renaissance-movie-lens_0
[2025-02-06T03:01:31.907Z] Running test renaissance-movie-lens_0 ...
[2025-02-06T03:01:31.907Z] ===============================================
[2025-02-06T03:01:31.907Z] renaissance-movie-lens_0 Start Time: Thu Feb 6 03:01:31 2025 Epoch Time (ms): 1738810891388
[2025-02-06T03:01:31.907Z] variation: NoOptions
[2025-02-06T03:01:31.907Z] JVM_OPTIONS:
[2025-02-06T03:01:31.907Z] { \
[2025-02-06T03:01:31.907Z] echo ""; echo "TEST SETUP:"; \
[2025-02-06T03:01:31.907Z] echo "Nothing to be done for setup."; \
[2025-02-06T03:01:31.907Z] mkdir -p "/Users/jenkins/workspace/Test_openjdk11_hs_extended.perf_x86-64_mac_testList_1/aqa-tests/TKG/../TKG/output_1738810181584/renaissance-movie-lens_0"; \
[2025-02-06T03:01:31.907Z] cd "/Users/jenkins/workspace/Test_openjdk11_hs_extended.perf_x86-64_mac_testList_1/aqa-tests/TKG/../TKG/output_1738810181584/renaissance-movie-lens_0"; \
[2025-02-06T03:01:31.907Z] echo ""; echo "TESTING:"; \
[2025-02-06T03:01:31.907Z] "/Users/jenkins/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/jenkins/workspace/Test_openjdk11_hs_extended.perf_x86-64_mac_testList_1/aqa-tests/TKG/../../jvmtest/perf/renaissance/renaissance.jar" --json ""/Users/jenkins/workspace/Test_openjdk11_hs_extended.perf_x86-64_mac_testList_1/aqa-tests/TKG/../TKG/output_1738810181584/renaissance-movie-lens_0"/movie-lens.json" movie-lens; \
[2025-02-06T03:01:31.907Z] if [ $? -eq 0 ]; then echo "-----------------------------------"; echo "renaissance-movie-lens_0""_PASSED"; echo "-----------------------------------"; cd /Users/jenkins/workspace/Test_openjdk11_hs_extended.perf_x86-64_mac_testList_1/aqa-tests/TKG/..; rm -f -r "/Users/jenkins/workspace/Test_openjdk11_hs_extended.perf_x86-64_mac_testList_1/aqa-tests/TKG/../TKG/output_1738810181584/renaissance-movie-lens_0"; else echo "-----------------------------------"; echo "renaissance-movie-lens_0""_FAILED"; echo "-----------------------------------"; fi; \
[2025-02-06T03:01:31.907Z] echo ""; echo "TEST TEARDOWN:"; \
[2025-02-06T03:01:31.907Z] echo "Nothing to be done for teardown."; \
[2025-02-06T03:01:31.907Z] } 2>&1 | tee -a "/Users/jenkins/workspace/Test_openjdk11_hs_extended.perf_x86-64_mac_testList_1/aqa-tests/TKG/../TKG/output_1738810181584/TestTargetResult";
[2025-02-06T03:01:31.907Z]
[2025-02-06T03:01:31.907Z] TEST SETUP:
[2025-02-06T03:01:31.908Z] Nothing to be done for setup.
[2025-02-06T03:01:31.908Z]
[2025-02-06T03:01:31.908Z] TESTING:
[2025-02-06T03:01:35.889Z] Using Spark's default log4j profile: org/apache/spark/log4j-defaults.properties
[2025-02-06T03:01:37.662Z] NOTE: 'movie-lens' benchmark uses Spark local executor with 8 (out of 8) threads.
[2025-02-06T03:01:41.643Z] Got 100004 ratings from 671 users on 9066 movies.
[2025-02-06T03:01:41.643Z] Training: 60056, validation: 20285, test: 19854
[2025-02-06T03:01:41.643Z] ====== movie-lens (apache-spark) [default], iteration 0 started ======
[2025-02-06T03:01:41.643Z] GC before operation: completed in 53.922 ms, heap usage 98.231 MB -> 37.207 MB.
