renaissance-movie-lens_0
[2024-09-05T00:48:53.402Z] Running test renaissance-movie-lens_0 ...
[2024-09-05T00:48:53.402Z] ===============================================
[2024-09-05T00:48:53.402Z] renaissance-movie-lens_0 Start Time: Wed Sep 4 17:48:52 2024 Epoch Time (ms): 1725497332156
[2024-09-05T00:48:53.402Z] variation: NoOptions
[2024-09-05T00:48:53.402Z] JVM_OPTIONS:
[2024-09-05T00:48:53.402Z] { \
[2024-09-05T00:48:53.402Z] echo ""; echo "TEST SETUP:"; \
[2024-09-05T00:48:53.402Z] echo "Nothing to be done for setup."; \
[2024-09-05T00:48:53.402Z] mkdir -p "/Users/admin/workspace/workspace/Test_openjdk17_hs_extended.perf_x86-64_mac_testList_0/aqa-tests/TKG/../TKG/output_17254950145893/renaissance-movie-lens_0"; \
[2024-09-05T00:48:53.402Z] cd "/Users/admin/workspace/workspace/Test_openjdk17_hs_extended.perf_x86-64_mac_testList_0/aqa-tests/TKG/../TKG/output_17254950145893/renaissance-movie-lens_0"; \
[2024-09-05T00:48:53.402Z] echo ""; echo "TESTING:"; \
[2024-09-05T00:48:53.402Z] "/Users/admin/workspace/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/admin/workspace/workspace/Test_openjdk17_hs_extended.perf_x86-64_mac_testList_0/aqa-tests/TKG/../../jvmtest/perf/renaissance/renaissance.jar" --json ""/Users/admin/workspace/workspace/Test_openjdk17_hs_extended.perf_x86-64_mac_testList_0/aqa-tests/TKG/../TKG/output_17254950145893/renaissance-movie-lens_0"/movie-lens.json" movie-lens; \
[2024-09-05T00:48:53.402Z] if [ $? -eq 0 ]; then echo "-----------------------------------"; echo "renaissance-movie-lens_0""_PASSED"; echo "-----------------------------------"; cd /Users/admin/workspace/workspace/Test_openjdk17_hs_extended.perf_x86-64_mac_testList_0/aqa-tests/TKG/..; rm -f -r "/Users/admin/workspace/workspace/Test_openjdk17_hs_extended.perf_x86-64_mac_testList_0/aqa-tests/TKG/../TKG/output_17254950145893/renaissance-movie-lens_0"; else echo "-----------------------------------"; echo "renaissance-movie-lens_0""_FAILED"; echo "-----------------------------------"; fi; \
[2024-09-05T00:48:53.402Z] echo ""; echo "TEST TEARDOWN:"; \
[2024-09-05T00:48:53.402Z] echo "Nothing to be done for teardown."; \
[2024-09-05T00:48:53.402Z] } 2>&1 | tee -a "/Users/admin/workspace/workspace/Test_openjdk17_hs_extended.perf_x86-64_mac_testList_0/aqa-tests/TKG/../TKG/output_17254950145893/TestTargetResult";
[2024-09-05T00:48:53.402Z]
[2024-09-05T00:48:53.402Z] TEST SETUP:
[2024-09-05T00:48:53.402Z] Nothing to be done for setup.
[2024-09-05T00:48:53.402Z]
[2024-09-05T00:48:53.402Z] TESTING:
[2024-09-05T00:49:07.016Z] Using Spark's default log4j profile: org/apache/spark/log4j-defaults.properties
[2024-09-05T00:49:12.797Z] NOTE: 'movie-lens' benchmark uses Spark local executor with 3 (out of 3) threads.
[2024-09-05T00:49:26.285Z] Got 100004 ratings from 671 users on 9066 movies.
[2024-09-05T00:49:30.696Z] Training: 60056, validation: 20285, test: 19854
[2024-09-05T00:49:30.696Z] ====== movie-lens (apache-spark) [default], iteration 0 started ======
[2024-09-05T00:49:32.303Z] GC before operation: completed in 999.395 ms, heap usage 123.338 MB -> 37.512 MB.
