 | Run 14099 |
| Creator | zenogantner |
| Program | MyMediaLite-matrix-factorization-k-5 |
| Dataset | eachmovie |
| Task type | CollaborativeFiltering |
| Created | 1y319d ago |
Done! 
| 43m27s | |
| 438M | |
| CollaborativeFiltering | |
| 1.06 | |
| 0.821 | |
| 1.17 | |
| 0.903 | |
Log file
Nothing to construct. ===== MAIN: learn based on training data ===== === START program1: ./run learn ../dataset6/train === START program4: ./run split ../dataset6/train ../program1/cv.train ../program1/cv.test n=2079628 total examples, aiming for t=1455740 training, but actually allocated u=1455740 l=0 mandatory training examples === END program4: ./run split ../dataset6/train ../program1/cv.train ../program1/cv.test --- OK [48s] ===== Cross-validator: trying hyperparameter 0.01 ===== === START _tune-hyperparameter0: ./run setHyperparameter 0.01 === END _tune-hyperparameter0: ./run setHyperparameter 0.01 --- OK [0s] === START _tune-hyperparameter0: ./run learn ../cv.train loading_time 8.3 ratings range: [0, 6] training data: 30000 users, 1621 items, 1455740 ratings, sparsity 97.0065 MatrixFactorization num_factors=5 regularization=0.01 learn_rate=0.005 num_iter=125 init_mean=0 init_stdev=0.1 training_time 00:07:27.7701920 memory 32 Save model to model.txt === END _tune-hyperparameter0: ./run learn ../cv.train --- OK [458s] === START _tune-hyperparameter0: ./run predict ../cv.test /home/mlcomp/worker/scratch/program1/cvTestPredictions0 loading_time 11.56 ratings range: [0, 6] training data: 30000 users, 1621 items, 1455740 ratings, sparsity 97.0065 test data: 29995 users, 1606 items, 623888 ratings, sparsity 98.70487 Load model from model.txt Set num_factors to 5 MatrixFactorization num_factors=5 regularization=0.015 learn_rate=0.01 num_iter=30 init_mean=0 init_stdev=0.1 RMSE 1.14739 MAE 0.87654 NMAE 0.14609 testing_time 00:00:00.4871570 predicting_time 00:00:03.7068600 memory 36 === END _tune-hyperparameter0: ./run predict ../cv.test /home/mlcomp/worker/scratch/program1/cvTestPredictions0 --- OK [19s] === START program5: ./run evaluate ../program1/cv.test /home/mlcomp/worker/scratch/program1/cvTestPredictions0 === END program5: ./run evaluate ../program1/cv.test /home/mlcomp/worker/scratch/program1/cvTestPredictions0 --- OK [7s] CV error rate 1.31650603457787 with hyperparameter 0.01 ===== Cross-validator: trying hyperparameter 0.1 ===== === START _tune-hyperparameter1: ./run setHyperparameter 0.1 === END _tune-hyperparameter1: ./run setHyperparameter 0.1 --- OK [0s] === START _tune-hyperparameter1: ./run learn ../cv.train loading_time 8.27 ratings range: [0, 6] training data: 30000 users, 1621 items, 1455740 ratings, sparsity 97.0065 MatrixFactorization num_factors=5 regularization=0.025 learn_rate=0.005 num_iter=125 init_mean=0 init_stdev=0.1 training_time 00:07:31.1841290 memory 32 Save model to model.txt === END _tune-hyperparameter1: ./run learn ../cv.train --- OK [462s] === START _tune-hyperparameter1: ./run predict ../cv.test /home/mlcomp/worker/scratch/program1/cvTestPredictions1 loading_time 11.78 ratings range: [0, 6] training data: 30000 users, 1621 items, 1455740 ratings, sparsity 97.0065 test data: 29995 users, 1606 items, 623888 ratings, sparsity 98.70487 Load model from model.txt Set num_factors to 5 MatrixFactorization num_factors=5 regularization=0.015 learn_rate=0.01 num_iter=30 init_mean=0 init_stdev=0.1 RMSE 1.14397 MAE 0.87684 NMAE 0.14614 testing_time 00:00:00.5878750 predicting_time 00:00:03.6905960 memory 36 === END _tune-hyperparameter1: ./run predict ../cv.test /home/mlcomp/worker/scratch/program1/cvTestPredictions1 --- OK [19s] === START program5: ./run evaluate ../program1/cv.test /home/mlcomp/worker/scratch/program1/cvTestPredictions1 === END program5: ./run evaluate ../program1/cv.test /home/mlcomp/worker/scratch/program1/cvTestPredictions1 --- OK [8s] CV error rate 1.30866071626135 with hyperparameter 0.1 ===== Cross-validator: trying hyperparameter 1.0 ===== === START _tune-hyperparameter2: ./run setHyperparameter 1.0 Unknown hyperparameter. === END _tune-hyperparameter2: ./run setHyperparameter 1.0 --- OK [0s] === START _tune-hyperparameter2: ./run learn ../cv.train loading_time 8.31 ratings range: [0, 6] training data: 30000 users, 1621 items, 1455740 ratings, sparsity 97.0065 MatrixFactorization num_factors=5 regularization=0.05 learn_rate=0.005 num_iter=125 init_mean=0 init_stdev=0.1 training_time 00:07:32.0191910 memory 32 Save model to model.txt === END _tune-hyperparameter2: ./run learn ../cv.train --- OK [463s] === START _tune-hyperparameter2: ./run predict ../cv.test /home/mlcomp/worker/scratch/program1/cvTestPredictions2 loading_time 11.69 ratings range: [0, 6] training data: 30000 users, 1621 items, 1455740 ratings, sparsity 97.0065 test data: 29995 users, 1606 items, 623888 ratings, sparsity 98.70487 Load model from model.txt Set num_factors to 5 MatrixFactorization num_factors=5 regularization=0.015 learn_rate=0.01 num_iter=30 init_mean=0 init_stdev=0.1 RMSE 1.14044 MAE 0.87867 NMAE 0.14645 testing_time 00:00:00.4171000 predicting_time 00:00:03.6980880 memory 36 === END _tune-hyperparameter2: ./run predict ../cv.test /home/mlcomp/worker/scratch/program1/cvTestPredictions2 --- OK [18s] === START program5: ./run evaluate ../program1/cv.test /home/mlcomp/worker/scratch/program1/cvTestPredictions2 === END program5: ./run evaluate ../program1/cv.test /home/mlcomp/worker/scratch/program1/cvTestPredictions2 --- OK [8s] CV error rate 1.30061118178108 with hyperparameter 1.0 ===== Cross-validator: trying hyperparameter 10.0 ===== === START _tune-hyperparameter3: ./run setHyperparameter 10.0 Unknown hyperparameter. === END _tune-hyperparameter3: ./run setHyperparameter 10.0 --- OK [0s] === START _tune-hyperparameter3: ./run learn ../cv.train loading_time 8.62 ratings range: [0, 6] training data: 30000 users, 1621 items, 1455740 ratings, sparsity 97.0065 MatrixFactorization num_factors=5 regularization=0.05 learn_rate=0.005 num_iter=125 init_mean=0 init_stdev=0.1 training_time 00:07:48.8952860 memory 32 Save model to model.txt === END _tune-hyperparameter3: ./run learn ../cv.train --- OK [481s] === START _tune-hyperparameter3: ./run predict ../cv.test /home/mlcomp/worker/scratch/program1/cvTestPredictions3 loading_time 11.69 ratings range: [0, 6] training data: 30000 users, 1621 items, 1455740 ratings, sparsity 97.0065 test data: 29995 users, 1606 items, 623888 ratings, sparsity 98.70487 Load model from model.txt Set num_factors to 5 MatrixFactorization num_factors=5 regularization=0.015 learn_rate=0.01 num_iter=30 init_mean=0 init_stdev=0.1 RMSE 1.14095 MAE 0.87898 NMAE 0.1465 testing_time 00:00:00.5592900 predicting_time 00:00:03.8226520 memory 36 === END _tune-hyperparameter3: ./run predict ../cv.test /home/mlcomp/worker/scratch/program1/cvTestPredictions3 --- OK [18s] === START program5: ./run evaluate ../program1/cv.test /home/mlcomp/worker/scratch/program1/cvTestPredictions3 === END program5: ./run evaluate ../program1/cv.test /home/mlcomp/worker/scratch/program1/cvTestPredictions3 --- OK [8s] CV error rate 1.30177638224185 with hyperparameter 10.0 ===== Cross-validator: trying hyperparameter 100.0 ===== === START _tune-hyperparameter4: ./run setHyperparameter 100.0 Unknown hyperparameter. === END _tune-hyperparameter4: ./run setHyperparameter 100.0 --- OK [0s] === START _tune-hyperparameter4: ./run learn ../cv.train loading_time 8.09 ratings range: [0, 6] training data: 30000 users, 1621 items, 1455740 ratings, sparsity 97.0065 MatrixFactorization num_factors=5 regularization=0.05 learn_rate=0.005 num_iter=125 init_mean=0 init_stdev=0.1 training_time 