renaissance-movie-lens_0
[2024-10-02T20:27:16.486Z] Running test renaissance-movie-lens_0 ...
[2024-10-02T20:27:16.486Z] ===============================================
[2024-10-02T20:27:16.486Z] renaissance-movie-lens_0 Start Time: Wed Oct 2 16:27:16 2024 Epoch Time (ms): 1727900836006
[2024-10-02T20:27:16.486Z] variation: NoOptions
[2024-10-02T20:27:16.486Z] JVM_OPTIONS:
[2024-10-02T20:27:16.486Z] { \
[2024-10-02T20:27:16.486Z] echo ""; echo "TEST SETUP:"; \
[2024-10-02T20:27:16.486Z] echo "Nothing to be done for setup."; \
[2024-10-02T20:27:16.486Z] mkdir -p "/Users/admin/workspace/workspace/Test_openjdk17_hs_extended.perf_aarch64_mac/aqa-tests/TKG/../TKG/output_17279005025415/renaissance-movie-lens_0"; \
[2024-10-02T20:27:16.486Z] cd "/Users/admin/workspace/workspace/Test_openjdk17_hs_extended.perf_aarch64_mac/aqa-tests/TKG/../TKG/output_17279005025415/renaissance-movie-lens_0"; \
[2024-10-02T20:27:16.486Z] echo ""; echo "TESTING:"; \
[2024-10-02T20:27:16.486Z] "/Users/admin/workspace/workspace/Test_openjdk17_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_openjdk17_hs_extended.perf_aarch64_mac/aqa-tests/TKG/../../jvmtest/perf/renaissance/renaissance.jar" --json ""/Users/admin/workspace/workspace/Test_openjdk17_hs_extended.perf_aarch64_mac/aqa-tests/TKG/../TKG/output_17279005025415/renaissance-movie-lens_0"/movie-lens.json" movie-lens; \
[2024-10-02T20:27:16.486Z] if [ $? -eq 0 ]; then echo "-----------------------------------"; echo "renaissance-movie-lens_0""_PASSED"; echo "-----------------------------------"; cd /Users/admin/workspace/workspace/Test_openjdk17_hs_extended.perf_aarch64_mac/aqa-tests/TKG/..; rm -f -r "/Users/admin/workspace/workspace/Test_openjdk17_hs_extended.perf_aarch64_mac/aqa-tests/TKG/../TKG/output_17279005025415/renaissance-movie-lens_0"; else echo "-----------------------------------"; echo "renaissance-movie-lens_0""_FAILED"; echo "-----------------------------------"; fi; \
[2024-10-02T20:27:16.486Z] echo ""; echo "TEST TEARDOWN:"; \
[2024-10-02T20:27:16.486Z] echo "Nothing to be done for teardown."; \
[2024-10-02T20:27:16.486Z] } 2>&1 | tee -a "/Users/admin/workspace/workspace/Test_openjdk17_hs_extended.perf_aarch64_mac/aqa-tests/TKG/../TKG/output_17279005025415/TestTargetResult";
[2024-10-02T20:27:16.486Z]
[2024-10-02T20:27:16.486Z] TEST SETUP:
[2024-10-02T20:27:16.486Z] Nothing to be done for setup.
[2024-10-02T20:27:16.486Z]
[2024-10-02T20:27:16.486Z] TESTING:
[2024-10-02T20:27:18.260Z] Using Spark's default log4j profile: org/apache/spark/log4j-defaults.properties
[2024-10-02T20:27:19.488Z] NOTE: 'movie-lens' benchmark uses Spark local executor with 3 (out of 3) threads.
[2024-10-02T20:27:21.335Z] Got 100004 ratings from 671 users on 9066 movies.
[2024-10-02T20:27:21.336Z] Training: 60056, validation: 20285, test: 19854
[2024-10-02T20:27:21.336Z] ====== movie-lens (apache-spark) [default], iteration 0 started ======
[2024-10-02T20:27:21.336Z] GC before operation: completed in 22.526 ms, heap usage 107.095 MB -> 37.180 MB.
