renaissance-movie-lens_0
[2024-05-29T20:38:07.156Z] Running test renaissance-movie-lens_0 ...
[2024-05-29T20:38:07.156Z] ===============================================
[2024-05-29T20:38:07.156Z] renaissance-movie-lens_0 Start Time: Wed May 29 16:38:06 2024 Epoch Time (ms): 1717015086864
[2024-05-29T20:38:07.156Z] variation: NoOptions
[2024-05-29T20:38:07.156Z] JVM_OPTIONS:
[2024-05-29T20:38:07.156Z] { \
[2024-05-29T20:38:07.156Z] echo ""; echo "TEST SETUP:"; \
[2024-05-29T20:38:07.156Z] echo "Nothing to be done for setup."; \
[2024-05-29T20:38:07.156Z] mkdir -p "/Users/admin/workspace/workspace/Test_openjdk17_hs_extended.perf_aarch64_mac_testList_0/aqa-tests/TKG/../TKG/output_17170147534968/renaissance-movie-lens_0"; \
[2024-05-29T20:38:07.156Z] cd "/Users/admin/workspace/workspace/Test_openjdk17_hs_extended.perf_aarch64_mac_testList_0/aqa-tests/TKG/../TKG/output_17170147534968/renaissance-movie-lens_0"; \
[2024-05-29T20:38:07.156Z] echo ""; echo "TESTING:"; \
[2024-05-29T20:38:07.156Z] "/Users/admin/workspace/workspace/Test_openjdk17_hs_extended.perf_aarch64_mac_testList_0/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_testList_0/aqa-tests/TKG/../../jvmtest/perf/renaissance/renaissance.jar" --json ""/Users/admin/workspace/workspace/Test_openjdk17_hs_extended.perf_aarch64_mac_testList_0/aqa-tests/TKG/../TKG/output_17170147534968/renaissance-movie-lens_0"/movie-lens.json" movie-lens; \
[2024-05-29T20:38:07.156Z] if [ $? -eq 0 ]; then echo "-----------------------------------"; echo "renaissance-movie-lens_0""_PASSED"; echo "-----------------------------------"; cd /Users/admin/workspace/workspace/Test_openjdk17_hs_extended.perf_aarch64_mac_testList_0/aqa-tests/TKG/..; rm -f -r "/Users/admin/workspace/workspace/Test_openjdk17_hs_extended.perf_aarch64_mac_testList_0/aqa-tests/TKG/../TKG/output_17170147534968/renaissance-movie-lens_0"; else echo "-----------------------------------"; echo "renaissance-movie-lens_0""_FAILED"; echo "-----------------------------------"; fi; \
[2024-05-29T20:38:07.156Z] echo ""; echo "TEST TEARDOWN:"; \
[2024-05-29T20:38:07.156Z] echo "Nothing to be done for teardown."; \
[2024-05-29T20:38:07.156Z] } 2>&1 | tee -a "/Users/admin/workspace/workspace/Test_openjdk17_hs_extended.perf_aarch64_mac_testList_0/aqa-tests/TKG/../TKG/output_17170147534968/TestTargetResult";
[2024-05-29T20:38:07.156Z]
[2024-05-29T20:38:07.156Z] TEST SETUP:
[2024-05-29T20:38:07.156Z] Nothing to be done for setup.
[2024-05-29T20:38:07.156Z]
[2024-05-29T20:38:07.156Z] TESTING:
[2024-05-29T20:38:08.987Z] Using Spark's default log4j profile: org/apache/spark/log4j-defaults.properties
[2024-05-29T20:38:09.781Z] NOTE: 'movie-lens' benchmark uses Spark local executor with 3 (out of 3) threads.
[2024-05-29T20:38:11.610Z] Got 100004 ratings from 671 users on 9066 movies.
[2024-05-29T20:38:11.610Z] Training: 60056, validation: 20285, test: 19854
[2024-05-29T20:38:11.610Z] ====== movie-lens (apache-spark) [default], iteration 0 started ======
[2024-05-29T20:38:11.610Z] GC before operation: completed in 25.415 ms, heap usage 72.337 MB -> 36.818 MB.
