renaissance-movie-lens_0
[2024-10-02T23:50:50.088Z] Running test renaissance-movie-lens_0 ...
[2024-10-02T23:50:50.088Z] ===============================================
[2024-10-02T23:50:50.088Z] renaissance-movie-lens_0 Start Time: Thu Oct 3 00:50:49 2024 Epoch Time (ms): 1727913049235
[2024-10-02T23:50:50.088Z] variation: NoOptions
[2024-10-02T23:50:50.088Z] JVM_OPTIONS:
[2024-10-02T23:50:50.088Z] { \
[2024-10-02T23:50:50.088Z] echo ""; echo "TEST SETUP:"; \
[2024-10-02T23:50:50.088Z] echo "Nothing to be done for setup."; \
[2024-10-02T23:50:50.088Z] mkdir -p "/Users/jenkins/workspace/Test_openjdk17_hs_extended.perf_x86-64_mac_testList_0/aqa-tests/TKG/../TKG/output_17279016772139/renaissance-movie-lens_0"; \
[2024-10-02T23:50:50.088Z] cd "/Users/jenkins/workspace/Test_openjdk17_hs_extended.perf_x86-64_mac_testList_0/aqa-tests/TKG/../TKG/output_17279016772139/renaissance-movie-lens_0"; \
[2024-10-02T23:50:50.088Z] echo ""; echo "TESTING:"; \
[2024-10-02T23:50:50.088Z] "/Users/jenkins/workspace/Test_openjdk17_hs_extended.perf_x86-64_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/jenkins/workspace/Test_openjdk17_hs_extended.perf_x86-64_mac_testList_0/aqa-tests/TKG/../../jvmtest/perf/renaissance/renaissance.jar" --json ""/Users/jenkins/workspace/Test_openjdk17_hs_extended.perf_x86-64_mac_testList_0/aqa-tests/TKG/../TKG/output_17279016772139/renaissance-movie-lens_0"/movie-lens.json" movie-lens; \
[2024-10-02T23:50:50.088Z] if [ $? -eq 0 ]; then echo "-----------------------------------"; echo "renaissance-movie-lens_0""_PASSED"; echo "-----------------------------------"; cd /Users/jenkins/workspace/Test_openjdk17_hs_extended.perf_x86-64_mac_testList_0/aqa-tests/TKG/..; rm -f -r "/Users/jenkins/workspace/Test_openjdk17_hs_extended.perf_x86-64_mac_testList_0/aqa-tests/TKG/../TKG/output_17279016772139/renaissance-movie-lens_0"; else echo "-----------------------------------"; echo "renaissance-movie-lens_0""_FAILED"; echo "-----------------------------------"; fi; \
[2024-10-02T23:50:50.088Z] echo ""; echo "TEST TEARDOWN:"; \
[2024-10-02T23:50:50.088Z] echo "Nothing to be done for teardown."; \
[2024-10-02T23:50:50.088Z] } 2>&1 | tee -a "/Users/jenkins/workspace/Test_openjdk17_hs_extended.perf_x86-64_mac_testList_0/aqa-tests/TKG/../TKG/output_17279016772139/TestTargetResult";
[2024-10-02T23:50:50.088Z]
[2024-10-02T23:50:50.088Z] TEST SETUP:
[2024-10-02T23:50:50.088Z] Nothing to be done for setup.
[2024-10-02T23:50:50.088Z]
[2024-10-02T23:50:50.088Z] TESTING:
[2024-10-02T23:50:53.178Z] Using Spark's default log4j profile: org/apache/spark/log4j-defaults.properties
[2024-10-02T23:50:54.921Z] NOTE: 'movie-lens' benchmark uses Spark local executor with 8 (out of 8) threads.
[2024-10-02T23:50:58.019Z] Got 100004 ratings from 671 users on 9066 movies.
[2024-10-02T23:50:58.372Z] Training: 60056, validation: 20285, test: 19854
[2024-10-02T23:50:58.372Z] ====== movie-lens (apache-spark) [default], iteration 0 started ======
[2024-10-02T23:50:58.372Z] GC before operation: completed in 59.447 ms, heap usage 43.526 MB -> 38.128 MB.
