renaissance-movie-lens_0
[2025-01-22T03:37:44.241Z] Running test renaissance-movie-lens_0 ...
[2025-01-22T03:37:44.241Z] ===============================================
[2025-01-22T03:37:44.241Z] renaissance-movie-lens_0 Start Time: Tue Jan 21 22:37:44 2025 Epoch Time (ms): 1737517064022
[2025-01-22T03:37:44.241Z] variation: NoOptions
[2025-01-22T03:37:44.241Z] JVM_OPTIONS:
[2025-01-22T03:37:44.241Z] { \
[2025-01-22T03:37:44.241Z] echo ""; echo "TEST SETUP:"; \
[2025-01-22T03:37:44.241Z] echo "Nothing to be done for setup."; \
[2025-01-22T03:37:44.241Z] mkdir -p "/Users/admin/workspace/workspace/Test_openjdk11_hs_extended.perf_aarch64_mac/aqa-tests/TKG/../TKG/output_17375166921655/renaissance-movie-lens_0"; \
[2025-01-22T03:37:44.241Z] cd "/Users/admin/workspace/workspace/Test_openjdk11_hs_extended.perf_aarch64_mac/aqa-tests/TKG/../TKG/output_17375166921655/renaissance-movie-lens_0"; \
[2025-01-22T03:37:44.241Z] echo ""; echo "TESTING:"; \
[2025-01-22T03:37:44.241Z] "/Users/admin/workspace/workspace/Test_openjdk11_hs_extended.perf_aarch64_mac/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_openjdk11_hs_extended.perf_aarch64_mac/aqa-tests/TKG/../../jvmtest/perf/renaissance/renaissance.jar" --json ""/Users/admin/workspace/workspace/Test_openjdk11_hs_extended.perf_aarch64_mac/aqa-tests/TKG/../TKG/output_17375166921655/renaissance-movie-lens_0"/movie-lens.json" movie-lens; \
[2025-01-22T03:37:44.241Z] if [ $? -eq 0 ]; then echo "-----------------------------------"; echo "renaissance-movie-lens_0""_PASSED"; echo "-----------------------------------"; cd /Users/admin/workspace/workspace/Test_openjdk11_hs_extended.perf_aarch64_mac/aqa-tests/TKG/..; rm -f -r "/Users/admin/workspace/workspace/Test_openjdk11_hs_extended.perf_aarch64_mac/aqa-tests/TKG/../TKG/output_17375166921655/renaissance-movie-lens_0"; else echo "-----------------------------------"; echo "renaissance-movie-lens_0""_FAILED"; echo "-----------------------------------"; fi; \
[2025-01-22T03:37:44.241Z] echo ""; echo "TEST TEARDOWN:"; \
[2025-01-22T03:37:44.241Z] echo "Nothing to be done for teardown."; \
[2025-01-22T03:37:44.241Z] } 2>&1 | tee -a "/Users/admin/workspace/workspace/Test_openjdk11_hs_extended.perf_aarch64_mac/aqa-tests/TKG/../TKG/output_17375166921655/TestTargetResult";
[2025-01-22T03:37:44.241Z]
[2025-01-22T03:37:44.241Z] TEST SETUP:
[2025-01-22T03:37:44.241Z] Nothing to be done for setup.
[2025-01-22T03:37:44.241Z]
[2025-01-22T03:37:44.241Z] TESTING:
[2025-01-22T03:37:46.168Z] Using Spark's default log4j profile: org/apache/spark/log4j-defaults.properties
[2025-01-22T03:37:46.998Z] NOTE: 'movie-lens' benchmark uses Spark local executor with 3 (out of 3) threads.
[2025-01-22T03:37:48.322Z] Got 100004 ratings from 671 users on 9066 movies.
[2025-01-22T03:37:48.322Z] Training: 60056, validation: 20285, test: 19854
[2025-01-22T03:37:48.322Z] ====== movie-lens (apache-spark) [default], iteration 0 started ======
[2025-01-22T03:37:48.322Z] GC before operation: completed in 23.271 ms, heap usage 66.673 MB -> 36.559 MB.
