renaissance-movie-lens_0
[2024-05-29T22:57:18.449Z] Running test renaissance-movie-lens_0 ...
[2024-05-29T22:57:18.449Z] ===============================================
[2024-05-29T22:57:18.449Z] renaissance-movie-lens_0 Start Time: Wed May 29 23:57:18 2024 Epoch Time (ms): 1717023438175
[2024-05-29T22:57:18.449Z] variation: NoOptions
[2024-05-29T22:57:18.449Z] JVM_OPTIONS:
[2024-05-29T22:57:18.449Z] { \
[2024-05-29T22:57:18.449Z] echo ""; echo "TEST SETUP:"; \
[2024-05-29T22:57:18.449Z] echo "Nothing to be done for setup."; \
[2024-05-29T22:57:18.449Z] mkdir -p "/Users/jenkins/workspace/Test_openjdk17_hs_extended.perf_x86-64_mac_testList_0/aqa-tests/TKG/../TKG/output_17170132419871/renaissance-movie-lens_0"; \
[2024-05-29T22:57:18.449Z] cd "/Users/jenkins/workspace/Test_openjdk17_hs_extended.perf_x86-64_mac_testList_0/aqa-tests/TKG/../TKG/output_17170132419871/renaissance-movie-lens_0"; \
[2024-05-29T22:57:18.449Z] echo ""; echo "TESTING:"; \
[2024-05-29T22:57:18.449Z] "/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_17170132419871/renaissance-movie-lens_0"/movie-lens.json" movie-lens; \
[2024-05-29T22:57:18.449Z] 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_17170132419871/renaissance-movie-lens_0"; else echo "-----------------------------------"; echo "renaissance-movie-lens_0""_FAILED"; echo "-----------------------------------"; fi; \
[2024-05-29T22:57:18.449Z] echo ""; echo "TEST TEARDOWN:"; \
[2024-05-29T22:57:18.449Z] echo "Nothing to be done for teardown."; \
[2024-05-29T22:57:18.450Z] } 2>&1 | tee -a "/Users/jenkins/workspace/Test_openjdk17_hs_extended.perf_x86-64_mac_testList_0/aqa-tests/TKG/../TKG/output_17170132419871/TestTargetResult";
[2024-05-29T22:57:18.450Z]
[2024-05-29T22:57:18.450Z] TEST SETUP:
[2024-05-29T22:57:18.450Z] Nothing to be done for setup.
[2024-05-29T22:57:18.450Z]
[2024-05-29T22:57:18.450Z] TESTING:
[2024-05-29T22:57:22.490Z] Using Spark's default log4j profile: org/apache/spark/log4j-defaults.properties
[2024-05-29T22:57:23.751Z] NOTE: 'movie-lens' benchmark uses Spark local executor with 8 (out of 8) threads.
[2024-05-29T22:57:26.953Z] Got 100004 ratings from 671 users on 9066 movies.
[2024-05-29T22:57:27.321Z] Training: 60056, validation: 20285, test: 19854
[2024-05-29T22:57:27.321Z] ====== movie-lens (apache-spark) [default], iteration 0 started ======
[2024-05-29T22:57:27.321Z] GC before operation: completed in 44.092 ms, heap usage 81.515 MB -> 38.292 MB.
[2024-05-29T22:57:55.694Z] RMSE (validation) = 3.6219689545487506 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-05-29T22:58:15.410Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-05-29T22:58:39.113Z] RMSE (validation) = 1.3105189674610933 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-05-29T22:58:55.565Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-05-29T22:59:04.859Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-05-29T22:59:14.164Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-05-29T22:59:25.427Z] RMSE (validation) = 0.9275717391338142 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-05-29T22:59:34.737Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-05-29T22:59:34.737Z] 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.
[2024-05-29T22:59:34.737Z] The best model improves the baseline by 14.43%.
[2024-05-29T22:59:35.107Z] Movies recommended for you:
[2024-05-29T22:59:35.107Z] WARNING: This benchmark provides no result that can be validated.