[2024-05-29T20:38:14.917Z] RMSE (validation) = 3.62196895454876 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-05-29T20:38:17.434Z] RMSE (validation) = 2.1340923221100736 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-05-29T20:38:19.294Z] RMSE (validation) = 1.3105190428502682 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-05-29T20:38:21.126Z] RMSE (validation) = 0.9920028100571827 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-05-29T20:38:21.917Z] RMSE (validation) = 1.212631715629696 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-05-29T20:38:23.198Z] RMSE (validation) = 1.1025694034386657 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-05-29T20:38:23.989Z] RMSE (validation) = 0.9259652469249674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-05-29T20:38:24.793Z] RMSE (validation) = 0.8982232214592321 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-05-29T20:38:24.793Z] 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.
[2024-05-29T20:38:24.793Z] The best model improves the baseline by 14.52%.
[2024-05-29T20:38:25.162Z] Movies recommended for you:
[2024-05-29T20:38:25.162Z] WARNING: This benchmark provides no result that can be validated.
[2024-05-29T20:38:25.162Z] There is no way to check that no silent failure occurred.
[2024-05-29T20:38:25.162Z] ====== movie-lens (apache-spark) [default], iteration 0 completed (13335.357 ms) ======
[2024-05-29T20:38:25.162Z] ====== movie-lens (apache-spark) [default], iteration 1 started ======
[2024-05-29T20:38:25.162Z] GC before operation: completed in 47.300 ms, heap usage 197.894 MB -> 52.896 MB.
[2024-05-29T20:38:26.441Z] RMSE (validation) = 3.62196895454876 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-05-29T20:38:27.716Z] RMSE (validation) = 2.1340923221100736 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-05-29T20:38:29.003Z] RMSE (validation) = 1.3105190428502682 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-05-29T20:38:30.282Z] RMSE (validation) = 0.9920028100571827 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-05-29T20:38:31.095Z] RMSE (validation) = 1.212631715629696 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-05-29T20:38:31.885Z] RMSE (validation) = 1.1025694034386657 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-05-29T20:38:32.685Z] RMSE (validation) = 0.9259652469249674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-05-29T20:38:33.490Z] RMSE (validation) = 0.8982232214592321 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-05-29T20:38:33.490Z] 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.
[2024-05-29T20:38:33.490Z] The best model improves the baseline by 14.52%.
[2024-05-29T20:38:33.856Z] Movies recommended for you:
[2024-05-29T20:38:33.856Z] WARNING: This benchmark provides no result that can be validated.
[2024-05-29T20:38:33.856Z] There is no way to check that no silent failure occurred.
[2024-05-29T20:38:33.856Z] ====== movie-lens (apache-spark) [default], iteration 1 completed (8627.432 ms) ======
[2024-05-29T20:38:33.856Z] ====== movie-lens (apache-spark) [default], iteration 2 started ======
[2024-05-29T20:38:33.856Z] GC before operation: completed in 32.331 ms, heap usage 305.521 MB -> 49.395 MB.
[2024-05-29T20:38:34.764Z] RMSE (validation) = 3.6219689545487603 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-05-29T20:38:36.032Z] RMSE (validation) = 2.1340923221100736 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-05-29T20:38:37.310Z] RMSE (validation) = 1.3105190428502682 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-05-29T20:38:38.586Z] RMSE (validation) = 0.9920028100571827 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-05-29T20:38:39.378Z] RMSE (validation) = 1.212631715629696 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-05-29T20:38:40.170Z] RMSE (validation) = 1.1025694034386657 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-05-29T20:38:40.971Z] RMSE (validation) = 0.9259652469249674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-05-29T20:38:41.756Z] RMSE (validation) = 0.8982232214592321 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-05-29T20:38:41.756Z] 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.
[2024-05-29T20:38:41.756Z] The best model improves the baseline by 14.52%.
[2024-05-29T20:38:41.756Z] Movies recommended for you:
[2024-05-29T20:38:41.756Z] WARNING: This benchmark provides no result that can be validated.
[2024-05-29T20:38:41.756Z] There is no way to check that no silent failure occurred.
[2024-05-29T20:38:41.756Z] ====== movie-lens (apache-spark) [default], iteration 2 completed (8030.217 ms) ======
[2024-05-29T20:38:41.756Z] ====== movie-lens (apache-spark) [default], iteration 3 started ======
[2024-05-29T20:38:41.756Z] GC before operation: completed in 41.913 ms, heap usage 123.348 MB -> 49.400 MB.