[2024-09-05T00:50:17.974Z] RMSE (validation) = 3.62196895454876 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-09-05T00:50:44.896Z] RMSE (validation) = 2.1340923221100736 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-09-05T00:51:03.913Z] RMSE (validation) = 1.3105190428502682 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-09-05T00:51:23.459Z] RMSE (validation) = 0.9920028100571827 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-09-05T00:51:35.075Z] RMSE (validation) = 1.212631715629696 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-09-05T00:51:54.975Z] RMSE (validation) = 1.1025694034386657 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-09-05T00:52:01.528Z] RMSE (validation) = 0.9259652469249674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-09-05T00:52:10.902Z] RMSE (validation) = 0.8982232214592321 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-09-05T00:52:11.379Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9063003101263983.
[2024-09-05T00:52:11.867Z] The best model improves the baseline by 14.52%.
[2024-09-05T00:52:11.867Z] Movies recommended for you:
[2024-09-05T00:52:11.867Z] WARNING: This benchmark provides no result that can be validated.
[2024-09-05T00:52:11.867Z] There is no way to check that no silent failure occurred.
[2024-09-05T00:52:11.867Z] ====== movie-lens (apache-spark) [default], iteration 0 completed (160018.816 ms) ======
[2024-09-05T00:52:11.867Z] ====== movie-lens (apache-spark) [default], iteration 1 started ======
[2024-09-05T00:52:12.546Z] GC before operation: completed in 205.365 ms, heap usage 455.157 MB -> 53.348 MB.
[2024-09-05T00:52:38.737Z] RMSE (validation) = 3.62196895454876 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-09-05T00:52:57.418Z] RMSE (validation) = 2.1340923221100736 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-09-05T00:53:17.049Z] RMSE (validation) = 1.3105190428502682 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-09-05T00:53:40.580Z] RMSE (validation) = 0.9920028100571827 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-09-05T00:53:48.983Z] RMSE (validation) = 1.212631715629696 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-09-05T00:54:00.339Z] RMSE (validation) = 1.1025694034386657 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-09-05T00:54:11.840Z] RMSE (validation) = 0.9259652469249674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-09-05T00:54:21.527Z] RMSE (validation) = 0.8982232214592321 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-09-05T00:54:22.089Z] 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.
[2024-09-05T00:54:22.527Z] The best model improves the baseline by 14.52%.
[2024-09-05T00:54:23.819Z] Movies recommended for you:
[2024-09-05T00:54:23.819Z] WARNING: This benchmark provides no result that can be validated.
[2024-09-05T00:54:23.819Z] There is no way to check that no silent failure occurred.
[2024-09-05T00:54:23.819Z] ====== movie-lens (apache-spark) [default], iteration 1 completed (130670.332 ms) ======
[2024-09-05T00:54:23.819Z] ====== movie-lens (apache-spark) [default], iteration 2 started ======
[2024-09-05T00:54:23.819Z] GC before operation: completed in 175.788 ms, heap usage 292.134 MB -> 49.776 MB.
[2024-09-05T00:54:39.673Z] RMSE (validation) = 3.62196895454876 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-09-05T00:55:02.297Z] RMSE (validation) = 2.1340923221100736 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-09-05T00:55:15.252Z] RMSE (validation) = 1.3105190428502682 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-09-05T00:55:34.959Z] RMSE (validation) = 0.9920028100571827 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-09-05T00:55:45.042Z] RMSE (validation) = 1.212631715629696 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-09-05T00:55:56.325Z] RMSE (validation) = 1.1025694034386657 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-09-05T00:56:06.909Z] RMSE (validation) = 0.9259652469249674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-09-05T00:56:26.038Z] RMSE (validation) = 0.8982232214592321 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-09-05T00:56:26.038Z] 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.
[2024-09-05T00:56:26.038Z] The best model improves the baseline by 14.52%.
[2024-09-05T00:56:26.038Z] Movies recommended for you:
[2024-09-05T00:56:26.038Z] WARNING: This benchmark provides no result that can be validated.
[2024-09-05T00:56:26.038Z] There is no way to check that no silent failure occurred.
[2024-09-05T00:56:26.038Z] ====== movie-lens (apache-spark) [default], iteration 2 completed (122772.451 ms) ======
[2024-09-05T00:56:26.038Z] ====== movie-lens (apache-spark) [default], iteration 3 started ======
[2024-09-05T00:56:26.038Z] GC before operation: completed in 183.467 ms, heap usage 172.104 MB -> 50.088 MB.