[2024-10-02T20:27:25.390Z] RMSE (validation) = 3.6219689545487603 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-10-02T20:27:27.206Z] RMSE (validation) = 2.1340923221100736 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-10-02T20:27:29.625Z] RMSE (validation) = 1.3105190428502682 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-10-02T20:27:30.852Z] RMSE (validation) = 0.9920028100571827 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-10-02T20:27:32.073Z] RMSE (validation) = 1.212631715629696 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-10-02T20:27:32.837Z] RMSE (validation) = 1.1025694034386657 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-10-02T20:27:33.595Z] RMSE (validation) = 0.9259652469249674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-10-02T20:27:34.357Z] RMSE (validation) = 0.8982232214592321 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-10-02T20:27:34.709Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9063003101263983.
[2024-10-02T20:27:34.709Z] The best model improves the baseline by 14.52%.
[2024-10-02T20:27:34.709Z] Movies recommended for you:
[2024-10-02T20:27:34.709Z] WARNING: This benchmark provides no result that can be validated.
[2024-10-02T20:27:34.709Z] There is no way to check that no silent failure occurred.
[2024-10-02T20:27:34.709Z] ====== movie-lens (apache-spark) [default], iteration 0 completed (13435.373 ms) ======
[2024-10-02T20:27:34.709Z] ====== movie-lens (apache-spark) [default], iteration 1 started ======
[2024-10-02T20:27:34.709Z] GC before operation: completed in 42.306 ms, heap usage 216.134 MB -> 52.789 MB.
[2024-10-02T20:27:36.476Z] RMSE (validation) = 3.6219689545487603 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-10-02T20:27:37.726Z] RMSE (validation) = 2.1340923221100736 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-10-02T20:27:39.554Z] RMSE (validation) = 1.3105190428502682 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-10-02T20:27:40.812Z] RMSE (validation) = 0.9920028100571827 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-10-02T20:27:41.574Z] RMSE (validation) = 1.212631715629696 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-10-02T20:27:42.366Z] RMSE (validation) = 1.1025694034386657 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-10-02T20:27:43.206Z] RMSE (validation) = 0.9259652469249674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-10-02T20:27:43.984Z] RMSE (validation) = 0.8982232214592321 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-10-02T20:27:44.342Z] 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.
[2024-10-02T20:27:44.342Z] The best model improves the baseline by 14.52%.
[2024-10-02T20:27:44.342Z] Movies recommended for you:
[2024-10-02T20:27:44.342Z] WARNING: This benchmark provides no result that can be validated.
[2024-10-02T20:27:44.342Z] There is no way to check that no silent failure occurred.
[2024-10-02T20:27:44.342Z] ====== movie-lens (apache-spark) [default], iteration 1 completed (9497.015 ms) ======
[2024-10-02T20:27:44.342Z] ====== movie-lens (apache-spark) [default], iteration 2 started ======
[2024-10-02T20:27:44.342Z] GC before operation: completed in 35.436 ms, heap usage 249.488 MB -> 49.551 MB.
[2024-10-02T20:27:45.576Z] RMSE (validation) = 3.62196895454876 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-10-02T20:27:46.805Z] RMSE (validation) = 2.1340923221100736 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-10-02T20:27:48.606Z] RMSE (validation) = 1.3105190428502682 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-10-02T20:27:49.363Z] RMSE (validation) = 0.9920028100571827 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-10-02T20:27:50.121Z] RMSE (validation) = 1.212631715629696 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-10-02T20:27:50.905Z] RMSE (validation) = 1.1025694034386657 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-10-02T20:27:51.683Z] RMSE (validation) = 0.9259652469249674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-10-02T20:27:52.449Z] RMSE (validation) = 0.8982232214592321 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-10-02T20:27:52.807Z] 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.
[2024-10-02T20:27:52.807Z] The best model improves the baseline by 14.52%.
[2024-10-02T20:27:52.807Z] Movies recommended for you:
[2024-10-02T20:27:52.807Z] WARNING: This benchmark provides no result that can be validated.
[2024-10-02T20:27:52.807Z] There is no way to check that no silent failure occurred.
[2024-10-02T20:27:52.807Z] ====== movie-lens (apache-spark) [default], iteration 2 completed (8452.988 ms) ======
[2024-10-02T20:27:52.807Z] ====== movie-lens (apache-spark) [default], iteration 3 started ======
[2024-10-02T20:27:52.807Z] GC before operation: completed in 29.140 ms, heap usage 206.033 MB -> 49.779 MB.