[2024-05-29T22:59:35.107Z] There is no way to check that no silent failure occurred.
[2024-05-29T22:59:35.107Z] ====== movie-lens (apache-spark) [default], iteration 0 completed (127712.670 ms) ======
[2024-05-29T22:59:35.107Z] ====== movie-lens (apache-spark) [default], iteration 1 started ======
[2024-05-29T22:59:35.107Z] GC before operation: completed in 65.775 ms, heap usage 427.869 MB -> 66.998 MB.
[2024-05-29T22:59:58.767Z] RMSE (validation) = 3.6219689545487506 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-05-29T23:00:18.699Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-05-29T23:00:42.344Z] RMSE (validation) = 1.3105189674610935 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-05-29T23:00:55.961Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-05-29T23:01:07.238Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-05-29T23:01:14.881Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-05-29T23:01:28.476Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-05-29T23:01:36.131Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-05-29T23:01:36.131Z] 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.
[2024-05-29T23:01:36.131Z] The best model improves the baseline by 14.43%.
[2024-05-29T23:01:36.498Z] Movies recommended for you:
[2024-05-29T23:01:36.498Z] WARNING: This benchmark provides no result that can be validated.
[2024-05-29T23:01:36.498Z] There is no way to check that no silent failure occurred.
[2024-05-29T23:01:36.498Z] ====== movie-lens (apache-spark) [default], iteration 1 completed (121302.239 ms) ======
[2024-05-29T23:01:36.498Z] ====== movie-lens (apache-spark) [default], iteration 2 started ======
[2024-05-29T23:01:36.498Z] GC before operation: completed in 71.972 ms, heap usage 283.533 MB -> 80.329 MB.
[2024-05-29T23:01:56.232Z] RMSE (validation) = 3.6219689545487506 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-05-29T23:02:15.971Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-05-29T23:02:39.705Z] RMSE (validation) = 1.3105189674610933 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-05-29T23:02:53.324Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-05-29T23:03:04.619Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-05-29T23:03:12.283Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-05-29T23:03:25.902Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-05-29T23:03:33.553Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-05-29T23:03:33.553Z] 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.
[2024-05-29T23:03:33.553Z] The best model improves the baseline by 14.43%.
[2024-05-29T23:03:33.553Z] Movies recommended for you:
[2024-05-29T23:03:33.553Z] WARNING: This benchmark provides no result that can be validated.
[2024-05-29T23:03:33.553Z] There is no way to check that no silent failure occurred.
[2024-05-29T23:03:33.553Z] ====== movie-lens (apache-spark) [default], iteration 2 completed (116575.472 ms) ======
[2024-05-29T23:03:33.553Z] ====== movie-lens (apache-spark) [default], iteration 3 started ======
[2024-05-29T23:03:33.553Z] GC before operation: completed in 70.904 ms, heap usage 233.264 MB -> 80.599 MB.
[2024-05-29T23:03:53.291Z] RMSE (validation) = 3.6219689545487506 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-05-29T23:04:13.025Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-05-29T23:04:37.098Z] RMSE (validation) = 1.3105189674610933 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-05-29T23:04:50.719Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-05-29T23:05:00.033Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-05-29T23:05:11.313Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-05-29T23:05:24.934Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-05-29T23:05:32.584Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-05-29T23:05:32.584Z] 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.
[2024-05-29T23:05:32.584Z] The best model improves the baseline by 14.43%.
[2024-05-29T23:05:32.584Z] Movies recommended for you:
[2024-05-29T23:05:32.584Z] WARNING: This benchmark provides no result that can be validated.
[2024-05-29T23:05:32.584Z] There is no way to check that no silent failure occurred.
[2024-05-29T23:05:32.584Z] ====== movie-lens (apache-spark) [default], iteration 3 completed (119317.801 ms) ======
[2024-05-29T23:05:32.584Z] ====== movie-lens (apache-spark) [default], iteration 4 started ======
[2024-05-29T23:05:32.584Z] GC before operation: completed in 85.179 ms, heap usage 322.313 MB -> 81.045 MB.
