15:22.587Z] RMSE (validation) = 0.9920028100571827 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2025-02-27T02:15:25.915Z] RMSE (validation) = 1.212631715629696 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2025-02-27T02:15:30.133Z] RMSE (validation) = 1.1025694034386657 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2025-02-27T02:15:33.610Z] RMSE (validation) = 0.9259652469249674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2025-02-27T02:15:37.876Z] RMSE (validation) = 0.8982232214592321 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2025-02-27T02:15:37.876Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9063003101263983.
[2025-02-27T02:15:37.876Z] The best model improves the baseline by 14.52%.
[2025-02-27T02:15:37.876Z] Movies recommended for you:
[2025-02-27T02:15:37.876Z] WARNING: This benchmark provides no result that can be validated.
[2025-02-27T02:15:37.876Z] There is no way to check that no silent failure occurred.
[2025-02-27T02:15:37.876Z] ====== movie-lens (apache-spark) [default], iteration 16 completed (44826.656 ms) ======
[2025-02-27T02:15:37.876Z] ====== movie-lens (apache-spark) [default], iteration 17 started ======
[2025-02-27T02:15:38.354Z] GC before operation: completed in 160.610 ms, heap usage 188.137 MB -> 49.894 MB.
[2025-02-27T02:15:47.868Z] RMSE (validation) = 3.6219689545487603 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2025-02-27T02:15:54.507Z] RMSE (validation) = 2.1340923221100736 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2025-02-27T02:16:01.243Z] RMSE (validation) = 1.3105190428502682 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2025-02-27T02:16:07.879Z] RMSE (validation) = 0.9920028100571827 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2025-02-27T02:16:11.410Z] RMSE (validation) = 1.212631715629696 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2025-02-27T02:16:16.771Z] RMSE (validation) = 1.1025694034386657 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2025-02-27T02:16:20.133Z] RMSE (validation) = 0.9259652469249674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2025-02-27T02:16:24.403Z] RMSE (validation) = 0.8982232214592321 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2025-02-27T02:16:24.792Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9063003101263983.
[2025-02-27T02:16:24.792Z] The best model improves the baseline by 14.52%.
[2025-02-27T02:16:24.792Z] Movies recommended for you:
[2025-02-27T02:16:24.792Z] WARNING: This benchmark provides no result that can be validated.
[2025-02-27T02:16:24.792Z] There is no way to check that no silent failure occurred.
[2025-02-27T02:16:24.792Z] ====== movie-lens (apache-spark) [default], iteration 17 completed (46804.423 ms) ======
[2025-02-27T02:16:24.792Z] ====== movie-lens (apache-spark) [default], iteration 18 started ======
[2025-02-27T02:16:25.242Z] GC before operation: completed in 191.522 ms, heap usage 347.028 MB -> 50.100 MB.
[2025-02-27T02:16:33.397Z] RMSE (validation) = 3.6219689545487603 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2025-02-27T02:16:40.122Z] RMSE (validation) = 2.1340923221100736 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2025-02-27T02:16:48.192Z] RMSE (validation) = 1.3105190428502682 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2025-02-27T02:16:54.804Z] RMSE (validation) = 0.9920028100571827 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2025-02-27T02:16:58.244Z] RMSE (validation) = 1.212631715629696 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2025-02-27T02:17:02.642Z] RMSE (validation) = 1.1025694034386657 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2025-02-27T02:17:06.254Z] RMSE (validation) = 0.9259652469249674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2025-02-27T02:17:09.926Z] RMSE (validation) = 0.8982232214592321 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2025-02-27T02:17:09.926Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9063003101263983.
[2025-02-27T02:17:09.926Z] The best model improves the baseline by 14.52%.
[2025-02-27T02:17:10.357Z] Movies recommended for you:
[2025-02-27T02:17:10.357Z] WARNING: This benchmark provides no result that can be validated.
[2025-02-27T02:17:10.357Z] There is no way to check that no silent failure occurred.
[2025-02-27T02:17:10.357Z] ====== movie-lens (apache-spark) [default], iteration 18 completed (45123.155 ms) ======
[2025-02-27T02:17:10.357Z] ====== movie-lens (apache-spark) [default], iteration 19 started ======
[2025-02-27T02:17:10.357Z] GC before operation: completed in 147.153 ms, heap usage 66.472 MB -> 53.496 MB.
[2025-02-27T02:17:14.782Z] 25/02/26 18:17:14 WARN BlockManager: Asked to remove block rdd_48448_1, which does not exist
[2025-02-27T02:17:17.618Z] RMSE (validation) = 3.6219689545487603 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2025-02-27T02:17:25.642Z] RMSE (validation) = 2.1340923221100736 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2025-02-27T02:17:32.335Z] RMSE (validation) = 1.3105190428502682 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2025-02-27T02:17:38.916Z] RMSE (validation) = 0.9920028100571827 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2025-02-27T02:17:43.203Z] RMSE (validation) = 1.212631715629696 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2025-02-27T02:17:46.605Z] RMSE (validation) = 1.1025694034386657 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2025-02-27T02:17:50.902Z] RMSE (validation) = 0.9259652469249674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2025-02-27T02:17:55.155Z] RMSE (validation) = 0.8982232214592321 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2025-02-27T02:17:55.155Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9063003101263983.
[2025-02-27T02:17:55.155Z] The best model improves the baseline by 14.52%.
[2025-02-27T02:17:55.959Z] Movies recommended for you:
[2025-02-27T02:17:55.959Z] WARNING: This benchmark provides no result that can be validated.
[2025-02-27T02:17:55.959Z] There is no way to check that no silent failure occurred.
[2025-02-27T02:17:55.959Z] ====== movie-lens (apache-spark) [default], iteration 19 completed (45211.604 ms) ======
[2025-02-27T02:17:57.894Z] -----------------------------------
[2025-02-27T02:17:57.894Z] renaissance-movie-lens_0_PASSED
[2025-02-27T02:17:57.894Z] -----------------------------------
[2025-02-27T02:17:57.894Z]
[2025-02-27T02:17:57.894Z] TEST TEARDOWN:
[2025-02-27T02:17:57.894Z] Nothing to be done for teardown.
[2025-02-27T02:17:57.894Z] renaissance-movie-lens_0 Finish Time: Wed Feb 26 18:17:56 2025 Epoch Time (ms): 1740622676998