[2024-10-02T23:51:26.519Z] RMSE (validation) = 3.6219689545487506 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-10-02T23:51:49.970Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-10-02T23:52:13.395Z] RMSE (validation) = 1.3105189674610933 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-10-02T23:52:29.577Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-10-02T23:52:40.681Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-10-02T23:52:49.832Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-10-02T23:53:06.049Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-10-02T23:53:15.207Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-10-02T23:53:15.207Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9073522634082535.
[2024-10-02T23:53:15.207Z] The best model improves the baseline by 14.43%.
[2024-10-02T23:53:15.207Z] Movies recommended for you:
[2024-10-02T23:53:15.207Z] WARNING: This benchmark provides no result that can be validated.
[2024-10-02T23:53:15.207Z] There is no way to check that no silent failure occurred.
[2024-10-02T23:53:15.207Z] ====== movie-lens (apache-spark) [default], iteration 0 completed (136399.371 ms) ======
[2024-10-02T23:53:15.207Z] ====== movie-lens (apache-spark) [default], iteration 1 started ======
[2024-10-02T23:53:15.207Z] GC before operation: completed in 81.733 ms, heap usage 814.335 MB -> 74.434 MB.
[2024-10-02T23:53:45.823Z] RMSE (validation) = 3.6219689545487506 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-10-02T23:54:09.285Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-10-02T23:54:37.441Z] RMSE (validation) = 1.3105189674610933 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-10-02T23:54:56.936Z] RMSE (validation) = 1.004540774800519 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-10-02T23:55:08.066Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-10-02T23:55:19.152Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-10-02T23:55:32.583Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-10-02T23:55:43.688Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-10-02T23:55:43.688Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9073522634082535.
[2024-10-02T23:55:43.688Z] The best model improves the baseline by 14.43%.
[2024-10-02T23:55:43.688Z] Movies recommended for you:
[2024-10-02T23:55:43.688Z] WARNING: This benchmark provides no result that can be validated.
[2024-10-02T23:55:43.688Z] There is no way to check that no silent failure occurred.
[2024-10-02T23:55:43.688Z] ====== movie-lens (apache-spark) [default], iteration 1 completed (148913.986 ms) ======
[2024-10-02T23:55:43.688Z] ====== movie-lens (apache-spark) [default], iteration 2 started ======
[2024-10-02T23:55:43.688Z] GC before operation: completed in 76.230 ms, heap usage 609.865 MB -> 80.495 MB.
[2024-10-02T23:56:07.150Z] RMSE (validation) = 3.6219689545487506 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-10-02T23:56:30.590Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-10-02T23:56:58.775Z] RMSE (validation) = 1.3105189674610935 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-10-02T23:57:18.277Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-10-02T23:57:29.377Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-10-02T23:57:42.858Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-10-02T23:57:56.290Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-10-02T23:58:07.386Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-10-02T23:58:07.386Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9073522634082535.
[2024-10-02T23:58:07.386Z] The best model improves the baseline by 14.43%.
[2024-10-02T23:58:07.386Z] Movies recommended for you:
[2024-10-02T23:58:07.386Z] WARNING: This benchmark provides no result that can be validated.
[2024-10-02T23:58:07.386Z] There is no way to check that no silent failure occurred.
[2024-10-02T23:58:07.386Z] ====== movie-lens (apache-spark) [default], iteration 2 completed (143695.523 ms) ======
[2024-10-02T23:58:07.386Z] ====== movie-lens (apache-spark) [default], iteration 3 started ======
[2024-10-02T23:58:07.738Z] GC before operation: completed in 84.636 ms, heap usage 498.354 MB -> 80.726 MB.
[2024-10-02T23:58:35.889Z] RMSE (validation) = 3.6219689545487506 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-10-02T23:58:59.331Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-10-02T23:59:22.786Z] RMSE (validation) = 1.3105189674610935 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-10-02T23:59:42.290Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-10-02T23:59:53.497Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-10-03T00:00:02.642Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-10-03T00:00:18.817Z] RMSE (validation) = 0.9275717391338142 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-10-03T00:00:27.961Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-10-03T00:00:27.961Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9073522634082535.