[2025-02-06T03:02:15.513Z] RMSE (validation) = 3.6219689545487506 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2025-02-06T03:02:35.083Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2025-02-06T03:03:03.381Z] RMSE (validation) = 1.3105189674610933 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2025-02-06T03:03:19.640Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2025-02-06T03:03:33.172Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2025-02-06T03:03:40.745Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2025-02-06T03:03:54.238Z] RMSE (validation) = 0.927571739133814 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2025-02-06T03:04:03.442Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2025-02-06T03:04:03.442Z] 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.
[2025-02-06T03:04:03.442Z] The best model improves the baseline by 14.43%.
[2025-02-06T03:04:03.442Z] Movies recommended for you:
[2025-02-06T03:04:03.442Z] WARNING: This benchmark provides no result that can be validated.
[2025-02-06T03:04:03.443Z] There is no way to check that no silent failure occurred.
[2025-02-06T03:04:03.443Z] ====== movie-lens (apache-spark) [default], iteration 0 completed (141589.911 ms) ======
[2025-02-06T03:04:03.443Z] ====== movie-lens (apache-spark) [default], iteration 1 started ======
[2025-02-06T03:04:03.443Z] GC before operation: completed in 184.550 ms, heap usage 1.003 GB -> 52.163 MB.
[2025-02-06T03:04:23.038Z] RMSE (validation) = 3.6219689545487506 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2025-02-06T03:04:42.617Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2025-02-06T03:05:06.171Z] RMSE (validation) = 1.3105189674610933 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2025-02-06T03:05:22.436Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2025-02-06T03:05:31.657Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2025-02-06T03:05:40.864Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2025-02-06T03:05:54.393Z] RMSE (validation) = 0.927571739133814 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2025-02-06T03:06:01.948Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2025-02-06T03:06:01.948Z] 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.
[2025-02-06T03:06:01.948Z] The best model improves the baseline by 14.43%.
[2025-02-06T03:06:01.948Z] Movies recommended for you:
[2025-02-06T03:06:01.948Z] WARNING: This benchmark provides no result that can be validated.
[2025-02-06T03:06:01.948Z] There is no way to check that no silent failure occurred.
[2025-02-06T03:06:01.948Z] ====== movie-lens (apache-spark) [default], iteration 1 completed (118356.182 ms) ======
[2025-02-06T03:06:01.948Z] ====== movie-lens (apache-spark) [default], iteration 2 started ======
[2025-02-06T03:06:01.948Z] GC before operation: completed in 209.935 ms, heap usage 826.012 MB -> 75.982 MB.
[2025-02-06T03:06:25.599Z] RMSE (validation) = 3.6219689545487506 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2025-02-06T03:06:45.170Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2025-02-06T03:07:13.419Z] RMSE (validation) = 1.3105189674610933 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2025-02-06T03:07:26.919Z] RMSE (validation) = 1.004540774800519 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2025-02-06T03:07:36.148Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2025-02-06T03:07:45.350Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2025-02-06T03:07:56.516Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2025-02-06T03:08:05.813Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2025-02-06T03:08:05.813Z] 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.
[2025-02-06T03:08:05.813Z] The best model improves the baseline by 14.43%.
[2025-02-06T03:08:05.813Z] Movies recommended for you:
[2025-02-06T03:08:05.813Z] WARNING: This benchmark provides no result that can be validated.
[2025-02-06T03:08:05.813Z] There is no way to check that no silent failure occurred.
[2025-02-06T03:08:05.813Z] ====== movie-lens (apache-spark) [default], iteration 2 completed (123731.119 ms) ======
[2025-02-06T03:08:05.813Z] ====== movie-lens (apache-spark) [default], iteration 3 started ======
[2025-02-06T03:08:05.813Z] GC before operation: completed in 192.099 ms, heap usage 1.084 GB -> 57.353 MB.
[2025-02-06T03:08:25.388Z] RMSE (validation) = 3.621968954548751 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2025-02-06T03:08:44.965Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2025-02-06T03:09:08.529Z] RMSE (validation) = 1.3105189674610933 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2025-02-06T03:09:28.112Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2025-02-06T03:09:35.680Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2025-02-06T03:09:46.881Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2025-02-06T03:10:00.375Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2025-02-06T03:10:07.944Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2025-02-06T03:10:08.302Z] 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.
