renaissance-movie-lens_0
[2025-02-26T21:56:56.722Z] Running test renaissance-movie-lens_0 ...
[2025-02-26T21:56:56.722Z] ===============================================
[2025-02-26T21:56:56.722Z] renaissance-movie-lens_0 Start Time: Wed Feb 26 16:56:56 2025 Epoch Time (ms): 1740607016320
[2025-02-26T21:56:56.722Z] variation: NoOptions
[2025-02-26T21:56:56.722Z] JVM_OPTIONS:
[2025-02-26T21:56:56.722Z] { \
[2025-02-26T21:56:56.722Z] echo ""; echo "TEST SETUP:"; \
[2025-02-26T21:56:56.722Z] echo "Nothing to be done for setup."; \
[2025-02-26T21:56:56.722Z] mkdir -p "/Users/admin/workspace/workspace/Test_openjdk17_hs_extended.perf_aarch64_mac/aqa-tests/TKG/../TKG/output_17406066794944/renaissance-movie-lens_0"; \
[2025-02-26T21:56:56.722Z] cd "/Users/admin/workspace/workspace/Test_openjdk17_hs_extended.perf_aarch64_mac/aqa-tests/TKG/../TKG/output_17406066794944/renaissance-movie-lens_0"; \
[2025-02-26T21:56:56.722Z] echo ""; echo "TESTING:"; \
[2025-02-26T21:56:56.722Z] "/Users/admin/workspace/workspace/Test_openjdk17_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_openjdk17_hs_extended.perf_aarch64_mac/aqa-tests/TKG/../../jvmtest/perf/renaissance/renaissance.jar" --json ""/Users/admin/workspace/workspace/Test_openjdk17_hs_extended.perf_aarch64_mac/aqa-tests/TKG/../TKG/output_17406066794944/renaissance-movie-lens_0"/movie-lens.json" movie-lens; \
[2025-02-26T21:56:56.722Z] if [ $? -eq 0 ]; then echo "-----------------------------------"; echo "renaissance-movie-lens_0""_PASSED"; echo "-----------------------------------"; cd /Users/admin/workspace/workspace/Test_openjdk17_hs_extended.perf_aarch64_mac/aqa-tests/TKG/..; rm -f -r "/Users/admin/workspace/workspace/Test_openjdk17_hs_extended.perf_aarch64_mac/aqa-tests/TKG/../TKG/output_17406066794944/renaissance-movie-lens_0"; else echo "-----------------------------------"; echo "renaissance-movie-lens_0""_FAILED"; echo "-----------------------------------"; fi; \
[2025-02-26T21:56:56.722Z] echo ""; echo "TEST TEARDOWN:"; \
[2025-02-26T21:56:56.722Z] echo "Nothing to be done for teardown."; \
[2025-02-26T21:56:56.722Z] } 2>&1 | tee -a "/Users/admin/workspace/workspace/Test_openjdk17_hs_extended.perf_aarch64_mac/aqa-tests/TKG/../TKG/output_17406066794944/TestTargetResult";
[2025-02-26T21:56:56.722Z]
[2025-02-26T21:56:56.722Z] TEST SETUP:
[2025-02-26T21:56:56.722Z] Nothing to be done for setup.
[2025-02-26T21:56:56.722Z]
[2025-02-26T21:56:56.722Z] TESTING:
[2025-02-26T21:56:58.626Z] Using Spark's default log4j profile: org/apache/spark/log4j-defaults.properties
[2025-02-26T21:56:59.465Z] NOTE: 'movie-lens' benchmark uses Spark local executor with 3 (out of 3) threads.
[2025-02-26T21:57:01.426Z] Got 100004 ratings from 671 users on 9066 movies.
[2025-02-26T21:57:01.426Z] Training: 60056, validation: 20285, test: 19854
[2025-02-26T21:57:01.426Z] ====== movie-lens (apache-spark) [default], iteration 0 started ======
[2025-02-26T21:57:01.426Z] GC before operation: completed in 22.572 ms, heap usage 73.199 MB -> 37.177 MB.