[2024-10-02T20:27:54.040Z] RMSE (validation) = 3.6219689545487603 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-10-02T20:27:55.817Z] RMSE (validation) = 2.1340923221100736 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-10-02T20:27:57.117Z] RMSE (validation) = 1.3105190428502682 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-10-02T20:27:57.880Z] RMSE (validation) = 0.9920028100571827 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-10-02T20:27:58.648Z] RMSE (validation) = 1.212631715629696 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-10-02T20:27:59.408Z] RMSE (validation) = 1.1025694034386657 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-10-02T20:28:00.656Z] RMSE (validation) = 0.9259652469249674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-10-02T20:28:01.021Z] RMSE (validation) = 0.8982232214592321 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-10-02T20:28:01.389Z] 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.
[2024-10-02T20:28:01.389Z] The best model improves the baseline by 14.52%.
[2024-10-02T20:28:01.389Z] Movies recommended for you:
[2024-10-02T20:28:01.389Z] WARNING: This benchmark provides no result that can be validated.
[2024-10-02T20:28:01.389Z] There is no way to check that no silent failure occurred.
[2024-10-02T20:28:01.389Z] ====== movie-lens (apache-spark) [default], iteration 3 completed (8505.983 ms) ======
[2024-10-02T20:28:01.389Z] ====== movie-lens (apache-spark) [default], iteration 4 started ======
[2024-10-02T20:28:01.389Z] GC before operation: completed in 34.450 ms, heap usage 206.689 MB -> 50.133 MB.
[2024-10-02T20:28:02.619Z] RMSE (validation) = 3.6219689545487603 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-10-02T20:28:03.848Z] RMSE (validation) = 2.1340923221100736 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-10-02T20:28:05.068Z] RMSE (validation) = 1.3105190428502682 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-10-02T20:28:06.309Z] RMSE (validation) = 0.9920028100571827 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-10-02T20:28:06.668Z] RMSE (validation) = 1.212631715629696 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-10-02T20:28:07.430Z] RMSE (validation) = 1.1025694034386657 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-10-02T20:28:08.195Z] RMSE (validation) = 0.9259652469249674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-10-02T20:28:08.994Z] RMSE (validation) = 0.8982232214592321 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-10-02T20:28:08.994Z] 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.
[2024-10-02T20:28:08.994Z] The best model improves the baseline by 14.52%.
[2024-10-02T20:28:08.994Z] Movies recommended for you:
[2024-10-02T20:28:08.994Z] WARNING: This benchmark provides no result that can be validated.
[2024-10-02T20:28:08.994Z] There is no way to check that no silent failure occurred.
[2024-10-02T20:28:08.994Z] ====== movie-lens (apache-spark) [default], iteration 4 completed (7752.010 ms) ======
[2024-10-02T20:28:08.994Z] ====== movie-lens (apache-spark) [default], iteration 5 started ======
[2024-10-02T20:28:08.994Z] GC before operation: completed in 33.762 ms, heap usage 206.245 MB -> 50.301 MB.
[2024-10-02T20:28:10.230Z] RMSE (validation) = 3.62196895454876 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-10-02T20:28:11.472Z] RMSE (validation) = 2.1340923221100736 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-10-02T20:28:12.717Z] RMSE (validation) = 1.3105190428502682 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-10-02T20:28:13.943Z] RMSE (validation) = 0.9920028100571827 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-10-02T20:28:14.712Z] RMSE (validation) = 1.212631715629696 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-10-02T20:28:15.486Z] RMSE (validation) = 1.1025694034386657 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-10-02T20:28:15.860Z] RMSE (validation) = 0.9259652469249674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-10-02T20:28:16.624Z] RMSE (validation) = 0.8982232214592321 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-10-02T20:28:16.977Z] 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.
[2024-10-02T20:28:16.977Z] The best model improves the baseline by 14.52%.
[2024-10-02T20:28:16.977Z] Movies recommended for you:
[2024-10-02T20:28:16.977Z] WARNING: This benchmark provides no result that can be validated.
[2024-10-02T20:28:16.977Z] There is no way to check that no silent failure occurred.
[2024-10-02T20:28:16.977Z] ====== movie-lens (apache-spark) [default], iteration 5 completed (7778.777 ms) ======
[2024-10-02T20:28:16.977Z] ====== movie-lens (apache-spark) [default], iteration 6 started ======
[2024-10-02T20:28:16.977Z] GC before operation: completed in 40.650 ms, heap usage 101.483 MB -> 51.158 MB.