[2025-02-06T03:10:08.302Z] The best model improves the baseline by 14.43%.
[2025-02-06T03:10:08.660Z] Movies recommended for you:
[2025-02-06T03:10:08.660Z] WARNING: This benchmark provides no result that can be validated.
[2025-02-06T03:10:08.660Z] There is no way to check that no silent failure occurred.
[2025-02-06T03:10:08.660Z] ====== movie-lens (apache-spark) [default], iteration 3 completed (122630.601 ms) ======
[2025-02-06T03:10:08.660Z] ====== movie-lens (apache-spark) [default], iteration 4 started ======
[2025-02-06T03:10:08.660Z] GC before operation: completed in 177.630 ms, heap usage 266.062 MB -> 57.544 MB.
[2025-02-06T03:10:32.197Z] RMSE (validation) = 3.6219689545487506 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2025-02-06T03:10:51.773Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2025-02-06T03:11:20.821Z] RMSE (validation) = 1.3105189674610935 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2025-02-06T03:11:34.318Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2025-02-06T03:11:45.490Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2025-02-06T03:11:54.702Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2025-02-06T03:12:05.879Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2025-02-06T03:12:15.113Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2025-02-06T03:12:15.471Z] 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.
[2025-02-06T03:12:15.471Z] The best model improves the baseline by 14.43%.
[2025-02-06T03:12:15.471Z] Movies recommended for you:
[2025-02-06T03:12:15.471Z] WARNING: This benchmark provides no result that can be validated.
[2025-02-06T03:12:15.471Z] There is no way to check that no silent failure occurred.
[2025-02-06T03:12:15.471Z] ====== movie-lens (apache-spark) [default], iteration 4 completed (126764.207 ms) ======
[2025-02-06T03:12:15.471Z] ====== movie-lens (apache-spark) [default], iteration 5 started ======
[2025-02-06T03:12:15.830Z] GC before operation: completed in 158.229 ms, heap usage 651.003 MB -> 57.769 MB.
[2025-02-06T03:12:39.357Z] RMSE (validation) = 3.6219689545487506 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2025-02-06T03:12:58.942Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2025-02-06T03:13:22.469Z] RMSE (validation) = 1.3105189674610935 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2025-02-06T03:13:38.742Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2025-02-06T03:13:47.961Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2025-02-06T03:13:57.187Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2025-02-06T03:14:08.379Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2025-02-06T03:14:17.611Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2025-02-06T03:14:17.611Z] 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.
[2025-02-06T03:14:17.611Z] The best model improves the baseline by 14.43%.
[2025-02-06T03:14:17.611Z] Movies recommended for you:
[2025-02-06T03:14:17.611Z] WARNING: This benchmark provides no result that can be validated.
[2025-02-06T03:14:17.611Z] There is no way to check that no silent failure occurred.
[2025-02-06T03:14:17.611Z] ====== movie-lens (apache-spark) [default], iteration 5 completed (121985.216 ms) ======
[2025-02-06T03:14:17.611Z] ====== movie-lens (apache-spark) [default], iteration 6 started ======
[2025-02-06T03:14:17.611Z] GC before operation: completed in 131.367 ms, heap usage 636.460 MB -> 57.708 MB.
[2025-02-06T03:14:37.186Z] RMSE (validation) = 3.621968954548751 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2025-02-06T03:14:56.810Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2025-02-06T03:15:25.071Z] RMSE (validation) = 1.3105189674610935 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2025-02-06T03:15:38.579Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2025-02-06T03:15:47.823Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2025-02-06T03:15:57.053Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2025-02-06T03:16:10.560Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2025-02-06T03:16:18.160Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2025-02-06T03:16:18.160Z] 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.
[2025-02-06T03:16:18.160Z] The best model improves the baseline by 14.43%.
[2025-02-06T03:16:18.160Z] Movies recommended for you:
[2025-02-06T03:16:18.160Z] WARNING: This benchmark provides no result that can be validated.
[2025-02-06T03:16:18.160Z] There is no way to check that no silent failure occurred.
