renaissance-movie-lens_0
[2024-08-08T00:53:30.137Z] Running test renaissance-movie-lens_0 ...
[2024-08-08T00:53:30.137Z] ===============================================
[2024-08-08T00:53:30.137Z] renaissance-movie-lens_0 Start Time: Thu Aug 8 00:53:29 2024 Epoch Time (ms): 1723078409173
[2024-08-08T00:53:30.137Z] variation: NoOptions
[2024-08-08T00:53:30.137Z] JVM_OPTIONS:
[2024-08-08T00:53:30.137Z] { \
[2024-08-08T00:53:30.137Z] echo ""; echo "TEST SETUP:"; \
[2024-08-08T00:53:30.137Z] echo "Nothing to be done for setup."; \
[2024-08-08T00:53:30.137Z] mkdir -p "/home/jenkins/workspace/Test_openjdk11_hs_extended.perf_aarch64_linux_testList_0/aqa-tests/TKG/../TKG/output_17230774662032/renaissance-movie-lens_0"; \
[2024-08-08T00:53:30.137Z] cd "/home/jenkins/workspace/Test_openjdk11_hs_extended.perf_aarch64_linux_testList_0/aqa-tests/TKG/../TKG/output_17230774662032/renaissance-movie-lens_0"; \
[2024-08-08T00:53:30.137Z] echo ""; echo "TESTING:"; \
[2024-08-08T00:53:30.137Z] "/home/jenkins/workspace/Test_openjdk11_hs_extended.perf_aarch64_linux_testList_0/jdkbinary/j2sdk-image/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 "/home/jenkins/workspace/Test_openjdk11_hs_extended.perf_aarch64_linux_testList_0/aqa-tests/TKG/../../jvmtest/perf/renaissance/renaissance.jar" --json ""/home/jenkins/workspace/Test_openjdk11_hs_extended.perf_aarch64_linux_testList_0/aqa-tests/TKG/../TKG/output_17230774662032/renaissance-movie-lens_0"/movie-lens.json" movie-lens; \
[2024-08-08T00:53:30.137Z] if [ $? -eq 0 ]; then echo "-----------------------------------"; echo "renaissance-movie-lens_0""_PASSED"; echo "-----------------------------------"; cd /home/jenkins/workspace/Test_openjdk11_hs_extended.perf_aarch64_linux_testList_0/aqa-tests/TKG/..; rm -f -r "/home/jenkins/workspace/Test_openjdk11_hs_extended.perf_aarch64_linux_testList_0/aqa-tests/TKG/../TKG/output_17230774662032/renaissance-movie-lens_0"; else echo "-----------------------------------"; echo "renaissance-movie-lens_0""_FAILED"; echo "-----------------------------------"; fi; \
[2024-08-08T00:53:30.137Z] echo ""; echo "TEST TEARDOWN:"; \
[2024-08-08T00:53:30.137Z] echo "Nothing to be done for teardown."; \
[2024-08-08T00:53:30.137Z] } 2>&1 | tee -a "/home/jenkins/workspace/Test_openjdk11_hs_extended.perf_aarch64_linux_testList_0/aqa-tests/TKG/../TKG/output_17230774662032/TestTargetResult";
[2024-08-08T00:53:30.137Z]
[2024-08-08T00:53:30.137Z] TEST SETUP:
[2024-08-08T00:53:30.137Z] Nothing to be done for setup.
[2024-08-08T00:53:30.137Z]
[2024-08-08T00:53:30.137Z] TESTING:
[2024-08-08T00:53:33.105Z] Using Spark's default log4j profile: org/apache/spark/log4j-defaults.properties
[2024-08-08T00:53:36.070Z] NOTE: 'movie-lens' benchmark uses Spark local executor with 8 (out of 8) threads.
[2024-08-08T00:53:40.158Z] Got 100004 ratings from 671 users on 9066 movies.
[2024-08-08T00:53:40.158Z] Training: 60056, validation: 20285, test: 19854
[2024-08-08T00:53:40.158Z] ====== movie-lens (apache-spark) [default], iteration 0 started ======
[2024-08-08T00:53:40.158Z] GC before operation: completed in 76.324 ms, heap usage 149.978 MB -> 37.190 MB.