[2025-02-26T21:57:04.883Z] RMSE (validation) = 3.6219689545487603 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2025-02-26T21:57:06.819Z] RMSE (validation) = 2.1340923221100736 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2025-02-26T21:57:08.782Z] RMSE (validation) = 1.3105190428502682 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2025-02-26T21:57:10.131Z] RMSE (validation) = 0.9920028100571827 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2025-02-26T21:57:11.484Z] RMSE (validation) = 1.212631715629696 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2025-02-26T21:57:12.330Z] RMSE (validation) = 1.1025694034386657 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2025-02-26T21:57:13.754Z] RMSE (validation) = 0.9259652469249674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2025-02-26T21:57:14.606Z] RMSE (validation) = 0.8982232214592321 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2025-02-26T21:57:14.606Z] 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.
[2025-02-26T21:57:14.606Z] The best model improves the baseline by 14.52%.
[2025-02-26T21:57:14.997Z] Movies recommended for you:
[2025-02-26T21:57:14.997Z] WARNING: This benchmark provides no result that can be validated.
[2025-02-26T21:57:14.997Z] There is no way to check that no silent failure occurred.
[2025-02-26T21:57:14.997Z] ====== movie-lens (apache-spark) [default], iteration 0 completed (13458.466 ms) ======
[2025-02-26T21:57:14.997Z] ====== movie-lens (apache-spark) [default], iteration 1 started ======
[2025-02-26T21:57:14.997Z] GC before operation: completed in 46.506 ms, heap usage 161.883 MB -> 52.658 MB.
[2025-02-26T21:57:16.345Z] RMSE (validation) = 3.6219689545487603 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2025-02-26T21:57:18.252Z] RMSE (validation) = 2.1340923221100736 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2025-02-26T21:57:19.611Z] RMSE (validation) = 1.3105190428502682 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2025-02-26T21:57:21.512Z] RMSE (validation) = 0.9920028100571827 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2025-02-26T21:57:22.358Z] RMSE (validation) = 1.212631715629696 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2025-02-26T21:57:23.199Z] RMSE (validation) = 1.1025694034386657 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2025-02-26T21:57:24.069Z] RMSE (validation) = 0.9259652469249674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2025-02-26T21:57:24.967Z] RMSE (validation) = 0.8982232214592321 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2025-02-26T21:57:24.967Z] 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.
[2025-02-26T21:57:24.967Z] The best model improves the baseline by 14.52%.
[2025-02-26T21:57:24.967Z] Movies recommended for you:
[2025-02-26T21:57:24.967Z] WARNING: This benchmark provides no result that can be validated.
[2025-02-26T21:57:24.967Z] There is no way to check that no silent failure occurred.
[2025-02-26T21:57:24.967Z] ====== movie-lens (apache-spark) [default], iteration 1 completed (10169.907 ms) ======
[2025-02-26T21:57:24.967Z] ====== movie-lens (apache-spark) [default], iteration 2 started ======
[2025-02-26T21:57:24.967Z] GC before operation: completed in 37.388 ms, heap usage 73.664 MB -> 49.235 MB.
[2025-02-26T21:57:26.303Z] RMSE (validation) = 3.6219689545487603 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2025-02-26T21:57:28.239Z] RMSE (validation) = 2.1340923221100736 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2025-02-26T21:57:29.596Z] RMSE (validation) = 1.3105190428502682 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2025-02-26T21:57:30.928Z] RMSE (validation) = 0.9920028100571827 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2025-02-26T21:57:31.766Z] RMSE (validation) = 1.212631715629696 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2025-02-26T21:57:32.599Z] RMSE (validation) = 1.1025694034386657 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2025-02-26T21:57:33.436Z] RMSE (validation) = 0.9259652469249674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2025-02-26T21:57:34.785Z] RMSE (validation) = 0.8982232214592321 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2025-02-26T21:57:34.785Z] 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.
[2025-02-26T21:57:34.785Z] The best model improves the baseline by 14.52%.
[2025-02-26T21:57:34.785Z] Movies recommended for you:
[2025-02-26T21:57:34.785Z] WARNING: This benchmark provides no result that can be validated.
[2025-02-26T21:57:34.785Z] There is no way to check that no silent failure occurred.
[2025-02-26T21:57:34.785Z] ====== movie-lens (apache-spark) [default], iteration 2 completed (9643.091 ms) ======
[2025-02-26T21:57:34.785Z] ====== movie-lens (apache-spark) [default], iteration 3 started ======
[2025-02-26T21:57:34.785Z] GC before operation: completed in 42.380 ms, heap usage 60.161 MB -> 52.300 MB.