[2024-05-29T23:05:56.247Z] RMSE (validation) = 3.621968954548751 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-05-29T23:06:12.630Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-05-29T23:06:36.300Z] RMSE (validation) = 1.3105189674610933 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-05-29T23:06:49.910Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-05-29T23:07:01.194Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-05-29T23:07:08.885Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-05-29T23:07:20.182Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-05-29T23:07:29.485Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-05-29T23:07:29.485Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9073522634082535.
[2024-05-29T23:07:29.485Z] The best model improves the baseline by 14.43%.
[2024-05-29T23:07:29.485Z] Movies recommended for you:
[2024-05-29T23:07:29.485Z] WARNING: This benchmark provides no result that can be validated.
[2024-05-29T23:07:29.485Z] There is no way to check that no silent failure occurred.
[2024-05-29T23:07:29.485Z] ====== movie-lens (apache-spark) [default], iteration 4 completed (116655.938 ms) ======
[2024-05-29T23:07:29.485Z] ====== movie-lens (apache-spark) [default], iteration 5 started ======
[2024-05-29T23:07:29.485Z] GC before operation: completed in 76.967 ms, heap usage 376.893 MB -> 81.285 MB.
[2024-05-29T23:07:49.222Z] RMSE (validation) = 3.6219689545487506 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-05-29T23:08:08.964Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-05-29T23:08:32.643Z] RMSE (validation) = 1.3105189674610933 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-05-29T23:08:49.057Z] RMSE (validation) = 1.004540774800519 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-05-29T23:08:57.686Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-05-29T23:09:06.986Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-05-29T23:09:18.263Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-05-29T23:09:27.571Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-05-29T23:09:27.571Z] 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.
[2024-05-29T23:09:27.571Z] The best model improves the baseline by 14.43%.
[2024-05-29T23:09:27.571Z] Movies recommended for you:
[2024-05-29T23:09:27.571Z] WARNING: This benchmark provides no result that can be validated.
[2024-05-29T23:09:27.571Z] There is no way to check that no silent failure occurred.
[2024-05-29T23:09:27.571Z] ====== movie-lens (apache-spark) [default], iteration 5 completed (118073.789 ms) ======
[2024-05-29T23:09:27.571Z] ====== movie-lens (apache-spark) [default], iteration 6 started ======
[2024-05-29T23:09:27.571Z] GC before operation: completed in 71.807 ms, heap usage 203.304 MB -> 81.070 MB.
[2024-05-29T23:09:47.279Z] RMSE (validation) = 3.621968954548751 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-05-29T23:10:06.977Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-05-29T23:10:26.732Z] RMSE (validation) = 1.3105189674610933 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-05-29T23:10:43.161Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-05-29T23:10:52.481Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-05-29T23:11:01.782Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-05-29T23:11:13.067Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-05-29T23:11:20.709Z] RMSE (validation) = 0.9001440981626696 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-05-29T23:11:20.709Z] 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.
[2024-05-29T23:11:21.077Z] The best model improves the baseline by 14.43%.
[2024-05-29T23:11:21.077Z] Movies recommended for you:
[2024-05-29T23:11:21.077Z] WARNING: This benchmark provides no result that can be validated.
[2024-05-29T23:11:21.077Z] There is no way to check that no silent failure occurred.
[2024-05-29T23:11:21.077Z] ====== movie-lens (apache-spark) [default], iteration 6 completed (113580.176 ms) ======
[2024-05-29T23:11:21.077Z] ====== movie-lens (apache-spark) [default], iteration 7 started ======
[2024-05-29T23:11:21.077Z] GC before operation: completed in 74.347 ms, heap usage 416.174 MB -> 81.391 MB.
