 | Run 14769 |
| Creator | internal |
| Program | MyMediaLite-matrix-factorization-k-40 |
| Dataset | movielens1m |
| Task type | CollaborativeFiltering |
| Created | 28d17h ago |
Done! 
| 12m36s | |
| 423M | |
| CollaborativeFiltering | |
| 12m2s | |
| 0.680 | |
| 0.537 | |
| 14s | |
| 0.897 | |
| 0.703 | |
Log file
Nothing to construct. ===== MAIN: learn based on training data ===== === START program1: ./run learn ../dataset2/train loading_time 3.35 ratings range: [0, 5] training data: 5000 users, 3692 items, 830307 ratings, sparsity 95.50213 MatrixFactorization num_factors=40 regularization=0.05 learn_rate=0.005 num_iter=125 init_mean=0 init_stdev=0.1 training_time 00:11:55.9040470 memory 19 Save model to model.txt === END program1: ./run learn ../dataset2/train --- OK [722s] ===== MAIN: predict/evaluate on train data ===== === START program3: ./run stripLabels ../dataset2/train ../program0/evalTrain.in === END program3: ./run stripLabels ../dataset2/train ../program0/evalTrain.in --- OK [4s] === START program1: ./run predict ../program0/evalTrain.in ../program0/evalTrain.out loading_time 6.64 ratings range: [0, 5] training data: 5000 users, 3692 items, 830307 ratings, sparsity 95.50213 test data: 5000 users, 3692 items, 830307 ratings, sparsity 95.50213 Load model from model.txt Set num_factors to 40 MatrixFactorization num_factors=40 regularization=0.015 learn_rate=0.01 num_iter=30 init_mean=0 init_stdev=0.1 RMSE 3.65509 MAE 3.5821 NMAE 0.71642 testing_time 00:00:00.8460480 predicting_time 00:00:04.0892270 memory 28 === END program1: ./run predict ../program0/evalTrain.in ../program0/evalTrain.out --- OK [14s] === START program4: ./run evaluate ../dataset2/train ../program0/evalTrain.out === END program4: ./run evaluate ../dataset2/train ../program0/evalTrain.out --- OK [8s] ===== MAIN: predict/evaluate on test data ===== === START program3: ./run stripLabels ../dataset2/test ../program0/evalTest.in === END program3: ./run stripLabels ../dataset2/test ../program0/evalTest.in --- OK [1s] === START program1: ./run predict ../program0/evalTest.in ../program0/evalTest.out loading_time 3.38 ratings range: [0, 5] training data: 5000 users, 3692 items, 830307 ratings, sparsity 95.50213 test data: 5000 users, 1648 items, 5000 ratings, sparsity 99.93932 Load model from model.txt Set num_factors to 40 MatrixFactorization num_factors=40 regularization=0.015 learn_rate=0.01 num_iter=30 init_mean=0 init_stdev=0.1 RMSE 3.76802 MAE 3.71046 NMAE 0.74209 testing_time 00:00:00.0059480 predicting_time 00:00:00.0753570 memory 16 === END program1: ./run predict ../program0/evalTest.in ../program0/evalTest.out --- OK [6s] === START program4: ./run evaluate ../dataset2/test ../program0/evalTest.out === END program4: ./run evaluate ../dataset2/test ../program0/evalTest.out --- OK [2s] real 12m38.992s user 12m27.003s sys 0m4.608s
Run specification 
Results
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