[2025-01-22T03:37:51.680Z] RMSE (validation) = 3.62196895454876 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2025-01-22T03:37:53.024Z] RMSE (validation) = 2.1340923221100736 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2025-01-22T03:37:54.948Z] RMSE (validation) = 1.3105190428502682 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2025-01-22T03:37:55.811Z] RMSE (validation) = 0.9920028100571827 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2025-01-22T03:37:56.640Z] RMSE (validation) = 1.212631715629696 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2025-01-22T03:37:57.497Z] RMSE (validation) = 1.1025694034386657 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2025-01-22T03:37:58.339Z] RMSE (validation) = 0.9259652469249674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2025-01-22T03:37:59.174Z] RMSE (validation) = 0.8982232214592321 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2025-01-22T03:37:59.174Z] 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.
[2025-01-22T03:37:59.174Z] The best model improves the baseline by 14.52%.
[2025-01-22T03:37:59.174Z] Movies recommended for you:
[2025-01-22T03:37:59.174Z] WARNING: This benchmark provides no result that can be validated.
[2025-01-22T03:37:59.174Z] There is no way to check that no silent failure occurred.
[2025-01-22T03:37:59.174Z] ====== movie-lens (apache-spark) [default], iteration 0 completed (10807.448 ms) ======
[2025-01-22T03:37:59.174Z] ====== movie-lens (apache-spark) [default], iteration 1 started ======
[2025-01-22T03:37:59.174Z] GC before operation: completed in 46.670 ms, heap usage 207.269 MB -> 48.158 MB.
[2025-01-22T03:38:00.529Z] RMSE (validation) = 3.62196895454876 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2025-01-22T03:38:01.899Z] RMSE (validation) = 2.1340923221100736 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2025-01-22T03:38:03.834Z] RMSE (validation) = 1.3105190428502682 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2025-01-22T03:38:05.189Z] RMSE (validation) = 0.9920028100571827 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2025-01-22T03:38:05.580Z] RMSE (validation) = 1.212631715629696 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2025-01-22T03:38:06.417Z] RMSE (validation) = 1.1025694034386657 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2025-01-22T03:38:07.248Z] RMSE (validation) = 0.9259652469249674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2025-01-22T03:38:08.097Z] RMSE (validation) = 0.8982232214592321 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2025-01-22T03:38:08.486Z] 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.
[2025-01-22T03:38:08.486Z] The best model improves the baseline by 14.52%.
[2025-01-22T03:38:08.486Z] Movies recommended for you:
[2025-01-22T03:38:08.486Z] WARNING: This benchmark provides no result that can be validated.
[2025-01-22T03:38:08.486Z] There is no way to check that no silent failure occurred.
[2025-01-22T03:38:08.486Z] ====== movie-lens (apache-spark) [default], iteration 1 completed (9064.281 ms) ======
[2025-01-22T03:38:08.486Z] ====== movie-lens (apache-spark) [default], iteration 2 started ======
[2025-01-22T03:38:08.486Z] GC before operation: completed in 44.228 ms, heap usage 180.094 MB -> 48.962 MB.
[2025-01-22T03:38:09.819Z] RMSE (validation) = 3.62196895454876 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2025-01-22T03:38:11.171Z] RMSE (validation) = 2.1340923221100736 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2025-01-22T03:38:12.515Z] RMSE (validation) = 1.3105190428502682 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2025-01-22T03:38:13.862Z] RMSE (validation) = 0.9920028100571827 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2025-01-22T03:38:14.253Z] RMSE (validation) = 1.212631715629696 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2025-01-22T03:38:15.094Z] RMSE (validation) = 1.1025694034386657 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2025-01-22T03:38:15.931Z] RMSE (validation) = 0.9259652469249674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2025-01-22T03:38:16.765Z] RMSE (validation) = 0.8982232214592321 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2025-01-22T03:38:16.765Z] 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.
[2025-01-22T03:38:16.765Z] The best model improves the baseline by 14.52%.
[2025-01-22T03:38:16.765Z] Movies recommended for you:
[2025-01-22T03:38:16.765Z] WARNING: This benchmark provides no result that can be validated.
[2025-01-22T03:38:16.765Z] There is no way to check that no silent failure occurred.
[2025-01-22T03:38:16.765Z] ====== movie-lens (apache-spark) [default], iteration 2 completed (8286.885 ms) ======
[2025-01-22T03:38:16.765Z] ====== movie-lens (apache-spark) [default], iteration 3 started ======
[2025-01-22T03:38:16.765Z] GC before operation: completed in 44.046 ms, heap usage 175.246 MB -> 49.229 MB.