[2024-10-03T00:00:27.961Z] The best model improves the baseline by 14.43%.
[2024-10-03T00:00:27.961Z] Movies recommended for you:
[2024-10-03T00:00:27.961Z] WARNING: This benchmark provides no result that can be validated.
[2024-10-03T00:00:27.961Z] There is no way to check that no silent failure occurred.
[2024-10-03T00:00:27.961Z] ====== movie-lens (apache-spark) [default], iteration 3 completed (139874.706 ms) ======
[2024-10-03T00:00:27.961Z] ====== movie-lens (apache-spark) [default], iteration 4 started ======
[2024-10-03T00:00:27.961Z] GC before operation: completed in 81.823 ms, heap usage 254.101 MB -> 80.987 MB.
[2024-10-03T00:00:51.414Z] RMSE (validation) = 3.6219689545487506 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-10-03T00:01:14.853Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-10-03T00:01:43.009Z] RMSE (validation) = 1.3105189674610933 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-10-03T00:02:02.502Z] RMSE (validation) = 1.004540774800519 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-10-03T00:02:13.604Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-10-03T00:02:22.776Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-10-03T00:02:36.193Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-10-03T00:02:47.286Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-10-03T00:02:47.286Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9073522634082535.
[2024-10-03T00:02:47.286Z] The best model improves the baseline by 14.43%.
[2024-10-03T00:02:47.286Z] Movies recommended for you:
[2024-10-03T00:02:47.286Z] WARNING: This benchmark provides no result that can be validated.
[2024-10-03T00:02:47.286Z] There is no way to check that no silent failure occurred.
[2024-10-03T00:02:47.286Z] ====== movie-lens (apache-spark) [default], iteration 4 completed (139419.400 ms) ======
[2024-10-03T00:02:47.286Z] ====== movie-lens (apache-spark) [default], iteration 5 started ======
[2024-10-03T00:02:47.286Z] GC before operation: completed in 79.709 ms, heap usage 334.858 MB -> 81.217 MB.
[2024-10-03T00:03:15.402Z] RMSE (validation) = 3.6219689545487506 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-10-03T00:03:34.908Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-10-03T00:04:03.094Z] RMSE (validation) = 1.3105189674610933 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-10-03T00:04:19.290Z] RMSE (validation) = 1.004540774800519 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-10-03T00:04:32.711Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-10-03T00:04:41.868Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-10-03T00:04:55.305Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-10-03T00:05:06.412Z] RMSE (validation) = 0.9001440981626694 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-10-03T00:05:06.412Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9073522634082535.
[2024-10-03T00:05:06.412Z] The best model improves the baseline by 14.43%.
[2024-10-03T00:05:06.412Z] Movies recommended for you:
[2024-10-03T00:05:06.412Z] WARNING: This benchmark provides no result that can be validated.
[2024-10-03T00:05:06.412Z] There is no way to check that no silent failure occurred.
[2024-10-03T00:05:06.412Z] ====== movie-lens (apache-spark) [default], iteration 5 completed (138814.210 ms) ======
[2024-10-03T00:05:06.412Z] ====== movie-lens (apache-spark) [default], iteration 6 started ======
[2024-10-03T00:05:06.412Z] GC before operation: completed in 78.091 ms, heap usage 603.601 MB -> 81.172 MB.
[2024-10-03T00:05:29.882Z] RMSE (validation) = 3.6219689545487506 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-10-03T00:05:53.340Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-10-03T00:06:16.787Z] RMSE (validation) = 1.3105189674610933 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-10-03T00:06:36.287Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-10-03T00:06:47.400Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-10-03T00:06:58.510Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-10-03T00:07:11.942Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-10-03T00:07:23.046Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-10-03T00:07:23.046Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9073522634082535.
[2024-10-03T00:07:23.046Z] The best model improves the baseline by 14.43%.
[2024-10-03T00:07:23.046Z] Movies recommended for you:
[2024-10-03T00:07:23.046Z] WARNING: This benchmark provides no result that can be validated.
[2024-10-03T00:07:23.046Z] There is no way to check that no silent failure occurred.