[2024-10-02T20:28:18.218Z] RMSE (validation) = 3.62196895454876 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-10-02T20:28:19.441Z] RMSE (validation) = 2.1340923221100736 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-10-02T20:28:20.688Z] RMSE (validation) = 1.3105190428502682 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-10-02T20:28:21.920Z] RMSE (validation) = 0.9920028100571827 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-10-02T20:28:22.679Z] RMSE (validation) = 1.212631715629696 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-10-02T20:28:23.432Z] RMSE (validation) = 1.1025694034386657 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-10-02T20:28:24.670Z] RMSE (validation) = 0.9259652469249674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-10-02T20:28:25.042Z] RMSE (validation) = 0.8982232214592321 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-10-02T20:28:25.392Z] 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.
[2024-10-02T20:28:25.392Z] The best model improves the baseline by 14.52%.
[2024-10-02T20:28:25.392Z] Movies recommended for you:
[2024-10-02T20:28:25.392Z] WARNING: This benchmark provides no result that can be validated.
[2024-10-02T20:28:25.392Z] There is no way to check that no silent failure occurred.
[2024-10-02T20:28:25.392Z] ====== movie-lens (apache-spark) [default], iteration 6 completed (8387.141 ms) ======
[2024-10-02T20:28:25.392Z] ====== movie-lens (apache-spark) [default], iteration 7 started ======
[2024-10-02T20:28:25.392Z] GC before operation: completed in 34.041 ms, heap usage 364.503 MB -> 50.537 MB.
[2024-10-02T20:28:26.645Z] RMSE (validation) = 3.62196895454876 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-10-02T20:28:27.870Z] RMSE (validation) = 2.1340923221100736 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-10-02T20:28:29.103Z] RMSE (validation) = 1.3105190428502682 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-10-02T20:28:30.350Z] RMSE (validation) = 0.9920028100571827 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-10-02T20:28:31.124Z] RMSE (validation) = 1.212631715629696 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-10-02T20:28:31.899Z] RMSE (validation) = 1.1025694034386657 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-10-02T20:28:32.690Z] RMSE (validation) = 0.9259652469249674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-10-02T20:28:33.492Z] RMSE (validation) = 0.8982232214592321 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-10-02T20:28:33.492Z] 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.
[2024-10-02T20:28:33.492Z] The best model improves the baseline by 14.52%.
[2024-10-02T20:28:33.855Z] Movies recommended for you:
[2024-10-02T20:28:33.855Z] WARNING: This benchmark provides no result that can be validated.
[2024-10-02T20:28:33.855Z] There is no way to check that no silent failure occurred.
[2024-10-02T20:28:33.855Z] ====== movie-lens (apache-spark) [default], iteration 7 completed (8293.374 ms) ======
[2024-10-02T20:28:33.855Z] ====== movie-lens (apache-spark) [default], iteration 8 started ======
[2024-10-02T20:28:33.855Z] GC before operation: completed in 43.320 ms, heap usage 196.507 MB -> 50.650 MB.
[2024-10-02T20:28:35.081Z] RMSE (validation) = 3.62196895454876 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-10-02T20:28:36.309Z] RMSE (validation) = 2.1340923221100736 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-10-02T20:28:37.540Z] RMSE (validation) = 1.3105190428502682 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-10-02T20:28:38.299Z] RMSE (validation) = 0.9920028100571827 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-10-02T20:28:39.064Z] RMSE (validation) = 1.212631715629696 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-10-02T20:28:39.822Z] RMSE (validation) = 1.1025694034386657 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-10-02T20:28:40.579Z] RMSE (validation) = 0.9259652469249674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-10-02T20:28:41.333Z] RMSE (validation) = 0.8982232214592321 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-10-02T20:28:41.333Z] 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.
[2024-10-02T20:28:41.333Z] The best model improves the baseline by 14.52%.
[2024-10-02T20:28:41.333Z] Movies recommended for you:
[2024-10-02T20:28:41.333Z] WARNING: This benchmark provides no result that can be validated.
[2024-10-02T20:28:41.333Z] There is no way to check that no silent failure occurred.