[2025-01-22T03:38:18.111Z] RMSE (validation) = 3.62196895454876 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2025-01-22T03:38:19.495Z] RMSE (validation) = 2.1340923221100736 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2025-01-22T03:38:20.853Z] RMSE (validation) = 1.3105190428502682 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2025-01-22T03:38:21.696Z] RMSE (validation) = 0.9920028100571827 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2025-01-22T03:38:22.526Z] RMSE (validation) = 1.212631715629696 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2025-01-22T03:38:23.356Z] RMSE (validation) = 1.1025694034386657 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2025-01-22T03:38:24.186Z] RMSE (validation) = 0.9259652469249674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2025-01-22T03:38:25.021Z] RMSE (validation) = 0.8982232214592321 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2025-01-22T03:38:25.021Z] 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.
[2025-01-22T03:38:25.021Z] The best model improves the baseline by 14.52%.
[2025-01-22T03:38:25.021Z] Movies recommended for you:
[2025-01-22T03:38:25.021Z] WARNING: This benchmark provides no result that can be validated.
[2025-01-22T03:38:25.021Z] There is no way to check that no silent failure occurred.
[2025-01-22T03:38:25.021Z] ====== movie-lens (apache-spark) [default], iteration 3 completed (8330.203 ms) ======
[2025-01-22T03:38:25.021Z] ====== movie-lens (apache-spark) [default], iteration 4 started ======
[2025-01-22T03:38:25.021Z] GC before operation: completed in 54.479 ms, heap usage 194.611 MB -> 49.537 MB.
[2025-01-22T03:38:26.355Z] RMSE (validation) = 3.6219689545487603 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2025-01-22T03:38:27.779Z] RMSE (validation) = 2.1340923221100736 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2025-01-22T03:38:28.635Z] RMSE (validation) = 1.3105190428502682 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2025-01-22T03:38:29.979Z] RMSE (validation) = 0.9920028100571827 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2025-01-22T03:38:30.823Z] RMSE (validation) = 1.212631715629696 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2025-01-22T03:38:31.667Z] RMSE (validation) = 1.1025694034386657 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2025-01-22T03:38:32.511Z] RMSE (validation) = 0.9259652469249674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2025-01-22T03:38:32.905Z] RMSE (validation) = 0.8982232214592321 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2025-01-22T03:38:33.293Z] 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.
[2025-01-22T03:38:33.293Z] The best model improves the baseline by 14.52%.
[2025-01-22T03:38:33.293Z] Movies recommended for you:
[2025-01-22T03:38:33.293Z] WARNING: This benchmark provides no result that can be validated.
[2025-01-22T03:38:33.293Z] There is no way to check that no silent failure occurred.
[2025-01-22T03:38:33.293Z] ====== movie-lens (apache-spark) [default], iteration 4 completed (8029.955 ms) ======
[2025-01-22T03:38:33.293Z] ====== movie-lens (apache-spark) [default], iteration 5 started ======
[2025-01-22T03:38:33.293Z] GC before operation: completed in 47.300 ms, heap usage 250.087 MB -> 49.751 MB.
[2025-01-22T03:38:34.661Z] RMSE (validation) = 3.6219689545487603 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2025-01-22T03:38:36.195Z] RMSE (validation) = 2.1340923221100736 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2025-01-22T03:38:37.036Z] RMSE (validation) = 1.3105190428502682 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2025-01-22T03:38:38.375Z] RMSE (validation) = 0.9920028100571827 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2025-01-22T03:38:39.229Z] RMSE (validation) = 1.212631715629696 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2025-01-22T03:38:39.622Z] RMSE (validation) = 1.1025694034386657 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2025-01-22T03:38:40.454Z] RMSE (validation) = 0.9259652469249674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2025-01-22T03:38:41.289Z] RMSE (validation) = 0.8982232214592321 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2025-01-22T03:38:41.289Z] 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.
[2025-01-22T03:38:41.289Z] The best model improves the baseline by 14.52%.
[2025-01-22T03:38:41.289Z] Movies recommended for you:
[2025-01-22T03:38:41.289Z] WARNING: This benchmark provides no result that can be validated.
[2025-01-22T03:38:41.289Z] There is no way to check that no silent failure occurred.
[2025-01-22T03:38:41.289Z] ====== movie-lens (apache-spark) [default], iteration 5 completed (8080.208 ms) ======
[2025-01-22T03:38:41.289Z] ====== movie-lens (apache-spark) [default], iteration 6 started ======
[2025-01-22T03:38:41.289Z] GC before operation: completed in 40.807 ms, heap usage 206.718 MB -> 49.754 MB.
