renaissance-movie-lens_0
[2025-02-26T22:18:05.033Z] Running test renaissance-movie-lens_0 ...
[2025-02-26T22:18:05.033Z] ===============================================
[2025-02-26T22:18:05.033Z] renaissance-movie-lens_0 Start Time: Wed Feb 26 17:18:04 2025 Epoch Time (ms): 1740608284960
[2025-02-26T22:18:05.033Z] variation: NoOptions
[2025-02-26T22:18:05.033Z] JVM_OPTIONS:
[2025-02-26T22:18:05.033Z] { \
[2025-02-26T22:18:05.033Z] echo ""; echo "TEST SETUP:"; \
[2025-02-26T22:18:05.033Z] echo "Nothing to be done for setup."; \
[2025-02-26T22:18:05.033Z] mkdir -p "/Users/admin/workspace/workspace/Test_openjdk11_hs_extended.perf_aarch64_mac/aqa-tests/TKG/../TKG/output_17406079039241/renaissance-movie-lens_0"; \
[2025-02-26T22:18:05.033Z] cd "/Users/admin/workspace/workspace/Test_openjdk11_hs_extended.perf_aarch64_mac/aqa-tests/TKG/../TKG/output_17406079039241/renaissance-movie-lens_0"; \
[2025-02-26T22:18:05.033Z] echo ""; echo "TESTING:"; \
[2025-02-26T22:18:05.033Z] "/Users/admin/workspace/workspace/Test_openjdk11_hs_extended.perf_aarch64_mac/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_aarch64_mac/aqa-tests/TKG/../../jvmtest/perf/renaissance/renaissance.jar" --json ""/Users/admin/workspace/workspace/Test_openjdk11_hs_extended.perf_aarch64_mac/aqa-tests/TKG/../TKG/output_17406079039241/renaissance-movie-lens_0"/movie-lens.json" movie-lens; \
[2025-02-26T22:18:05.033Z] if [ $? -eq 0 ]; then echo "-----------------------------------"; echo "renaissance-movie-lens_0""_PASSED"; echo "-----------------------------------"; cd /Users/admin/workspace/workspace/Test_openjdk11_hs_extended.perf_aarch64_mac/aqa-tests/TKG/..; rm -f -r "/Users/admin/workspace/workspace/Test_openjdk11_hs_extended.perf_aarch64_mac/aqa-tests/TKG/../TKG/output_17406079039241/renaissance-movie-lens_0"; else echo "-----------------------------------"; echo "renaissance-movie-lens_0""_FAILED"; echo "-----------------------------------"; fi; \
[2025-02-26T22:18:05.033Z] echo ""; echo "TEST TEARDOWN:"; \
[2025-02-26T22:18:05.033Z] echo "Nothing to be done for teardown."; \
[2025-02-26T22:18:05.033Z] } 2>&1 | tee -a "/Users/admin/workspace/workspace/Test_openjdk11_hs_extended.perf_aarch64_mac/aqa-tests/TKG/../TKG/output_17406079039241/TestTargetResult";
[2025-02-26T22:18:05.033Z]
[2025-02-26T22:18:05.033Z] TEST SETUP:
[2025-02-26T22:18:05.033Z] Nothing to be done for setup.
[2025-02-26T22:18:05.033Z]
[2025-02-26T22:18:05.033Z] TESTING:
[2025-02-26T22:18:06.970Z] Using Spark's default log4j profile: org/apache/spark/log4j-defaults.properties
[2025-02-26T22:18:07.806Z] NOTE: 'movie-lens' benchmark uses Spark local executor with 3 (out of 3) threads.
[2025-02-26T22:18:09.168Z] Got 100004 ratings from 671 users on 9066 movies.
[2025-02-26T22:18:09.168Z] Training: 60056, validation: 20285, test: 19854
[2025-02-26T22:18:09.168Z] ====== movie-lens (apache-spark) [default], iteration 0 started ======
[2025-02-26T22:18:09.168Z] GC before operation: completed in 23.600 ms, heap usage 115.767 MB -> 36.573 MB.