[2025-02-26T21:57:36.120Z] RMSE (validation) = 3.6219689545487603 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2025-02-26T21:57:37.478Z] RMSE (validation) = 2.1340923221100736 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2025-02-26T21:57:39.399Z] RMSE (validation) = 1.3105190428502682 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2025-02-26T21:57:40.750Z] RMSE (validation) = 0.9920028100571827 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2025-02-26T21:57:41.619Z] RMSE (validation) = 1.212631715629696 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2025-02-26T21:57:42.478Z] RMSE (validation) = 1.1025694034386657 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2025-02-26T21:57:43.316Z] RMSE (validation) = 0.9259652469249674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2025-02-26T21:57:44.150Z] RMSE (validation) = 0.8982232214592321 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2025-02-26T21:57:44.150Z] 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.
[2025-02-26T21:57:44.150Z] The best model improves the baseline by 14.52%.
[2025-02-26T21:57:44.538Z] Movies recommended for you:
[2025-02-26T21:57:44.538Z] WARNING: This benchmark provides no result that can be validated.
[2025-02-26T21:57:44.538Z] There is no way to check that no silent failure occurred.
[2025-02-26T21:57:44.538Z] ====== movie-lens (apache-spark) [default], iteration 3 completed (9599.303 ms) ======
[2025-02-26T21:57:44.538Z] ====== movie-lens (apache-spark) [default], iteration 4 started ======
[2025-02-26T21:57:44.538Z] GC before operation: completed in 40.875 ms, heap usage 296.201 MB -> 50.178 MB.
[2025-02-26T21:57:45.896Z] RMSE (validation) = 3.62196895454876 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2025-02-26T21:57:47.224Z] RMSE (validation) = 2.1340923221100736 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2025-02-26T21:57:48.566Z] RMSE (validation) = 1.3105190428502682 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2025-02-26T21:57:49.948Z] RMSE (validation) = 0.9920028100571827 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2025-02-26T21:57:51.328Z] RMSE (validation) = 1.212631715629696 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2025-02-26T21:57:51.729Z] RMSE (validation) = 1.1025694034386657 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2025-02-26T21:57:52.576Z] RMSE (validation) = 0.9259652469249674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2025-02-26T21:57:53.431Z] RMSE (validation) = 0.8982232214592321 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2025-02-26T21:57:53.822Z] 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.
[2025-02-26T21:57:53.822Z] The best model improves the baseline by 14.52%.
[2025-02-26T21:57:53.822Z] Movies recommended for you:
[2025-02-26T21:57:53.822Z] WARNING: This benchmark provides no result that can be validated.
[2025-02-26T21:57:53.822Z] There is no way to check that no silent failure occurred.
[2025-02-26T21:57:53.822Z] ====== movie-lens (apache-spark) [default], iteration 4 completed (9354.278 ms) ======
[2025-02-26T21:57:53.822Z] ====== movie-lens (apache-spark) [default], iteration 5 started ======
[2025-02-26T21:57:53.822Z] GC before operation: completed in 42.702 ms, heap usage 148.767 MB -> 50.147 MB.
[2025-02-26T21:57:55.168Z] RMSE (validation) = 3.62196895454876 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2025-02-26T21:57:56.513Z] RMSE (validation) = 2.1340923221100736 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2025-02-26T21:57:58.440Z] RMSE (validation) = 1.3105190428502682 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2025-02-26T21:57:59.788Z] RMSE (validation) = 0.9920028100571827 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2025-02-26T21:58:00.635Z] RMSE (validation) = 1.212631715629696 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2025-02-26T21:58:01.497Z] RMSE (validation) = 1.1025694034386657 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2025-02-26T21:58:02.352Z] RMSE (validation) = 0.9259652469249674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2025-02-26T21:58:03.193Z] RMSE (validation) = 0.8982232214592321 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2025-02-26T21:58:03.193Z] 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.
[2025-02-26T21:58:03.193Z] The best model improves the baseline by 14.52%.
[2025-02-26T21:58:03.593Z] Movies recommended for you:
[2025-02-26T21:58:03.593Z] WARNING: This benchmark provides no result that can be validated.
[2025-02-26T21:58:03.593Z] There is no way to check that no silent failure occurred.
[2025-02-26T21:58:03.593Z] ====== movie-lens (apache-spark) [default], iteration 5 completed (9606.034 ms) ======
[2025-02-26T21:58:03.593Z] ====== movie-lens (apache-spark) [default], iteration 6 started ======
[2025-02-26T21:58:03.593Z] GC before operation: completed in 36.349 ms, heap usage 63.473 MB -> 49.990 MB.
