renaissance-movie-lens_0
[2023-04-19T11:06:47.520Z] Running test renaissance-movie-lens_0 ...
[2023-04-19T11:06:47.520Z] ===============================================
[2023-04-19T11:06:47.520Z] renaissance-movie-lens_0 Start Time: Wed Apr 19 06:06:42 2023 Epoch Time (ms): 1681902402876
[2023-04-19T11:06:48.064Z] variation: NoOptions
[2023-04-19T11:06:48.064Z] JVM_OPTIONS:
[2023-04-19T11:06:48.064Z] { \
[2023-04-19T11:06:48.064Z] echo ""; echo "TEST SETUP:"; \
[2023-04-19T11:06:48.064Z] echo "Nothing to be done for setup."; \
[2023-04-19T11:06:48.064Z] mkdir -p "E:/workspace/Test_openjdk8_hs_extended.perf_x86-64_windows/aqa-tests/\\TKG\\output_16819009566849\\renaissance-movie-lens_0"; \
[2023-04-19T11:06:48.064Z] cd "E:/workspace/Test_openjdk8_hs_extended.perf_x86-64_windows/aqa-tests/\\TKG\\output_16819009566849\\renaissance-movie-lens_0"; \
[2023-04-19T11:06:48.064Z] echo ""; echo "TESTING:"; \
[2023-04-19T11:06:48.064Z] "E:/workspace/Test_openjdk8_hs_extended.perf_x86-64_windows/openjdkbinary/j2sdk-image\\bin\\java" -jar "E:/workspace/Test_openjdk8_hs_extended.perf_x86-64_windows/aqa-tests///..//jvmtest\\perf\\renaissance\\renaissance.jar" --json ""E:/workspace/Test_openjdk8_hs_extended.perf_x86-64_windows/aqa-tests/\\TKG\\output_16819009566849\\renaissance-movie-lens_0"\\movie-lens.json" movie-lens; \
[2023-04-19T11:06:48.064Z] if [ $? -eq 0 ]; then echo "-----------------------------------"; echo "renaissance-movie-lens_0""_PASSED"; echo "-----------------------------------"; cd E:/workspace/Test_openjdk8_hs_extended.perf_x86-64_windows/aqa-tests/; rm -f -r "E:/workspace/Test_openjdk8_hs_extended.perf_x86-64_windows/aqa-tests/\\TKG\\output_16819009566849\\renaissance-movie-lens_0"; else echo "-----------------------------------"; echo "renaissance-movie-lens_0""_FAILED"; echo "-----------------------------------"; fi; \
[2023-04-19T11:06:48.064Z] echo ""; echo "TEST TEARDOWN:"; \
[2023-04-19T11:06:48.064Z] echo "Nothing to be done for teardown."; \
[2023-04-19T11:06:48.064Z] } 2>&1 | tee -a "E:/workspace/Test_openjdk8_hs_extended.perf_x86-64_windows/aqa-tests/\\TKG\\output_16819009566849\\TestTargetResult";
[2023-04-19T11:06:48.064Z]
[2023-04-19T11:06:48.064Z] TEST SETUP:
[2023-04-19T11:06:48.064Z] Nothing to be done for setup.
[2023-04-19T11:06:48.064Z]
[2023-04-19T11:06:48.064Z] TESTING:
[2023-04-19T11:06:52.302Z] Using Spark's default log4j profile: org/apache/spark/log4j-defaults.properties
[2023-04-19T11:06:54.809Z] NOTE: 'movie-lens' benchmark uses Spark local executor with 8 (out of 8) threads.
[2023-04-19T11:06:59.048Z] Got 100004 ratings from 671 users on 9066 movies.
[2023-04-19T11:06:59.048Z] Training: 60056, validation: 20285, test: 19854
[2023-04-19T11:06:59.048Z] ====== movie-lens (apache-spark) [default], iteration 0 started ======
[2023-04-19T11:06:59.594Z] GC before operation: completed in 324.996 ms, heap usage 121.639 MB -> 27.577 MB.
