 | Run 14117 |
| Creator | zenogantner |
| Program | MyMediaLite-biased-matrix-factorization-k-40 |
| Dataset | movielens1m |
| Task type | CollaborativeFiltering |
| Created | 28d17h ago |
Done! 
| 4h46m | |
| 431M | |
| CollaborativeFiltering | |
| 4h46m | |
| 0.703 | |
| 0.549 | |
| 17s | |
| 0.916 | |
| 0.717 | |
Log file
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[3353s] === START _tune-hyperparameter4: ./run predict ../cv.test /home/mlcomp/worker/scratch/program1/cvTestPredictions4 loading_time 4.59 ratings range: [0, 5] training data: 5000 users, 3646 items, 581215 ratings, sparsity 96.81177 test data: 4998 users, 3510 items, 249092 ratings, sparsity 98.5801 Load model from model.txt Set num_factors to 5000 BiasedMatrixFactorization num_factors=40 bias_reg=0.0001 reg_u=0.015 reg_i=0.015 learn_rate=0.01 num_iter=30 bold_driver=False init_mean=0 init_stdev=0.1 optimize_mae=False RMSE 0.88026 MAE 0.69226 NMAE 0.13845 testing_time 00:00:00.4819820 predicting_time 00:00:01.7541170 memory 16 === END _tune-hyperparameter4: ./run predict ../cv.test /home/mlcomp/worker/scratch/program1/cvTestPredictions4 --- OK [10s] === 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 [4s] CV error rate 0.774861317734535 with hyperparameter 100.0 Best hyperparameter value is 10.0; got CV error rate 0.774570158716766 === END program1: ./run learn ../dataset6/train --- OK [17162s] ===== 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 [5s] === START program1: ./run predict ../program0/evalTrain.in ../program0/evalTrain.out === START _tune-hyperparameter-best: ./run predict ../../program0/evalTrain.in ../../program0/evalTrain.out loading_time 7.24 ratings range: [0, 5] training data: 5000 users, 3646 items, 581215 ratings, sparsity 96.81177 test data: 5000 users, 3692 items, 830307 ratings, sparsity 95.50213 Load model from model.txt Set num_factors to 5000 BiasedMatrixFactorization num_factors=40 bias_reg=0.0001 reg_u=0.015 reg_i=0.015 learn_rate=0.01 num_iter=30 bold_driver=False init_mean=0 init_stdev=0.1 optimize_mae=False RMSE 3.62191 MAE 3.53772 NMAE 0.70754 testing_time 00:00:01.3992270 predicting_time 00:00:05.6182740 memory 25 === END _tune-hyperparameter-best: ./run predict ../../program0/evalTrain.in ../../program0/evalTrain.out --- OK [17s] === END program1: ./run predict ../program0/evalTrain.in ../program0/evalTrain.out --- OK [17s] === START program8: ./run evaluate ../dataset6/train ../program0/evalTrain.out === END program8: ./run evaluate ../dataset6/train ../program0/evalTrain.out --- OK [10s] ===== 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 [1s] === START program1: ./run predict ../program0/evalTest.in ../program0/evalTest.out === START _tune-hyperparameter-best: ./run predict ../../program0/evalTest.in ../../program0/evalTest.out loading_time 3.28 ratings range: [0, 5] training data: 5000 users, 3646 items, 581215 ratings, sparsity 96.81177 test data: 5000 users, 1648 items, 5000 ratings, sparsity 99.93932 Load model from model.txt Set num_factors to 5000 BiasedMatrixFactorization num_factors=40 bias_reg=0.0001 reg_u=0.015 reg_i=0.015 learn_rate=0.01 num_iter=30 bold_driver=False init_mean=0 init_stdev=0.1 optimize_mae=False RMSE 3.73834 MAE 3.6675 NMAE 0.7335 testing_time 00:00:00.0472090 predicting_time 00:00:00.0185570 memory 12 === END _tune-hyperparameter-best: ./run predict ../../program0/evalTest.in ../../program0/evalTest.out --- OK [6s] === END program1: ./run predict ../program0/evalTest.in ../program0/evalTest.out --- OK [6s] === START program8: ./run evaluate ../dataset6/test ../program0/evalTest.out === END program8: ./run evaluate ../dataset6/test ../program0/evalTest.out --- OK [2s] real 286m54.321s user 191m21.154s sys 0m11.805s
Run specification 
Results
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