[2025-02-26T21:58:04.940Z] RMSE (validation) = 3.62196895454876 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2025-02-26T21:58:06.888Z] RMSE (validation) = 2.1340923221100736 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2025-02-26T21:58:08.236Z] RMSE (validation) = 1.3105190428502682 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2025-02-26T21:58:09.571Z] RMSE (validation) = 0.9920028100571827 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2025-02-26T21:58:10.401Z] RMSE (validation) = 1.212631715629696 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2025-02-26T21:58:11.238Z] RMSE (validation) = 1.1025694034386657 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2025-02-26T21:58:12.069Z] RMSE (validation) = 0.9259652469249674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2025-02-26T21:58:13.445Z] RMSE (validation) = 0.8982232214592321 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2025-02-26T21:58:13.445Z] 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.
[2025-02-26T21:58:13.445Z] The best model improves the baseline by 14.52%.
[2025-02-26T21:58:13.445Z] Movies recommended for you:
[2025-02-26T21:58:13.445Z] WARNING: This benchmark provides no result that can be validated.
[2025-02-26T21:58:13.445Z] There is no way to check that no silent failure occurred.
[2025-02-26T21:58:13.445Z] ====== movie-lens (apache-spark) [default], iteration 6 completed (9939.876 ms) ======
[2025-02-26T21:58:13.446Z] ====== movie-lens (apache-spark) [default], iteration 7 started ======
[2025-02-26T21:58:13.446Z] GC before operation: completed in 54.098 ms, heap usage 415.823 MB -> 53.624 MB.
[2025-02-26T21:58:15.357Z] RMSE (validation) = 3.62196895454876 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2025-02-26T21:58:16.717Z] RMSE (validation) = 2.1340923221100736 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2025-02-26T21:58:18.074Z] RMSE (validation) = 1.3105190428502682 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2025-02-26T21:58:19.416Z] RMSE (validation) = 0.9920028100571827 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2025-02-26T21:58:20.246Z] RMSE (validation) = 1.212631715629696 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2025-02-26T21:58:21.632Z] RMSE (validation) = 1.1025694034386657 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2025-02-26T21:58:22.517Z] RMSE (validation) = 0.9259652469249674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2025-02-26T21:58:22.929Z] RMSE (validation) = 0.8982232214592321 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2025-02-26T21:58:23.322Z] 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.
[2025-02-26T21:58:23.322Z] The best model improves the baseline by 14.52%.
[2025-02-26T21:58:23.322Z] Movies recommended for you:
[2025-02-26T21:58:23.322Z] WARNING: This benchmark provides no result that can be validated.
[2025-02-26T21:58:23.322Z] There is no way to check that no silent failure occurred.
[2025-02-26T21:58:23.322Z] ====== movie-lens (apache-spark) [default], iteration 7 completed (9881.096 ms) ======
[2025-02-26T21:58:23.322Z] ====== movie-lens (apache-spark) [default], iteration 8 started ======
[2025-02-26T21:58:23.322Z] GC before operation: completed in 48.312 ms, heap usage 247.489 MB -> 50.620 MB.
[2025-02-26T21:58:26.063Z] RMSE (validation) = 3.62196895454876 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2025-02-26T21:58:26.909Z] RMSE (validation) = 2.1340923221100736 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2025-02-26T21:58:28.829Z] RMSE (validation) = 1.3105190428502682 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2025-02-26T21:58:30.170Z] RMSE (validation) = 0.9920028100571827 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2025-02-26T21:58:31.014Z] RMSE (validation) = 1.212631715629696 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2025-02-26T21:58:31.860Z] RMSE (validation) = 1.1025694034386657 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2025-02-26T21:58:32.699Z] RMSE (validation) = 0.9259652469249674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2025-02-26T21:58:33.551Z] RMSE (validation) = 0.8982232214592321 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2025-02-26T21:58:33.551Z] 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.
[2025-02-26T21:58:33.940Z] The best model improves the baseline by 14.52%.
[2025-02-26T21:58:33.940Z] Movies recommended for you:
[2025-02-26T21:58:33.940Z] WARNING: This benchmark provides no result that can be validated.
[2025-02-26T21:58:33.940Z] There is no way to check that no silent failure occurred.