[2023-04-19T11:07:06.064Z] RMSE (validation) = 3.621968954548751 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2023-04-19T11:07:10.293Z] RMSE (validation) = 2.1340923219297814 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2023-04-19T11:07:14.516Z] RMSE (validation) = 1.310518969403201 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2023-04-19T11:07:18.742Z] RMSE (validation) = 1.0045407704488025 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2023-04-19T11:07:21.257Z] RMSE (validation) = 1.221833058605191 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2023-04-19T11:07:23.769Z] RMSE (validation) = 1.1174953757118413 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2023-04-19T11:07:26.289Z] RMSE (validation) = 0.9275717388537591 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2023-04-19T11:07:28.800Z] RMSE (validation) = 0.9001440970559653 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2023-04-19T11:07:28.800Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9073522617949712.
[2023-04-19T11:07:28.800Z] The best model improves the baseline by 14.43%.
[2023-04-19T11:07:29.339Z] Movies recommended for you:
[2023-04-19T11:07:29.339Z] WARNING: This benchmark provides no result that can be validated.
[2023-04-19T11:07:29.339Z] There is no way to check that no silent failure occurred.
[2023-04-19T11:07:29.339Z] ====== movie-lens (apache-spark) [default], iteration 0 completed (29741.714 ms) ======
[2023-04-19T11:07:29.339Z] ====== movie-lens (apache-spark) [default], iteration 1 started ======
[2023-04-19T11:07:29.339Z] GC before operation: completed in 395.989 ms, heap usage 618.054 MB -> 54.697 MB.
[2023-04-19T11:07:34.599Z] RMSE (validation) = 3.621968954548751 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2023-04-19T11:07:38.844Z] RMSE (validation) = 2.1340923219297814 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2023-04-19T11:07:44.112Z] RMSE (validation) = 1.3105189694032011 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2023-04-19T11:07:48.440Z] RMSE (validation) = 1.0045407704488027 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2023-04-19T11:07:50.949Z] RMSE (validation) = 1.221833058605191 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2023-04-19T11:07:53.451Z] RMSE (validation) = 1.117495375711841 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2023-04-19T11:07:55.953Z] RMSE (validation) = 0.9275717388537591 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2023-04-19T11:07:58.457Z] RMSE (validation) = 0.9001440970559653 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2023-04-19T11:07:58.997Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9073522617949712.
[2023-04-19T11:07:58.997Z] The best model improves the baseline by 14.43%.
[2023-04-19T11:07:59.539Z] Movies recommended for you:
[2023-04-19T11:07:59.539Z] WARNING: This benchmark provides no result that can be validated.
[2023-04-19T11:07:59.539Z] There is no way to check that no silent failure occurred.
[2023-04-19T11:07:59.539Z] ====== movie-lens (apache-spark) [default], iteration 1 completed (29705.739 ms) ======
[2023-04-19T11:07:59.539Z] ====== movie-lens (apache-spark) [default], iteration 2 started ======
[2023-04-19T11:07:59.539Z] GC before operation: completed in 220.881 ms, heap usage 321.043 MB -> 43.635 MB.
[2023-04-19T11:08:03.767Z] RMSE (validation) = 3.6219689545487506 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2023-04-19T11:08:07.996Z] RMSE (validation) = 2.1340923219297814 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2023-04-19T11:08:12.233Z] RMSE (validation) = 1.3105189694032011 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2023-04-19T11:08:16.461Z] RMSE (validation) = 1.0045407704488027 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2023-04-19T11:08:18.977Z] RMSE (validation) = 1.221833058605191 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2023-04-19T11:08:21.483Z] RMSE (validation) = 1.1174953757118413 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2023-04-19T11:08:24.804Z] RMSE (validation) = 0.9275717388537591 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2023-04-19T11:08:27.314Z] RMSE (validation) = 0.9001440970559653 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2023-04-19T11:08:27.315Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9073522617949711.
[2023-04-19T11:08:27.315Z] The best model improves the baseline by 14.43%.
[2023-04-19T11:08:27.315Z] Movies recommended for you:
[2023-04-19T11:08:27.315Z] WARNING: This benchmark provides no result that can be validated.
[2023-04-19T11:08:27.315Z] There is no way to check that no silent failure occurred.
[2023-04-19T11:08:27.315Z] ====== movie-lens (apache-spark) [default], iteration 2 completed (27954.894 ms) ======
[2023-04-19T11:08:27.315Z] ====== movie-lens (apache-spark) [default], iteration 3 started ======
[2023-04-19T11:08:27.857Z] GC before operation: completed in 178.163 ms, heap usage 628.607 MB -> 48.226 MB.