[2025-02-26T22:18:12.557Z] RMSE (validation) = 3.62196895454876 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2025-02-26T22:18:13.920Z] RMSE (validation) = 2.1340923221100736 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2025-02-26T22:18:15.303Z] RMSE (validation) = 1.3105190428502682 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2025-02-26T22:18:17.252Z] RMSE (validation) = 0.9920028100571827 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2025-02-26T22:18:18.666Z] RMSE (validation) = 1.212631715629696 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2025-02-26T22:18:19.536Z] RMSE (validation) = 1.1025694034386657 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2025-02-26T22:18:20.373Z] RMSE (validation) = 0.9259652469249674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2025-02-26T22:18:21.232Z] RMSE (validation) = 0.8982232214592321 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2025-02-26T22:18:21.232Z] 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.
[2025-02-26T22:18:21.232Z] The best model improves the baseline by 14.52%.
[2025-02-26T22:18:21.232Z] Movies recommended for you:
[2025-02-26T22:18:21.232Z] WARNING: This benchmark provides no result that can be validated.
[2025-02-26T22:18:21.232Z] There is no way to check that no silent failure occurred.
[2025-02-26T22:18:21.232Z] ====== movie-lens (apache-spark) [default], iteration 0 completed (12104.363 ms) ======
[2025-02-26T22:18:21.232Z] ====== movie-lens (apache-spark) [default], iteration 1 started ======
[2025-02-26T22:18:21.232Z] GC before operation: completed in 47.541 ms, heap usage 378.109 MB -> 51.713 MB.
[2025-02-26T22:18:23.175Z] RMSE (validation) = 3.62196895454876 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2025-02-26T22:18:24.035Z] RMSE (validation) = 2.1340923221100736 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2025-02-26T22:18:25.992Z] RMSE (validation) = 1.3105190428502682 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2025-02-26T22:18:26.838Z] RMSE (validation) = 0.9920028100571827 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2025-02-26T22:18:27.679Z] RMSE (validation) = 1.212631715629696 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2025-02-26T22:18:28.515Z] RMSE (validation) = 1.1025694034386657 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2025-02-26T22:18:29.438Z] RMSE (validation) = 0.9259652469249674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2025-02-26T22:18:30.282Z] RMSE (validation) = 0.8982232214592321 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2025-02-26T22:18:30.282Z] 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.
[2025-02-26T22:18:30.282Z] The best model improves the baseline by 14.52%.
[2025-02-26T22:18:30.282Z] Movies recommended for you:
[2025-02-26T22:18:30.282Z] WARNING: This benchmark provides no result that can be validated.
[2025-02-26T22:18:30.282Z] There is no way to check that no silent failure occurred.
[2025-02-26T22:18:30.282Z] ====== movie-lens (apache-spark) [default], iteration 1 completed (8945.271 ms) ======
[2025-02-26T22:18:30.282Z] ====== movie-lens (apache-spark) [default], iteration 2 started ======
[2025-02-26T22:18:30.282Z] GC before operation: completed in 53.390 ms, heap usage 134.058 MB -> 52.051 MB.
[2025-02-26T22:18:31.632Z] RMSE (validation) = 3.62196895454876 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2025-02-26T22:18:33.573Z] RMSE (validation) = 2.1340923221100736 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2025-02-26T22:18:34.952Z] RMSE (validation) = 1.3105190428502682 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2025-02-26T22:18:36.287Z] RMSE (validation) = 0.9920028100571827 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2025-02-26T22:18:37.265Z] RMSE (validation) = 1.212631715629696 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2025-02-26T22:18:37.669Z] RMSE (validation) = 1.1025694034386657 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2025-02-26T22:18:39.028Z] RMSE (validation) = 0.9259652469249674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2025-02-26T22:18:39.867Z] RMSE (validation) = 0.8982232214592321 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2025-02-26T22:18:39.867Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9063003101263983.
[2025-02-26T22:18:39.867Z] The best model improves the baseline by 14.52%.
[2025-02-26T22:18:39.867Z] Movies recommended for you:
[2025-02-26T22:18:39.867Z] WARNING: This benchmark provides no result that can be validated.
[2025-02-26T22:18:39.867Z] There is no way to check that no silent failure occurred.
[2025-02-26T22:18:39.867Z] ====== movie-lens (apache-spark) [default], iteration 2 completed (9525.450 ms) ======
[2025-02-26T22:18:39.867Z] ====== movie-lens (apache-spark) [default], iteration 3 started ======
[2025-02-26T22:18:39.867Z] GC before operation: completed in 44.835 ms, heap usage 121.780 MB -> 49.061 MB.