00:07:26.9679790 memory 32 Save model to model.txt === END _tune-hyperparameter4: ./run learn ../cv.train --- OK [457s] === START _tune-hyperparameter4: ./run predict ../cv.test /home/mlcomp/worker/scratch/program1/cvTestPredictions4 loading_time 11.51 ratings range: [0, 6] training data: 30000 users, 1621 items, 1455740 ratings, sparsity 97.0065 test data: 29995 users, 1606 items, 623888 ratings, sparsity 98.70487 Load model from model.txt Set num_factors to 5 MatrixFactorization num_factors=5 regularization=0.015 learn_rate=0.01 num_iter=30 init_mean=0 init_stdev=0.1 RMSE 1.14117 MAE 0.87902 NMAE 0.1465 testing_time 00:00:00.4663680 predicting_time 00:00:03.7142070 memory 36 === END _tune-hyperparameter4: ./run predict ../cv.test /home/mlcomp/worker/scratch/program1/cvTestPredictions4 --- OK [18s] === START program5: ./run evaluate ../program1/cv.test /home/mlcomp/worker/scratch/program1/cvTestPredictions4 === END program5: ./run evaluate ../program1/cv.test /home/mlcomp/worker/scratch/program1/cvTestPredictions4 --- OK [8s] CV error rate 1.30227900988339 with hyperparameter 100.0 Best hyperparameter value is 1.0; got CV error rate 1.30061118178108 === END program1: ./run learn ../dataset6/train --- OK [2501s] ===== MAIN: predict/evaluate on train data ===== === START program7: ./run stripLabels ../dataset6/train ../program0/evalTrain.in === END program7: ./run stripLabels ../dataset6/train ../program0/evalTrain.in --- OK [8s] === START program1: ./run predict ../program0/evalTrain.in ../program0/evalTrain.out === START _tune-hyperparameter-best: ./run predict ../../program0/evalTrain.in ../../program0/evalTrain.out loading_time 18.48 ratings range: [0, 6] training data: 30000 users, 1621 items, 1455740 ratings, sparsity 97.0065 test data: 30000 users, 1623 items, 2079628 ratings, sparsity 95.72884 Load model from model.txt Set num_factors to 5 MatrixFactorization num_factors=5 regularization=0.015 learn_rate=0.01 num_iter=30 init_mean=0 init_stdev=0.1 RMSE 4.15686 MAE 4.0128 NMAE 0.6688 testing_time 00:00:01.4769560 predicting_time 00:00:12.6805430 memory 58 === END _tune-hyperparameter-best: ./run predict ../../program0/evalTrain.in ../../program0/evalTrain.out --- OK [37s] === END program1: ./run predict ../program0/evalTrain.in ../program0/evalTrain.out --- OK [37s] === START program8: ./run evaluate ../dataset6/train ../program0/evalTrain.out === END program8: ./run evaluate ../dataset6/train ../program0/evalTrain.out --- OK [45s] ===== MAIN: predict/evaluate on test data ===== === START program7: ./run stripLabels ../dataset6/test ../program0/evalTest.in === END program7: ./run stripLabels ../dataset6/test ../program0/evalTest.in --- OK [2s] === START program1: ./run predict ../program0/evalTest.in ../program0/evalTest.out === START _tune-hyperparameter-best: ./run predict ../../program0/evalTest.in ../../program0/evalTest.out loading_time 8.36 ratings range: [0, 6] training data: 30000 users, 1621 items, 1455740 ratings, sparsity 97.0065 test data: 29998 users, 1123 items, 29998 ratings, sparsity 99.91095 Load model from model.txt Set num_factors to 5 MatrixFactorization num_factors=5 regularization=0.015 learn_rate=0.01 num_iter=30 init_mean=0 init_stdev=0.1 RMSE 4.24354 MAE 4.11177 NMAE 0.6853 testing_time 00:00:00.0125650 predicting_time 00:00:00.2321870 memory 27 === END _tune-hyperparameter-best: ./run predict ../../program0/evalTest.in ../../program0/evalTest.out --- OK [11s] === END program1: ./run predict ../program0/evalTest.in ../program0/evalTest.out --- OK [11s] === START program8: ./run evaluate ../dataset6/test ../program0/evalTest.out === END program8: ./run evaluate ../dataset6/test ../program0/evalTest.out --- OK [2s] real 43m30.299s user 27m50.204s sys 0m18.285s
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