[2024-10-03T00:07:23.046Z] ====== movie-lens (apache-spark) [default], iteration 6 completed (136080.920 ms) ======
[2024-10-03T00:07:23.046Z] ====== movie-lens (apache-spark) [default], iteration 7 started ======
[2024-10-03T00:07:23.046Z] GC before operation: completed in 82.436 ms, heap usage 380.738 MB -> 81.323 MB.
[2024-10-03T00:07:51.236Z] RMSE (validation) = 3.6219689545487506 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-10-03T00:08:21.554Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-10-03T00:08:44.994Z] RMSE (validation) = 1.3105189674610933 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-10-03T00:09:04.524Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-10-03T00:09:15.631Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-10-03T00:09:26.745Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-10-03T00:09:40.211Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-10-03T00:09:49.370Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-10-03T00:09:49.723Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9073522634082535.
[2024-10-03T00:09:49.723Z] The best model improves the baseline by 14.43%.
[2024-10-03T00:09:50.078Z] Movies recommended for you:
[2024-10-03T00:09:50.078Z] WARNING: This benchmark provides no result that can be validated.
[2024-10-03T00:09:50.078Z] There is no way to check that no silent failure occurred.
[2024-10-03T00:09:50.078Z] ====== movie-lens (apache-spark) [default], iteration 7 completed (147839.120 ms) ======
[2024-10-03T00:09:50.078Z] ====== movie-lens (apache-spark) [default], iteration 8 started ======
[2024-10-03T00:09:50.078Z] GC before operation: completed in 82.151 ms, heap usage 622.273 MB -> 81.676 MB.
[2024-10-03T00:10:18.284Z] RMSE (validation) = 3.6219689545487506 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-10-03T00:10:37.793Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-10-03T00:11:05.961Z] RMSE (validation) = 1.3105189674610935 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-10-03T00:11:22.171Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-10-03T00:11:35.619Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-10-03T00:11:44.843Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-10-03T00:11:58.290Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-10-03T00:12:09.399Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-10-03T00:12:09.399Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9073522634082535.
[2024-10-03T00:12:09.399Z] The best model improves the baseline by 14.43%.
[2024-10-03T00:12:09.399Z] Movies recommended for you:
[2024-10-03T00:12:09.399Z] WARNING: This benchmark provides no result that can be validated.
[2024-10-03T00:12:09.399Z] There is no way to check that no silent failure occurred.
[2024-10-03T00:12:09.399Z] ====== movie-lens (apache-spark) [default], iteration 8 completed (138786.806 ms) ======
[2024-10-03T00:12:09.399Z] ====== movie-lens (apache-spark) [default], iteration 9 started ======
[2024-10-03T00:12:09.399Z] GC before operation: completed in 82.414 ms, heap usage 902.749 MB -> 81.524 MB.
[2024-10-03T00:12:32.843Z] RMSE (validation) = 3.6219689545487506 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-10-03T00:13:03.270Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-10-03T00:13:32.448Z] RMSE (validation) = 1.3105189674610933 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-10-03T00:13:51.956Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-10-03T00:14:01.111Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-10-03T00:14:12.237Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-10-03T00:14:25.664Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-10-03T00:14:34.853Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-10-03T00:14:35.205Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9073522634082535.
[2024-10-03T00:14:35.205Z] The best model improves the baseline by 14.43%.
[2024-10-03T00:14:35.205Z] Movies recommended for you:
[2024-10-03T00:14:35.205Z] WARNING: This benchmark provides no result that can be validated.
[2024-10-03T00:14:35.205Z] There is no way to check that no silent failure occurred.
[2024-10-03T00:14:35.205Z] ====== movie-lens (apache-spark) [default], iteration 9 completed (146288.695 ms) ======
[2024-10-03T00:14:35.205Z] ====== movie-lens (apache-spark) [default], iteration 10 started ======
[2024-10-03T00:14:35.205Z] GC before operation: completed in 84.643 ms, heap usage 540.469 MB -> 81.599 MB.