[2025-02-26T22:18:41.237Z] RMSE (validation) = 3.62196895454876 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2025-02-26T22:18:43.161Z] RMSE (validation) = 2.1340923221100736 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2025-02-26T22:18:44.520Z] RMSE (validation) = 1.3105190428502682 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2025-02-26T22:18:45.365Z] RMSE (validation) = 0.9920028100571827 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2025-02-26T22:18:46.219Z] RMSE (validation) = 1.212631715629696 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2025-02-26T22:18:47.073Z] RMSE (validation) = 1.1025694034386657 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2025-02-26T22:18:47.922Z] RMSE (validation) = 0.9259652469249674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2025-02-26T22:18:48.764Z] RMSE (validation) = 0.8982232214592321 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2025-02-26T22:18:48.764Z] 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.
[2025-02-26T22:18:49.170Z] The best model improves the baseline by 14.52%.
[2025-02-26T22:18:49.170Z] Movies recommended for you:
[2025-02-26T22:18:49.170Z] WARNING: This benchmark provides no result that can be validated.
[2025-02-26T22:18:49.170Z] There is no way to check that no silent failure occurred.
[2025-02-26T22:18:49.170Z] ====== movie-lens (apache-spark) [default], iteration 3 completed (9086.439 ms) ======
[2025-02-26T22:18:49.170Z] ====== movie-lens (apache-spark) [default], iteration 4 started ======
[2025-02-26T22:18:49.170Z] GC before operation: completed in 44.068 ms, heap usage 212.052 MB -> 49.471 MB.
[2025-02-26T22:18:51.160Z] RMSE (validation) = 3.6219689545487603 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2025-02-26T22:18:52.520Z] RMSE (validation) = 2.1340923221100736 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2025-02-26T22:18:54.512Z] RMSE (validation) = 1.3105190428502682 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2025-02-26T22:18:55.864Z] RMSE (validation) = 0.9920028100571827 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2025-02-26T22:18:56.705Z] RMSE (validation) = 1.212631715629696 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2025-02-26T22:18:58.059Z] RMSE (validation) = 1.1025694034386657 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2025-02-26T22:18:58.898Z] RMSE (validation) = 0.9259652469249674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2025-02-26T22:18:59.743Z] RMSE (validation) = 0.8982232214592321 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2025-02-26T22:19:00.157Z] 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.
[2025-02-26T22:19:00.157Z] The best model improves the baseline by 14.52%.
[2025-02-26T22:19:00.157Z] Movies recommended for you:
[2025-02-26T22:19:00.157Z] WARNING: This benchmark provides no result that can be validated.
[2025-02-26T22:19:00.157Z] There is no way to check that no silent failure occurred.
[2025-02-26T22:19:00.157Z] ====== movie-lens (apache-spark) [default], iteration 4 completed (11025.116 ms) ======
[2025-02-26T22:19:00.157Z] ====== movie-lens (apache-spark) [default], iteration 5 started ======
[2025-02-26T22:19:00.157Z] GC before operation: completed in 54.551 ms, heap usage 69.816 MB -> 49.526 MB.
[2025-02-26T22:19:02.136Z] RMSE (validation) = 3.6219689545487603 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2025-02-26T22:19:03.494Z] RMSE (validation) = 2.1340923221100736 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2025-02-26T22:19:06.123Z] RMSE (validation) = 1.3105190428502682 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2025-02-26T22:19:07.472Z] RMSE (validation) = 0.9920028100571827 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2025-02-26T22:19:08.323Z] RMSE (validation) = 1.212631715629696 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2025-02-26T22:19:09.182Z] RMSE (validation) = 1.1025694034386657 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2025-02-26T22:19:10.032Z] RMSE (validation) = 0.9259652469249674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2025-02-26T22:19:11.432Z] RMSE (validation) = 0.8982232214592321 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2025-02-26T22:19:11.432Z] 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.
[2025-02-26T22:19:11.432Z] The best model improves the baseline by 14.52%.
[2025-02-26T22:19:11.432Z] Movies recommended for you:
[2025-02-26T22:19:11.432Z] WARNING: This benchmark provides no result that can be validated.
[2025-02-26T22:19:11.432Z] There is no way to check that no silent failure occurred.
[2025-02-26T22:19:11.432Z] ====== movie-lens (apache-spark) [default], iteration 5 completed (11258.068 ms) ======
[2025-02-26T22:19:11.432Z] ====== movie-lens (apache-spark) [default], iteration 6 started ======
[2025-02-26T22:19:11.432Z] GC before operation: completed in 47.315 ms, heap usage 76.385 MB -> 49.480 MB.