[2024-05-29T20:38:43.037Z] RMSE (validation) = 3.6219689545487603 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-05-29T20:38:44.406Z] RMSE (validation) = 2.1340923221100736 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-05-29T20:38:45.687Z] RMSE (validation) = 1.3105190428502682 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-05-29T20:38:46.982Z] RMSE (validation) = 0.9920028100571827 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-05-29T20:38:47.356Z] RMSE (validation) = 1.212631715629696 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-05-29T20:38:48.164Z] RMSE (validation) = 1.1025694034386657 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-05-29T20:38:48.954Z] RMSE (validation) = 0.9259652469249674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-05-29T20:38:49.767Z] RMSE (validation) = 0.8982232214592321 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-05-29T20:38:49.767Z] 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.
[2024-05-29T20:38:49.767Z] The best model improves the baseline by 14.52%.
[2024-05-29T20:38:49.767Z] Movies recommended for you:
[2024-05-29T20:38:49.767Z] WARNING: This benchmark provides no result that can be validated.
[2024-05-29T20:38:49.767Z] There is no way to check that no silent failure occurred.
[2024-05-29T20:38:49.767Z] ====== movie-lens (apache-spark) [default], iteration 3 completed (8080.170 ms) ======
[2024-05-29T20:38:49.767Z] ====== movie-lens (apache-spark) [default], iteration 4 started ======
[2024-05-29T20:38:49.767Z] GC before operation: completed in 28.930 ms, heap usage 96.616 MB -> 50.301 MB.
[2024-05-29T20:38:51.116Z] RMSE (validation) = 3.62196895454876 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-05-29T20:38:52.394Z] RMSE (validation) = 2.1340923221100736 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-05-29T20:38:53.667Z] RMSE (validation) = 1.3105190428502682 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-05-29T20:38:55.013Z] RMSE (validation) = 0.9920028100571827 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-05-29T20:38:55.385Z] RMSE (validation) = 1.212631715629696 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-05-29T20:38:56.171Z] RMSE (validation) = 1.1025694034386657 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-05-29T20:38:57.001Z] RMSE (validation) = 0.9259652469249674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-05-29T20:38:57.791Z] RMSE (validation) = 0.8982232214592321 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-05-29T20:38:58.160Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9063003101263983.
[2024-05-29T20:38:58.160Z] The best model improves the baseline by 14.52%.
[2024-05-29T20:38:58.160Z] Movies recommended for you:
[2024-05-29T20:38:58.160Z] WARNING: This benchmark provides no result that can be validated.
[2024-05-29T20:38:58.160Z] There is no way to check that no silent failure occurred.
[2024-05-29T20:38:58.160Z] ====== movie-lens (apache-spark) [default], iteration 4 completed (8239.731 ms) ======
[2024-05-29T20:38:58.160Z] ====== movie-lens (apache-spark) [default], iteration 5 started ======
[2024-05-29T20:38:58.160Z] GC before operation: completed in 40.597 ms, heap usage 257.441 MB -> 50.029 MB.
[2024-05-29T20:38:59.455Z] RMSE (validation) = 3.6219689545487603 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-05-29T20:39:00.775Z] RMSE (validation) = 2.1340923221100736 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-05-29T20:39:02.617Z] RMSE (validation) = 1.3105190428502682 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-05-29T20:39:03.430Z] RMSE (validation) = 0.9920028100571827 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-05-29T20:39:04.728Z] RMSE (validation) = 1.212631715629696 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-05-29T20:39:05.096Z] RMSE (validation) = 1.1025694034386657 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-05-29T20:39:06.368Z] RMSE (validation) = 0.9259652469249674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-05-29T20:39:07.168Z] RMSE (validation) = 0.8982232214592321 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-05-29T20:39:07.169Z] 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.
[2024-05-29T20:39:07.169Z] The best model improves the baseline by 14.52%.
[2024-05-29T20:39:07.169Z] Movies recommended for you:
[2024-05-29T20:39:07.169Z] WARNING: This benchmark provides no result that can be validated.
[2024-05-29T20:39:07.169Z] There is no way to check that no silent failure occurred.