[2023-04-19T11:08:32.091Z] RMSE (validation) = 3.621968954548751 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2023-04-19T11:08:36.327Z] RMSE (validation) = 2.1340923219297814 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2023-04-19T11:08:40.567Z] RMSE (validation) = 1.310518969403201 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2023-04-19T11:08:44.805Z] RMSE (validation) = 1.0045407704488027 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2023-04-19T11:08:47.318Z] RMSE (validation) = 1.221833058605191 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2023-04-19T11:08:49.106Z] RMSE (validation) = 1.117495375711841 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2023-04-19T11:08:52.435Z] RMSE (validation) = 0.9275717388537592 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2023-04-19T11:08:54.947Z] RMSE (validation) = 0.9001440970559653 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2023-04-19T11:08:54.947Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9073522617949712.
[2023-04-19T11:08:54.947Z] The best model improves the baseline by 14.43%.
[2023-04-19T11:08:54.947Z] Movies recommended for you:
[2023-04-19T11:08:54.947Z] WARNING: This benchmark provides no result that can be validated.
[2023-04-19T11:08:54.947Z] There is no way to check that no silent failure occurred.
[2023-04-19T11:08:54.947Z] ====== movie-lens (apache-spark) [default], iteration 3 completed (27487.626 ms) ======
[2023-04-19T11:08:54.947Z] ====== movie-lens (apache-spark) [default], iteration 4 started ======
[2023-04-19T11:08:55.494Z] GC before operation: completed in 200.683 ms, heap usage 620.237 MB -> 54.019 MB.
[2023-04-19T11:08:59.731Z] RMSE (validation) = 3.621968954548751 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2023-04-19T11:09:05.029Z] RMSE (validation) = 2.1340923219297814 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2023-04-19T11:09:09.260Z] RMSE (validation) = 1.3105189694032011 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2023-04-19T11:09:12.573Z] RMSE (validation) = 1.0045407704488025 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2023-04-19T11:09:15.890Z] RMSE (validation) = 1.221833058605191 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2023-04-19T11:09:18.397Z] RMSE (validation) = 1.1174953757118413 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2023-04-19T11:09:20.908Z] RMSE (validation) = 0.9275717388537591 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2023-04-19T11:09:23.414Z] RMSE (validation) = 0.9001440970559653 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2023-04-19T11:09:23.955Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9073522617949712.
[2023-04-19T11:09:23.955Z] The best model improves the baseline by 14.43%.
[2023-04-19T11:09:23.955Z] Movies recommended for you:
[2023-04-19T11:09:23.955Z] WARNING: This benchmark provides no result that can be validated.
[2023-04-19T11:09:23.955Z] There is no way to check that no silent failure occurred.
[2023-04-19T11:09:23.955Z] ====== movie-lens (apache-spark) [default], iteration 4 completed (28727.978 ms) ======
[2023-04-19T11:09:23.955Z] ====== movie-lens (apache-spark) [default], iteration 5 started ======
[2023-04-19T11:09:24.495Z] GC before operation: completed in 182.428 ms, heap usage 628.271 MB -> 51.546 MB.
[2023-04-19T11:09:28.723Z] RMSE (validation) = 3.6219689545487506 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2023-04-19T11:09:32.947Z] RMSE (validation) = 2.1340923219297814 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2023-04-19T11:09:37.181Z] RMSE (validation) = 1.310518969403201 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2023-04-19T11:09:41.420Z] RMSE (validation) = 1.0045407704488027 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2023-04-19T11:09:44.246Z] RMSE (validation) = 1.221833058605191 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2023-04-19T11:09:46.753Z] RMSE (validation) = 1.117495375711841 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2023-04-19T11:09:49.256Z] RMSE (validation) = 0.9275717388537591 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2023-04-19T11:09:51.759Z] RMSE (validation) = 0.9001440970559653 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2023-04-19T11:09:51.759Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9073522617949711.
[2023-04-19T11:09:51.759Z] The best model improves the baseline by 14.43%.
[2023-04-19T11:09:51.759Z] Movies recommended for you:
[2023-04-19T11:09:51.759Z] WARNING: This benchmark provides no result that can be validated.
[2023-04-19T11:09:51.759Z] There is no way to check that no silent failure occurred.
[2023-04-19T11:09:51.759Z] ====== movie-lens (apache-spark) [default], iteration 5 completed (27606.867 ms) ======
[2023-04-19T11:09:51.759Z] ====== movie-lens (apache-spark) [default], iteration 6 started ======
[2023-04-19T11:09:51.759Z] GC before operation: completed in 155.086 ms, heap usage 342.738 MB -> 51.680 MB.