[2025-02-06T03:16:18.160Z] ====== movie-lens (apache-spark) [default], iteration 6 completed (120055.550 ms) ======
[2025-02-06T03:16:18.160Z] ====== movie-lens (apache-spark) [default], iteration 7 started ======
[2025-02-06T03:16:18.160Z] GC before operation: completed in 130.914 ms, heap usage 497.740 MB -> 57.868 MB.
[2025-02-06T03:16:37.754Z] RMSE (validation) = 3.6219689545487506 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2025-02-06T03:16:57.343Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2025-02-06T03:17:20.866Z] RMSE (validation) = 1.3105189674610935 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2025-02-06T03:17:37.132Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2025-02-06T03:17:46.355Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2025-02-06T03:17:55.623Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2025-02-06T03:18:12.949Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2025-02-06T03:18:20.504Z] RMSE (validation) = 0.9001440981626694 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2025-02-06T03:18:20.875Z] 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.
[2025-02-06T03:18:20.875Z] The best model improves the baseline by 14.43%.
[2025-02-06T03:18:20.875Z] Movies recommended for you:
[2025-02-06T03:18:20.875Z] WARNING: This benchmark provides no result that can be validated.
[2025-02-06T03:18:20.875Z] There is no way to check that no silent failure occurred.
[2025-02-06T03:18:20.875Z] ====== movie-lens (apache-spark) [default], iteration 7 completed (123002.296 ms) ======
[2025-02-06T03:18:20.875Z] ====== movie-lens (apache-spark) [default], iteration 8 started ======
[2025-02-06T03:18:21.233Z] GC before operation: completed in 145.002 ms, heap usage 364.204 MB -> 58.079 MB.
[2025-02-06T03:18:44.775Z] RMSE (validation) = 3.6219689545487506 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2025-02-06T03:19:04.355Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2025-02-06T03:19:27.876Z] RMSE (validation) = 1.3105189674610933 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2025-02-06T03:19:44.149Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2025-02-06T03:19:53.387Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2025-02-06T03:20:02.596Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2025-02-06T03:20:16.089Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2025-02-06T03:20:23.680Z] RMSE (validation) = 0.9001440981626694 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2025-02-06T03:20:23.680Z] 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.
[2025-02-06T03:20:23.680Z] The best model improves the baseline by 14.43%.
[2025-02-06T03:20:23.680Z] Movies recommended for you:
[2025-02-06T03:20:23.680Z] WARNING: This benchmark provides no result that can be validated.
[2025-02-06T03:20:23.680Z] There is no way to check that no silent failure occurred.
[2025-02-06T03:20:23.680Z] ====== movie-lens (apache-spark) [default], iteration 8 completed (122316.372 ms) ======
[2025-02-06T03:20:23.680Z] ====== movie-lens (apache-spark) [default], iteration 9 started ======
[2025-02-06T03:20:23.680Z] GC before operation: completed in 169.038 ms, heap usage 984.893 MB -> 61.481 MB.
[2025-02-06T03:20:43.342Z] RMSE (validation) = 3.6219689545487506 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2025-02-06T03:21:02.930Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2025-02-06T03:21:26.439Z] RMSE (validation) = 1.3105189674610933 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2025-02-06T03:21:42.745Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2025-02-06T03:21:56.258Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2025-02-06T03:22:03.813Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2025-02-06T03:22:17.294Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2025-02-06T03:22:26.489Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2025-02-06T03:22:26.489Z] 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.
[2025-02-06T03:22:26.489Z] The best model improves the baseline by 14.43%.
[2025-02-06T03:22:27.144Z] Movies recommended for you:
[2025-02-06T03:22:27.144Z] WARNING: This benchmark provides no result that can be validated.
[2025-02-06T03:22:27.144Z] There is no way to check that no silent failure occurred.
[2025-02-06T03:22:27.144Z] ====== movie-lens (apache-spark) [default], iteration 9 completed (123484.813 ms) ======
[2025-02-06T03:22:27.144Z] ====== movie-lens (apache-spark) [default], iteration 10 started ======
[2025-02-06T03:22:27.144Z] GC before operation: completed in 182.479 ms, heap usage 700.756 MB -> 58.994 MB.