[2024-05-29T20:39:07.169Z] ====== movie-lens (apache-spark) [default], iteration 5 completed (9056.862 ms) ======
[2024-05-29T20:39:07.169Z] ====== movie-lens (apache-spark) [default], iteration 6 started ======
[2024-05-29T20:39:07.169Z] GC before operation: completed in 36.864 ms, heap usage 122.703 MB -> 49.898 MB.
[2024-05-29T20:39:08.999Z] RMSE (validation) = 3.6219689545487603 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-05-29T20:39:10.270Z] RMSE (validation) = 2.1340923221100736 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-05-29T20:39:11.618Z] RMSE (validation) = 1.3105190428502682 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-05-29T20:39:12.406Z] RMSE (validation) = 0.9920028100571827 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-05-29T20:39:13.687Z] RMSE (validation) = 1.212631715629696 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-05-29T20:39:14.485Z] RMSE (validation) = 1.1025694034386657 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-05-29T20:39:15.278Z] RMSE (validation) = 0.9259652469249674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-05-29T20:39:16.079Z] RMSE (validation) = 0.8982232214592321 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-05-29T20:39:16.079Z] 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.
[2024-05-29T20:39:16.079Z] The best model improves the baseline by 14.52%.
[2024-05-29T20:39:16.079Z] Movies recommended for you:
[2024-05-29T20:39:16.079Z] WARNING: This benchmark provides no result that can be validated.
[2024-05-29T20:39:16.079Z] There is no way to check that no silent failure occurred.
[2024-05-29T20:39:16.079Z] ====== movie-lens (apache-spark) [default], iteration 6 completed (8755.331 ms) ======
[2024-05-29T20:39:16.079Z] ====== movie-lens (apache-spark) [default], iteration 7 started ======
[2024-05-29T20:39:16.079Z] GC before operation: completed in 38.570 ms, heap usage 306.426 MB -> 50.242 MB.
[2024-05-29T20:39:17.365Z] RMSE (validation) = 3.62196895454876 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-05-29T20:39:18.645Z] RMSE (validation) = 2.1340923221100736 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-05-29T20:39:20.484Z] RMSE (validation) = 1.3105190428502682 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-05-29T20:39:21.294Z] RMSE (validation) = 0.9920028100571827 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-05-29T20:39:22.095Z] RMSE (validation) = 1.212631715629696 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-05-29T20:39:22.883Z] RMSE (validation) = 1.1025694034386657 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-05-29T20:39:23.682Z] RMSE (validation) = 0.9259652469249674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-05-29T20:39:24.467Z] RMSE (validation) = 0.8982232214592321 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-05-29T20:39:24.467Z] 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.
[2024-05-29T20:39:24.467Z] The best model improves the baseline by 14.52%.
[2024-05-29T20:39:24.467Z] Movies recommended for you:
[2024-05-29T20:39:24.467Z] WARNING: This benchmark provides no result that can be validated.
[2024-05-29T20:39:24.467Z] There is no way to check that no silent failure occurred.
[2024-05-29T20:39:24.467Z] ====== movie-lens (apache-spark) [default], iteration 7 completed (8455.916 ms) ======
[2024-05-29T20:39:24.467Z] ====== movie-lens (apache-spark) [default], iteration 8 started ======
[2024-05-29T20:39:24.467Z] GC before operation: completed in 29.646 ms, heap usage 121.920 MB -> 50.421 MB.
[2024-05-29T20:39:25.770Z] RMSE (validation) = 3.62196895454876 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-05-29T20:39:27.053Z] RMSE (validation) = 2.1340923221100736 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-05-29T20:39:28.322Z] RMSE (validation) = 1.3105190428502682 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-05-29T20:39:29.595Z] RMSE (validation) = 0.9920028100571827 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-05-29T20:39:30.392Z] RMSE (validation) = 1.212631715629696 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-05-29T20:39:31.189Z] RMSE (validation) = 1.1025694034386657 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-05-29T20:39:31.984Z] RMSE (validation) = 0.9259652469249674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-05-29T20:39:32.357Z] RMSE (validation) = 0.8982232214592321 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-05-29T20:39:32.723Z] 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.
[2024-05-29T20:39:32.723Z] The best model improves the baseline by 14.52%.
[2024-05-29T20:39:32.723Z] Movies recommended for you:
[2024-05-29T20:39:32.723Z] WARNING: This benchmark provides no result that can be validated.
[2024-05-29T20:39:32.723Z] There is no way to check that no silent failure occurred.