[2024-09-05T00:56:44.128Z] RMSE (validation) = 3.6219689545487603 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-09-05T00:57:03.324Z] RMSE (validation) = 2.1340923221100736 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-09-05T00:57:31.800Z] RMSE (validation) = 1.3105190428502682 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-09-05T00:57:43.002Z] RMSE (validation) = 0.9920028100571827 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-09-05T00:57:53.252Z] RMSE (validation) = 1.212631715629696 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-09-05T00:58:02.727Z] RMSE (validation) = 1.1025694034386657 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-09-05T00:58:11.515Z] RMSE (validation) = 0.9259652469249674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-09-05T00:58:20.925Z] RMSE (validation) = 0.8982232214592321 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-09-05T00:58:23.210Z] 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.
[2024-09-05T00:58:23.210Z] The best model improves the baseline by 14.52%.
[2024-09-05T00:58:23.210Z] Movies recommended for you:
[2024-09-05T00:58:23.210Z] WARNING: This benchmark provides no result that can be validated.
[2024-09-05T00:58:23.210Z] There is no way to check that no silent failure occurred.
[2024-09-05T00:58:23.210Z] ====== movie-lens (apache-spark) [default], iteration 3 completed (117263.122 ms) ======
[2024-09-05T00:58:23.210Z] ====== movie-lens (apache-spark) [default], iteration 4 started ======
[2024-09-05T00:58:23.210Z] GC before operation: completed in 155.844 ms, heap usage 491.106 MB -> 53.827 MB.
[2024-09-05T00:58:42.624Z] RMSE (validation) = 3.62196895454876 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-09-05T00:58:58.179Z] RMSE (validation) = 2.1340923221100736 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-09-05T00:59:14.501Z] RMSE (validation) = 1.3105190428502682 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-09-05T00:59:29.779Z] RMSE (validation) = 0.9920028100571827 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-09-05T00:59:38.131Z] RMSE (validation) = 1.212631715629696 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-09-05T00:59:47.786Z] RMSE (validation) = 1.1025694034386657 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-09-05T00:59:55.617Z] RMSE (validation) = 0.9259652469249674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-09-05T01:00:06.893Z] RMSE (validation) = 0.8982232214592321 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-09-05T01:00:07.331Z] 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.
[2024-09-05T01:00:07.331Z] The best model improves the baseline by 14.52%.
[2024-09-05T01:00:07.331Z] Movies recommended for you:
[2024-09-05T01:00:07.331Z] WARNING: This benchmark provides no result that can be validated.
[2024-09-05T01:00:07.331Z] There is no way to check that no silent failure occurred.
[2024-09-05T01:00:07.331Z] ====== movie-lens (apache-spark) [default], iteration 4 completed (104000.099 ms) ======
[2024-09-05T01:00:07.331Z] ====== movie-lens (apache-spark) [default], iteration 5 started ======
[2024-09-05T01:00:07.742Z] GC before operation: completed in 266.705 ms, heap usage 375.972 MB -> 54.054 MB.
[2024-09-05T01:00:23.427Z] RMSE (validation) = 3.62196895454876 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-09-05T01:00:45.591Z] RMSE (validation) = 2.1340923221100736 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-09-05T01:01:06.399Z] RMSE (validation) = 1.3105190428502682 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-09-05T01:01:19.142Z] RMSE (validation) = 0.9920028100571827 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-09-05T01:01:32.773Z] RMSE (validation) = 1.212631715629696 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-09-05T01:01:42.003Z] RMSE (validation) = 1.1025694034386657 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-09-05T01:01:51.291Z] RMSE (validation) = 0.9259652469249674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-09-05T01:01:59.305Z] RMSE (validation) = 0.8982232214592321 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-09-05T01:01:59.746Z] 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.
[2024-09-05T01:02:00.205Z] The best model improves the baseline by 14.52%.
[2024-09-05T01:02:00.205Z] Movies recommended for you:
[2024-09-05T01:02:00.205Z] WARNING: This benchmark provides no result that can be validated.
[2024-09-05T01:02:00.205Z] There is no way to check that no silent failure occurred.