[2024-10-03T00:15:03.347Z] RMSE (validation) = 3.621968954548751 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-10-03T00:15:22.827Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-10-03T00:15:50.968Z] RMSE (validation) = 1.3105189674610933 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-10-03T00:16:07.165Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-10-03T00:16:20.615Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-10-03T00:16:29.769Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-10-03T00:16:43.234Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-10-03T00:16:54.335Z] RMSE (validation) = 0.9001440981626694 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-10-03T00:16:54.336Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9073522634082535.
[2024-10-03T00:16:54.336Z] The best model improves the baseline by 14.43%.
[2024-10-03T00:16:54.336Z] Movies recommended for you:
[2024-10-03T00:16:54.336Z] WARNING: This benchmark provides no result that can be validated.
[2024-10-03T00:16:54.336Z] There is no way to check that no silent failure occurred.
[2024-10-03T00:16:54.336Z] ====== movie-lens (apache-spark) [default], iteration 10 completed (138010.459 ms) ======
[2024-10-03T00:16:54.336Z] ====== movie-lens (apache-spark) [default], iteration 11 started ======
[2024-10-03T00:16:54.336Z] GC before operation: completed in 88.738 ms, heap usage 643.851 MB -> 81.327 MB.
[2024-10-03T00:17:17.759Z] RMSE (validation) = 3.6219689545487506 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-10-03T00:17:41.225Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-10-03T00:18:09.348Z] RMSE (validation) = 1.3105189674610933 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-10-03T00:18:28.829Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-10-03T00:18:42.393Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-10-03T00:18:51.540Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-10-03T00:19:07.743Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-10-03T00:19:16.902Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-10-03T00:19:16.902Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9073522634082535.
[2024-10-03T00:19:16.902Z] The best model improves the baseline by 14.43%.
[2024-10-03T00:19:16.902Z] Movies recommended for you:
[2024-10-03T00:19:16.902Z] WARNING: This benchmark provides no result that can be validated.
[2024-10-03T00:19:16.902Z] There is no way to check that no silent failure occurred.
[2024-10-03T00:19:16.902Z] ====== movie-lens (apache-spark) [default], iteration 11 completed (142901.948 ms) ======
[2024-10-03T00:19:16.902Z] ====== movie-lens (apache-spark) [default], iteration 12 started ======
[2024-10-03T00:19:16.902Z] GC before operation: completed in 77.366 ms, heap usage 272.404 MB -> 81.496 MB.
[2024-10-03T00:19:45.048Z] RMSE (validation) = 3.621968954548751 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-10-03T00:20:04.547Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-10-03T00:20:32.693Z] RMSE (validation) = 1.3105189674610935 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-10-03T00:20:48.925Z] RMSE (validation) = 1.004540774800519 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-10-03T00:21:02.359Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-10-03T00:21:11.505Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-10-03T00:21:24.943Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-10-03T00:21:36.056Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-10-03T00:21:36.056Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9073522634082535.
[2024-10-03T00:21:36.056Z] The best model improves the baseline by 14.43%.
[2024-10-03T00:21:36.056Z] Movies recommended for you:
[2024-10-03T00:21:36.056Z] WARNING: This benchmark provides no result that can be validated.
[2024-10-03T00:21:36.056Z] There is no way to check that no silent failure occurred.
[2024-10-03T00:21:36.056Z] ====== movie-lens (apache-spark) [default], iteration 12 completed (139254.520 ms) ======
[2024-10-03T00:21:36.056Z] ====== movie-lens (apache-spark) [default], iteration 13 started ======
[2024-10-03T00:21:36.056Z] GC before operation: completed in 71.576 ms, heap usage 118.640 MB -> 67.292 MB.
[2024-10-03T00:21:59.539Z] RMSE (validation) = 3.6219689545487506 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-10-03T00:22:25.259Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-10-03T00:22:53.433Z] RMSE (validation) = 1.3105189674610933 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-10-03T00:23:12.928Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-10-03T00:23:22.114Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-10-03T00:23:33.245Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-10-03T00:23:46.688Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-10-03T00:23:55.844Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-10-03T00:23:56.606Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9073522634082535.
[2024-10-03T00:23:56.606Z] The best model improves the baseline by 14.43%.