[2025-01-22T03:38:42.643Z] RMSE (validation) = 3.62196895454876 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2025-01-22T03:38:43.982Z] RMSE (validation) = 2.1340923221100736 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2025-01-22T03:38:45.328Z] RMSE (validation) = 1.3105190428502682 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2025-01-22T03:38:46.169Z] RMSE (validation) = 0.9920028100571827 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2025-01-22T03:38:47.002Z] RMSE (validation) = 1.212631715629696 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2025-01-22T03:38:47.865Z] RMSE (validation) = 1.1025694034386657 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2025-01-22T03:38:48.268Z] RMSE (validation) = 0.9259652469249674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2025-01-22T03:38:49.307Z] RMSE (validation) = 0.8982232214592321 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2025-01-22T03:38:49.307Z] 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.
[2025-01-22T03:38:49.307Z] The best model improves the baseline by 14.52%.
[2025-01-22T03:38:49.307Z] Movies recommended for you:
[2025-01-22T03:38:49.307Z] WARNING: This benchmark provides no result that can be validated.
[2025-01-22T03:38:49.307Z] There is no way to check that no silent failure occurred.
[2025-01-22T03:38:49.307Z] ====== movie-lens (apache-spark) [default], iteration 6 completed (7959.886 ms) ======
[2025-01-22T03:38:49.307Z] ====== movie-lens (apache-spark) [default], iteration 7 started ======
[2025-01-22T03:38:49.928Z] GC before operation: completed in 40.829 ms, heap usage 87.946 MB -> 49.810 MB.
[2025-01-22T03:38:50.771Z] RMSE (validation) = 3.6219689545487603 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2025-01-22T03:38:51.617Z] RMSE (validation) = 2.1340923221100736 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2025-01-22T03:38:52.995Z] RMSE (validation) = 1.3105190428502682 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2025-01-22T03:38:54.332Z] RMSE (validation) = 0.9920028100571827 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2025-01-22T03:38:55.187Z] RMSE (validation) = 1.212631715629696 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2025-01-22T03:38:56.034Z] RMSE (validation) = 1.1025694034386657 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2025-01-22T03:38:56.434Z] RMSE (validation) = 0.9259652469249674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2025-01-22T03:38:57.282Z] RMSE (validation) = 0.8982232214592321 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2025-01-22T03:38:57.282Z] 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.
[2025-01-22T03:38:57.282Z] The best model improves the baseline by 14.52%.
[2025-01-22T03:38:57.282Z] Movies recommended for you:
[2025-01-22T03:38:57.282Z] WARNING: This benchmark provides no result that can be validated.
[2025-01-22T03:38:57.282Z] There is no way to check that no silent failure occurred.
[2025-01-22T03:38:57.282Z] ====== movie-lens (apache-spark) [default], iteration 7 completed (8030.458 ms) ======
[2025-01-22T03:38:57.282Z] ====== movie-lens (apache-spark) [default], iteration 8 started ======
[2025-01-22T03:38:57.282Z] GC before operation: completed in 43.386 ms, heap usage 304.879 MB -> 50.286 MB.
[2025-01-22T03:38:58.638Z] RMSE (validation) = 3.6219689545487603 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2025-01-22T03:38:59.968Z] RMSE (validation) = 2.1340923221100736 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2025-01-22T03:39:00.832Z] RMSE (validation) = 1.3105190428502682 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2025-01-22T03:39:02.216Z] RMSE (validation) = 0.9920028100571827 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2025-01-22T03:39:03.054Z] RMSE (validation) = 1.212631715629696 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2025-01-22T03:39:03.456Z] RMSE (validation) = 1.1025694034386657 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2025-01-22T03:39:04.317Z] RMSE (validation) = 0.9259652469249674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2025-01-22T03:39:05.171Z] RMSE (validation) = 0.8982232214592321 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2025-01-22T03:39:05.171Z] 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.
[2025-01-22T03:39:05.171Z] The best model improves the baseline by 14.52%.
[2025-01-22T03:39:05.171Z] Movies recommended for you:
[2025-01-22T03:39:05.171Z] WARNING: This benchmark provides no result that can be validated.
[2025-01-22T03:39:05.171Z] There is no way to check that no silent failure occurred.