[2025-02-26T22:19:13.367Z] RMSE (validation) = 3.6219689545487603 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2025-02-26T22:19:14.750Z] RMSE (validation) = 2.1340923221100736 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2025-02-26T22:19:16.708Z] RMSE (validation) = 1.3105190428502682 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2025-02-26T22:19:18.075Z] RMSE (validation) = 0.9920028100571827 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2025-02-26T22:19:19.448Z] RMSE (validation) = 1.212631715629696 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2025-02-26T22:19:20.302Z] RMSE (validation) = 1.1025694034386657 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2025-02-26T22:19:21.164Z] RMSE (validation) = 0.9259652469249674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2025-02-26T22:19:22.520Z] RMSE (validation) = 0.8982232214592321 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2025-02-26T22:19:22.520Z] 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.
[2025-02-26T22:19:22.520Z] The best model improves the baseline by 14.52%.
[2025-02-26T22:19:22.520Z] Movies recommended for you:
[2025-02-26T22:19:22.520Z] WARNING: This benchmark provides no result that can be validated.
[2025-02-26T22:19:22.520Z] There is no way to check that no silent failure occurred.
[2025-02-26T22:19:22.520Z] ====== movie-lens (apache-spark) [default], iteration 6 completed (11220.783 ms) ======
[2025-02-26T22:19:22.520Z] ====== movie-lens (apache-spark) [default], iteration 7 started ======
[2025-02-26T22:19:22.913Z] GC before operation: completed in 65.872 ms, heap usage 250.138 MB -> 49.876 MB.
[2025-02-26T22:19:24.266Z] RMSE (validation) = 3.6219689545487603 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2025-02-26T22:19:26.230Z] RMSE (validation) = 2.1340923221100736 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2025-02-26T22:19:27.585Z] RMSE (validation) = 1.3105190428502682 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2025-02-26T22:19:28.972Z] RMSE (validation) = 0.9920028100571827 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2025-02-26T22:19:29.863Z] RMSE (validation) = 1.212631715629696 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2025-02-26T22:19:31.210Z] RMSE (validation) = 1.1025694034386657 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2025-02-26T22:19:32.043Z] RMSE (validation) = 0.9259652469249674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2025-02-26T22:19:32.889Z] RMSE (validation) = 0.8982232214592321 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2025-02-26T22:19:33.294Z] 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.
[2025-02-26T22:19:33.294Z] The best model improves the baseline by 14.52%.
[2025-02-26T22:19:33.294Z] Movies recommended for you:
[2025-02-26T22:19:33.294Z] WARNING: This benchmark provides no result that can be validated.
[2025-02-26T22:19:33.294Z] There is no way to check that no silent failure occurred.
[2025-02-26T22:19:33.294Z] ====== movie-lens (apache-spark) [default], iteration 7 completed (10413.326 ms) ======
[2025-02-26T22:19:33.294Z] ====== movie-lens (apache-spark) [default], iteration 8 started ======
[2025-02-26T22:19:33.294Z] GC before operation: completed in 55.865 ms, heap usage 260.265 MB -> 50.159 MB.
[2025-02-26T22:19:35.230Z] RMSE (validation) = 3.62196895454876 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2025-02-26T22:19:36.581Z] RMSE (validation) = 2.1340923221100736 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2025-02-26T22:19:38.537Z] RMSE (validation) = 1.3105190428502682 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2025-02-26T22:19:39.908Z] RMSE (validation) = 0.9920028100571827 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2025-02-26T22:19:40.756Z] RMSE (validation) = 1.212631715629696 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2025-02-26T22:19:42.111Z] RMSE (validation) = 1.1025694034386657 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2025-02-26T22:19:42.956Z] RMSE (validation) = 0.9259652469249674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2025-02-26T22:19:43.821Z] RMSE (validation) = 0.8982232214592321 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2025-02-26T22:19:43.821Z] 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.
[2025-02-26T22:19:43.821Z] The best model improves the baseline by 14.52%.
[2025-02-26T22:19:43.821Z] Movies recommended for you:
[2025-02-26T22:19:43.821Z] WARNING: This benchmark provides no result that can be validated.
[2025-02-26T22:19:43.821Z] There is no way to check that no silent failure occurred.