[2024-10-03T00:23:56.606Z] Movies recommended for you:
[2024-10-03T00:23:56.606Z] WARNING: This benchmark provides no result that can be validated.
[2024-10-03T00:23:56.606Z] There is no way to check that no silent failure occurred.
[2024-10-03T00:23:56.606Z] ====== movie-lens (apache-spark) [default], iteration 13 completed (140908.592 ms) ======
[2024-10-03T00:23:56.606Z] ====== movie-lens (apache-spark) [default], iteration 14 started ======
[2024-10-03T00:23:56.606Z] GC before operation: completed in 84.173 ms, heap usage 413.137 MB -> 81.476 MB.
[2024-10-03T00:24:20.057Z] RMSE (validation) = 3.621968954548751 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-10-03T00:24:43.494Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-10-03T00:25:11.717Z] RMSE (validation) = 1.3105189674610935 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-10-03T00:25:31.230Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-10-03T00:25:42.335Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-10-03T00:25:51.495Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-10-03T00:26:04.924Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-10-03T00:26:16.036Z] RMSE (validation) = 0.9001440981626694 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-10-03T00:26:16.036Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9073522634082535.
[2024-10-03T00:26:16.036Z] The best model improves the baseline by 14.43%.
[2024-10-03T00:26:16.036Z] Movies recommended for you:
[2024-10-03T00:26:16.036Z] WARNING: This benchmark provides no result that can be validated.
[2024-10-03T00:26:16.036Z] There is no way to check that no silent failure occurred.
[2024-10-03T00:26:16.037Z] ====== movie-lens (apache-spark) [default], iteration 14 completed (138968.945 ms) ======
[2024-10-03T00:26:16.037Z] ====== movie-lens (apache-spark) [default], iteration 15 started ======
[2024-10-03T00:26:16.037Z] GC before operation: completed in 87.460 ms, heap usage 231.139 MB -> 81.485 MB.
[2024-10-03T00:26:44.228Z] RMSE (validation) = 3.621968954548751 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-10-03T00:27:03.733Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-10-03T00:27:31.880Z] RMSE (validation) = 1.3105189674610933 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-10-03T00:27:48.074Z] RMSE (validation) = 1.004540774800519 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-10-03T00:28:01.522Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-10-03T00:28:10.657Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-10-03T00:28:26.837Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-10-03T00:28:34.391Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-10-03T00:28:34.743Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9073522634082535.
[2024-10-03T00:28:34.743Z] The best model improves the baseline by 14.43%.
[2024-10-03T00:28:34.743Z] Movies recommended for you:
[2024-10-03T00:28:34.743Z] WARNING: This benchmark provides no result that can be validated.
[2024-10-03T00:28:34.743Z] There is no way to check that no silent failure occurred.
[2024-10-03T00:28:34.743Z] ====== movie-lens (apache-spark) [default], iteration 15 completed (139065.825 ms) ======
[2024-10-03T00:28:34.743Z] ====== movie-lens (apache-spark) [default], iteration 16 started ======
[2024-10-03T00:28:34.743Z] GC before operation: completed in 83.291 ms, heap usage 416.337 MB -> 81.681 MB.
[2024-10-03T00:29:02.874Z] RMSE (validation) = 3.621968954548751 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-10-03T00:29:22.378Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-10-03T00:29:50.510Z] RMSE (validation) = 1.3105189674610935 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-10-03T00:30:09.999Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-10-03T00:30:19.171Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-10-03T00:30:30.292Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-10-03T00:30:43.725Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-10-03T00:30:54.842Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-10-03T00:30:54.842Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9073522634082535.
[2024-10-03T00:30:54.842Z] The best model improves the baseline by 14.43%.
[2024-10-03T00:30:54.842Z] Movies recommended for you:
[2024-10-03T00:30:54.842Z] WARNING: This benchmark provides no result that can be validated.
[2024-10-03T00:30:54.842Z] There is no way to check that no silent failure occurred.
[2024-10-03T00:30:54.842Z] ====== movie-lens (apache-spark) [default], iteration 16 completed (139129.710 ms) ======
[2024-10-03T00:30:54.842Z] ====== movie-lens (apache-spark) [default], iteration 17 started ======
[2024-10-03T00:30:54.842Z] GC before operation: completed in 69.589 ms, heap usage 711.037 MB -> 59.087 MB.