[2024-10-02T20:28:41.333Z] ====== movie-lens (apache-spark) [default], iteration 8 completed (7585.997 ms) ======
[2024-10-02T20:28:41.333Z] ====== movie-lens (apache-spark) [default], iteration 9 started ======
[2024-10-02T20:28:41.333Z] GC before operation: completed in 30.571 ms, heap usage 198.916 MB -> 50.629 MB.
[2024-10-02T20:28:42.559Z] RMSE (validation) = 3.6219689545487603 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-10-02T20:28:43.322Z] RMSE (validation) = 2.1340923221100736 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-10-02T20:28:44.558Z] RMSE (validation) = 1.3105190428502682 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-10-02T20:28:45.354Z] RMSE (validation) = 0.9920028100571827 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-10-02T20:28:46.113Z] RMSE (validation) = 1.212631715629696 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-10-02T20:28:46.916Z] RMSE (validation) = 1.1025694034386657 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-10-02T20:28:47.278Z] RMSE (validation) = 0.9259652469249674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-10-02T20:28:48.154Z] RMSE (validation) = 0.8982232214592321 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-10-02T20:28:48.154Z] 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.
[2024-10-02T20:28:48.154Z] The best model improves the baseline by 14.52%.
[2024-10-02T20:28:48.154Z] Movies recommended for you:
[2024-10-02T20:28:48.154Z] WARNING: This benchmark provides no result that can be validated.
[2024-10-02T20:28:48.154Z] There is no way to check that no silent failure occurred.
[2024-10-02T20:28:48.154Z] ====== movie-lens (apache-spark) [default], iteration 9 completed (6881.816 ms) ======
[2024-10-02T20:28:48.154Z] ====== movie-lens (apache-spark) [default], iteration 10 started ======
[2024-10-02T20:28:48.154Z] GC before operation: completed in 31.919 ms, heap usage 63.757 MB -> 50.724 MB.
[2024-10-02T20:28:49.388Z] RMSE (validation) = 3.6219689545487603 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-10-02T20:28:50.616Z] RMSE (validation) = 2.1340923221100736 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-10-02T20:28:51.844Z] RMSE (validation) = 1.3105190428502682 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-10-02T20:28:53.073Z] RMSE (validation) = 0.9920028100571827 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-10-02T20:28:53.823Z] RMSE (validation) = 1.212631715629696 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-10-02T20:28:54.594Z] RMSE (validation) = 1.1025694034386657 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-10-02T20:28:55.959Z] RMSE (validation) = 0.9259652469249674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-10-02T20:28:57.218Z] RMSE (validation) = 0.8982232214592321 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-10-02T20:28:57.218Z] 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.
[2024-10-02T20:28:57.218Z] The best model improves the baseline by 14.52%.
[2024-10-02T20:28:57.218Z] Movies recommended for you:
[2024-10-02T20:28:57.218Z] WARNING: This benchmark provides no result that can be validated.
[2024-10-02T20:28:57.218Z] There is no way to check that no silent failure occurred.
[2024-10-02T20:28:57.218Z] ====== movie-lens (apache-spark) [default], iteration 10 completed (8925.808 ms) ======
[2024-10-02T20:28:57.218Z] ====== movie-lens (apache-spark) [default], iteration 11 started ======
[2024-10-02T20:28:57.218Z] GC before operation: completed in 31.195 ms, heap usage 420.042 MB -> 53.742 MB.
[2024-10-02T20:28:57.981Z] RMSE (validation) = 3.62196895454876 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-10-02T20:28:59.208Z] RMSE (validation) = 2.1340923221100736 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-10-02T20:29:00.439Z] RMSE (validation) = 1.3105190428502682 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-10-02T20:29:01.200Z] RMSE (validation) = 0.9920028100571827 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-10-02T20:29:01.974Z] RMSE (validation) = 1.212631715629696 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-10-02T20:29:02.339Z] RMSE (validation) = 1.1025694034386657 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-10-02T20:29:03.097Z] RMSE (validation) = 0.9259652469249674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-10-02T20:29:03.863Z] RMSE (validation) = 0.8982232214592321 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-10-02T20:29:03.863Z] 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.
[2024-10-02T20:29:03.863Z] The best model improves the baseline by 14.52%.
[2024-10-02T20:29:03.863Z] Movies recommended for you:
[2024-10-02T20:29:03.863Z] WARNING: This benchmark provides no result that can be validated.