[2025-02-26T22:19:43.821Z] ====== movie-lens (apache-spark) [default], iteration 8 completed (10647.345 ms) ======
[2025-02-26T22:19:43.821Z] ====== movie-lens (apache-spark) [default], iteration 9 started ======
[2025-02-26T22:19:43.821Z] GC before operation: completed in 43.478 ms, heap usage 196.807 MB -> 49.954 MB.
[2025-02-26T22:19:45.871Z] RMSE (validation) = 3.62196895454876 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2025-02-26T22:19:47.260Z] RMSE (validation) = 2.1340923221100736 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2025-02-26T22:19:49.262Z] RMSE (validation) = 1.3105190428502682 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2025-02-26T22:19:50.615Z] RMSE (validation) = 0.9920028100571827 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2025-02-26T22:19:51.462Z] RMSE (validation) = 1.212631715629696 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2025-02-26T22:19:52.311Z] RMSE (validation) = 1.1025694034386657 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2025-02-26T22:19:53.149Z] RMSE (validation) = 0.9259652469249674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2025-02-26T22:19:54.543Z] RMSE (validation) = 0.8982232214592321 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2025-02-26T22:19:54.543Z] 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.
[2025-02-26T22:19:54.543Z] The best model improves the baseline by 14.52%.
[2025-02-26T22:19:54.543Z] Movies recommended for you:
[2025-02-26T22:19:54.543Z] WARNING: This benchmark provides no result that can be validated.
[2025-02-26T22:19:54.543Z] There is no way to check that no silent failure occurred.
[2025-02-26T22:19:54.543Z] ====== movie-lens (apache-spark) [default], iteration 9 completed (10544.755 ms) ======
[2025-02-26T22:19:54.543Z] ====== movie-lens (apache-spark) [default], iteration 10 started ======
[2025-02-26T22:19:54.543Z] GC before operation: completed in 61.696 ms, heap usage 84.626 MB -> 49.971 MB.
[2025-02-26T22:19:55.889Z] RMSE (validation) = 3.62196895454876 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2025-02-26T22:19:57.834Z] RMSE (validation) = 2.1340923221100736 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2025-02-26T22:19:59.190Z] RMSE (validation) = 1.3105190428502682 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2025-02-26T22:20:00.572Z] RMSE (validation) = 0.9920028100571827 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2025-02-26T22:20:01.446Z] RMSE (validation) = 1.212631715629696 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2025-02-26T22:20:02.300Z] RMSE (validation) = 1.1025694034386657 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2025-02-26T22:20:03.165Z] RMSE (validation) = 0.9259652469249674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2025-02-26T22:20:04.552Z] RMSE (validation) = 0.8982232214592321 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2025-02-26T22:20:04.552Z] 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.
[2025-02-26T22:20:04.552Z] The best model improves the baseline by 14.52%.
[2025-02-26T22:20:04.552Z] Movies recommended for you:
[2025-02-26T22:20:04.552Z] WARNING: This benchmark provides no result that can be validated.
[2025-02-26T22:20:04.552Z] There is no way to check that no silent failure occurred.
[2025-02-26T22:20:04.552Z] ====== movie-lens (apache-spark) [default], iteration 10 completed (10117.067 ms) ======
[2025-02-26T22:20:04.552Z] ====== movie-lens (apache-spark) [default], iteration 11 started ======
[2025-02-26T22:20:04.552Z] GC before operation: completed in 49.725 ms, heap usage 200.325 MB -> 49.820 MB.
[2025-02-26T22:20:06.505Z] RMSE (validation) = 3.62196895454876 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2025-02-26T22:20:07.851Z] RMSE (validation) = 2.1340923221100736 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2025-02-26T22:20:09.800Z] RMSE (validation) = 1.3105190428502682 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2025-02-26T22:20:11.159Z] RMSE (validation) = 0.9920028100571827 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2025-02-26T22:20:12.011Z] RMSE (validation) = 1.212631715629696 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2025-02-26T22:20:12.852Z] RMSE (validation) = 1.1025694034386657 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2025-02-26T22:20:14.215Z] RMSE (validation) = 0.9259652469249674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2025-02-26T22:20:15.094Z] RMSE (validation) = 0.8982232214592321 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2025-02-26T22:20:15.094Z] 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.
[2025-02-26T22:20:15.094Z] The best model improves the baseline by 14.52%.
[2025-02-26T22:20:15.094Z] Movies recommended for you:
[2025-02-26T22:20:15.094Z] WARNING: This benchmark provides no result that can be validated.