[2025-02-06T03:22:46.708Z] RMSE (validation) = 3.621968954548751 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2025-02-06T03:23:10.227Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2025-02-06T03:23:33.766Z] RMSE (validation) = 1.3105189674610933 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2025-02-06T03:23:50.031Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2025-02-06T03:24:01.177Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2025-02-06T03:24:08.736Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2025-02-06T03:24:22.224Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2025-02-06T03:24:29.791Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2025-02-06T03:24:29.791Z] 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.
[2025-02-06T03:24:29.791Z] The best model improves the baseline by 14.43%.
[2025-02-06T03:24:29.791Z] Movies recommended for you:
[2025-02-06T03:24:29.791Z] WARNING: This benchmark provides no result that can be validated.
[2025-02-06T03:24:29.791Z] There is no way to check that no silent failure occurred.
[2025-02-06T03:24:29.791Z] ====== movie-lens (apache-spark) [default], iteration 10 completed (122569.098 ms) ======
[2025-02-06T03:24:29.791Z] ====== movie-lens (apache-spark) [default], iteration 11 started ======
[2025-02-06T03:24:29.791Z] GC before operation: completed in 133.491 ms, heap usage 356.304 MB -> 58.757 MB.
[2025-02-06T03:24:49.425Z] RMSE (validation) = 3.6219689545487506 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2025-02-06T03:25:09.026Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2025-02-06T03:25:32.564Z] RMSE (validation) = 1.3105189674610933 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2025-02-06T03:25:46.124Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2025-02-06T03:25:57.302Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2025-02-06T03:26:06.517Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2025-02-06T03:26:20.031Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2025-02-06T03:26:31.231Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2025-02-06T03:26:31.231Z] 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.
[2025-02-06T03:26:31.231Z] The best model improves the baseline by 14.43%.
[2025-02-06T03:26:31.231Z] Movies recommended for you:
[2025-02-06T03:26:31.231Z] WARNING: This benchmark provides no result that can be validated.
[2025-02-06T03:26:31.231Z] There is no way to check that no silent failure occurred.
[2025-02-06T03:26:31.231Z] ====== movie-lens (apache-spark) [default], iteration 11 completed (120505.896 ms) ======
[2025-02-06T03:26:31.231Z] ====== movie-lens (apache-spark) [default], iteration 12 started ======
[2025-02-06T03:26:31.231Z] GC before operation: completed in 164.999 ms, heap usage 706.328 MB -> 58.959 MB.
[2025-02-06T03:26:56.625Z] RMSE (validation) = 3.621968954548751 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2025-02-06T03:27:20.137Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2025-02-06T03:27:43.660Z] RMSE (validation) = 1.3105189674610933 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2025-02-06T03:27:59.935Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2025-02-06T03:28:09.149Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2025-02-06T03:28:20.321Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2025-02-06T03:28:33.832Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2025-02-06T03:28:43.037Z] RMSE (validation) = 0.9001440981626694 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2025-02-06T03:28:43.037Z] 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.
[2025-02-06T03:28:43.398Z] The best model improves the baseline by 14.43%.
[2025-02-06T03:28:43.398Z] Movies recommended for you:
[2025-02-06T03:28:43.398Z] WARNING: This benchmark provides no result that can be validated.
[2025-02-06T03:28:43.398Z] There is no way to check that no silent failure occurred.
[2025-02-06T03:28:43.398Z] ====== movie-lens (apache-spark) [default], iteration 12 completed (132731.267 ms) ======
[2025-02-06T03:28:43.398Z] ====== movie-lens (apache-spark) [default], iteration 13 started ======
[2025-02-06T03:28:43.398Z] GC before operation: completed in 137.473 ms, heap usage 286.800 MB -> 62.643 MB.
[2025-02-06T03:29:06.925Z] RMSE (validation) = 3.621968954548751 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2025-02-06T03:29:23.217Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2025-02-06T03:29:46.753Z] RMSE (validation) = 1.3105189674610935 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2025-02-06T03:30:03.053Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2025-02-06T03:30:12.252Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2025-02-06T03:30:21.463Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2025-02-06T03:30:34.959Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2025-02-06T03:30:42.524Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2025-02-06T03:30:42.524Z] 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.
[2025-02-06T03:30:42.524Z] The best model improves the baseline by 14.43%.