[2023-04-19T11:09:57.035Z] RMSE (validation) = 3.6219689545487506 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2023-04-19T11:10:01.274Z] RMSE (validation) = 2.1340923219297814 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2023-04-19T11:10:05.507Z] RMSE (validation) = 1.310518969403201 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2023-04-19T11:10:08.825Z] RMSE (validation) = 1.0045407704488027 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2023-04-19T11:10:11.330Z] RMSE (validation) = 1.221833058605191 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2023-04-19T11:10:13.838Z] RMSE (validation) = 1.1174953757118413 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2023-04-19T11:10:16.346Z] RMSE (validation) = 0.9275717388537591 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2023-04-19T11:10:18.851Z] RMSE (validation) = 0.9001440970559653 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2023-04-19T11:10:19.394Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9073522617949712.
[2023-04-19T11:10:19.394Z] The best model improves the baseline by 14.43%.
[2023-04-19T11:10:19.394Z] Movies recommended for you:
[2023-04-19T11:10:19.394Z] WARNING: This benchmark provides no result that can be validated.
[2023-04-19T11:10:19.394Z] There is no way to check that no silent failure occurred.
[2023-04-19T11:10:19.394Z] ====== movie-lens (apache-spark) [default], iteration 6 completed (27427.978 ms) ======
[2023-04-19T11:10:19.395Z] ====== movie-lens (apache-spark) [default], iteration 7 started ======
[2023-04-19T11:10:19.395Z] GC before operation: completed in 140.835 ms, heap usage 757.116 MB -> 59.249 MB.
[2023-04-19T11:10:24.665Z] RMSE (validation) = 3.621968954548751 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2023-04-19T11:10:28.906Z] RMSE (validation) = 2.1340923219297814 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2023-04-19T11:10:33.160Z] RMSE (validation) = 1.310518969403201 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2023-04-19T11:10:37.392Z] RMSE (validation) = 1.0045407704488027 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2023-04-19T11:10:40.713Z] RMSE (validation) = 1.221833058605191 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2023-04-19T11:10:43.222Z] RMSE (validation) = 1.117495375711841 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2023-04-19T11:10:45.025Z] RMSE (validation) = 0.9275717388537591 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2023-04-19T11:10:46.810Z] RMSE (validation) = 0.9001440970559652 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2023-04-19T11:10:47.350Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9073522617949712.
[2023-04-19T11:10:47.350Z] The best model improves the baseline by 14.43%.
[2023-04-19T11:10:47.350Z] Movies recommended for you:
[2023-04-19T11:10:47.350Z] WARNING: This benchmark provides no result that can be validated.
[2023-04-19T11:10:47.350Z] There is no way to check that no silent failure occurred.
[2023-04-19T11:10:47.350Z] ====== movie-lens (apache-spark) [default], iteration 7 completed (27911.394 ms) ======
[2023-04-19T11:10:47.350Z] ====== movie-lens (apache-spark) [default], iteration 8 started ======
[2023-04-19T11:10:47.350Z] GC before operation: completed in 139.764 ms, heap usage 537.178 MB -> 51.212 MB.
[2023-04-19T11:10:52.630Z] RMSE (validation) = 3.6219689545487506 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2023-04-19T11:10:56.860Z] RMSE (validation) = 2.1340923219297814 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2023-04-19T11:11:01.146Z] RMSE (validation) = 1.310518969403201 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2023-04-19T11:11:05.381Z] RMSE (validation) = 1.0045407704488027 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2023-04-19T11:11:07.887Z] RMSE (validation) = 1.221833058605191 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2023-04-19T11:11:10.393Z] RMSE (validation) = 1.117495375711841 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2023-04-19T11:11:12.915Z] RMSE (validation) = 0.9275717388537591 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2023-04-19T11:11:15.419Z] RMSE (validation) = 0.9001440970559653 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2023-04-19T11:11:15.419Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9073522617949711.
[2023-04-19T11:11:15.419Z] The best model improves the baseline by 14.43%.
[2023-04-19T11:11:15.419Z] Movies recommended for you:
[2023-04-19T11:11:15.419Z] WARNING: This benchmark provides no result that can be validated.
[2023-04-19T11:11:15.419Z] There is no way to check that no silent failure occurred.