[2025-02-26T21:58:33.940Z] ====== movie-lens (apache-spark) [default], iteration 8 completed (10465.325 ms) ======
[2025-02-26T21:58:33.940Z] ====== movie-lens (apache-spark) [default], iteration 9 started ======
[2025-02-26T21:58:33.940Z] GC before operation: completed in 41.989 ms, heap usage 124.552 MB -> 50.334 MB.
[2025-02-26T21:58:35.283Z] RMSE (validation) = 3.6219689545487603 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2025-02-26T21:58:36.628Z] RMSE (validation) = 2.1340923221100736 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2025-02-26T21:58:38.562Z] RMSE (validation) = 1.3105190428502682 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2025-02-26T21:58:39.897Z] RMSE (validation) = 0.9920028100571827 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2025-02-26T21:58:40.760Z] RMSE (validation) = 1.212631715629696 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2025-02-26T21:58:41.150Z] RMSE (validation) = 1.1025694034386657 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2025-02-26T21:58:42.031Z] RMSE (validation) = 0.9259652469249674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2025-02-26T21:58:42.871Z] RMSE (validation) = 0.8982232214592321 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2025-02-26T21:58:43.266Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9063003101263983.
[2025-02-26T21:58:43.266Z] The best model improves the baseline by 14.52%.
[2025-02-26T21:58:43.266Z] Movies recommended for you:
[2025-02-26T21:58:43.266Z] WARNING: This benchmark provides no result that can be validated.
[2025-02-26T21:58:43.266Z] There is no way to check that no silent failure occurred.
[2025-02-26T21:58:43.266Z] ====== movie-lens (apache-spark) [default], iteration 9 completed (9302.216 ms) ======
[2025-02-26T21:58:43.266Z] ====== movie-lens (apache-spark) [default], iteration 10 started ======
[2025-02-26T21:58:43.266Z] GC before operation: completed in 37.441 ms, heap usage 244.674 MB -> 50.623 MB.
[2025-02-26T21:58:44.597Z] RMSE (validation) = 3.62196895454876 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2025-02-26T21:58:45.946Z] RMSE (validation) = 2.1340923221100736 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2025-02-26T21:58:47.297Z] RMSE (validation) = 1.3105190428502682 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2025-02-26T21:58:48.684Z] RMSE (validation) = 0.9920028100571827 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2025-02-26T21:58:49.551Z] RMSE (validation) = 1.212631715629696 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2025-02-26T21:58:50.388Z] RMSE (validation) = 1.1025694034386657 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2025-02-26T21:58:51.222Z] RMSE (validation) = 0.9259652469249674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2025-02-26T21:58:52.054Z] RMSE (validation) = 0.8982232214592321 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2025-02-26T21:58:52.451Z] 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.
[2025-02-26T21:58:52.451Z] The best model improves the baseline by 14.52%.
[2025-02-26T21:58:52.451Z] Movies recommended for you:
[2025-02-26T21:58:52.451Z] WARNING: This benchmark provides no result that can be validated.
[2025-02-26T21:58:52.451Z] There is no way to check that no silent failure occurred.
[2025-02-26T21:58:52.451Z] ====== movie-lens (apache-spark) [default], iteration 10 completed (9122.960 ms) ======
[2025-02-26T21:58:52.451Z] ====== movie-lens (apache-spark) [default], iteration 11 started ======
[2025-02-26T21:58:52.451Z] GC before operation: completed in 39.774 ms, heap usage 69.951 MB -> 50.244 MB.
[2025-02-26T21:58:53.814Z] RMSE (validation) = 3.62196895454876 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2025-02-26T21:58:55.160Z] RMSE (validation) = 2.1340923221100736 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2025-02-26T21:58:57.103Z] RMSE (validation) = 1.3105190428502682 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2025-02-26T21:58:58.446Z] RMSE (validation) = 0.9920028100571827 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2025-02-26T21:58:58.858Z] RMSE (validation) = 1.212631715629696 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2025-02-26T21:58:59.717Z] RMSE (validation) = 1.1025694034386657 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2025-02-26T21:59:00.565Z] RMSE (validation) = 0.9259652469249674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2025-02-26T21:59:01.433Z] RMSE (validation) = 0.8982232214592321 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2025-02-26T21:59:01.433Z] 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.
[2025-02-26T21:59:01.433Z] The best model improves the baseline by 14.52%.
[2025-02-26T21:59:01.827Z] Movies recommended for you:
[2025-02-26T21:59:01.827Z] WARNING: This benchmark provides no result that can be validated.