[2024-10-02T20:29:03.863Z] There is no way to check that no silent failure occurred.
[2024-10-02T20:29:03.863Z] ====== movie-lens (apache-spark) [default], iteration 11 completed (6727.224 ms) ======
[2024-10-02T20:29:03.863Z] ====== movie-lens (apache-spark) [default], iteration 12 started ======
[2024-10-02T20:29:03.863Z] GC before operation: completed in 30.611 ms, heap usage 140.250 MB -> 50.454 MB.
[2024-10-02T20:29:05.085Z] RMSE (validation) = 3.62196895454876 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-10-02T20:29:05.834Z] RMSE (validation) = 2.1340923221100736 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-10-02T20:29:07.062Z] RMSE (validation) = 1.3105190428502682 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-10-02T20:29:08.298Z] RMSE (validation) = 0.9920028100571827 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-10-02T20:29:08.660Z] RMSE (validation) = 1.212631715629696 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-10-02T20:29:09.460Z] RMSE (validation) = 1.1025694034386657 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-10-02T20:29:09.823Z] RMSE (validation) = 0.9259652469249674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-10-02T20:29:10.185Z] RMSE (validation) = 0.8982232214592321 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-10-02T20:29:10.538Z] 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.
[2024-10-02T20:29:10.538Z] The best model improves the baseline by 14.52%.
[2024-10-02T20:29:10.538Z] Movies recommended for you:
[2024-10-02T20:29:10.538Z] WARNING: This benchmark provides no result that can be validated.
[2024-10-02T20:29:10.538Z] There is no way to check that no silent failure occurred.
[2024-10-02T20:29:10.538Z] ====== movie-lens (apache-spark) [default], iteration 12 completed (6483.477 ms) ======
[2024-10-02T20:29:10.538Z] ====== movie-lens (apache-spark) [default], iteration 13 started ======
[2024-10-02T20:29:10.538Z] GC before operation: completed in 32.038 ms, heap usage 215.096 MB -> 50.796 MB.
[2024-10-02T20:29:11.300Z] RMSE (validation) = 3.62196895454876 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-10-02T20:29:12.516Z] RMSE (validation) = 2.1340923221100736 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-10-02T20:29:13.267Z] RMSE (validation) = 1.3105190428502682 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-10-02T20:29:14.487Z] RMSE (validation) = 0.9920028100571827 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-10-02T20:29:14.841Z] RMSE (validation) = 1.212631715629696 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-10-02T20:29:15.603Z] RMSE (validation) = 1.1025694034386657 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-10-02T20:29:15.966Z] RMSE (validation) = 0.9259652469249674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-10-02T20:29:16.741Z] RMSE (validation) = 0.8982232214592321 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-10-02T20:29:16.741Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9063003101263983.
[2024-10-02T20:29:16.741Z] The best model improves the baseline by 14.52%.
[2024-10-02T20:29:16.741Z] Movies recommended for you:
[2024-10-02T20:29:16.741Z] WARNING: This benchmark provides no result that can be validated.
[2024-10-02T20:29:16.741Z] There is no way to check that no silent failure occurred.
[2024-10-02T20:29:16.741Z] ====== movie-lens (apache-spark) [default], iteration 13 completed (6301.841 ms) ======
[2024-10-02T20:29:16.741Z] ====== movie-lens (apache-spark) [default], iteration 14 started ======
[2024-10-02T20:29:16.741Z] GC before operation: completed in 29.613 ms, heap usage 275.221 MB -> 50.574 MB.
[2024-10-02T20:29:17.963Z] RMSE (validation) = 3.6219689545487603 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-10-02T20:29:18.713Z] RMSE (validation) = 2.1340923221100736 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-10-02T20:29:19.935Z] RMSE (validation) = 1.3105190428502682 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-10-02T20:29:20.691Z] RMSE (validation) = 0.9920028100571827 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-10-02T20:29:21.055Z] RMSE (validation) = 1.212631715629696 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-10-02T20:29:21.807Z] RMSE (validation) = 1.1025694034386657 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-10-02T20:29:22.577Z] RMSE (validation) = 0.9259652469249674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-10-02T20:29:22.941Z] RMSE (validation) = 0.8982232214592321 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-10-02T20:29:22.941Z] 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.