[2024-08-08T00:53:48.211Z] RMSE (validation) = 3.621968954548751 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-08-08T00:53:52.309Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-08-08T00:53:55.273Z] RMSE (validation) = 1.3105189674610933 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-08-08T00:53:58.244Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-08-08T00:54:01.235Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-08-08T00:54:02.170Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-08-08T00:54:04.095Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-08-08T00:54:06.018Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-08-08T00:54:06.018Z] 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.
[2024-08-08T00:54:06.018Z] The best model improves the baseline by 14.43%.
[2024-08-08T00:54:06.018Z] Movies recommended for you:
[2024-08-08T00:54:06.018Z] WARNING: This benchmark provides no result that can be validated.
[2024-08-08T00:54:06.018Z] There is no way to check that no silent failure occurred.
[2024-08-08T00:54:06.018Z] ====== movie-lens (apache-spark) [default], iteration 0 completed (25952.766 ms) ======
[2024-08-08T00:54:06.018Z] ====== movie-lens (apache-spark) [default], iteration 1 started ======
[2024-08-08T00:54:06.019Z] GC before operation: completed in 136.266 ms, heap usage 243.451 MB -> 50.531 MB.
[2024-08-08T00:54:08.991Z] RMSE (validation) = 3.621968954548751 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-08-08T00:54:11.971Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-08-08T00:54:13.894Z] RMSE (validation) = 1.3105189674610935 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-08-08T00:54:16.868Z] RMSE (validation) = 1.004540774800519 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-08-08T00:54:17.804Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-08-08T00:54:19.727Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-08-08T00:54:20.665Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-08-08T00:54:23.032Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-08-08T00:54:23.032Z] 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.
[2024-08-08T00:54:23.032Z] The best model improves the baseline by 14.43%.
[2024-08-08T00:54:23.032Z] Movies recommended for you:
[2024-08-08T00:54:23.032Z] WARNING: This benchmark provides no result that can be validated.
[2024-08-08T00:54:23.032Z] There is no way to check that no silent failure occurred.
[2024-08-08T00:54:23.032Z] ====== movie-lens (apache-spark) [default], iteration 1 completed (16553.531 ms) ======
[2024-08-08T00:54:23.032Z] ====== movie-lens (apache-spark) [default], iteration 2 started ======
[2024-08-08T00:54:23.032Z] GC before operation: completed in 133.955 ms, heap usage 312.245 MB -> 51.017 MB.
[2024-08-08T00:54:26.004Z] RMSE (validation) = 3.621968954548751 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-08-08T00:54:27.929Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-08-08T00:54:30.900Z] RMSE (validation) = 1.3105189674610933 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-08-08T00:54:32.823Z] RMSE (validation) = 1.004540774800519 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-08-08T00:54:33.759Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-08-08T00:54:35.682Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-08-08T00:54:36.621Z] RMSE (validation) = 0.9275717391338142 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-08-08T00:54:38.546Z] RMSE (validation) = 0.9001440981626694 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-08-08T00:54:38.546Z] 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.
[2024-08-08T00:54:38.546Z] The best model improves the baseline by 14.43%.
[2024-08-08T00:54:38.546Z] Movies recommended for you:
[2024-08-08T00:54:38.546Z] WARNING: This benchmark provides no result that can be validated.
[2024-08-08T00:54:38.546Z] There is no way to check that no silent failure occurred.
[2024-08-08T00:54:38.546Z] ====== movie-lens (apache-spark) [default], iteration 2 completed (15527.612 ms) ======
[2024-08-08T00:54:38.546Z] ====== movie-lens (apache-spark) [default], iteration 3 started ======
[2024-08-08T00:54:38.546Z] GC before operation: completed in 145.618 ms, heap usage 205.256 MB -> 54.641 MB.
[2024-08-08T00:54:41.513Z] RMSE (validation) = 3.6219689545487506 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-08-08T00:54:43.435Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-08-08T00:54:45.358Z] RMSE (validation) = 1.3105189674610935 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-08-08T00:54:47.279Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-08-08T00:54:49.200Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-08-08T00:54:50.138Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-08-08T00:54:51.074Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-08-08T00:54:53.007Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-08-08T00:54:53.007Z] 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.