[2024-05-29T23:11:44.748Z] RMSE (validation) = 3.6219689545487506 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-05-29T23:12:04.454Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-05-29T23:12:24.263Z] RMSE (validation) = 1.3105189674610935 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-05-29T23:12:40.660Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-05-29T23:12:50.010Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-05-29T23:12:59.311Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-05-29T23:13:10.590Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-05-29T23:13:18.267Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-05-29T23:13:19.050Z] 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.
[2024-05-29T23:13:19.050Z] The best model improves the baseline by 14.43%.
[2024-05-29T23:13:19.050Z] Movies recommended for you:
[2024-05-29T23:13:19.050Z] WARNING: This benchmark provides no result that can be validated.
[2024-05-29T23:13:19.050Z] There is no way to check that no silent failure occurred.
[2024-05-29T23:13:19.050Z] ====== movie-lens (apache-spark) [default], iteration 7 completed (117929.333 ms) ======
[2024-05-29T23:13:19.050Z] ====== movie-lens (apache-spark) [default], iteration 8 started ======
[2024-05-29T23:13:19.050Z] GC before operation: completed in 73.681 ms, heap usage 491.312 MB -> 81.723 MB.
[2024-05-29T23:13:38.774Z] RMSE (validation) = 3.621968954548751 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-05-29T23:13:58.484Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-05-29T23:14:29.289Z] RMSE (validation) = 1.3105189674610935 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-05-29T23:14:48.256Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-05-29T23:14:57.570Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-05-29T23:15:06.868Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-05-29T23:15:18.134Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-05-29T23:15:25.773Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-05-29T23:15:26.138Z] 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.
[2024-05-29T23:15:26.138Z] The best model improves the baseline by 14.43%.
[2024-05-29T23:15:26.138Z] Movies recommended for you:
[2024-05-29T23:15:26.138Z] WARNING: This benchmark provides no result that can be validated.
[2024-05-29T23:15:26.138Z] There is no way to check that no silent failure occurred.
[2024-05-29T23:15:26.138Z] ====== movie-lens (apache-spark) [default], iteration 8 completed (127174.866 ms) ======
[2024-05-29T23:15:26.138Z] ====== movie-lens (apache-spark) [default], iteration 9 started ======
[2024-05-29T23:15:26.506Z] GC before operation: completed in 75.656 ms, heap usage 806.691 MB -> 81.530 MB.
[2024-05-29T23:15:50.152Z] RMSE (validation) = 3.6219689545487506 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-05-29T23:16:06.542Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-05-29T23:16:30.225Z] RMSE (validation) = 1.3105189674610935 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-05-29T23:16:46.609Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-05-29T23:16:55.915Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-05-29T23:17:03.549Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-05-29T23:17:14.819Z] RMSE (validation) = 0.927571739133814 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-05-29T23:17:24.155Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-05-29T23:17:24.155Z] 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.
[2024-05-29T23:17:24.155Z] The best model improves the baseline by 14.43%.
[2024-05-29T23:17:24.155Z] Movies recommended for you:
[2024-05-29T23:17:24.155Z] WARNING: This benchmark provides no result that can be validated.
[2024-05-29T23:17:24.155Z] There is no way to check that no silent failure occurred.
[2024-05-29T23:17:24.155Z] ====== movie-lens (apache-spark) [default], iteration 9 completed (117183.619 ms) ======
[2024-05-29T23:17:24.155Z] ====== movie-lens (apache-spark) [default], iteration 10 started ======
[2024-05-29T23:17:24.155Z] GC before operation: completed in 76.306 ms, heap usage 235.744 MB -> 81.514 MB.
[2024-05-29T23:17:43.879Z] RMSE (validation) = 3.621968954548751 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-05-29T23:18:12.310Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-05-29T23:18:40.713Z] RMSE (validation) = 1.3105189674610935 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-05-29T23:18:54.322Z] RMSE (validation) = 1.004540774800519 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-05-29T23:19:05.603Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-05-29T23:19:13.251Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-05-29T23:19:24.525Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-05-29T23:19:33.832Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-05-29T23:19:33.832Z] 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.
[2024-05-29T23:19:33.832Z] The best model improves the baseline by 14.43%.