[2024-10-02T20:29:22.941Z] The best model improves the baseline by 14.52%.
[2024-10-02T20:29:23.290Z] Movies recommended for you:
[2024-10-02T20:29:23.290Z] WARNING: This benchmark provides no result that can be validated.
[2024-10-02T20:29:23.290Z] There is no way to check that no silent failure occurred.
[2024-10-02T20:29:23.290Z] ====== movie-lens (apache-spark) [default], iteration 14 completed (6301.459 ms) ======
[2024-10-02T20:29:23.291Z] ====== movie-lens (apache-spark) [default], iteration 15 started ======
[2024-10-02T20:29:23.291Z] GC before operation: completed in 32.665 ms, heap usage 195.190 MB -> 50.701 MB.
[2024-10-02T20:29:24.042Z] RMSE (validation) = 3.62196895454876 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-10-02T20:29:24.810Z] RMSE (validation) = 2.1340923221100736 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-10-02T20:29:26.024Z] RMSE (validation) = 1.3105190428502682 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-10-02T20:29:26.790Z] RMSE (validation) = 0.9920028100571827 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-10-02T20:29:27.553Z] RMSE (validation) = 1.212631715629696 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-10-02T20:29:27.906Z] RMSE (validation) = 1.1025694034386657 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-10-02T20:29:28.672Z] RMSE (validation) = 0.9259652469249674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-10-02T20:29:29.029Z] RMSE (validation) = 0.8982232214592321 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-10-02T20:29:29.029Z] 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.
[2024-10-02T20:29:29.029Z] The best model improves the baseline by 14.52%.
[2024-10-02T20:29:29.029Z] Movies recommended for you:
[2024-10-02T20:29:29.029Z] WARNING: This benchmark provides no result that can be validated.
[2024-10-02T20:29:29.029Z] There is no way to check that no silent failure occurred.
[2024-10-02T20:29:29.029Z] ====== movie-lens (apache-spark) [default], iteration 15 completed (5989.707 ms) ======
[2024-10-02T20:29:29.029Z] ====== movie-lens (apache-spark) [default], iteration 16 started ======
[2024-10-02T20:29:29.029Z] GC before operation: completed in 30.281 ms, heap usage 317.180 MB -> 50.929 MB.
[2024-10-02T20:29:30.264Z] RMSE (validation) = 3.6219689545487603 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-10-02T20:29:31.027Z] RMSE (validation) = 2.1340923221100736 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-10-02T20:29:32.241Z] RMSE (validation) = 1.3105190428502682 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-10-02T20:29:33.005Z] RMSE (validation) = 0.9920028100571827 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-10-02T20:29:33.380Z] RMSE (validation) = 1.212631715629696 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-10-02T20:29:34.139Z] RMSE (validation) = 1.1025694034386657 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-10-02T20:29:34.680Z] RMSE (validation) = 0.9259652469249674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-10-02T20:29:35.453Z] RMSE (validation) = 0.8982232214592321 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-10-02T20:29:35.453Z] 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.
[2024-10-02T20:29:35.453Z] The best model improves the baseline by 14.52%.
[2024-10-02T20:29:35.453Z] Movies recommended for you:
[2024-10-02T20:29:35.453Z] WARNING: This benchmark provides no result that can be validated.
[2024-10-02T20:29:35.453Z] There is no way to check that no silent failure occurred.
[2024-10-02T20:29:35.453Z] ====== movie-lens (apache-spark) [default], iteration 16 completed (6211.918 ms) ======
[2024-10-02T20:29:35.453Z] ====== movie-lens (apache-spark) [default], iteration 17 started ======
[2024-10-02T20:29:35.453Z] GC before operation: completed in 29.421 ms, heap usage 150.230 MB -> 50.570 MB.
[2024-10-02T20:29:36.690Z] RMSE (validation) = 3.62196895454876 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-10-02T20:29:37.473Z] RMSE (validation) = 2.1340923221100736 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-10-02T20:29:38.696Z] RMSE (validation) = 1.3105190428502682 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-10-02T20:29:39.450Z] RMSE (validation) = 0.9920028100571827 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-10-02T20:29:40.200Z] RMSE (validation) = 1.212631715629696 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-10-02T20:29:40.549Z] RMSE (validation) = 1.1025694034386657 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-10-02T20:29:41.321Z] RMSE (validation) = 0.9259652469249674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-10-02T20:29:41.680Z] RMSE (validation) = 0.8982232214592321 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-10-02T20:29:42.028Z] 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.