[2024-05-29T20:39:32.723Z] ====== movie-lens (apache-spark) [default], iteration 8 completed (8148.307 ms) ======
[2024-05-29T20:39:32.723Z] ====== movie-lens (apache-spark) [default], iteration 9 started ======
[2024-05-29T20:39:32.723Z] GC before operation: completed in 29.592 ms, heap usage 245.057 MB -> 50.414 MB.
[2024-05-29T20:39:34.015Z] RMSE (validation) = 3.62196895454876 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-05-29T20:39:34.810Z] RMSE (validation) = 2.1340923221100736 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-05-29T20:39:36.089Z] RMSE (validation) = 1.3105190428502682 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-05-29T20:39:36.872Z] RMSE (validation) = 0.9920028100571827 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-05-29T20:39:37.669Z] RMSE (validation) = 1.212631715629696 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-05-29T20:39:38.469Z] RMSE (validation) = 1.1025694034386657 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-05-29T20:39:38.837Z] RMSE (validation) = 0.9259652469249674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-05-29T20:39:39.632Z] RMSE (validation) = 0.8982232214592321 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-05-29T20:39:39.632Z] 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.
[2024-05-29T20:39:39.632Z] The best model improves the baseline by 14.52%.
[2024-05-29T20:39:39.632Z] Movies recommended for you:
[2024-05-29T20:39:39.632Z] WARNING: This benchmark provides no result that can be validated.
[2024-05-29T20:39:39.632Z] There is no way to check that no silent failure occurred.
[2024-05-29T20:39:39.632Z] ====== movie-lens (apache-spark) [default], iteration 9 completed (6934.804 ms) ======
[2024-05-29T20:39:39.632Z] ====== movie-lens (apache-spark) [default], iteration 10 started ======
[2024-05-29T20:39:39.632Z] GC before operation: completed in 39.728 ms, heap usage 94.139 MB -> 50.386 MB.
[2024-05-29T20:39:40.918Z] RMSE (validation) = 3.6219689545487603 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-05-29T20:39:41.717Z] RMSE (validation) = 2.1340923221100736 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-05-29T20:39:43.007Z] RMSE (validation) = 1.3105190428502682 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-05-29T20:39:44.285Z] RMSE (validation) = 0.9920028100571827 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-05-29T20:39:45.083Z] RMSE (validation) = 1.212631715629696 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-05-29T20:39:45.893Z] RMSE (validation) = 1.1025694034386657 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-05-29T20:39:46.775Z] RMSE (validation) = 0.9259652469249674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-05-29T20:39:47.577Z] RMSE (validation) = 0.8982232214592321 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-05-29T20:39:47.577Z] 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.
[2024-05-29T20:39:47.577Z] The best model improves the baseline by 14.52%.
[2024-05-29T20:39:47.577Z] Movies recommended for you:
[2024-05-29T20:39:47.577Z] WARNING: This benchmark provides no result that can be validated.
[2024-05-29T20:39:47.577Z] There is no way to check that no silent failure occurred.
[2024-05-29T20:39:47.577Z] ====== movie-lens (apache-spark) [default], iteration 10 completed (7992.458 ms) ======
[2024-05-29T20:39:47.577Z] ====== movie-lens (apache-spark) [default], iteration 11 started ======
[2024-05-29T20:39:47.943Z] GC before operation: completed in 41.630 ms, heap usage 361.407 MB -> 53.479 MB.
[2024-05-29T20:39:49.217Z] RMSE (validation) = 3.62196895454876 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-05-29T20:39:50.496Z] RMSE (validation) = 2.1340923221100736 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-05-29T20:39:51.788Z] RMSE (validation) = 1.3105190428502682 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-05-29T20:39:53.064Z] RMSE (validation) = 0.9920028100571827 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-05-29T20:39:53.853Z] RMSE (validation) = 1.212631715629696 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-05-29T20:39:54.235Z] RMSE (validation) = 1.1025694034386657 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-05-29T20:39:55.038Z] RMSE (validation) = 0.9259652469249674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-05-29T20:39:55.840Z] RMSE (validation) = 0.8982232214592321 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-05-29T20:39:55.840Z] 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.
[2024-05-29T20:39:55.840Z] The best model improves the baseline by 14.52%.