[2024-05-29T23:19:34.197Z] Movies recommended for you:
[2024-05-29T23:19:34.197Z] WARNING: This benchmark provides no result that can be validated.
[2024-05-29T23:19:34.197Z] There is no way to check that no silent failure occurred.
[2024-05-29T23:19:34.197Z] ====== movie-lens (apache-spark) [default], iteration 10 completed (130466.543 ms) ======
[2024-05-29T23:19:34.197Z] ====== movie-lens (apache-spark) [default], iteration 11 started ======
[2024-05-29T23:19:34.197Z] GC before operation: completed in 75.768 ms, heap usage 511.465 MB -> 81.356 MB.
[2024-05-29T23:19:53.934Z] RMSE (validation) = 3.6219689545487506 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-05-29T23:20:13.670Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-05-29T23:20:33.420Z] RMSE (validation) = 1.3105189674610933 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-05-29T23:20:49.854Z] RMSE (validation) = 1.004540774800519 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-05-29T23:20:59.154Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-05-29T23:21:08.494Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-05-29T23:21:19.769Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-05-29T23:21:27.414Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-05-29T23:21:27.784Z] 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.
[2024-05-29T23:21:27.784Z] The best model improves the baseline by 14.43%.
[2024-05-29T23:21:27.784Z] Movies recommended for you:
[2024-05-29T23:21:27.784Z] WARNING: This benchmark provides no result that can be validated.
[2024-05-29T23:21:27.784Z] There is no way to check that no silent failure occurred.
[2024-05-29T23:21:27.784Z] ====== movie-lens (apache-spark) [default], iteration 11 completed (113616.103 ms) ======
[2024-05-29T23:21:27.784Z] ====== movie-lens (apache-spark) [default], iteration 12 started ======
[2024-05-29T23:21:27.784Z] GC before operation: completed in 72.557 ms, heap usage 649.566 MB -> 81.554 MB.
[2024-05-29T23:21:51.492Z] RMSE (validation) = 3.621968954548751 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-05-29T23:22:11.190Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-05-29T23:22:34.914Z] RMSE (validation) = 1.3105189674610935 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-05-29T23:22:51.302Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-05-29T23:23:00.623Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-05-29T23:23:08.269Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-05-29T23:23:19.543Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-05-29T23:23:28.874Z] RMSE (validation) = 0.9001440981626694 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-05-29T23:23:28.874Z] 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.
[2024-05-29T23:23:28.874Z] The best model improves the baseline by 14.43%.
[2024-05-29T23:23:29.241Z] Movies recommended for you:
[2024-05-29T23:23:29.241Z] WARNING: This benchmark provides no result that can be validated.
[2024-05-29T23:23:29.241Z] There is no way to check that no silent failure occurred.
[2024-05-29T23:23:29.241Z] ====== movie-lens (apache-spark) [default], iteration 12 completed (121293.185 ms) ======
[2024-05-29T23:23:29.241Z] ====== movie-lens (apache-spark) [default], iteration 13 started ======
[2024-05-29T23:23:29.241Z] GC before operation: completed in 76.278 ms, heap usage 231.227 MB -> 81.604 MB.
[2024-05-29T23:23:52.925Z] RMSE (validation) = 3.6219689545487506 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-05-29T23:24:12.624Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-05-29T23:24:32.350Z] RMSE (validation) = 1.3105189674610933 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-05-29T23:24:48.806Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-05-29T23:24:58.098Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-05-29T23:25:05.763Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-05-29T23:25:17.042Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-05-29T23:25:26.379Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-05-29T23:25:26.379Z] 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.
[2024-05-29T23:25:26.379Z] The best model improves the baseline by 14.43%.
[2024-05-29T23:25:26.379Z] Movies recommended for you:
[2024-05-29T23:25:26.379Z] WARNING: This benchmark provides no result that can be validated.
[2024-05-29T23:25:26.379Z] There is no way to check that no silent failure occurred.