[2025-02-06T03:30:42.524Z] Movies recommended for you:
[2025-02-06T03:30:42.524Z] WARNING: This benchmark provides no result that can be validated.
[2025-02-06T03:30:42.524Z] There is no way to check that no silent failure occurred.
[2025-02-06T03:30:42.524Z] ====== movie-lens (apache-spark) [default], iteration 13 completed (118970.844 ms) ======
[2025-02-06T03:30:42.524Z] ====== movie-lens (apache-spark) [default], iteration 14 started ======
[2025-02-06T03:30:42.524Z] GC before operation: completed in 138.888 ms, heap usage 772.377 MB -> 58.844 MB.
[2025-02-06T03:31:02.097Z] RMSE (validation) = 3.6219689545487506 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2025-02-06T03:31:25.621Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2025-02-06T03:31:49.152Z] RMSE (validation) = 1.3105189674610933 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2025-02-06T03:32:02.647Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2025-02-06T03:32:13.812Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2025-02-06T03:32:23.054Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2025-02-06T03:32:34.225Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2025-02-06T03:32:43.438Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2025-02-06T03:32:43.438Z] 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.
[2025-02-06T03:32:43.438Z] The best model improves the baseline by 14.43%.
[2025-02-06T03:32:43.438Z] Movies recommended for you:
[2025-02-06T03:32:43.438Z] WARNING: This benchmark provides no result that can be validated.
[2025-02-06T03:32:43.438Z] There is no way to check that no silent failure occurred.
[2025-02-06T03:32:43.438Z] ====== movie-lens (apache-spark) [default], iteration 14 completed (120582.604 ms) ======
[2025-02-06T03:32:43.438Z] ====== movie-lens (apache-spark) [default], iteration 15 started ======
[2025-02-06T03:32:43.438Z] GC before operation: completed in 133.869 ms, heap usage 341.442 MB -> 59.038 MB.
[2025-02-06T03:33:03.011Z] RMSE (validation) = 3.6219689545487506 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2025-02-06T03:33:22.592Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2025-02-06T03:33:46.131Z] RMSE (validation) = 1.3105189674610935 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2025-02-06T03:34:02.404Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2025-02-06T03:34:19.036Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2025-02-06T03:34:28.236Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2025-02-06T03:34:43.438Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2025-02-06T03:34:52.665Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2025-02-06T03:34:52.665Z] 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.
[2025-02-06T03:34:52.665Z] The best model improves the baseline by 14.43%.
[2025-02-06T03:34:53.028Z] Movies recommended for you:
[2025-02-06T03:34:53.028Z] WARNING: This benchmark provides no result that can be validated.
[2025-02-06T03:34:53.028Z] There is no way to check that no silent failure occurred.
[2025-02-06T03:34:53.028Z] ====== movie-lens (apache-spark) [default], iteration 15 completed (129555.521 ms) ======
[2025-02-06T03:34:53.028Z] ====== movie-lens (apache-spark) [default], iteration 16 started ======
[2025-02-06T03:34:53.028Z] GC before operation: completed in 177.658 ms, heap usage 208.677 MB -> 58.981 MB.
[2025-02-06T03:35:16.690Z] RMSE (validation) = 3.621968954548751 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2025-02-06T03:35:40.351Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2025-02-06T03:36:03.873Z] RMSE (validation) = 1.3105189674610933 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2025-02-06T03:36:26.540Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2025-02-06T03:36:42.571Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2025-02-06T03:36:51.780Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2025-02-06T03:37:05.287Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2025-02-06T03:37:12.844Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2025-02-06T03:37:13.609Z] 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.
[2025-02-06T03:37:13.609Z] The best model improves the baseline by 14.43%.
[2025-02-06T03:37:13.609Z] Movies recommended for you:
[2025-02-06T03:37:13.609Z] WARNING: This benchmark provides no result that can be validated.
[2025-02-06T03:37:13.609Z] There is no way to check that no silent failure occurred.
[2025-02-06T03:37:13.609Z] ====== movie-lens (apache-spark) [default], iteration 16 completed (140509.977 ms) ======
[2025-02-06T03:37:13.609Z] ====== movie-lens (apache-spark) [default], iteration 17 started ======
[2025-02-06T03:37:13.609Z] GC before operation: completed in 146.049 ms, heap usage 128.473 MB -> 58.739 MB.