[2024-05-29T20:39:55.840Z] Movies recommended for you:
[2024-05-29T20:39:55.840Z] WARNING: This benchmark provides no result that can be validated.
[2024-05-29T20:39:55.840Z] There is no way to check that no silent failure occurred.
[2024-05-29T20:39:55.840Z] ====== movie-lens (apache-spark) [default], iteration 11 completed (8243.807 ms) ======
[2024-05-29T20:39:55.840Z] ====== movie-lens (apache-spark) [default], iteration 12 started ======
[2024-05-29T20:39:56.212Z] GC before operation: completed in 33.019 ms, heap usage 175.795 MB -> 50.362 MB.
[2024-05-29T20:39:57.008Z] RMSE (validation) = 3.62196895454876 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-05-29T20:39:58.460Z] RMSE (validation) = 2.1340923221100736 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-05-29T20:39:59.250Z] RMSE (validation) = 1.3105190428502682 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-05-29T20:40:00.534Z] RMSE (validation) = 0.9920028100571827 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-05-29T20:40:01.331Z] RMSE (validation) = 1.212631715629696 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-05-29T20:40:02.149Z] RMSE (validation) = 1.1025694034386657 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-05-29T20:40:02.985Z] RMSE (validation) = 0.9259652469249674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-05-29T20:40:03.363Z] RMSE (validation) = 0.8982232214592321 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-05-29T20:40:03.738Z] 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.
[2024-05-29T20:40:03.738Z] The best model improves the baseline by 14.52%.
[2024-05-29T20:40:03.738Z] Movies recommended for you:
[2024-05-29T20:40:03.738Z] WARNING: This benchmark provides no result that can be validated.
[2024-05-29T20:40:03.738Z] There is no way to check that no silent failure occurred.
[2024-05-29T20:40:03.739Z] ====== movie-lens (apache-spark) [default], iteration 12 completed (7575.704 ms) ======
[2024-05-29T20:40:03.739Z] ====== movie-lens (apache-spark) [default], iteration 13 started ======
[2024-05-29T20:40:03.739Z] GC before operation: completed in 43.920 ms, heap usage 287.860 MB -> 50.751 MB.
[2024-05-29T20:40:04.581Z] RMSE (validation) = 3.6219689545487603 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-05-29T20:40:05.862Z] RMSE (validation) = 2.1340923221100736 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-05-29T20:40:07.136Z] RMSE (validation) = 1.3105190428502682 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-05-29T20:40:07.926Z] RMSE (validation) = 0.9920028100571827 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-05-29T20:40:08.291Z] RMSE (validation) = 1.212631715629696 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-05-29T20:40:09.099Z] RMSE (validation) = 1.1025694034386657 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-05-29T20:40:09.482Z] RMSE (validation) = 0.9259652469249674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-05-29T20:40:10.287Z] RMSE (validation) = 0.8982232214592321 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-05-29T20:40:10.287Z] 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.
[2024-05-29T20:40:10.287Z] The best model improves the baseline by 14.52%.
[2024-05-29T20:40:10.287Z] Movies recommended for you:
[2024-05-29T20:40:10.287Z] WARNING: This benchmark provides no result that can be validated.
[2024-05-29T20:40:10.287Z] There is no way to check that no silent failure occurred.
[2024-05-29T20:40:10.287Z] ====== movie-lens (apache-spark) [default], iteration 13 completed (6748.917 ms) ======
[2024-05-29T20:40:10.287Z] ====== movie-lens (apache-spark) [default], iteration 14 started ======
[2024-05-29T20:40:10.287Z] GC before operation: completed in 33.620 ms, heap usage 170.704 MB -> 50.328 MB.
[2024-05-29T20:40:11.557Z] RMSE (validation) = 3.62196895454876 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-05-29T20:40:12.351Z] RMSE (validation) = 2.1340923221100736 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-05-29T20:40:14.231Z] RMSE (validation) = 1.3105190428502682 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-05-29T20:40:15.020Z] RMSE (validation) = 0.9920028100571827 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-05-29T20:40:15.817Z] RMSE (validation) = 1.212631715629696 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-05-29T20:40:16.188Z] RMSE (validation) = 1.1025694034386657 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-05-29T20:40:16.981Z] RMSE (validation) = 0.9259652469249674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-05-29T20:40:17.791Z] RMSE (validation) = 0.8982232214592321 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-05-29T20:40:17.791Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9063003101263983.