[2024-10-02T20:29:42.028Z] The best model improves the baseline by 14.52%.
[2024-10-02T20:29:42.028Z] Movies recommended for you:
[2024-10-02T20:29:42.028Z] WARNING: This benchmark provides no result that can be validated.
[2024-10-02T20:29:42.028Z] There is no way to check that no silent failure occurred.
[2024-10-02T20:29:42.028Z] ====== movie-lens (apache-spark) [default], iteration 17 completed (6491.589 ms) ======
[2024-10-02T20:29:42.028Z] ====== movie-lens (apache-spark) [default], iteration 18 started ======
[2024-10-02T20:29:42.028Z] GC before operation: completed in 29.205 ms, heap usage 260.010 MB -> 50.726 MB.
[2024-10-02T20:29:42.791Z] RMSE (validation) = 3.62196895454876 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-10-02T20:29:44.019Z] RMSE (validation) = 2.1340923221100736 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-10-02T20:29:44.787Z] RMSE (validation) = 1.3105190428502682 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-10-02T20:29:46.019Z] RMSE (validation) = 0.9920028100571827 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-10-02T20:29:46.370Z] RMSE (validation) = 1.212631715629696 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-10-02T20:29:46.722Z] RMSE (validation) = 1.1025694034386657 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-10-02T20:29:47.549Z] RMSE (validation) = 0.9259652469249674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-10-02T20:29:47.906Z] RMSE (validation) = 0.8982232214592321 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-10-02T20:29:47.906Z] 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.
[2024-10-02T20:29:47.907Z] The best model improves the baseline by 14.52%.
[2024-10-02T20:29:47.907Z] Movies recommended for you:
[2024-10-02T20:29:47.907Z] WARNING: This benchmark provides no result that can be validated.
[2024-10-02T20:29:47.907Z] There is no way to check that no silent failure occurred.
[2024-10-02T20:29:47.907Z] ====== movie-lens (apache-spark) [default], iteration 18 completed (6062.729 ms) ======
[2024-10-02T20:29:47.907Z] ====== movie-lens (apache-spark) [default], iteration 19 started ======
[2024-10-02T20:29:47.907Z] GC before operation: completed in 28.855 ms, heap usage 169.284 MB -> 51.022 MB.
[2024-10-02T20:29:48.669Z] RMSE (validation) = 3.6219689545487603 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-10-02T20:29:49.913Z] RMSE (validation) = 2.1340923221100736 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-10-02T20:29:50.691Z] RMSE (validation) = 1.3105190428502682 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-10-02T20:29:51.445Z] RMSE (validation) = 0.9920028100571827 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-10-02T20:29:51.809Z] RMSE (validation) = 1.212631715629696 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-10-02T20:29:52.566Z] RMSE (validation) = 1.1025694034386657 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-10-02T20:29:52.923Z] RMSE (validation) = 0.9259652469249674 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-10-02T20:29:53.675Z] RMSE (validation) = 0.8982232214592321 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-10-02T20:29:53.675Z] 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.
[2024-10-02T20:29:53.675Z] The best model improves the baseline by 14.52%.
[2024-10-02T20:29:53.675Z] Movies recommended for you:
[2024-10-02T20:29:53.675Z] WARNING: This benchmark provides no result that can be validated.
[2024-10-02T20:29:53.675Z] There is no way to check that no silent failure occurred.
[2024-10-02T20:29:53.675Z] ====== movie-lens (apache-spark) [default], iteration 19 completed (5753.075 ms) ======
[2024-10-02T20:29:54.030Z] -----------------------------------
[2024-10-02T20:29:54.030Z] renaissance-movie-lens_0_PASSED
[2024-10-02T20:29:54.030Z] -----------------------------------
[2024-10-02T20:29:54.030Z]
[2024-10-02T20:29:54.030Z] TEST TEARDOWN:
[2024-10-02T20:29:54.030Z] Nothing to be done for teardown.
[2024-10-02T20:29:54.030Z] renaissance-movie-lens_0 Finish Time: Wed Oct 2 16:29:53 2024 Epoch Time (ms): 1727900993765