[2024-08-08T00:54:53.007Z] The best model improves the baseline by 14.43%.
[2024-08-08T00:54:53.007Z] Movies recommended for you:
[2024-08-08T00:54:53.007Z] WARNING: This benchmark provides no result that can be validated.
[2024-08-08T00:54:53.007Z] There is no way to check that no silent failure occurred.
[2024-08-08T00:54:53.007Z] ====== movie-lens (apache-spark) [default], iteration 3 completed (14351.453 ms) ======
[2024-08-08T00:54:53.007Z] ====== movie-lens (apache-spark) [default], iteration 4 started ======
[2024-08-08T00:54:53.007Z] GC before operation: completed in 148.686 ms, heap usage 264.541 MB -> 51.640 MB.
[2024-08-08T00:54:55.977Z] RMSE (validation) = 3.6219689545487506 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-08-08T00:54:57.904Z] RMSE (validation) = 2.1340923217118064 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-08-08T00:54:59.827Z] RMSE (validation) = 1.3105189674610933 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-08-08T00:55:01.748Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-08-08T00:55:02.684Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-08-08T00:55:04.609Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-08-08T00:55:05.544Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-08-08T00:55:06.503Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-08-08T00:55:06.503Z] 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.
[2024-08-08T00:55:06.503Z] The best model improves the baseline by 14.43%.
[2024-08-08T00:55:07.438Z] Movies recommended for you:
[2024-08-08T00:55:07.438Z] WARNING: This benchmark provides no result that can be validated.
[2024-08-08T00:55:07.438Z] There is no way to check that no silent failure occurred.
[2024-08-08T00:55:07.438Z] ====== movie-lens (apache-spark) [default], iteration 4 completed (13785.744 ms) ======
[2024-08-08T00:55:07.438Z] ====== movie-lens (apache-spark) [default], iteration 5 started ======
[2024-08-08T00:55:07.438Z] GC before operation: completed in 142.896 ms, heap usage 147.737 MB -> 51.758 MB.
[2024-08-08T00:55:09.481Z] RMSE (validation) = 3.6219689545487506 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-08-08T00:55:11.401Z] RMSE (validation) = 2.1340923217118064 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-08-08T00:55:13.324Z] RMSE (validation) = 1.3105189674610933 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-08-08T00:55:15.244Z] RMSE (validation) = 1.004540774800519 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-08-08T00:55:16.179Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-08-08T00:55:18.128Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-08-08T00:55:19.062Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-08-08T00:55:20.990Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-08-08T00:55:20.990Z] 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.
[2024-08-08T00:55:20.990Z] The best model improves the baseline by 14.43%.
[2024-08-08T00:55:20.990Z] Movies recommended for you:
[2024-08-08T00:55:20.990Z] WARNING: This benchmark provides no result that can be validated.
[2024-08-08T00:55:20.990Z] There is no way to check that no silent failure occurred.
[2024-08-08T00:55:20.990Z] ====== movie-lens (apache-spark) [default], iteration 5 completed (13680.353 ms) ======
[2024-08-08T00:55:20.990Z] ====== movie-lens (apache-spark) [default], iteration 6 started ======
[2024-08-08T00:55:20.990Z] GC before operation: completed in 151.357 ms, heap usage 535.045 MB -> 55.218 MB.
[2024-08-08T00:55:23.955Z] RMSE (validation) = 3.621968954548751 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-08-08T00:55:26.919Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-08-08T00:55:28.839Z] RMSE (validation) = 1.3105189674610933 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-08-08T00:55:31.809Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-08-08T00:55:33.182Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-08-08T00:55:34.118Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-08-08T00:55:36.037Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-08-08T00:55:37.955Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-08-08T00:55:37.955Z] 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.
[2024-08-08T00:55:37.955Z] The best model improves the baseline by 14.43%.
[2024-08-08T00:55:37.955Z] Movies recommended for you:
[2024-08-08T00:55:37.955Z] WARNING: This benchmark provides no result that can be validated.
[2024-08-08T00:55:37.955Z] There is no way to check that no silent failure occurred.