[2024-05-29T20:40:17.791Z] The best model improves the baseline by 14.52%.
[2024-05-29T20:40:17.791Z] Movies recommended for you:
[2024-05-29T20:40:17.791Z] WARNING: This benchmark provides no result that can be validated.
[2024-05-29T20:40:17.791Z] There is no way to check that no silent failure occurred.
[2024-05-29T20:40:17.791Z] ====== movie-lens (apache-spark) [default], iteration 14 completed (7293.218 ms) ======
[2024-05-29T20:40:17.791Z] ====== movie-lens (apache-spark) [default], iteration 15 started ======
[2024-05-29T20:40:17.791Z] GC before operation: completed in 31.693 ms, heap usage 202.298 MB -> 50.405 MB.
[2024-05-29T20:40:18.609Z] RMSE (validation) = 3.6219689545487603 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-05-29T20:40:19.906Z] RMSE (validation) = 2.1340923221100736 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-05-29T20:40:21.221Z] RMSE (validation) = 1.3105190428502682 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-05-29T20:40:22.589Z] RMSE (validation) = 0.9920028100571827 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-05-29T20:40:23.378Z] RMSE (validation) = 1.212631715629696 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-05-29T20:40:24.170Z] RMSE (validation) = 1.1025694034386657 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-05-29T20:40:24.977Z] RMSE (validation) = 0.9259652469249674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-05-29T20:40:25.789Z] RMSE (validation) = 0.8982232214592321 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-05-29T20:40:25.789Z] 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.
[2024-05-29T20:40:25.789Z] The best model improves the baseline by 14.52%.
[2024-05-29T20:40:25.789Z] Movies recommended for you:
[2024-05-29T20:40:25.789Z] WARNING: This benchmark provides no result that can be validated.
[2024-05-29T20:40:25.789Z] There is no way to check that no silent failure occurred.
[2024-05-29T20:40:25.789Z] ====== movie-lens (apache-spark) [default], iteration 15 completed (8182.856 ms) ======
[2024-05-29T20:40:25.789Z] ====== movie-lens (apache-spark) [default], iteration 16 started ======
[2024-05-29T20:40:26.155Z] GC before operation: completed in 37.520 ms, heap usage 121.952 MB -> 50.414 MB.
[2024-05-29T20:40:27.472Z] RMSE (validation) = 3.6219689545487603 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-05-29T20:40:28.262Z] RMSE (validation) = 2.1340923221100736 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-05-29T20:40:29.534Z] RMSE (validation) = 1.3105190428502682 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-05-29T20:40:30.354Z] RMSE (validation) = 0.9920028100571827 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-05-29T20:40:31.146Z] RMSE (validation) = 1.212631715629696 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-05-29T20:40:31.947Z] RMSE (validation) = 1.1025694034386657 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-05-29T20:40:32.732Z] RMSE (validation) = 0.9259652469249674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-05-29T20:40:33.522Z] RMSE (validation) = 0.8982232214592321 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-05-29T20:40:33.523Z] 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.
[2024-05-29T20:40:33.523Z] The best model improves the baseline by 14.52%.
[2024-05-29T20:40:33.523Z] Movies recommended for you:
[2024-05-29T20:40:33.523Z] WARNING: This benchmark provides no result that can be validated.
[2024-05-29T20:40:33.523Z] There is no way to check that no silent failure occurred.
[2024-05-29T20:40:33.523Z] ====== movie-lens (apache-spark) [default], iteration 16 completed (7655.917 ms) ======
[2024-05-29T20:40:33.523Z] ====== movie-lens (apache-spark) [default], iteration 17 started ======
[2024-05-29T20:40:33.523Z] GC before operation: completed in 30.955 ms, heap usage 279.567 MB -> 50.633 MB.
[2024-05-29T20:40:34.815Z] RMSE (validation) = 3.62196895454876 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-05-29T20:40:36.095Z] RMSE (validation) = 2.1340923221100736 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-05-29T20:40:37.369Z] RMSE (validation) = 1.3105190428502682 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-05-29T20:40:38.424Z] RMSE (validation) = 0.9920028100571827 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-05-29T20:40:39.205Z] RMSE (validation) = 1.212631715629696 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-05-29T20:40:39.988Z] RMSE (validation) = 1.1025694034386657 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-05-29T20:40:40.772Z] RMSE (validation) = 0.9259652469249674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-05-29T20:40:41.560Z] RMSE (validation) = 0.8982232214592321 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-05-29T20:40:41.560Z] 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.