[2023-04-19T11:11:15.419Z] ====== movie-lens (apache-spark) [default], iteration 8 completed (27962.366 ms) ======
[2023-04-19T11:11:15.419Z] ====== movie-lens (apache-spark) [default], iteration 9 started ======
[2023-04-19T11:11:15.965Z] GC before operation: completed in 157.414 ms, heap usage 148.058 MB -> 54.662 MB.
[2023-04-19T11:11:21.225Z] RMSE (validation) = 3.621968954548751 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2023-04-19T11:11:25.452Z] RMSE (validation) = 2.1340923219297814 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2023-04-19T11:11:29.681Z] RMSE (validation) = 1.3105189694032011 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2023-04-19T11:11:33.913Z] RMSE (validation) = 1.0045407704488027 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2023-04-19T11:11:36.751Z] RMSE (validation) = 1.221833058605191 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2023-04-19T11:11:39.257Z] RMSE (validation) = 1.117495375711841 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2023-04-19T11:11:41.042Z] RMSE (validation) = 0.9275717388537591 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2023-04-19T11:11:44.361Z] RMSE (validation) = 0.9001440970559653 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2023-04-19T11:11:44.361Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9073522617949711.
[2023-04-19T11:11:44.361Z] The best model improves the baseline by 14.43%.
[2023-04-19T11:11:44.361Z] Movies recommended for you:
[2023-04-19T11:11:44.361Z] WARNING: This benchmark provides no result that can be validated.
[2023-04-19T11:11:44.361Z] There is no way to check that no silent failure occurred.
[2023-04-19T11:11:44.361Z] ====== movie-lens (apache-spark) [default], iteration 9 completed (28562.548 ms) ======
[2023-04-19T11:11:44.361Z] ====== movie-lens (apache-spark) [default], iteration 10 started ======
[2023-04-19T11:11:44.361Z] GC before operation: completed in 187.802 ms, heap usage 554.001 MB -> 48.913 MB.
[2023-04-19T11:11:49.631Z] RMSE (validation) = 3.621968954548751 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2023-04-19T11:11:52.945Z] RMSE (validation) = 2.1340923219297814 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2023-04-19T11:11:57.171Z] RMSE (validation) = 1.310518969403201 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2023-04-19T11:12:01.395Z] RMSE (validation) = 1.0045407704488027 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2023-04-19T11:12:03.901Z] RMSE (validation) = 1.221833058605191 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2023-04-19T11:12:06.405Z] RMSE (validation) = 1.1174953757118413 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2023-04-19T11:12:08.930Z] RMSE (validation) = 0.9275717388537591 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2023-04-19T11:12:11.469Z] RMSE (validation) = 0.9001440970559652 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2023-04-19T11:12:11.469Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9073522617949712.
[2023-04-19T11:12:12.389Z] The best model improves the baseline by 14.43%.
[2023-04-19T11:12:12.389Z] Movies recommended for you:
[2023-04-19T11:12:12.389Z] WARNING: This benchmark provides no result that can be validated.
[2023-04-19T11:12:12.389Z] There is no way to check that no silent failure occurred.
[2023-04-19T11:12:12.389Z] ====== movie-lens (apache-spark) [default], iteration 10 completed (27380.704 ms) ======
[2023-04-19T11:12:12.389Z] ====== movie-lens (apache-spark) [default], iteration 11 started ======
[2023-04-19T11:12:12.389Z] GC before operation: completed in 168.859 ms, heap usage 609.766 MB -> 53.282 MB.
[2023-04-19T11:12:17.651Z] RMSE (validation) = 3.621968954548751 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2023-04-19T11:12:20.967Z] RMSE (validation) = 2.1340923219297814 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2023-04-19T11:12:26.242Z] RMSE (validation) = 1.310518969403201 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2023-04-19T11:12:30.481Z] RMSE (validation) = 1.0045407704488027 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2023-04-19T11:12:32.263Z] RMSE (validation) = 1.221833058605191 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2023-04-19T11:12:34.769Z] RMSE (validation) = 1.117495375711841 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2023-04-19T11:12:37.280Z] RMSE (validation) = 0.9275717388537591 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2023-04-19T11:12:40.598Z] RMSE (validation) = 0.9001440970559652 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2023-04-19T11:12:40.598Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9073522617949711.
[2023-04-19T11:12:40.598Z] The best model improves the baseline by 14.43%.
[2023-04-19T11:12:40.598Z] Movies recommended for you:
[2023-04-19T11:12:40.598Z] WARNING: This benchmark provides no result that can be validated.