[2025-02-26T22:20:15.094Z] There is no way to check that no silent failure occurred.
[2025-02-26T22:20:15.094Z] ====== movie-lens (apache-spark) [default], iteration 11 completed (10558.998 ms) ======
[2025-02-26T22:20:15.094Z] ====== movie-lens (apache-spark) [default], iteration 12 started ======
[2025-02-26T22:20:15.487Z] GC before operation: completed in 49.311 ms, heap usage 113.415 MB -> 49.909 MB.
[2025-02-26T22:20:16.835Z] RMSE (validation) = 3.62196895454876 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2025-02-26T22:20:18.881Z] RMSE (validation) = 2.1340923221100736 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2025-02-26T22:20:19.729Z] RMSE (validation) = 1.3105190428502682 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2025-02-26T22:20:21.087Z] RMSE (validation) = 0.9920028100571827 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2025-02-26T22:20:21.936Z] RMSE (validation) = 1.212631715629696 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2025-02-26T22:20:22.788Z] RMSE (validation) = 1.1025694034386657 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2025-02-26T22:20:23.195Z] RMSE (validation) = 0.9259652469249674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2025-02-26T22:20:24.076Z] RMSE (validation) = 0.8982232214592321 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2025-02-26T22:20:24.474Z] 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.
[2025-02-26T22:20:24.474Z] The best model improves the baseline by 14.52%.
[2025-02-26T22:20:24.474Z] Movies recommended for you:
[2025-02-26T22:20:24.474Z] WARNING: This benchmark provides no result that can be validated.
[2025-02-26T22:20:24.474Z] There is no way to check that no silent failure occurred.
[2025-02-26T22:20:24.474Z] ====== movie-lens (apache-spark) [default], iteration 12 completed (9133.139 ms) ======
[2025-02-26T22:20:24.474Z] ====== movie-lens (apache-spark) [default], iteration 13 started ======
[2025-02-26T22:20:24.474Z] GC before operation: completed in 60.625 ms, heap usage 199.275 MB -> 50.162 MB.
[2025-02-26T22:20:25.855Z] RMSE (validation) = 3.6219689545487603 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2025-02-26T22:20:27.256Z] RMSE (validation) = 2.1340923221100736 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2025-02-26T22:20:28.613Z] RMSE (validation) = 1.3105190428502682 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2025-02-26T22:20:29.970Z] RMSE (validation) = 0.9920028100571827 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2025-02-26T22:20:30.805Z] RMSE (validation) = 1.212631715629696 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2025-02-26T22:20:31.665Z] RMSE (validation) = 1.1025694034386657 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2025-02-26T22:20:32.079Z] RMSE (validation) = 0.9259652469249674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2025-02-26T22:20:32.921Z] RMSE (validation) = 0.8982232214592321 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2025-02-26T22:20:32.921Z] 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.
[2025-02-26T22:20:32.921Z] The best model improves the baseline by 14.52%.
[2025-02-26T22:20:33.324Z] Movies recommended for you:
[2025-02-26T22:20:33.324Z] WARNING: This benchmark provides no result that can be validated.
[2025-02-26T22:20:33.324Z] There is no way to check that no silent failure occurred.
[2025-02-26T22:20:33.325Z] ====== movie-lens (apache-spark) [default], iteration 13 completed (8635.033 ms) ======
[2025-02-26T22:20:33.325Z] ====== movie-lens (apache-spark) [default], iteration 14 started ======
[2025-02-26T22:20:33.325Z] GC before operation: completed in 49.333 ms, heap usage 173.424 MB -> 49.959 MB.
[2025-02-26T22:20:34.697Z] RMSE (validation) = 3.62196895454876 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2025-02-26T22:20:36.064Z] RMSE (validation) = 2.1340923221100736 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2025-02-26T22:20:38.003Z] RMSE (validation) = 1.3105190428502682 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2025-02-26T22:20:39.365Z] RMSE (validation) = 0.9920028100571827 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2025-02-26T22:20:40.709Z] RMSE (validation) = 1.212631715629696 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2025-02-26T22:20:41.616Z] RMSE (validation) = 1.1025694034386657 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2025-02-26T22:20:42.472Z] RMSE (validation) = 0.9259652469249674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2025-02-26T22:20:43.320Z] RMSE (validation) = 0.8982232214592321 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2025-02-26T22:20:43.320Z] 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.