[2024-08-08T00:55:37.955Z] ====== movie-lens (apache-spark) [default], iteration 6 completed (17117.638 ms) ======
[2024-08-08T00:55:37.955Z] ====== movie-lens (apache-spark) [default], iteration 7 started ======
[2024-08-08T00:55:37.955Z] GC before operation: completed in 134.849 ms, heap usage 266.552 MB -> 51.982 MB.
[2024-08-08T00:55:40.919Z] RMSE (validation) = 3.6219689545487506 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-08-08T00:55:42.838Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-08-08T00:55:44.757Z] RMSE (validation) = 1.3105189674610935 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-08-08T00:55:47.723Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-08-08T00:55:48.674Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-08-08T00:55:49.611Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-08-08T00:55:50.546Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-08-08T00:55:52.532Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-08-08T00:55:52.532Z] 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.
[2024-08-08T00:55:52.532Z] The best model improves the baseline by 14.43%.
[2024-08-08T00:55:52.532Z] Movies recommended for you:
[2024-08-08T00:55:52.532Z] WARNING: This benchmark provides no result that can be validated.
[2024-08-08T00:55:52.532Z] There is no way to check that no silent failure occurred.
[2024-08-08T00:55:52.532Z] ====== movie-lens (apache-spark) [default], iteration 7 completed (14005.130 ms) ======
[2024-08-08T00:55:52.532Z] ====== movie-lens (apache-spark) [default], iteration 8 started ======
[2024-08-08T00:55:52.532Z] GC before operation: completed in 135.348 ms, heap usage 306.644 MB -> 52.289 MB.
[2024-08-08T00:55:54.454Z] RMSE (validation) = 3.621968954548751 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-08-08T00:55:56.375Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-08-08T00:55:58.294Z] RMSE (validation) = 1.3105189674610933 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-08-08T00:56:00.211Z] RMSE (validation) = 1.004540774800519 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-08-08T00:56:01.144Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-08-08T00:56:03.065Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-08-08T00:56:04.000Z] RMSE (validation) = 0.9275717391338142 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-08-08T00:56:04.935Z] RMSE (validation) = 0.9001440981626694 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-08-08T00:56:04.935Z] 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.
[2024-08-08T00:56:04.935Z] The best model improves the baseline by 14.43%.
[2024-08-08T00:56:04.935Z] Movies recommended for you:
[2024-08-08T00:56:04.935Z] WARNING: This benchmark provides no result that can be validated.
[2024-08-08T00:56:04.935Z] There is no way to check that no silent failure occurred.
[2024-08-08T00:56:04.935Z] ====== movie-lens (apache-spark) [default], iteration 8 completed (12916.388 ms) ======
[2024-08-08T00:56:04.935Z] ====== movie-lens (apache-spark) [default], iteration 9 started ======
[2024-08-08T00:56:05.869Z] GC before operation: completed in 138.637 ms, heap usage 532.504 MB -> 55.510 MB.
[2024-08-08T00:56:07.786Z] RMSE (validation) = 3.6219689545487506 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-08-08T00:56:09.703Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-08-08T00:56:11.654Z] RMSE (validation) = 1.3105189674610935 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-08-08T00:56:13.608Z] RMSE (validation) = 1.004540774800519 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-08-08T00:56:14.542Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-08-08T00:56:15.475Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-08-08T00:56:17.394Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-08-08T00:56:18.328Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-08-08T00:56:18.329Z] 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.
[2024-08-08T00:56:18.329Z] The best model improves the baseline by 14.43%.
[2024-08-08T00:56:18.329Z] Movies recommended for you:
[2024-08-08T00:56:18.329Z] WARNING: This benchmark provides no result that can be validated.
[2024-08-08T00:56:18.329Z] There is no way to check that no silent failure occurred.
[2024-08-08T00:56:18.329Z] ====== movie-lens (apache-spark) [default], iteration 9 completed (12958.567 ms) ======
[2024-08-08T00:56:18.329Z] ====== movie-lens (apache-spark) [default], iteration 10 started ======
[2024-08-08T00:56:18.329Z] GC before operation: completed in 147.568 ms, heap usage 511.641 MB -> 55.669 MB.