[2023-04-19T11:12:40.598Z] There is no way to check that no silent failure occurred.
[2023-04-19T11:12:40.599Z] ====== movie-lens (apache-spark) [default], iteration 11 completed (28748.876 ms) ======
[2023-04-19T11:12:40.599Z] ====== movie-lens (apache-spark) [default], iteration 12 started ======
[2023-04-19T11:12:41.144Z] GC before operation: completed in 152.169 ms, heap usage 671.977 MB -> 55.187 MB.
[2023-04-19T11:12:45.385Z] RMSE (validation) = 3.6219689545487506 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2023-04-19T11:12:50.206Z] RMSE (validation) = 2.1340923219297814 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2023-04-19T11:12:54.214Z] RMSE (validation) = 1.310518969403201 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2023-04-19T11:12:58.502Z] RMSE (validation) = 1.0045407704488027 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2023-04-19T11:13:00.287Z] RMSE (validation) = 1.221833058605191 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2023-04-19T11:13:02.796Z] RMSE (validation) = 1.1174953757118413 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2023-04-19T11:13:06.255Z] RMSE (validation) = 0.9275717388537591 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2023-04-19T11:13:08.770Z] RMSE (validation) = 0.9001440970559653 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2023-04-19T11:13:08.770Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9073522617949711.
[2023-04-19T11:13:08.770Z] The best model improves the baseline by 14.43%.
[2023-04-19T11:13:08.770Z] Movies recommended for you:
[2023-04-19T11:13:08.770Z] WARNING: This benchmark provides no result that can be validated.
[2023-04-19T11:13:08.770Z] There is no way to check that no silent failure occurred.
[2023-04-19T11:13:08.770Z] ====== movie-lens (apache-spark) [default], iteration 12 completed (27849.133 ms) ======
[2023-04-19T11:13:08.770Z] ====== movie-lens (apache-spark) [default], iteration 13 started ======
[2023-04-19T11:13:08.770Z] GC before operation: completed in 180.740 ms, heap usage 511.978 MB -> 49.080 MB.
[2023-04-19T11:13:13.008Z] RMSE (validation) = 3.621968954548751 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2023-04-19T11:13:17.247Z] RMSE (validation) = 2.1340923219297814 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2023-04-19T11:13:21.486Z] RMSE (validation) = 1.310518969403201 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2023-04-19T11:13:25.731Z] RMSE (validation) = 1.0045407704488027 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2023-04-19T11:13:29.060Z] RMSE (validation) = 1.221833058605191 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2023-04-19T11:13:31.573Z] RMSE (validation) = 1.117495375711841 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2023-04-19T11:13:34.917Z] RMSE (validation) = 0.9275717388537591 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2023-04-19T11:13:36.706Z] RMSE (validation) = 0.9001440970559652 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2023-04-19T11:13:37.248Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9073522617949712.
[2023-04-19T11:13:37.248Z] The best model improves the baseline by 14.43%.
[2023-04-19T11:13:37.249Z] Movies recommended for you:
[2023-04-19T11:13:37.249Z] WARNING: This benchmark provides no result that can be validated.
[2023-04-19T11:13:37.249Z] There is no way to check that no silent failure occurred.
[2023-04-19T11:13:37.249Z] ====== movie-lens (apache-spark) [default], iteration 13 completed (28334.208 ms) ======
[2023-04-19T11:13:37.249Z] ====== movie-lens (apache-spark) [default], iteration 14 started ======
[2023-04-19T11:13:37.249Z] GC before operation: completed in 147.555 ms, heap usage 690.947 MB -> 55.426 MB.
[2023-04-19T11:13:42.533Z] RMSE (validation) = 3.621968954548751 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2023-04-19T11:13:45.857Z] RMSE (validation) = 2.1340923219297814 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2023-04-19T11:13:51.141Z] RMSE (validation) = 1.3105189694032011 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2023-04-19T11:13:55.378Z] RMSE (validation) = 1.0045407704488027 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2023-04-19T11:13:57.170Z] RMSE (validation) = 1.221833058605191 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2023-04-19T11:13:59.690Z] RMSE (validation) = 1.1174953757118413 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2023-04-19T11:14:02.207Z] RMSE (validation) = 0.9275717388537591 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2023-04-19T11:14:04.717Z] RMSE (validation) = 0.9001440970559653 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2023-04-19T11:14:04.717Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9073522617949712.