[2025-02-26T22:20:43.715Z] The best model improves the baseline by 14.52%.
[2025-02-26T22:20:43.715Z] Movies recommended for you:
[2025-02-26T22:20:43.715Z] WARNING: This benchmark provides no result that can be validated.
[2025-02-26T22:20:43.715Z] There is no way to check that no silent failure occurred.
[2025-02-26T22:20:43.715Z] ====== movie-lens (apache-spark) [default], iteration 14 completed (10387.763 ms) ======
[2025-02-26T22:20:43.715Z] ====== movie-lens (apache-spark) [default], iteration 15 started ======
[2025-02-26T22:20:43.715Z] GC before operation: completed in 69.999 ms, heap usage 74.957 MB -> 49.907 MB.
[2025-02-26T22:20:45.078Z] RMSE (validation) = 3.62196895454876 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2025-02-26T22:20:47.000Z] RMSE (validation) = 2.1340923221100736 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2025-02-26T22:20:48.489Z] RMSE (validation) = 1.3105190428502682 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2025-02-26T22:20:50.438Z] RMSE (validation) = 0.9920028100571827 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2025-02-26T22:20:51.281Z] RMSE (validation) = 1.212631715629696 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2025-02-26T22:20:52.124Z] RMSE (validation) = 1.1025694034386657 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2025-02-26T22:20:53.485Z] RMSE (validation) = 0.9259652469249674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2025-02-26T22:20:54.334Z] RMSE (validation) = 0.8982232214592321 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2025-02-26T22:20:54.334Z] 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.
[2025-02-26T22:20:54.334Z] The best model improves the baseline by 14.52%.
[2025-02-26T22:20:54.725Z] Movies recommended for you:
[2025-02-26T22:20:54.725Z] WARNING: This benchmark provides no result that can be validated.
[2025-02-26T22:20:54.725Z] There is no way to check that no silent failure occurred.
[2025-02-26T22:20:54.725Z] ====== movie-lens (apache-spark) [default], iteration 15 completed (10933.585 ms) ======
[2025-02-26T22:20:54.725Z] ====== movie-lens (apache-spark) [default], iteration 16 started ======
[2025-02-26T22:20:54.725Z] GC before operation: completed in 49.220 ms, heap usage 130.461 MB -> 50.074 MB.
[2025-02-26T22:20:56.114Z] RMSE (validation) = 3.6219689545487603 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2025-02-26T22:20:58.065Z] RMSE (validation) = 2.1340923221100736 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2025-02-26T22:20:59.432Z] RMSE (validation) = 1.3105190428502682 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2025-02-26T22:21:01.405Z] RMSE (validation) = 0.9920028100571827 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2025-02-26T22:21:02.254Z] RMSE (validation) = 1.212631715629696 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2025-02-26T22:21:03.126Z] RMSE (validation) = 1.1025694034386657 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2025-02-26T22:21:03.997Z] RMSE (validation) = 0.9259652469249674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2025-02-26T22:21:05.394Z] RMSE (validation) = 0.8982232214592321 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2025-02-26T22:21:05.394Z] 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.
[2025-02-26T22:21:05.394Z] The best model improves the baseline by 14.52%.
[2025-02-26T22:21:05.394Z] Movies recommended for you:
[2025-02-26T22:21:05.394Z] WARNING: This benchmark provides no result that can be validated.
[2025-02-26T22:21:05.394Z] There is no way to check that no silent failure occurred.
[2025-02-26T22:21:05.394Z] ====== movie-lens (apache-spark) [default], iteration 16 completed (10722.890 ms) ======
[2025-02-26T22:21:05.394Z] ====== movie-lens (apache-spark) [default], iteration 17 started ======
[2025-02-26T22:21:05.394Z] GC before operation: completed in 55.925 ms, heap usage 250.161 MB -> 50.057 MB.
[2025-02-26T22:21:07.385Z] RMSE (validation) = 3.62196895454876 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2025-02-26T22:21:08.758Z] RMSE (validation) = 2.1340923221100736 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2025-02-26T22:21:10.727Z] RMSE (validation) = 1.3105190428502682 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2025-02-26T22:21:12.146Z] RMSE (validation) = 0.9920028100571827 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2025-02-26T22:21:13.556Z] RMSE (validation) = 1.212631715629696 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2025-02-26T22:21:14.419Z] RMSE (validation) = 1.1025694034386657 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2025-02-26T22:21:15.282Z] RMSE (validation) = 0.9259652469249674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2025-02-26T22:21:16.158Z] RMSE (validation) = 0.8982232214592321 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2025-02-26T22:21:16.565Z] 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.