[2024-08-08T00:56:20.253Z] RMSE (validation) = 3.621968954548751 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-08-08T00:56:22.205Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-08-08T00:56:24.123Z] RMSE (validation) = 1.3105189674610933 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-08-08T00:56:26.044Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-08-08T00:56:27.965Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-08-08T00:56:28.909Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-08-08T00:56:29.845Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-08-08T00:56:30.779Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-08-08T00:56:31.718Z] 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.
[2024-08-08T00:56:31.718Z] The best model improves the baseline by 14.43%.
[2024-08-08T00:56:31.718Z] Movies recommended for you:
[2024-08-08T00:56:31.718Z] WARNING: This benchmark provides no result that can be validated.
[2024-08-08T00:56:31.718Z] There is no way to check that no silent failure occurred.
[2024-08-08T00:56:31.718Z] ====== movie-lens (apache-spark) [default], iteration 10 completed (12884.276 ms) ======
[2024-08-08T00:56:31.718Z] ====== movie-lens (apache-spark) [default], iteration 11 started ======
[2024-08-08T00:56:31.718Z] GC before operation: completed in 134.338 ms, heap usage 124.310 MB -> 51.773 MB.
[2024-08-08T00:56:33.637Z] RMSE (validation) = 3.621968954548751 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-08-08T00:56:35.557Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-08-08T00:56:37.477Z] RMSE (validation) = 1.3105189674610933 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-08-08T00:56:39.395Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-08-08T00:56:40.333Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-08-08T00:56:41.267Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-08-08T00:56:43.882Z] RMSE (validation) = 0.9275717391338142 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-08-08T00:56:43.882Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-08-08T00:56:43.882Z] 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.
[2024-08-08T00:56:43.882Z] The best model improves the baseline by 14.43%.
[2024-08-08T00:56:43.882Z] Movies recommended for you:
[2024-08-08T00:56:43.882Z] WARNING: This benchmark provides no result that can be validated.
[2024-08-08T00:56:43.882Z] There is no way to check that no silent failure occurred.
[2024-08-08T00:56:43.882Z] ====== movie-lens (apache-spark) [default], iteration 11 completed (12679.120 ms) ======
[2024-08-08T00:56:43.882Z] ====== movie-lens (apache-spark) [default], iteration 12 started ======
[2024-08-08T00:56:44.868Z] GC before operation: completed in 135.389 ms, heap usage 102.260 MB -> 52.062 MB.
[2024-08-08T00:56:46.883Z] RMSE (validation) = 3.621968954548751 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-08-08T00:56:48.803Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-08-08T00:56:50.793Z] RMSE (validation) = 1.3105189674610935 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-08-08T00:56:52.732Z] RMSE (validation) = 1.004540774800519 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-08-08T00:56:53.669Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-08-08T00:56:54.607Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-08-08T00:56:55.544Z] RMSE (validation) = 0.9275717391338142 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-08-08T00:56:57.469Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-08-08T00:56:57.469Z] 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.
[2024-08-08T00:56:57.469Z] The best model improves the baseline by 14.43%.
[2024-08-08T00:56:57.469Z] Movies recommended for you:
[2024-08-08T00:56:57.469Z] WARNING: This benchmark provides no result that can be validated.
[2024-08-08T00:56:57.469Z] There is no way to check that no silent failure occurred.
[2024-08-08T00:56:57.469Z] ====== movie-lens (apache-spark) [default], iteration 12 completed (13091.009 ms) ======
[2024-08-08T00:56:57.469Z] ====== movie-lens (apache-spark) [default], iteration 13 started ======
[2024-08-08T00:56:57.469Z] GC before operation: completed in 137.990 ms, heap usage 591.238 MB -> 55.815 MB.
[2024-08-08T00:56:59.395Z] RMSE (validation) = 3.621968954548751 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-08-08T00:57:01.320Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-08-08T00:57:03.244Z] RMSE (validation) = 1.3105189674610933 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-08-08T00:57:05.167Z] RMSE (validation) = 1.004540774800519 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-08-08T00:57:07.095Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-08-08T00:57:08.039Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-08-08T00:57:08.976Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-08-08T00:57:09.912Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-08-08T00:57:10.851Z] 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.