[2023-04-19T11:14:04.717Z] The best model improves the baseline by 14.43%.
[2023-04-19T11:14:05.260Z] Movies recommended for you:
[2023-04-19T11:14:05.260Z] WARNING: This benchmark provides no result that can be validated.
[2023-04-19T11:14:05.260Z] There is no way to check that no silent failure occurred.
[2023-04-19T11:14:05.260Z] ====== movie-lens (apache-spark) [default], iteration 14 completed (27556.053 ms) ======
[2023-04-19T11:14:05.260Z] ====== movie-lens (apache-spark) [default], iteration 15 started ======
[2023-04-19T11:14:05.260Z] GC before operation: completed in 130.760 ms, heap usage 531.268 MB -> 51.287 MB.
[2023-04-19T11:14:09.491Z] RMSE (validation) = 3.621968954548751 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2023-04-19T11:14:13.858Z] RMSE (validation) = 2.1340923219297814 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2023-04-19T11:14:18.096Z] RMSE (validation) = 1.3105189694032011 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2023-04-19T11:14:22.338Z] RMSE (validation) = 1.0045407704488027 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2023-04-19T11:14:24.858Z] RMSE (validation) = 1.221833058605191 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2023-04-19T11:14:28.178Z] RMSE (validation) = 1.1174953757118413 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2023-04-19T11:14:29.963Z] RMSE (validation) = 0.9275717388537591 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2023-04-19T11:14:32.474Z] RMSE (validation) = 0.9001440970559653 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2023-04-19T11:14:32.474Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9073522617949711.
[2023-04-19T11:14:32.474Z] The best model improves the baseline by 14.43%.
[2023-04-19T11:14:32.474Z] Movies recommended for you:
[2023-04-19T11:14:32.474Z] WARNING: This benchmark provides no result that can be validated.
[2023-04-19T11:14:32.474Z] There is no way to check that no silent failure occurred.
[2023-04-19T11:14:32.474Z] ====== movie-lens (apache-spark) [default], iteration 15 completed (27527.429 ms) ======
[2023-04-19T11:14:32.474Z] ====== movie-lens (apache-spark) [default], iteration 16 started ======
[2023-04-19T11:14:33.019Z] GC before operation: completed in 161.610 ms, heap usage 713.853 MB -> 63.671 MB.
[2023-04-19T11:14:38.307Z] RMSE (validation) = 3.621968954548751 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2023-04-19T11:14:43.585Z] RMSE (validation) = 2.1340923219297814 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2023-04-19T11:14:46.911Z] RMSE (validation) = 1.310518969403201 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2023-04-19T11:14:51.161Z] RMSE (validation) = 1.0045407704488027 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2023-04-19T11:14:53.675Z] RMSE (validation) = 1.221833058605191 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2023-04-19T11:14:57.004Z] RMSE (validation) = 1.1174953757118413 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2023-04-19T11:14:59.511Z] RMSE (validation) = 0.9275717388537591 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2023-04-19T11:15:02.016Z] RMSE (validation) = 0.9001440970559653 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2023-04-19T11:15:02.016Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9073522617949712.
[2023-04-19T11:15:02.016Z] The best model improves the baseline by 14.43%.
[2023-04-19T11:15:02.016Z] Movies recommended for you:
[2023-04-19T11:15:02.016Z] WARNING: This benchmark provides no result that can be validated.
[2023-04-19T11:15:02.016Z] There is no way to check that no silent failure occurred.
[2023-04-19T11:15:02.016Z] ====== movie-lens (apache-spark) [default], iteration 16 completed (29297.449 ms) ======
[2023-04-19T11:15:02.016Z] ====== movie-lens (apache-spark) [default], iteration 17 started ======
[2023-04-19T11:15:02.016Z] GC before operation: completed in 146.015 ms, heap usage 602.813 MB -> 49.102 MB.
[2023-04-19T11:15:06.249Z] RMSE (validation) = 3.6219689545487506 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2023-04-19T11:15:11.519Z] RMSE (validation) = 2.1340923219297814 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2023-04-19T11:15:14.842Z] RMSE (validation) = 1.310518969403201 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2023-04-19T11:15:19.097Z] RMSE (validation) = 1.0045407704488027 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2023-04-19T11:15:21.824Z] RMSE (validation) = 1.221833058605191 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2023-04-19T11:15:24.336Z] RMSE (validation) = 1.1174953757118413 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2023-04-19T11:15:26.851Z] RMSE (validation) = 0.9275717388537591 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2023-04-19T11:15:29.365Z] RMSE (validation) = 0.9001440970559653 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2023-04-19T11:15:29.365Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9073522617949712.