[2024-05-29T23:25:26.379Z] ====== movie-lens (apache-spark) [default], iteration 13 completed (116686.253 ms) ======
[2024-05-29T23:25:26.379Z] ====== movie-lens (apache-spark) [default], iteration 14 started ======
[2024-05-29T23:25:26.379Z] GC before operation: completed in 77.855 ms, heap usage 555.501 MB -> 81.315 MB.
[2024-05-29T23:25:46.094Z] RMSE (validation) = 3.6219689545487506 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-05-29T23:26:10.242Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-05-29T23:26:33.898Z] RMSE (validation) = 1.3105189674610935 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-05-29T23:26:47.520Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-05-29T23:26:58.835Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-05-29T23:27:06.481Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-05-29T23:27:17.741Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-05-29T23:27:27.085Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-05-29T23:27:27.452Z] 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.
[2024-05-29T23:27:27.452Z] The best model improves the baseline by 14.43%.
[2024-05-29T23:27:27.452Z] Movies recommended for you:
[2024-05-29T23:27:27.452Z] WARNING: This benchmark provides no result that can be validated.
[2024-05-29T23:27:27.452Z] There is no way to check that no silent failure occurred.
[2024-05-29T23:27:27.452Z] ====== movie-lens (apache-spark) [default], iteration 14 completed (121505.071 ms) ======
[2024-05-29T23:27:27.452Z] ====== movie-lens (apache-spark) [default], iteration 15 started ======
[2024-05-29T23:27:27.452Z] GC before operation: completed in 58.755 ms, heap usage 194.035 MB -> 56.605 MB.
[2024-05-29T23:27:47.273Z] RMSE (validation) = 3.6219689545487506 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-05-29T23:28:07.040Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-05-29T23:28:26.769Z] RMSE (validation) = 1.3105189674610935 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-05-29T23:28:43.162Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-05-29T23:28:52.460Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-05-29T23:29:01.848Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-05-29T23:29:13.102Z] RMSE (validation) = 0.9275717391338142 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-05-29T23:29:20.741Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-05-29T23:29:20.741Z] 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.
[2024-05-29T23:29:20.741Z] The best model improves the baseline by 14.43%.
[2024-05-29T23:29:21.117Z] Movies recommended for you:
[2024-05-29T23:29:21.117Z] WARNING: This benchmark provides no result that can be validated.
[2024-05-29T23:29:21.117Z] There is no way to check that no silent failure occurred.
[2024-05-29T23:29:21.117Z] ====== movie-lens (apache-spark) [default], iteration 15 completed (113511.806 ms) ======
[2024-05-29T23:29:21.117Z] ====== movie-lens (apache-spark) [default], iteration 16 started ======
[2024-05-29T23:29:21.117Z] GC before operation: completed in 78.060 ms, heap usage 588.275 MB -> 81.666 MB.
[2024-05-29T23:29:44.830Z] RMSE (validation) = 3.621968954548751 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-05-29T23:30:04.523Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-05-29T23:30:24.279Z] RMSE (validation) = 1.3105189674610935 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-05-29T23:30:40.722Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-05-29T23:30:50.015Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-05-29T23:30:57.730Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-05-29T23:31:09.022Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-05-29T23:31:18.345Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-05-29T23:31:18.345Z] 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.
[2024-05-29T23:31:18.345Z] The best model improves the baseline by 14.43%.
[2024-05-29T23:31:18.345Z] Movies recommended for you:
[2024-05-29T23:31:18.345Z] WARNING: This benchmark provides no result that can be validated.
[2024-05-29T23:31:18.345Z] There is no way to check that no silent failure occurred.
[2024-05-29T23:31:18.345Z] ====== movie-lens (apache-spark) [default], iteration 16 completed (117090.664 ms) ======
[2024-05-29T23:31:18.345Z] ====== movie-lens (apache-spark) [default], iteration 17 started ======
[2024-05-29T23:31:18.345Z] GC before operation: completed in 71.670 ms, heap usage 505.367 MB -> 81.671 MB.