[2024-05-29T20:40:41.560Z] The best model improves the baseline by 14.52%.
[2024-05-29T20:40:41.560Z] Movies recommended for you:
[2024-05-29T20:40:41.560Z] WARNING: This benchmark provides no result that can be validated.
[2024-05-29T20:40:41.560Z] There is no way to check that no silent failure occurred.
[2024-05-29T20:40:41.560Z] ====== movie-lens (apache-spark) [default], iteration 17 completed (7904.961 ms) ======
[2024-05-29T20:40:41.560Z] ====== movie-lens (apache-spark) [default], iteration 18 started ======
[2024-05-29T20:40:41.560Z] GC before operation: completed in 40.310 ms, heap usage 93.324 MB -> 50.383 MB.
[2024-05-29T20:40:42.831Z] RMSE (validation) = 3.62196895454876 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-05-29T20:40:44.103Z] RMSE (validation) = 2.1340923221100736 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-05-29T20:40:45.372Z] RMSE (validation) = 1.3105190428502682 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-05-29T20:40:46.156Z] RMSE (validation) = 0.9920028100571827 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-05-29T20:40:46.956Z] RMSE (validation) = 1.212631715629696 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-05-29T20:40:47.327Z] RMSE (validation) = 1.1025694034386657 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-05-29T20:40:48.195Z] RMSE (validation) = 0.9259652469249674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-05-29T20:40:49.057Z] RMSE (validation) = 0.8982232214592321 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-05-29T20:40:49.057Z] 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.
[2024-05-29T20:40:49.057Z] The best model improves the baseline by 14.52%.
[2024-05-29T20:40:49.057Z] Movies recommended for you:
[2024-05-29T20:40:49.057Z] WARNING: This benchmark provides no result that can be validated.
[2024-05-29T20:40:49.057Z] There is no way to check that no silent failure occurred.
[2024-05-29T20:40:49.057Z] ====== movie-lens (apache-spark) [default], iteration 18 completed (7418.626 ms) ======
[2024-05-29T20:40:49.057Z] ====== movie-lens (apache-spark) [default], iteration 19 started ======
[2024-05-29T20:40:49.057Z] GC before operation: completed in 31.385 ms, heap usage 155.610 MB -> 50.546 MB.
[2024-05-29T20:40:50.364Z] RMSE (validation) = 3.62196895454876 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-05-29T20:40:51.641Z] RMSE (validation) = 2.1340923221100736 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-05-29T20:40:52.915Z] RMSE (validation) = 1.3105190428502682 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-05-29T20:40:54.204Z] RMSE (validation) = 0.9920028100571827 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-05-29T20:40:55.017Z] RMSE (validation) = 1.212631715629696 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-05-29T20:40:55.385Z] RMSE (validation) = 1.1025694034386657 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-05-29T20:40:56.198Z] RMSE (validation) = 0.9259652469249674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-05-29T20:40:56.995Z] RMSE (validation) = 0.8982232214592321 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-05-29T20:40:56.995Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9063003101263983.
[2024-05-29T20:40:56.995Z] The best model improves the baseline by 14.52%.
[2024-05-29T20:40:56.995Z] Movies recommended for you:
[2024-05-29T20:40:56.995Z] WARNING: This benchmark provides no result that can be validated.
[2024-05-29T20:40:56.995Z] There is no way to check that no silent failure occurred.
[2024-05-29T20:40:56.995Z] ====== movie-lens (apache-spark) [default], iteration 19 completed (7825.595 ms) ======
[2024-05-29T20:40:56.995Z] -----------------------------------
[2024-05-29T20:40:56.995Z] renaissance-movie-lens_0_PASSED
[2024-05-29T20:40:56.995Z] -----------------------------------
[2024-05-29T20:40:56.995Z]
[2024-05-29T20:40:56.995Z] TEST TEARDOWN:
[2024-05-29T20:40:56.995Z] Nothing to be done for teardown.
[2024-05-29T20:40:56.995Z] renaissance-movie-lens_0 Finish Time: Wed May 29 16:40:56 2024 Epoch Time (ms): 1717015256851