[2025-01-22T03:39:05.171Z] ====== movie-lens (apache-spark) [default], iteration 8 completed (7700.596 ms) ======
[2025-01-22T03:39:05.171Z] ====== movie-lens (apache-spark) [default], iteration 9 started ======
[2025-01-22T03:39:05.171Z] GC before operation: completed in 41.030 ms, heap usage 121.889 MB -> 49.877 MB.
[2025-01-22T03:39:06.548Z] RMSE (validation) = 3.62196895454876 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2025-01-22T03:39:07.396Z] RMSE (validation) = 2.1340923221100736 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2025-01-22T03:39:08.756Z] RMSE (validation) = 1.3105190428502682 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2025-01-22T03:39:10.116Z] RMSE (validation) = 0.9920028100571827 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2025-01-22T03:39:10.516Z] RMSE (validation) = 1.212631715629696 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2025-01-22T03:39:11.348Z] RMSE (validation) = 1.1025694034386657 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2025-01-22T03:39:12.191Z] RMSE (validation) = 0.9259652469249674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2025-01-22T03:39:13.039Z] RMSE (validation) = 0.8982232214592321 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2025-01-22T03:39:13.039Z] 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.
[2025-01-22T03:39:13.039Z] The best model improves the baseline by 14.52%.
[2025-01-22T03:39:13.039Z] Movies recommended for you:
[2025-01-22T03:39:13.039Z] WARNING: This benchmark provides no result that can be validated.
[2025-01-22T03:39:13.039Z] There is no way to check that no silent failure occurred.
[2025-01-22T03:39:13.039Z] ====== movie-lens (apache-spark) [default], iteration 9 completed (8005.749 ms) ======
[2025-01-22T03:39:13.039Z] ====== movie-lens (apache-spark) [default], iteration 10 started ======
[2025-01-22T03:39:13.039Z] GC before operation: completed in 43.441 ms, heap usage 76.148 MB -> 49.871 MB.
[2025-01-22T03:39:14.372Z] RMSE (validation) = 3.6219689545487603 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2025-01-22T03:39:15.784Z] RMSE (validation) = 2.1340923221100736 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2025-01-22T03:39:16.634Z] RMSE (validation) = 1.3105190428502682 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2025-01-22T03:39:17.967Z] RMSE (validation) = 0.9920028100571827 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2025-01-22T03:39:18.355Z] RMSE (validation) = 1.212631715629696 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2025-01-22T03:39:19.187Z] RMSE (validation) = 1.1025694034386657 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2025-01-22T03:39:20.024Z] RMSE (validation) = 0.9259652469249674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2025-01-22T03:39:20.419Z] RMSE (validation) = 0.8982232214592321 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2025-01-22T03:39:20.419Z] 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.
[2025-01-22T03:39:20.419Z] The best model improves the baseline by 14.52%.
[2025-01-22T03:39:20.419Z] Movies recommended for you:
[2025-01-22T03:39:20.419Z] WARNING: This benchmark provides no result that can be validated.
[2025-01-22T03:39:20.419Z] There is no way to check that no silent failure occurred.
[2025-01-22T03:39:20.419Z] ====== movie-lens (apache-spark) [default], iteration 10 completed (7390.840 ms) ======
[2025-01-22T03:39:20.419Z] ====== movie-lens (apache-spark) [default], iteration 11 started ======
[2025-01-22T03:39:20.811Z] GC before operation: completed in 39.669 ms, heap usage 191.797 MB -> 49.888 MB.
[2025-01-22T03:39:21.727Z] RMSE (validation) = 3.62196895454876 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2025-01-22T03:39:22.569Z] RMSE (validation) = 2.1340923221100736 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2025-01-22T03:39:23.910Z] RMSE (validation) = 1.3105190428502682 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2025-01-22T03:39:24.768Z] RMSE (validation) = 0.9920028100571827 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2025-01-22T03:39:25.604Z] RMSE (validation) = 1.212631715629696 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2025-01-22T03:39:26.434Z] RMSE (validation) = 1.1025694034386657 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2025-01-22T03:39:27.271Z] RMSE (validation) = 0.9259652469249674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2025-01-22T03:39:27.661Z] RMSE (validation) = 0.8982232214592321 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2025-01-22T03:39:28.047Z] 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.
[2025-01-22T03:39:28.047Z] The best model improves the baseline by 14.52%.
[2025-01-22T03:39:28.047Z] Movies recommended for you:
[2025-01-22T03:39:28.047Z] WARNING: This benchmark provides no result that can be validated.