[2025-02-26T22:21:16.565Z] The best model improves the baseline by 14.52%.
[2025-02-26T22:21:16.565Z] Movies recommended for you:
[2025-02-26T22:21:16.565Z] WARNING: This benchmark provides no result that can be validated.
[2025-02-26T22:21:16.566Z] There is no way to check that no silent failure occurred.
[2025-02-26T22:21:16.566Z] ====== movie-lens (apache-spark) [default], iteration 17 completed (11092.136 ms) ======
[2025-02-26T22:21:16.566Z] ====== movie-lens (apache-spark) [default], iteration 18 started ======
[2025-02-26T22:21:16.566Z] GC before operation: completed in 63.354 ms, heap usage 185.332 MB -> 50.074 MB.
[2025-02-26T22:21:18.514Z] RMSE (validation) = 3.6219689545487603 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2025-02-26T22:21:19.886Z] RMSE (validation) = 2.1340923221100736 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2025-02-26T22:21:21.838Z] RMSE (validation) = 1.3105190428502682 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2025-02-26T22:21:23.232Z] RMSE (validation) = 0.9920028100571827 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2025-02-26T22:21:24.081Z] RMSE (validation) = 1.212631715629696 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2025-02-26T22:21:25.435Z] RMSE (validation) = 1.1025694034386657 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2025-02-26T22:21:26.281Z] RMSE (validation) = 0.9259652469249674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2025-02-26T22:21:27.169Z] RMSE (validation) = 0.8982232214592321 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2025-02-26T22:21:27.579Z] 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.
[2025-02-26T22:21:27.579Z] The best model improves the baseline by 14.52%.
[2025-02-26T22:21:27.579Z] Movies recommended for you:
[2025-02-26T22:21:27.579Z] WARNING: This benchmark provides no result that can be validated.
[2025-02-26T22:21:27.579Z] There is no way to check that no silent failure occurred.
[2025-02-26T22:21:27.579Z] ====== movie-lens (apache-spark) [default], iteration 18 completed (10968.044 ms) ======
[2025-02-26T22:21:27.579Z] ====== movie-lens (apache-spark) [default], iteration 19 started ======
[2025-02-26T22:21:27.579Z] GC before operation: completed in 59.895 ms, heap usage 57.827 MB -> 50.146 MB.
[2025-02-26T22:21:29.592Z] RMSE (validation) = 3.62196895454876 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2025-02-26T22:21:30.977Z] RMSE (validation) = 2.1340923221100736 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2025-02-26T22:21:32.951Z] RMSE (validation) = 1.3105190428502682 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2025-02-26T22:21:34.373Z] RMSE (validation) = 0.9920028100571827 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2025-02-26T22:21:35.267Z] RMSE (validation) = 1.212631715629696 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2025-02-26T22:21:36.642Z] RMSE (validation) = 1.1025694034386657 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2025-02-26T22:21:37.491Z] RMSE (validation) = 0.9259652469249674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2025-02-26T22:21:38.342Z] RMSE (validation) = 0.8982232214592321 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2025-02-26T22:21:38.342Z] 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.
[2025-02-26T22:21:38.342Z] The best model improves the baseline by 14.52%.
[2025-02-26T22:21:38.735Z] Movies recommended for you:
[2025-02-26T22:21:38.735Z] WARNING: This benchmark provides no result that can be validated.
[2025-02-26T22:21:38.735Z] There is no way to check that no silent failure occurred.
[2025-02-26T22:21:38.735Z] ====== movie-lens (apache-spark) [default], iteration 19 completed (10975.925 ms) ======
[2025-02-26T22:21:38.735Z] -----------------------------------
[2025-02-26T22:21:38.735Z] renaissance-movie-lens_0_PASSED
[2025-02-26T22:21:38.735Z] -----------------------------------
[2025-02-26T22:21:38.735Z]
[2025-02-26T22:21:38.735Z] TEST TEARDOWN:
[2025-02-26T22:21:38.735Z] Nothing to be done for teardown.
[2025-02-26T22:21:38.735Z] renaissance-movie-lens_0 Finish Time: Wed Feb 26 17:21:38 2025 Epoch Time (ms): 1740608498644