[2024-09-05T01:02:00.205Z] ====== movie-lens (apache-spark) [default], iteration 5 completed (112622.467 ms) ======
[2024-09-05T01:02:00.205Z] ====== movie-lens (apache-spark) [default], iteration 6 started ======
[2024-09-05T01:02:00.686Z] GC before operation: completed in 240.164 ms, heap usage 754.032 MB -> 54.885 MB.
[2024-09-05T01:02:22.432Z] RMSE (validation) = 3.62196895454876 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-09-05T01:02:35.960Z] RMSE (validation) = 2.1340923221100736 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-09-05T01:02:51.984Z] RMSE (validation) = 1.3105190428502682 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-09-05T01:03:10.671Z] RMSE (validation) = 0.9920028100571827 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-09-05T01:03:18.551Z] RMSE (validation) = 1.212631715629696 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-09-05T01:03:26.281Z] RMSE (validation) = 1.1025694034386657 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-09-05T01:03:45.643Z] RMSE (validation) = 0.9259652469249674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-09-05T01:03:53.094Z] RMSE (validation) = 0.8982232214592321 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-09-05T01:03:53.633Z] 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.
[2024-09-05T01:03:53.633Z] The best model improves the baseline by 14.52%.
[2024-09-05T01:03:55.099Z] Movies recommended for you:
[2024-09-05T01:03:55.099Z] WARNING: This benchmark provides no result that can be validated.
[2024-09-05T01:03:55.099Z] There is no way to check that no silent failure occurred.
[2024-09-05T01:03:55.099Z] ====== movie-lens (apache-spark) [default], iteration 6 completed (114479.428 ms) ======
[2024-09-05T01:03:55.099Z] ====== movie-lens (apache-spark) [default], iteration 7 started ======
[2024-09-05T01:03:55.499Z] GC before operation: completed in 442.595 ms, heap usage 149.703 MB -> 50.700 MB.
[2024-09-05T01:04:15.107Z] RMSE (validation) = 3.62196895454876 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-09-05T01:04:31.403Z] RMSE (validation) = 2.1340923221100736 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-09-05T01:04:47.616Z] RMSE (validation) = 1.3105190428502682 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-09-05T01:05:01.730Z] RMSE (validation) = 0.9920028100571827 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-09-05T01:05:09.267Z] RMSE (validation) = 1.212631715629696 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-09-05T01:05:21.146Z] RMSE (validation) = 1.1025694034386657 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-09-05T01:05:31.666Z] RMSE (validation) = 0.9259652469249674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-09-05T01:05:38.721Z] RMSE (validation) = 0.8982232214592321 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-09-05T01:05:39.675Z] 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.
[2024-09-05T01:05:39.675Z] The best model improves the baseline by 14.52%.
[2024-09-05T01:05:39.675Z] Movies recommended for you:
[2024-09-05T01:05:39.675Z] WARNING: This benchmark provides no result that can be validated.
[2024-09-05T01:05:39.675Z] There is no way to check that no silent failure occurred.
[2024-09-05T01:05:39.675Z] ====== movie-lens (apache-spark) [default], iteration 7 completed (104395.314 ms) ======
[2024-09-05T01:05:39.675Z] ====== movie-lens (apache-spark) [default], iteration 8 started ======
[2024-09-05T01:05:40.148Z] GC before operation: completed in 406.002 ms, heap usage 293.168 MB -> 51.239 MB.
[2024-09-05T01:05:58.730Z] RMSE (validation) = 3.62196895454876 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-09-05T01:06:14.478Z] RMSE (validation) = 2.1340923221100736 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-09-05T01:06:30.580Z] RMSE (validation) = 1.3105190428502682 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-09-05T01:06:46.014Z] RMSE (validation) = 0.9920028100571827 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-09-05T01:06:56.839Z] RMSE (validation) = 1.212631715629696 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-09-05T01:07:04.118Z] RMSE (validation) = 1.1025694034386657 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-09-05T01:07:12.466Z] RMSE (validation) = 0.9259652469249674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-09-05T01:07:29.096Z] RMSE (validation) = 0.8982232214592321 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-09-05T01:07:29.096Z] 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.
[2024-09-05T01:07:29.096Z] The best model improves the baseline by 14.52%.
[2024-09-05T01:07:29.683Z] Movies recommended for you:
[2024-09-05T01:07:29.683Z] WARNING: This benchmark provides no result that can be validated.
[2024-09-05T01:07:29.683Z] There is no way to check that no silent failure occurred.