[2025-01-22T03:39:28.047Z] There is no way to check that no silent failure occurred.
[2025-01-22T03:39:28.047Z] ====== movie-lens (apache-spark) [default], iteration 11 completed (7303.967 ms) ======
[2025-01-22T03:39:28.047Z] ====== movie-lens (apache-spark) [default], iteration 12 started ======
[2025-01-22T03:39:28.047Z] GC before operation: completed in 42.717 ms, heap usage 293.099 MB -> 50.163 MB.
[2025-01-22T03:39:29.389Z] RMSE (validation) = 3.62196895454876 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2025-01-22T03:39:30.237Z] RMSE (validation) = 2.1340923221100736 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2025-01-22T03:39:31.560Z] RMSE (validation) = 1.3105190428502682 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2025-01-22T03:39:32.904Z] RMSE (validation) = 0.9920028100571827 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2025-01-22T03:39:33.740Z] RMSE (validation) = 1.212631715629696 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2025-01-22T03:39:34.133Z] RMSE (validation) = 1.1025694034386657 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2025-01-22T03:39:34.961Z] RMSE (validation) = 0.9259652469249674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2025-01-22T03:39:35.792Z] RMSE (validation) = 0.8982232214592321 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2025-01-22T03:39:36.179Z] 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.
[2025-01-22T03:39:36.179Z] The best model improves the baseline by 14.52%.
[2025-01-22T03:39:36.179Z] Movies recommended for you:
[2025-01-22T03:39:36.179Z] WARNING: This benchmark provides no result that can be validated.
[2025-01-22T03:39:36.179Z] There is no way to check that no silent failure occurred.
[2025-01-22T03:39:36.179Z] ====== movie-lens (apache-spark) [default], iteration 12 completed (8150.280 ms) ======
[2025-01-22T03:39:36.179Z] ====== movie-lens (apache-spark) [default], iteration 13 started ======
[2025-01-22T03:39:36.179Z] GC before operation: completed in 39.489 ms, heap usage 157.995 MB -> 50.147 MB.
[2025-01-22T03:39:37.517Z] RMSE (validation) = 3.62196895454876 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2025-01-22T03:39:38.352Z] RMSE (validation) = 2.1340923221100736 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2025-01-22T03:39:39.694Z] RMSE (validation) = 1.3105190428502682 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2025-01-22T03:39:40.541Z] RMSE (validation) = 0.9920028100571827 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2025-01-22T03:39:41.388Z] RMSE (validation) = 1.212631715629696 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2025-01-22T03:39:42.234Z] RMSE (validation) = 1.1025694034386657 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2025-01-22T03:39:42.622Z] RMSE (validation) = 0.9259652469249674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2025-01-22T03:39:43.463Z] RMSE (validation) = 0.8982232214592321 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2025-01-22T03:39:43.463Z] 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.
[2025-01-22T03:39:43.463Z] The best model improves the baseline by 14.52%.
[2025-01-22T03:39:43.463Z] Movies recommended for you:
[2025-01-22T03:39:43.463Z] WARNING: This benchmark provides no result that can be validated.
[2025-01-22T03:39:43.463Z] There is no way to check that no silent failure occurred.
[2025-01-22T03:39:43.463Z] ====== movie-lens (apache-spark) [default], iteration 13 completed (7373.317 ms) ======
[2025-01-22T03:39:43.463Z] ====== movie-lens (apache-spark) [default], iteration 14 started ======
[2025-01-22T03:39:43.463Z] GC before operation: completed in 46.293 ms, heap usage 303.420 MB -> 50.009 MB.
[2025-01-22T03:39:44.798Z] RMSE (validation) = 3.62196895454876 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2025-01-22T03:39:46.146Z] RMSE (validation) = 2.1340923221100736 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2025-01-22T03:39:47.476Z] RMSE (validation) = 1.3105190428502682 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2025-01-22T03:39:48.302Z] RMSE (validation) = 0.9920028100571827 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2025-01-22T03:39:49.147Z] RMSE (validation) = 1.212631715629696 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2025-01-22T03:39:49.970Z] RMSE (validation) = 1.1025694034386657 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2025-01-22T03:39:50.805Z] RMSE (validation) = 0.9259652469249674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2025-01-22T03:39:51.205Z] RMSE (validation) = 0.8982232214592321 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2025-01-22T03:39:51.593Z] 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.