[2023-04-19T11:15:29.365Z] The best model improves the baseline by 14.43%.
[2023-04-19T11:15:29.913Z] Movies recommended for you:
[2023-04-19T11:15:29.913Z] WARNING: This benchmark provides no result that can be validated.
[2023-04-19T11:15:29.913Z] There is no way to check that no silent failure occurred.
[2023-04-19T11:15:29.913Z] ====== movie-lens (apache-spark) [default], iteration 17 completed (27433.396 ms) ======
[2023-04-19T11:15:29.913Z] ====== movie-lens (apache-spark) [default], iteration 18 started ======
[2023-04-19T11:15:29.913Z] GC before operation: completed in 141.758 ms, heap usage 715.787 MB -> 63.445 MB.
[2023-04-19T11:15:35.186Z] RMSE (validation) = 3.621968954548751 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2023-04-19T11:15:40.465Z] RMSE (validation) = 2.1340923219297814 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2023-04-19T11:15:43.783Z] RMSE (validation) = 1.3105189694032011 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2023-04-19T11:15:49.059Z] RMSE (validation) = 1.0045407704488027 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2023-04-19T11:15:51.579Z] RMSE (validation) = 1.221833058605191 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2023-04-19T11:15:53.369Z] RMSE (validation) = 1.1174953757118413 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2023-04-19T11:15:55.883Z] RMSE (validation) = 0.9275717388537591 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2023-04-19T11:15:58.401Z] RMSE (validation) = 0.9001440970559653 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2023-04-19T11:15:58.401Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9073522617949712.
[2023-04-19T11:15:58.401Z] The best model improves the baseline by 14.43%.
[2023-04-19T11:15:58.948Z] Movies recommended for you:
[2023-04-19T11:15:58.948Z] WARNING: This benchmark provides no result that can be validated.
[2023-04-19T11:15:58.948Z] There is no way to check that no silent failure occurred.
[2023-04-19T11:15:58.948Z] ====== movie-lens (apache-spark) [default], iteration 18 completed (29055.685 ms) ======
[2023-04-19T11:15:58.948Z] ====== movie-lens (apache-spark) [default], iteration 19 started ======
[2023-04-19T11:15:58.948Z] GC before operation: completed in 134.343 ms, heap usage 192.824 MB -> 55.404 MB.
[2023-04-19T11:16:04.228Z] RMSE (validation) = 3.6219689545487506 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2023-04-19T11:16:08.458Z] RMSE (validation) = 2.1340923219297814 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2023-04-19T11:16:12.694Z] RMSE (validation) = 1.310518969403201 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2023-04-19T11:16:17.970Z] RMSE (validation) = 1.0045407704488027 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2023-04-19T11:16:19.757Z] RMSE (validation) = 1.221833058605191 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2023-04-19T11:16:21.544Z] RMSE (validation) = 1.1174953757118413 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2023-04-19T11:16:24.062Z] RMSE (validation) = 0.9275717388537591 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2023-04-19T11:16:26.576Z] RMSE (validation) = 0.9001440970559653 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2023-04-19T11:16:27.119Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9073522617949711.
[2023-04-19T11:16:27.119Z] The best model improves the baseline by 14.43%.
[2023-04-19T11:16:27.119Z] Movies recommended for you:
[2023-04-19T11:16:27.119Z] WARNING: This benchmark provides no result that can be validated.
[2023-04-19T11:16:27.119Z] There is no way to check that no silent failure occurred.
[2023-04-19T11:16:27.119Z] ====== movie-lens (apache-spark) [default], iteration 19 completed (28223.504 ms) ======
[2023-04-19T11:16:28.906Z] -----------------------------------
[2023-04-19T11:16:28.906Z] renaissance-movie-lens_0_PASSED
[2023-04-19T11:16:28.906Z] -----------------------------------
[2023-04-19T11:16:28.906Z]
[2023-04-19T11:16:28.906Z] TEST TEARDOWN:
[2023-04-19T11:16:28.906Z] Nothing to be done for teardown.
[2023-04-19T11:16:28.906Z] renaissance-movie-lens_0 Finish Time: Wed Apr 19 06:16:24 2023 Epoch Time (ms): 1681902984078