[2024-09-05T01:07:29.683Z] ====== movie-lens (apache-spark) [default], iteration 8 completed (109255.444 ms) ======
[2024-09-05T01:07:29.683Z] ====== movie-lens (apache-spark) [default], iteration 9 started ======
[2024-09-05T01:07:29.683Z] GC before operation: completed in 174.089 ms, heap usage 307.057 MB -> 51.007 MB.
[2024-09-05T01:07:46.116Z] RMSE (validation) = 3.62196895454876 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-09-05T01:08:12.794Z] RMSE (validation) = 2.1340923221100736 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-09-05T01:08:26.118Z] RMSE (validation) = 1.3105190428502682 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-09-05T01:08:40.324Z] RMSE (validation) = 0.9920028100571827 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-09-05T01:08:50.476Z] RMSE (validation) = 1.212631715629696 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-09-05T01:09:00.109Z] RMSE (validation) = 1.1025694034386657 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-09-05T01:09:11.405Z] RMSE (validation) = 0.9259652469249674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-09-05T01:09:19.333Z] RMSE (validation) = 0.8982232214592321 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-09-05T01:09:20.939Z] 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.
[2024-09-05T01:09:21.739Z] The best model improves the baseline by 14.52%.
[2024-09-05T01:09:26.375Z] Movies recommended for you:
[2024-09-05T01:09:26.375Z] WARNING: This benchmark provides no result that can be validated.
[2024-09-05T01:09:26.375Z] There is no way to check that no silent failure occurred.
[2024-09-05T01:09:26.375Z] ====== movie-lens (apache-spark) [default], iteration 9 completed (116009.527 ms) ======
[2024-09-05T01:09:26.375Z] ====== movie-lens (apache-spark) [default], iteration 10 started ======
[2024-09-05T01:09:28.788Z] GC before operation: completed in 1307.487 ms, heap usage 356.739 MB -> 51.086 MB.
[2024-09-05T01:09:44.611Z] RMSE (validation) = 3.62196895454876 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-09-05T01:09:59.796Z] RMSE (validation) = 2.1340923221100736 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-09-05T01:10:19.110Z] RMSE (validation) = 1.3105190428502682 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-09-05T01:10:34.857Z] RMSE (validation) = 0.9920028100571827 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-09-05T01:10:46.244Z] RMSE (validation) = 1.212631715629696 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-09-05T01:10:57.979Z] RMSE (validation) = 1.1025694034386657 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-09-05T01:11:09.164Z] RMSE (validation) = 0.9259652469249674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-09-05T01:11:17.347Z] RMSE (validation) = 0.8982232214592321 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-09-05T01:11:17.913Z] 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.
[2024-09-05T01:11:17.913Z] The best model improves the baseline by 14.52%.
[2024-09-05T01:11:19.892Z] Movies recommended for you:
[2024-09-05T01:11:19.892Z] WARNING: This benchmark provides no result that can be validated.
[2024-09-05T01:11:19.892Z] There is no way to check that no silent failure occurred.
[2024-09-05T01:11:19.892Z] ====== movie-lens (apache-spark) [default], iteration 10 completed (112578.865 ms) ======
[2024-09-05T01:11:19.892Z] ====== movie-lens (apache-spark) [default], iteration 11 started ======
[2024-09-05T01:11:19.892Z] GC before operation: completed in 221.647 ms, heap usage 612.370 MB -> 54.408 MB.
[2024-09-05T01:11:39.587Z] RMSE (validation) = 3.62196895454876 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-09-05T01:11:55.965Z] RMSE (validation) = 2.1340923221100736 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-09-05T01:12:19.299Z] RMSE (validation) = 1.3105190428502682 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-09-05T01:12:35.077Z] RMSE (validation) = 0.9920028100571827 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-09-05T01:12:42.787Z] RMSE (validation) = 1.212631715629696 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-09-05T01:12:52.017Z] RMSE (validation) = 1.1025694034386657 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-09-05T01:13:09.819Z] RMSE (validation) = 0.9259652469249674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-09-05T01:13:15.110Z] RMSE (validation) = 0.8982232214592321 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-09-05T01:13:16.483Z] 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.
[2024-09-05T01:13:16.483Z] The best model improves the baseline by 14.52%.