[2025-02-26T21:59:01.827Z] There is no way to check that no silent failure occurred.
[2025-02-26T21:59:01.827Z] ====== movie-lens (apache-spark) [default], iteration 11 completed (9246.707 ms) ======
[2025-02-26T21:59:01.827Z] ====== movie-lens (apache-spark) [default], iteration 12 started ======
[2025-02-26T21:59:01.827Z] GC before operation: completed in 42.777 ms, heap usage 376.699 MB -> 53.934 MB.
[2025-02-26T21:59:03.200Z] RMSE (validation) = 3.6219689545487603 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2025-02-26T21:59:04.568Z] RMSE (validation) = 2.1340923221100736 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2025-02-26T21:59:05.922Z] RMSE (validation) = 1.3105190428502682 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2025-02-26T21:59:07.263Z] RMSE (validation) = 0.9920028100571827 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2025-02-26T21:59:07.692Z] RMSE (validation) = 1.212631715629696 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2025-02-26T21:59:09.043Z] RMSE (validation) = 1.1025694034386657 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2025-02-26T21:59:09.461Z] RMSE (validation) = 0.9259652469249674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2025-02-26T21:59:10.313Z] RMSE (validation) = 0.8982232214592321 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2025-02-26T21:59:10.838Z] 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.
[2025-02-26T21:59:10.838Z] The best model improves the baseline by 14.52%.
[2025-02-26T21:59:10.838Z] Movies recommended for you:
[2025-02-26T21:59:10.838Z] WARNING: This benchmark provides no result that can be validated.
[2025-02-26T21:59:10.838Z] There is no way to check that no silent failure occurred.
[2025-02-26T21:59:10.838Z] ====== movie-lens (apache-spark) [default], iteration 12 completed (8986.969 ms) ======
[2025-02-26T21:59:10.838Z] ====== movie-lens (apache-spark) [default], iteration 13 started ======
[2025-02-26T21:59:10.838Z] GC before operation: completed in 35.783 ms, heap usage 66.082 MB -> 50.603 MB.
[2025-02-26T21:59:12.187Z] RMSE (validation) = 3.6219689545487603 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2025-02-26T21:59:13.029Z] RMSE (validation) = 2.1340923221100736 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2025-02-26T21:59:14.962Z] RMSE (validation) = 1.3105190428502682 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2025-02-26T21:59:16.342Z] RMSE (validation) = 0.9920028100571827 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2025-02-26T21:59:16.746Z] RMSE (validation) = 1.212631715629696 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2025-02-26T21:59:17.631Z] RMSE (validation) = 1.1025694034386657 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2025-02-26T21:59:18.038Z] RMSE (validation) = 0.9259652469249674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2025-02-26T21:59:18.917Z] RMSE (validation) = 0.8982232214592321 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2025-02-26T21:59:18.917Z] 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.
[2025-02-26T21:59:18.917Z] The best model improves the baseline by 14.52%.
[2025-02-26T21:59:18.917Z] Movies recommended for you:
[2025-02-26T21:59:18.917Z] WARNING: This benchmark provides no result that can be validated.
[2025-02-26T21:59:18.917Z] There is no way to check that no silent failure occurred.
[2025-02-26T21:59:18.917Z] ====== movie-lens (apache-spark) [default], iteration 13 completed (8399.566 ms) ======
[2025-02-26T21:59:18.917Z] ====== movie-lens (apache-spark) [default], iteration 14 started ======
[2025-02-26T21:59:19.308Z] GC before operation: completed in 40.860 ms, heap usage 265.011 MB -> 50.566 MB.
[2025-02-26T21:59:20.724Z] RMSE (validation) = 3.62196895454876 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2025-02-26T21:59:22.095Z] RMSE (validation) = 2.1340923221100736 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2025-02-26T21:59:23.497Z] RMSE (validation) = 1.3105190428502682 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2025-02-26T21:59:24.398Z] RMSE (validation) = 0.9920028100571827 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2025-02-26T21:59:25.239Z] RMSE (validation) = 1.212631715629696 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2025-02-26T21:59:26.089Z] RMSE (validation) = 1.1025694034386657 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2025-02-26T21:59:26.970Z] RMSE (validation) = 0.9259652469249674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2025-02-26T21:59:27.389Z] RMSE (validation) = 0.8982232214592321 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2025-02-26T21:59:27.780Z] 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.