[2024-08-08T00:57:10.851Z] The best model improves the baseline by 14.43%.
[2024-08-08T00:57:10.851Z] Movies recommended for you:
[2024-08-08T00:57:10.851Z] WARNING: This benchmark provides no result that can be validated.
[2024-08-08T00:57:10.851Z] There is no way to check that no silent failure occurred.
[2024-08-08T00:57:10.851Z] ====== movie-lens (apache-spark) [default], iteration 13 completed (12941.194 ms) ======
[2024-08-08T00:57:10.851Z] ====== movie-lens (apache-spark) [default], iteration 14 started ======
[2024-08-08T00:57:10.851Z] GC before operation: completed in 133.909 ms, heap usage 187.620 MB -> 51.934 MB.
[2024-08-08T00:57:12.775Z] RMSE (validation) = 3.621968954548751 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-08-08T00:57:14.699Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-08-08T00:57:16.621Z] RMSE (validation) = 1.3105189674610933 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-08-08T00:57:18.541Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-08-08T00:57:19.479Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-08-08T00:57:20.414Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-08-08T00:57:22.340Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-08-08T00:57:23.275Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-08-08T00:57:23.275Z] 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.
[2024-08-08T00:57:23.275Z] The best model improves the baseline by 14.43%.
[2024-08-08T00:57:23.275Z] Movies recommended for you:
[2024-08-08T00:57:23.275Z] WARNING: This benchmark provides no result that can be validated.
[2024-08-08T00:57:23.275Z] There is no way to check that no silent failure occurred.
[2024-08-08T00:57:23.275Z] ====== movie-lens (apache-spark) [default], iteration 14 completed (12687.621 ms) ======
[2024-08-08T00:57:23.275Z] ====== movie-lens (apache-spark) [default], iteration 15 started ======
[2024-08-08T00:57:23.275Z] GC before operation: completed in 132.349 ms, heap usage 199.056 MB -> 52.188 MB.
[2024-08-08T00:57:25.195Z] RMSE (validation) = 3.621968954548751 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-08-08T00:57:27.121Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-08-08T00:57:29.040Z] RMSE (validation) = 1.3105189674610933 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-08-08T00:57:30.968Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-08-08T00:57:31.905Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-08-08T00:57:33.827Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-08-08T00:57:34.763Z] RMSE (validation) = 0.927571739133814 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-08-08T00:57:35.698Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-08-08T00:57:35.698Z] 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.
[2024-08-08T00:57:35.698Z] The best model improves the baseline by 14.43%.
[2024-08-08T00:57:36.633Z] Movies recommended for you:
[2024-08-08T00:57:36.633Z] WARNING: This benchmark provides no result that can be validated.
[2024-08-08T00:57:36.633Z] There is no way to check that no silent failure occurred.
[2024-08-08T00:57:36.633Z] ====== movie-lens (apache-spark) [default], iteration 15 completed (12701.400 ms) ======
[2024-08-08T00:57:36.633Z] ====== movie-lens (apache-spark) [default], iteration 16 started ======
[2024-08-08T00:57:36.633Z] GC before operation: completed in 150.860 ms, heap usage 509.361 MB -> 55.731 MB.
[2024-08-08T00:57:38.554Z] RMSE (validation) = 3.621968954548751 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-08-08T00:57:40.474Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-08-08T00:57:42.393Z] RMSE (validation) = 1.3105189674610935 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-08-08T00:57:44.314Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-08-08T00:57:45.251Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-08-08T00:57:46.197Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-08-08T00:57:47.131Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-08-08T00:57:49.053Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-08-08T00:57:49.053Z] 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.
[2024-08-08T00:57:49.053Z] The best model improves the baseline by 14.43%.
[2024-08-08T00:57:49.053Z] Movies recommended for you:
[2024-08-08T00:57:49.053Z] WARNING: This benchmark provides no result that can be validated.
[2024-08-08T00:57:49.053Z] There is no way to check that no silent failure occurred.
[2024-08-08T00:57:49.053Z] ====== movie-lens (apache-spark) [default], iteration 16 completed (12598.901 ms) ======
[2024-08-08T00:57:49.053Z] ====== movie-lens (apache-spark) [default], iteration 17 started ======
[2024-08-08T00:57:49.053Z] GC before operation: completed in 135.849 ms, heap usage 298.760 MB -> 52.215 MB.