[2024-09-05T01:13:16.981Z] Movies recommended for you:
[2024-09-05T01:13:16.981Z] WARNING: This benchmark provides no result that can be validated.
[2024-09-05T01:13:16.981Z] There is no way to check that no silent failure occurred.
[2024-09-05T01:13:16.981Z] ====== movie-lens (apache-spark) [default], iteration 11 completed (117012.060 ms) ======
[2024-09-05T01:13:16.981Z] ====== movie-lens (apache-spark) [default], iteration 12 started ======
[2024-09-05T01:13:16.981Z] GC before operation: completed in 254.591 ms, heap usage 132.147 MB -> 50.801 MB.
[2024-09-05T01:13:33.068Z] RMSE (validation) = 3.62196895454876 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-09-05T01:13:59.669Z] RMSE (validation) = 2.1340923221100736 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-09-05T01:14:12.280Z] RMSE (validation) = 1.3105190428502682 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-09-05T01:14:28.723Z] RMSE (validation) = 0.9920028100571827 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-09-05T01:14:38.096Z] RMSE (validation) = 1.212631715629696 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-09-05T01:14:47.740Z] RMSE (validation) = 1.1025694034386657 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-09-05T01:14:58.511Z] RMSE (validation) = 0.9259652469249674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-09-05T01:15:15.307Z] RMSE (validation) = 0.8982232214592321 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-09-05T01:15:15.307Z] 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.
[2024-09-05T01:15:15.307Z] The best model improves the baseline by 14.52%.
[2024-09-05T01:15:15.733Z] Movies recommended for you:
[2024-09-05T01:15:15.733Z] WARNING: This benchmark provides no result that can be validated.
[2024-09-05T01:15:15.733Z] There is no way to check that no silent failure occurred.
[2024-09-05T01:15:15.733Z] ====== movie-lens (apache-spark) [default], iteration 12 completed (118241.792 ms) ======
[2024-09-05T01:15:15.733Z] ====== movie-lens (apache-spark) [default], iteration 13 started ======
[2024-09-05T01:15:15.733Z] GC before operation: completed in 218.879 ms, heap usage 320.586 MB -> 51.162 MB.
[2024-09-05T01:15:31.624Z] RMSE (validation) = 3.62196895454876 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-09-05T01:15:50.494Z] RMSE (validation) = 2.1340923221100736 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-09-05T01:16:04.005Z] RMSE (validation) = 1.3105190428502682 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-09-05T01:16:23.214Z] RMSE (validation) = 0.9920028100571827 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-09-05T01:16:31.761Z] RMSE (validation) = 1.212631715629696 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-09-05T01:16:49.201Z] RMSE (validation) = 1.1025694034386657 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-09-05T01:16:58.888Z] RMSE (validation) = 0.9259652469249674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-09-05T01:17:08.487Z] RMSE (validation) = 0.8982232214592321 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-09-05T01:17:09.039Z] 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.
[2024-09-05T01:17:09.912Z] The best model improves the baseline by 14.52%.
[2024-09-05T01:17:10.620Z] Movies recommended for you:
[2024-09-05T01:17:10.620Z] WARNING: This benchmark provides no result that can be validated.
[2024-09-05T01:17:10.620Z] There is no way to check that no silent failure occurred.
[2024-09-05T01:17:10.620Z] ====== movie-lens (apache-spark) [default], iteration 13 completed (114661.757 ms) ======
[2024-09-05T01:17:10.620Z] ====== movie-lens (apache-spark) [default], iteration 14 started ======
[2024-09-05T01:17:10.620Z] GC before operation: completed in 261.051 ms, heap usage 390.003 MB -> 54.154 MB.
[2024-09-05T01:17:30.801Z] RMSE (validation) = 3.6219689545487603 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-09-05T01:17:46.704Z] RMSE (validation) = 2.1340923221100736 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-09-05T01:18:02.563Z] RMSE (validation) = 1.3105190428502682 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-09-05T01:18:21.274Z] RMSE (validation) = 0.9920028100571827 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-09-05T01:18:28.757Z] RMSE (validation) = 1.212631715629696 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-09-05T01:18:36.020Z] RMSE (validation) = 1.1025694034386657 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-09-05T01:18:44.349Z] RMSE (validation) = 0.9259652469249674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-09-05T01:18:51.989Z] RMSE (validation) = 0.8982232214592321 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-09-05T01:18:53.120Z] 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.