[2025-01-22T03:39:51.593Z] The best model improves the baseline by 14.52%.
[2025-01-22T03:39:51.593Z] Movies recommended for you:
[2025-01-22T03:39:51.593Z] WARNING: This benchmark provides no result that can be validated.
[2025-01-22T03:39:51.593Z] There is no way to check that no silent failure occurred.
[2025-01-22T03:39:51.593Z] ====== movie-lens (apache-spark) [default], iteration 14 completed (7963.676 ms) ======
[2025-01-22T03:39:51.593Z] ====== movie-lens (apache-spark) [default], iteration 15 started ======
[2025-01-22T03:39:51.593Z] GC before operation: completed in 47.570 ms, heap usage 194.342 MB -> 49.998 MB.
[2025-01-22T03:39:52.974Z] RMSE (validation) = 3.6219689545487603 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2025-01-22T03:39:53.821Z] RMSE (validation) = 2.1340923221100736 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2025-01-22T03:39:55.150Z] RMSE (validation) = 1.3105190428502682 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2025-01-22T03:39:56.491Z] RMSE (validation) = 0.9920028100571827 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2025-01-22T03:39:57.333Z] RMSE (validation) = 1.212631715629696 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2025-01-22T03:39:57.723Z] RMSE (validation) = 1.1025694034386657 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2025-01-22T03:39:58.557Z] RMSE (validation) = 0.9259652469249674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2025-01-22T03:39:59.420Z] RMSE (validation) = 0.8982232214592321 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2025-01-22T03:39:59.420Z] 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.
[2025-01-22T03:39:59.420Z] The best model improves the baseline by 14.52%.
[2025-01-22T03:39:59.420Z] Movies recommended for you:
[2025-01-22T03:39:59.420Z] WARNING: This benchmark provides no result that can be validated.
[2025-01-22T03:39:59.420Z] There is no way to check that no silent failure occurred.
[2025-01-22T03:39:59.420Z] ====== movie-lens (apache-spark) [default], iteration 15 completed (7831.647 ms) ======
[2025-01-22T03:39:59.420Z] ====== movie-lens (apache-spark) [default], iteration 16 started ======
[2025-01-22T03:39:59.420Z] GC before operation: completed in 40.481 ms, heap usage 78.517 MB -> 50.052 MB.
[2025-01-22T03:40:00.754Z] RMSE (validation) = 3.6219689545487603 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2025-01-22T03:40:02.155Z] RMSE (validation) = 2.1340923221100736 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2025-01-22T03:40:03.549Z] RMSE (validation) = 1.3105190428502682 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2025-01-22T03:40:04.389Z] RMSE (validation) = 0.9920028100571827 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2025-01-22T03:40:05.240Z] RMSE (validation) = 1.212631715629696 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2025-01-22T03:40:06.075Z] RMSE (validation) = 1.1025694034386657 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2025-01-22T03:40:06.476Z] RMSE (validation) = 0.9259652469249674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2025-01-22T03:40:07.308Z] RMSE (validation) = 0.8982232214592321 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2025-01-22T03:40:07.308Z] 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.
[2025-01-22T03:40:07.308Z] The best model improves the baseline by 14.52%.
[2025-01-22T03:40:07.308Z] Movies recommended for you:
[2025-01-22T03:40:07.308Z] WARNING: This benchmark provides no result that can be validated.
[2025-01-22T03:40:07.308Z] There is no way to check that no silent failure occurred.
[2025-01-22T03:40:07.308Z] ====== movie-lens (apache-spark) [default], iteration 16 completed (7886.548 ms) ======
[2025-01-22T03:40:07.308Z] ====== movie-lens (apache-spark) [default], iteration 17 started ======
[2025-01-22T03:40:07.308Z] GC before operation: completed in 43.840 ms, heap usage 142.933 MB -> 49.980 MB.
[2025-01-22T03:40:08.652Z] RMSE (validation) = 3.62196895454876 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2025-01-22T03:40:09.976Z] RMSE (validation) = 2.1340923221100736 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2025-01-22T03:40:11.334Z] RMSE (validation) = 1.3105190428502682 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2025-01-22T03:40:12.166Z] RMSE (validation) = 0.9920028100571827 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2025-01-22T03:40:13.001Z] RMSE (validation) = 1.212631715629696 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2025-01-22T03:40:13.845Z] RMSE (validation) = 1.1025694034386657 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2025-01-22T03:40:14.702Z] RMSE (validation) = 0.9259652469249674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2025-01-22T03:40:15.098Z] RMSE (validation) = 0.8982232214592321 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2025-01-22T03:40:15.098Z] 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.