[2024-05-29T23:31:38.096Z] RMSE (validation) = 3.621968954548751 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-05-29T23:31:57.833Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-05-29T23:32:26.390Z] RMSE (validation) = 1.3105189674610933 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-05-29T23:32:40.100Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-05-29T23:32:49.476Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-05-29T23:32:57.131Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-05-29T23:33:10.739Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-05-29T23:33:18.457Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-05-29T23:33:18.457Z] 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.
[2024-05-29T23:33:18.457Z] The best model improves the baseline by 14.43%.
[2024-05-29T23:33:18.457Z] Movies recommended for you:
[2024-05-29T23:33:18.457Z] WARNING: This benchmark provides no result that can be validated.
[2024-05-29T23:33:18.457Z] There is no way to check that no silent failure occurred.
[2024-05-29T23:33:18.457Z] ====== movie-lens (apache-spark) [default], iteration 17 completed (119727.798 ms) ======
[2024-05-29T23:33:18.457Z] ====== movie-lens (apache-spark) [default], iteration 18 started ======
[2024-05-29T23:33:18.457Z] GC before operation: completed in 74.710 ms, heap usage 195.384 MB -> 81.518 MB.
[2024-05-29T23:33:42.119Z] RMSE (validation) = 3.6219689545487506 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-05-29T23:33:58.542Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-05-29T23:34:22.221Z] RMSE (validation) = 1.3105189674610935 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-05-29T23:34:35.831Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-05-29T23:34:47.108Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-05-29T23:34:54.798Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-05-29T23:35:08.436Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-05-29T23:35:16.090Z] RMSE (validation) = 0.9001440981626694 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-05-29T23:35:16.090Z] 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.
[2024-05-29T23:35:16.090Z] The best model improves the baseline by 14.43%.
[2024-05-29T23:35:16.090Z] Movies recommended for you:
[2024-05-29T23:35:16.090Z] WARNING: This benchmark provides no result that can be validated.
[2024-05-29T23:35:16.090Z] There is no way to check that no silent failure occurred.
[2024-05-29T23:35:16.090Z] ====== movie-lens (apache-spark) [default], iteration 18 completed (117522.140 ms) ======
[2024-05-29T23:35:16.090Z] ====== movie-lens (apache-spark) [default], iteration 19 started ======
[2024-05-29T23:35:16.090Z] GC before operation: completed in 74.341 ms, heap usage 226.436 MB -> 81.649 MB.
[2024-05-29T23:35:35.835Z] RMSE (validation) = 3.6219689545487506 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-05-29T23:35:55.554Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-05-29T23:36:19.215Z] RMSE (validation) = 1.3105189674610933 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-05-29T23:36:38.911Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-05-29T23:36:48.274Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-05-29T23:36:55.940Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-05-29T23:37:09.557Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-05-29T23:37:17.208Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-05-29T23:37:17.208Z] 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.
[2024-05-29T23:37:17.208Z] The best model improves the baseline by 14.43%.
[2024-05-29T23:37:17.208Z] Movies recommended for you:
[2024-05-29T23:37:17.208Z] WARNING: This benchmark provides no result that can be validated.
[2024-05-29T23:37:17.208Z] There is no way to check that no silent failure occurred.
[2024-05-29T23:37:17.208Z] ====== movie-lens (apache-spark) [default], iteration 19 completed (121044.423 ms) ======
[2024-05-29T23:37:17.987Z] -----------------------------------
[2024-05-29T23:37:17.987Z] renaissance-movie-lens_0_PASSED
[2024-05-29T23:37:17.987Z] -----------------------------------
[2024-05-29T23:37:17.987Z]
[2024-05-29T23:37:17.987Z] TEST TEARDOWN:
[2024-05-29T23:37:17.987Z] Nothing to be done for teardown.
[2024-05-29T23:37:18.354Z] renaissance-movie-lens_0 Finish Time: Thu May 30 00:37:17 2024 Epoch Time (ms): 1717025837979