[2024-09-05T01:18:53.120Z] The best model improves the baseline by 14.52%.
[2024-09-05T01:18:54.211Z] Movies recommended for you:
[2024-09-05T01:18:54.211Z] WARNING: This benchmark provides no result that can be validated.
[2024-09-05T01:18:54.211Z] There is no way to check that no silent failure occurred.
[2024-09-05T01:18:54.211Z] ====== movie-lens (apache-spark) [default], iteration 14 completed (103497.411 ms) ======
[2024-09-05T01:18:54.211Z] ====== movie-lens (apache-spark) [default], iteration 15 started ======
[2024-09-05T01:18:54.211Z] GC before operation: completed in 152.740 ms, heap usage 157.475 MB -> 51.012 MB.
[2024-09-05T01:19:12.764Z] RMSE (validation) = 3.62196895454876 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-09-05T01:19:28.868Z] RMSE (validation) = 2.1340923221100736 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-09-05T01:19:48.298Z] RMSE (validation) = 1.3105190428502682 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-09-05T01:20:01.557Z] RMSE (validation) = 0.9920028100571827 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-09-05T01:20:08.079Z] RMSE (validation) = 1.212631715629696 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-09-05T01:20:19.451Z] RMSE (validation) = 1.1025694034386657 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-09-05T01:20:30.518Z] RMSE (validation) = 0.9259652469249674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-09-05T01:20:36.619Z] RMSE (validation) = 0.8982232214592321 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-09-05T01:20:38.638Z] 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.
[2024-09-05T01:20:39.832Z] The best model improves the baseline by 14.52%.
[2024-09-05T01:20:39.832Z] Movies recommended for you:
[2024-09-05T01:20:39.832Z] WARNING: This benchmark provides no result that can be validated.
[2024-09-05T01:20:39.832Z] There is no way to check that no silent failure occurred.
[2024-09-05T01:20:39.832Z] ====== movie-lens (apache-spark) [default], iteration 15 completed (105740.824 ms) ======
[2024-09-05T01:20:39.832Z] ====== movie-lens (apache-spark) [default], iteration 16 started ======
[2024-09-05T01:20:40.309Z] GC before operation: completed in 245.086 ms, heap usage 432.383 MB -> 54.500 MB.
[2024-09-05T01:20:59.262Z] RMSE (validation) = 3.62196895454876 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-09-05T01:21:18.037Z] RMSE (validation) = 2.1340923221100736 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-09-05T01:21:36.727Z] RMSE (validation) = 1.3105190428502682 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-09-05T01:21:52.431Z] RMSE (validation) = 0.9920028100571827 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-09-05T01:22:00.461Z] RMSE (validation) = 1.212631715629696 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-09-05T01:22:08.357Z] RMSE (validation) = 1.1025694034386657 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-09-05T01:22:19.028Z] RMSE (validation) = 0.9259652469249674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-09-05T01:22:28.145Z] RMSE (validation) = 0.8982232214592321 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-09-05T01:22:29.300Z] 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.
[2024-09-05T01:22:29.300Z] The best model improves the baseline by 14.52%.
[2024-09-05T01:22:30.047Z] Movies recommended for you:
[2024-09-05T01:22:30.047Z] WARNING: This benchmark provides no result that can be validated.
[2024-09-05T01:22:30.047Z] There is no way to check that no silent failure occurred.
[2024-09-05T01:22:30.047Z] ====== movie-lens (apache-spark) [default], iteration 16 completed (109744.582 ms) ======
[2024-09-05T01:22:30.047Z] ====== movie-lens (apache-spark) [default], iteration 17 started ======
[2024-09-05T01:22:31.410Z] GC before operation: completed in 1396.153 ms, heap usage 475.650 MB -> 54.316 MB.
[2024-09-05T01:22:47.355Z] RMSE (validation) = 3.62196895454876 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-09-05T01:23:06.473Z] RMSE (validation) = 2.1340923221100736 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-09-05T01:23:25.319Z] RMSE (validation) = 1.3105190428502682 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-09-05T01:23:48.808Z] RMSE (validation) = 0.9920028100571827 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-09-05T01:23:56.227Z] RMSE (validation) = 1.212631715629696 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-09-05T01:24:05.241Z] RMSE (validation) = 1.1025694034386657 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-09-05T01:24:16.118Z] RMSE (validation) = 0.9259652469249674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-09-05T01:24:27.491Z] RMSE (validation) = 0.8982232214592321 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-09-05T01:24:28.020Z] 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.