[2025-01-22T03:40:15.098Z] The best model improves the baseline by 14.52%.
[2025-01-22T03:40:15.503Z] Movies recommended for you:
[2025-01-22T03:40:15.503Z] WARNING: This benchmark provides no result that can be validated.
[2025-01-22T03:40:15.503Z] There is no way to check that no silent failure occurred.
[2025-01-22T03:40:15.503Z] ====== movie-lens (apache-spark) [default], iteration 17 completed (7888.085 ms) ======
[2025-01-22T03:40:15.503Z] ====== movie-lens (apache-spark) [default], iteration 18 started ======
[2025-01-22T03:40:15.503Z] GC before operation: completed in 40.606 ms, heap usage 122.402 MB -> 49.990 MB.
[2025-01-22T03:40:16.339Z] RMSE (validation) = 3.6219689545487603 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2025-01-22T03:40:17.671Z] RMSE (validation) = 2.1340923221100736 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2025-01-22T03:40:19.009Z] RMSE (validation) = 1.3105190428502682 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2025-01-22T03:40:19.844Z] RMSE (validation) = 0.9920028100571827 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2025-01-22T03:40:20.682Z] RMSE (validation) = 1.212631715629696 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2025-01-22T03:40:21.510Z] RMSE (validation) = 1.1025694034386657 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2025-01-22T03:40:21.911Z] RMSE (validation) = 0.9259652469249674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2025-01-22T03:40:22.775Z] RMSE (validation) = 0.8982232214592321 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2025-01-22T03:40:22.775Z] 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.
[2025-01-22T03:40:22.775Z] The best model improves the baseline by 14.52%.
[2025-01-22T03:40:22.775Z] Movies recommended for you:
[2025-01-22T03:40:22.775Z] WARNING: This benchmark provides no result that can be validated.
[2025-01-22T03:40:22.775Z] There is no way to check that no silent failure occurred.
[2025-01-22T03:40:22.775Z] ====== movie-lens (apache-spark) [default], iteration 18 completed (7595.086 ms) ======
[2025-01-22T03:40:22.775Z] ====== movie-lens (apache-spark) [default], iteration 19 started ======
[2025-01-22T03:40:23.206Z] GC before operation: completed in 50.695 ms, heap usage 120.047 MB -> 50.101 MB.
[2025-01-22T03:40:24.054Z] RMSE (validation) = 3.62196895454876 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2025-01-22T03:40:25.404Z] RMSE (validation) = 2.1340923221100736 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2025-01-22T03:40:26.745Z] RMSE (validation) = 1.3105190428502682 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2025-01-22T03:40:28.101Z] RMSE (validation) = 0.9920028100571827 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2025-01-22T03:40:28.493Z] RMSE (validation) = 1.212631715629696 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2025-01-22T03:40:29.333Z] RMSE (validation) = 1.1025694034386657 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2025-01-22T03:40:29.727Z] RMSE (validation) = 0.9259652469249674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2025-01-22T03:40:30.577Z] RMSE (validation) = 0.8982232214592321 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2025-01-22T03:40:30.577Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9063003101263983.
[2025-01-22T03:40:30.577Z] The best model improves the baseline by 14.52%.
[2025-01-22T03:40:30.577Z] Movies recommended for you:
[2025-01-22T03:40:30.577Z] WARNING: This benchmark provides no result that can be validated.
[2025-01-22T03:40:30.577Z] There is no way to check that no silent failure occurred.
[2025-01-22T03:40:30.577Z] ====== movie-lens (apache-spark) [default], iteration 19 completed (7769.145 ms) ======
[2025-01-22T03:40:30.965Z] -----------------------------------
[2025-01-22T03:40:30.965Z] renaissance-movie-lens_0_PASSED
[2025-01-22T03:40:30.965Z] -----------------------------------
[2025-01-22T03:40:30.965Z]
[2025-01-22T03:40:30.965Z] TEST TEARDOWN:
[2025-01-22T03:40:30.965Z] Nothing to be done for teardown.
[2025-01-22T03:40:30.965Z] renaissance-movie-lens_0 Finish Time: Tue Jan 21 22:40:30 2025 Epoch Time (ms): 1737517230737