[2025-02-06T03:37:37.143Z] RMSE (validation) = 3.6219689545487506 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2025-02-06T03:37:53.408Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2025-02-06T03:38:16.921Z] RMSE (validation) = 1.3105189674610933 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2025-02-06T03:38:30.410Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2025-02-06T03:38:41.564Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2025-02-06T03:38:50.756Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2025-02-06T03:39:01.917Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2025-02-06T03:39:11.126Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2025-02-06T03:39:11.126Z] 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.
[2025-02-06T03:39:11.126Z] The best model improves the baseline by 14.43%.
[2025-02-06T03:39:11.126Z] Movies recommended for you:
[2025-02-06T03:39:11.126Z] WARNING: This benchmark provides no result that can be validated.
[2025-02-06T03:39:11.126Z] There is no way to check that no silent failure occurred.
[2025-02-06T03:39:11.126Z] ====== movie-lens (apache-spark) [default], iteration 17 completed (116712.219 ms) ======
[2025-02-06T03:39:11.126Z] ====== movie-lens (apache-spark) [default], iteration 18 started ======
[2025-02-06T03:39:11.126Z] GC before operation: completed in 166.851 ms, heap usage 130.471 MB -> 63.089 MB.
[2025-02-06T03:39:30.742Z] RMSE (validation) = 3.6219689545487506 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2025-02-06T03:39:50.323Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2025-02-06T03:40:18.584Z] RMSE (validation) = 1.3105189674610933 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2025-02-06T03:40:34.847Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2025-02-06T03:40:46.005Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2025-02-06T03:40:55.207Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2025-02-06T03:41:08.716Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2025-02-06T03:41:17.913Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2025-02-06T03:41:18.676Z] 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.
[2025-02-06T03:41:18.676Z] The best model improves the baseline by 14.43%.
[2025-02-06T03:41:18.676Z] Movies recommended for you:
[2025-02-06T03:41:18.676Z] WARNING: This benchmark provides no result that can be validated.
[2025-02-06T03:41:18.676Z] There is no way to check that no silent failure occurred.
[2025-02-06T03:41:18.676Z] ====== movie-lens (apache-spark) [default], iteration 18 completed (128052.458 ms) ======
[2025-02-06T03:41:18.676Z] ====== movie-lens (apache-spark) [default], iteration 19 started ======
[2025-02-06T03:41:18.676Z] GC before operation: completed in 142.095 ms, heap usage 249.688 MB -> 56.771 MB.
[2025-02-06T03:41:42.191Z] RMSE (validation) = 3.621968954548751 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2025-02-06T03:42:01.757Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2025-02-06T03:42:29.979Z] RMSE (validation) = 1.3105189674610935 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2025-02-06T03:42:46.236Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2025-02-06T03:42:57.415Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2025-02-06T03:43:06.622Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2025-02-06T03:43:20.108Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2025-02-06T03:43:29.317Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2025-02-06T03:43:29.317Z] 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.
[2025-02-06T03:43:29.317Z] The best model improves the baseline by 14.43%.
[2025-02-06T03:43:29.317Z] Movies recommended for you:
[2025-02-06T03:43:29.317Z] WARNING: This benchmark provides no result that can be validated.
[2025-02-06T03:43:29.317Z] There is no way to check that no silent failure occurred.
[2025-02-06T03:43:29.317Z] ====== movie-lens (apache-spark) [default], iteration 19 completed (130732.011 ms) ======
[2025-02-06T03:43:30.547Z] -----------------------------------
[2025-02-06T03:43:30.547Z] renaissance-movie-lens_0_PASSED
[2025-02-06T03:43:30.547Z] -----------------------------------
[2025-02-06T03:43:30.547Z]
[2025-02-06T03:43:30.547Z] TEST TEARDOWN:
[2025-02-06T03:43:30.547Z] Nothing to be done for teardown.
[2025-02-06T03:43:30.547Z] renaissance-movie-lens_0 Finish Time: Thu Feb 6 03:43:30 2025 Epoch Time (ms): 1738813410363