[2024-08-08T00:57:50.975Z] RMSE (validation) = 3.621968954548751 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-08-08T00:57:53.509Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-08-08T00:57:55.430Z] RMSE (validation) = 1.3105189674610933 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-08-08T00:57:57.359Z] RMSE (validation) = 1.004540774800519 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-08-08T00:57:58.294Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-08-08T00:57:59.230Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-08-08T00:58:00.166Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-08-08T00:58:02.085Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-08-08T00:58:02.085Z] 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.
[2024-08-08T00:58:02.085Z] The best model improves the baseline by 14.43%.
[2024-08-08T00:58:02.085Z] Movies recommended for you:
[2024-08-08T00:58:02.085Z] WARNING: This benchmark provides no result that can be validated.
[2024-08-08T00:58:02.085Z] There is no way to check that no silent failure occurred.
[2024-08-08T00:58:02.085Z] ====== movie-lens (apache-spark) [default], iteration 17 completed (12899.444 ms) ======
[2024-08-08T00:58:02.085Z] ====== movie-lens (apache-spark) [default], iteration 18 started ======
[2024-08-08T00:58:02.085Z] GC before operation: completed in 133.192 ms, heap usage 300.847 MB -> 52.198 MB.
[2024-08-08T00:58:04.006Z] RMSE (validation) = 3.6219689545487506 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-08-08T00:58:05.925Z] RMSE (validation) = 2.1340923217118064 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-08-08T00:58:07.848Z] RMSE (validation) = 1.3105189674610935 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-08-08T00:58:09.771Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-08-08T00:58:10.709Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-08-08T00:58:11.644Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-08-08T00:58:13.563Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-08-08T00:58:14.496Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-08-08T00:58:14.496Z] 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.
[2024-08-08T00:58:14.496Z] The best model improves the baseline by 14.43%.
[2024-08-08T00:58:14.496Z] Movies recommended for you:
[2024-08-08T00:58:14.496Z] WARNING: This benchmark provides no result that can be validated.
[2024-08-08T00:58:14.496Z] There is no way to check that no silent failure occurred.
[2024-08-08T00:58:14.496Z] ====== movie-lens (apache-spark) [default], iteration 18 completed (12520.870 ms) ======
[2024-08-08T00:58:14.496Z] ====== movie-lens (apache-spark) [default], iteration 19 started ======
[2024-08-08T00:58:14.496Z] GC before operation: completed in 133.758 ms, heap usage 267.323 MB -> 52.367 MB.
[2024-08-08T00:58:16.413Z] RMSE (validation) = 3.621968954548751 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-08-08T00:58:18.337Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-08-08T00:58:21.329Z] RMSE (validation) = 1.3105189674610933 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-08-08T00:58:22.263Z] RMSE (validation) = 1.004540774800519 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-08-08T00:58:24.184Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-08-08T00:58:25.117Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-08-08T00:58:26.053Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-08-08T00:58:26.986Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-08-08T00:58:26.986Z] 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.
[2024-08-08T00:58:26.986Z] The best model improves the baseline by 14.43%.
[2024-08-08T00:58:27.921Z] Movies recommended for you:
[2024-08-08T00:58:27.921Z] WARNING: This benchmark provides no result that can be validated.
[2024-08-08T00:58:27.921Z] There is no way to check that no silent failure occurred.
[2024-08-08T00:58:27.921Z] ====== movie-lens (apache-spark) [default], iteration 19 completed (12676.840 ms) ======
[2024-08-08T00:58:28.857Z] -----------------------------------
[2024-08-08T00:58:28.857Z] renaissance-movie-lens_0_PASSED
[2024-08-08T00:58:28.857Z] -----------------------------------
[2024-08-08T00:58:28.857Z]
[2024-08-08T00:58:28.857Z] TEST TEARDOWN:
[2024-08-08T00:58:28.857Z] Nothing to be done for teardown.
[2024-08-08T00:58:28.857Z] renaissance-movie-lens_0 Finish Time: Thu Aug 8 00:58:28 2024 Epoch Time (ms): 1723078708070