[2024-09-05T01:24:28.020Z] The best model improves the baseline by 14.52%.
[2024-09-05T01:24:28.020Z] Movies recommended for you:
[2024-09-05T01:24:28.020Z] WARNING: This benchmark provides no result that can be validated.
[2024-09-05T01:24:28.020Z] There is no way to check that no silent failure occurred.
[2024-09-05T01:24:28.020Z] ====== movie-lens (apache-spark) [default], iteration 17 completed (116753.142 ms) ======
[2024-09-05T01:24:28.020Z] ====== movie-lens (apache-spark) [default], iteration 18 started ======
[2024-09-05T01:24:28.020Z] GC before operation: completed in 167.647 ms, heap usage 280.598 MB -> 51.030 MB.
[2024-09-05T01:24:47.363Z] RMSE (validation) = 3.62196895454876 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-09-05T01:25:03.477Z] RMSE (validation) = 2.1340923221100736 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-09-05T01:25:20.077Z] RMSE (validation) = 1.3105190428502682 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-09-05T01:25:38.561Z] RMSE (validation) = 0.9920028100571827 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-09-05T01:25:44.788Z] RMSE (validation) = 1.212631715629696 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-09-05T01:25:52.660Z] RMSE (validation) = 1.1025694034386657 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-09-05T01:26:03.697Z] RMSE (validation) = 0.9259652469249674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-09-05T01:26:11.684Z] RMSE (validation) = 0.8982232214592321 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-09-05T01:26:14.652Z] 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.
[2024-09-05T01:26:14.652Z] The best model improves the baseline by 14.52%.
[2024-09-05T01:26:14.652Z] Movies recommended for you:
[2024-09-05T01:26:14.652Z] WARNING: This benchmark provides no result that can be validated.
[2024-09-05T01:26:14.652Z] There is no way to check that no silent failure occurred.
[2024-09-05T01:26:14.652Z] ====== movie-lens (apache-spark) [default], iteration 18 completed (106391.324 ms) ======
[2024-09-05T01:26:14.653Z] ====== movie-lens (apache-spark) [default], iteration 19 started ======
[2024-09-05T01:26:15.224Z] GC before operation: completed in 292.358 ms, heap usage 397.177 MB -> 54.665 MB.
[2024-09-05T01:26:31.480Z] RMSE (validation) = 3.6219689545487603 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-09-05T01:26:51.091Z] RMSE (validation) = 2.1340923221100736 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-09-05T01:27:09.547Z] RMSE (validation) = 1.3105190428502682 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-09-05T01:27:26.131Z] RMSE (validation) = 0.9920028100571827 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-09-05T01:27:34.026Z] RMSE (validation) = 1.212631715629696 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-09-05T01:27:44.856Z] RMSE (validation) = 1.1025694034386657 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-09-05T01:27:55.757Z] RMSE (validation) = 0.9259652469249674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-09-05T01:28:05.131Z] RMSE (validation) = 0.8982232214592321 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-09-05T01:28:05.131Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9063003101263983.
[2024-09-05T01:28:05.131Z] The best model improves the baseline by 14.52%.
[2024-09-05T01:28:05.624Z] Movies recommended for you:
[2024-09-05T01:28:05.624Z] WARNING: This benchmark provides no result that can be validated.
[2024-09-05T01:28:05.624Z] There is no way to check that no silent failure occurred.
[2024-09-05T01:28:05.624Z] ====== movie-lens (apache-spark) [default], iteration 19 completed (110575.412 ms) ======
[2024-09-05T01:28:11.286Z] -----------------------------------
[2024-09-05T01:28:11.286Z] renaissance-movie-lens_0_PASSED
[2024-09-05T01:28:11.286Z] -----------------------------------
[2024-09-05T01:28:11.286Z]
[2024-09-05T01:28:11.286Z] TEST TEARDOWN:
[2024-09-05T01:28:11.286Z] Nothing to be done for teardown.
[2024-09-05T01:28:11.702Z] renaissance-movie-lens_0 Finish Time: Wed Sep 4 18:28:10 2024 Epoch Time (ms): 1725499691083