[2025-02-26T21:59:27.780Z] The best model improves the baseline by 14.52%.
[2025-02-26T21:59:27.780Z] Movies recommended for you:
[2025-02-26T21:59:27.780Z] WARNING: This benchmark provides no result that can be validated.
[2025-02-26T21:59:27.780Z] There is no way to check that no silent failure occurred.
[2025-02-26T21:59:27.780Z] ====== movie-lens (apache-spark) [default], iteration 14 completed (8643.550 ms) ======
[2025-02-26T21:59:27.780Z] ====== movie-lens (apache-spark) [default], iteration 15 started ======
[2025-02-26T21:59:27.780Z] GC before operation: completed in 36.966 ms, heap usage 96.164 MB -> 50.601 MB.
[2025-02-26T21:59:29.125Z] RMSE (validation) = 3.62196895454876 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2025-02-26T21:59:30.472Z] RMSE (validation) = 2.1340923221100736 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2025-02-26T21:59:31.834Z] RMSE (validation) = 1.3105190428502682 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2025-02-26T21:59:33.203Z] RMSE (validation) = 0.9920028100571827 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2025-02-26T21:59:34.048Z] RMSE (validation) = 1.212631715629696 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2025-02-26T21:59:34.888Z] RMSE (validation) = 1.1025694034386657 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2025-02-26T21:59:35.737Z] RMSE (validation) = 0.9259652469249674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2025-02-26T21:59:36.597Z] RMSE (validation) = 0.8982232214592321 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2025-02-26T21:59:36.993Z] 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.
[2025-02-26T21:59:36.993Z] The best model improves the baseline by 14.52%.
[2025-02-26T21:59:36.993Z] Movies recommended for you:
[2025-02-26T21:59:36.993Z] WARNING: This benchmark provides no result that can be validated.
[2025-02-26T21:59:36.993Z] There is no way to check that no silent failure occurred.
[2025-02-26T21:59:36.993Z] ====== movie-lens (apache-spark) [default], iteration 15 completed (9097.254 ms) ======
[2025-02-26T21:59:36.993Z] ====== movie-lens (apache-spark) [default], iteration 16 started ======
[2025-02-26T21:59:36.993Z] GC before operation: completed in 42.746 ms, heap usage 62.516 MB -> 54.053 MB.
[2025-02-26T21:59:38.363Z] RMSE (validation) = 3.62196895454876 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2025-02-26T21:59:39.705Z] RMSE (validation) = 2.1340923221100736 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2025-02-26T21:59:41.060Z] RMSE (validation) = 1.3105190428502682 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2025-02-26T21:59:42.411Z] RMSE (validation) = 0.9920028100571827 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2025-02-26T21:59:43.301Z] RMSE (validation) = 1.212631715629696 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2025-02-26T21:59:44.642Z] RMSE (validation) = 1.1025694034386657 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2025-02-26T21:59:45.525Z] RMSE (validation) = 0.9259652469249674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2025-02-26T21:59:45.930Z] RMSE (validation) = 0.8982232214592321 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2025-02-26T21:59:46.320Z] 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.
[2025-02-26T21:59:46.320Z] The best model improves the baseline by 14.52%.
[2025-02-26T21:59:46.320Z] Movies recommended for you:
[2025-02-26T21:59:46.320Z] WARNING: This benchmark provides no result that can be validated.
[2025-02-26T21:59:46.320Z] There is no way to check that no silent failure occurred.
[2025-02-26T21:59:46.320Z] ====== movie-lens (apache-spark) [default], iteration 16 completed (9270.211 ms) ======
[2025-02-26T21:59:46.320Z] ====== movie-lens (apache-spark) [default], iteration 17 started ======
[2025-02-26T21:59:46.320Z] GC before operation: completed in 40.575 ms, heap usage 167.899 MB -> 50.497 MB.
[2025-02-26T21:59:47.679Z] RMSE (validation) = 3.62196895454876 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2025-02-26T21:59:49.034Z] RMSE (validation) = 2.1340923221100736 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2025-02-26T21:59:50.382Z] RMSE (validation) = 1.3105190428502682 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2025-02-26T21:59:51.742Z] RMSE (validation) = 0.9920028100571827 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2025-02-26T21:59:52.584Z] RMSE (validation) = 1.212631715629696 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2025-02-26T21:59:53.443Z] RMSE (validation) = 1.1025694034386657 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2025-02-26T21:59:54.276Z] RMSE (validation) = 0.9259652469249674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2025-02-26T21:59:55.118Z] RMSE (validation) = 0.8982232214592321 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2025-02-26T21:59:55.118Z] 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.