[2024-10-03T00:31:23.014Z] RMSE (validation) = 3.6219689545487506 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-10-03T00:31:46.503Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-10-03T00:32:09.991Z] RMSE (validation) = 1.3105189674610935 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-10-03T00:32:29.533Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-10-03T00:32:46.181Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-10-03T00:32:57.314Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-10-03T00:33:10.738Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-10-03T00:33:19.894Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-10-03T00:33:20.255Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9073522634082535.
[2024-10-03T00:33:20.255Z] The best model improves the baseline by 14.43%.
[2024-10-03T00:33:20.255Z] Movies recommended for you:
[2024-10-03T00:33:20.255Z] WARNING: This benchmark provides no result that can be validated.
[2024-10-03T00:33:20.255Z] There is no way to check that no silent failure occurred.
[2024-10-03T00:33:20.255Z] ====== movie-lens (apache-spark) [default], iteration 17 completed (146342.068 ms) ======
[2024-10-03T00:33:20.255Z] ====== movie-lens (apache-spark) [default], iteration 18 started ======
[2024-10-03T00:33:20.618Z] GC before operation: completed in 74.569 ms, heap usage 899.203 MB -> 69.565 MB.
[2024-10-03T00:33:44.093Z] RMSE (validation) = 3.6219689545487506 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-10-03T00:34:07.538Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-10-03T00:34:30.979Z] RMSE (validation) = 1.3105189674610935 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-10-03T00:34:50.527Z] RMSE (validation) = 1.004540774800519 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-10-03T00:35:01.627Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-10-03T00:35:10.774Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-10-03T00:35:24.266Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-10-03T00:35:35.368Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-10-03T00:35:35.368Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9073522634082535.
[2024-10-03T00:35:35.368Z] The best model improves the baseline by 14.43%.
[2024-10-03T00:35:35.368Z] Movies recommended for you:
[2024-10-03T00:35:35.368Z] WARNING: This benchmark provides no result that can be validated.
[2024-10-03T00:35:35.368Z] There is no way to check that no silent failure occurred.
[2024-10-03T00:35:35.368Z] ====== movie-lens (apache-spark) [default], iteration 18 completed (134136.893 ms) ======
[2024-10-03T00:35:35.368Z] ====== movie-lens (apache-spark) [default], iteration 19 started ======
[2024-10-03T00:35:35.368Z] GC before operation: completed in 84.419 ms, heap usage 878.243 MB -> 81.920 MB.
[2024-10-03T00:36:03.726Z] RMSE (validation) = 3.6219689545487506 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-10-03T00:36:27.198Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-10-03T00:36:55.371Z] RMSE (validation) = 1.3105189674610933 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-10-03T00:37:11.576Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-10-03T00:37:22.728Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-10-03T00:37:33.836Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-10-03T00:37:47.290Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-10-03T00:37:56.443Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-10-03T00:37:57.195Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9073522634082535.
[2024-10-03T00:37:57.195Z] The best model improves the baseline by 14.43%.
[2024-10-03T00:37:57.195Z] Movies recommended for you:
[2024-10-03T00:37:57.195Z] WARNING: This benchmark provides no result that can be validated.
[2024-10-03T00:37:57.195Z] There is no way to check that no silent failure occurred.
[2024-10-03T00:37:57.195Z] ====== movie-lens (apache-spark) [default], iteration 19 completed (142408.958 ms) ======
[2024-10-03T00:37:58.417Z] -----------------------------------
[2024-10-03T00:37:58.417Z] renaissance-movie-lens_0_PASSED
[2024-10-03T00:37:58.417Z] -----------------------------------
[2024-10-03T00:37:58.770Z]
[2024-10-03T00:37:58.770Z] TEST TEARDOWN:
[2024-10-03T00:37:58.770Z] Nothing to be done for teardown.
[2024-10-03T00:37:58.770Z] renaissance-movie-lens_0 Finish Time: Thu Oct 3 01:37:58 2024 Epoch Time (ms): 1727915878455