[2025-02-26T21:59:55.118Z] The best model improves the baseline by 14.52%.
[2025-02-26T21:59:55.118Z] Movies recommended for you:
[2025-02-26T21:59:55.118Z] WARNING: This benchmark provides no result that can be validated.
[2025-02-26T21:59:55.118Z] There is no way to check that no silent failure occurred.
[2025-02-26T21:59:55.118Z] ====== movie-lens (apache-spark) [default], iteration 17 completed (8858.558 ms) ======
[2025-02-26T21:59:55.118Z] ====== movie-lens (apache-spark) [default], iteration 18 started ======
[2025-02-26T21:59:55.118Z] GC before operation: completed in 34.254 ms, heap usage 234.156 MB -> 50.689 MB.
[2025-02-26T21:59:56.477Z] RMSE (validation) = 3.62196895454876 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2025-02-26T21:59:57.829Z] RMSE (validation) = 2.1340923221100736 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2025-02-26T21:59:59.217Z] RMSE (validation) = 1.3105190428502682 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2025-02-26T22:00:00.579Z] RMSE (validation) = 0.9920028100571827 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2025-02-26T22:00:01.422Z] RMSE (validation) = 1.212631715629696 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2025-02-26T22:00:02.292Z] RMSE (validation) = 1.1025694034386657 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2025-02-26T22:00:03.153Z] RMSE (validation) = 0.9259652469249674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2025-02-26T22:00:04.041Z] RMSE (validation) = 0.8982232214592321 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2025-02-26T22:00:04.041Z] 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.
[2025-02-26T22:00:04.041Z] The best model improves the baseline by 14.52%.
[2025-02-26T22:00:04.041Z] Movies recommended for you:
[2025-02-26T22:00:04.041Z] WARNING: This benchmark provides no result that can be validated.
[2025-02-26T22:00:04.041Z] There is no way to check that no silent failure occurred.
[2025-02-26T22:00:04.041Z] ====== movie-lens (apache-spark) [default], iteration 18 completed (8990.890 ms) ======
[2025-02-26T22:00:04.041Z] ====== movie-lens (apache-spark) [default], iteration 19 started ======
[2025-02-26T22:00:04.041Z] GC before operation: completed in 36.605 ms, heap usage 125.635 MB -> 50.685 MB.
[2025-02-26T22:00:05.429Z] RMSE (validation) = 3.6219689545487603 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2025-02-26T22:00:06.788Z] RMSE (validation) = 2.1340923221100736 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2025-02-26T22:00:07.628Z] RMSE (validation) = 1.3105190428502682 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2025-02-26T22:00:09.078Z] RMSE (validation) = 0.9920028100571827 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2025-02-26T22:00:09.940Z] RMSE (validation) = 1.212631715629696 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2025-02-26T22:00:10.788Z] RMSE (validation) = 1.1025694034386657 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2025-02-26T22:00:11.647Z] RMSE (validation) = 0.9259652469249674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2025-02-26T22:00:12.485Z] RMSE (validation) = 0.8982232214592321 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2025-02-26T22:00:12.485Z] 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.
[2025-02-26T22:00:12.485Z] The best model improves the baseline by 14.52%.
[2025-02-26T22:00:12.485Z] Movies recommended for you:
[2025-02-26T22:00:12.485Z] WARNING: This benchmark provides no result that can be validated.
[2025-02-26T22:00:12.485Z] There is no way to check that no silent failure occurred.
[2025-02-26T22:00:12.485Z] ====== movie-lens (apache-spark) [default], iteration 19 completed (8416.298 ms) ======
[2025-02-26T22:00:13.019Z] -----------------------------------
[2025-02-26T22:00:13.019Z] renaissance-movie-lens_0_PASSED
[2025-02-26T22:00:13.019Z] -----------------------------------
[2025-02-26T22:00:13.019Z]
[2025-02-26T22:00:13.019Z] TEST TEARDOWN:
[2025-02-26T22:00:13.019Z] Nothing to be done for teardown.
[2025-02-26T22:00:13.019Z] renaissance-movie-lens_0 Finish Time: Wed Feb 26 17:00:12 2025 Epoch Time (ms): 1740607212632