 | Run 14771 |
| Creator | internal |
| Program | MyMediaLite-matrix-factorization-k-40 |
| Dataset | eachmovie |
| Task type | CollaborativeFiltering |
| Created | 1y341d ago |
Done! 
| 34m56s | |
| 435M | |
| CollaborativeFiltering | |
| 0.766 | |
| 0.584 | |
| 1.14 | |
| 0.873 | |
Log file
Nothing to construct. ===== MAIN: learn based on training data ===== === START program1: ./run learn ../dataset2/train loading_time 8.86 ratings range: [0, 6] training data: 30000 users, 1623 items, 2079628 ratings, sparsity 95.72884 MatrixFactorization num_factors=40 regularization=0.05 learn_rate=0.005 num_iter=125 init_mean=0 init_stdev=0.1 training_time 00:32:58.4800030 memory 52 Save model to model.txt === END program1: ./run learn ../dataset2/train --- OK [1995s] ===== 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 [8s] === START program1: ./run predict ../program0/evalTrain.in ../program0/evalTrain.out loading_time 17.69 ratings range: [0, 6] training data: 30000 users, 1623 items, 2079628 ratings, sparsity 95.72884 test data: 30000 users, 1623 items, 2079628 ratings, sparsity 95.72884 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 4.18422 MAE 4.01078 NMAE 0.66846 testing_time 00:00:02.7160970 predicting_time 00:00:11.5341160 memory 76 === END program1: ./run predict ../program0/evalTrain.in ../program0/evalTrain.out --- OK [40s] === START program4: ./run evaluate ../dataset2/train ../program0/evalTrain.out === END program4: ./run evaluate ../dataset2/train ../program0/evalTrain.out --- OK [35s] ===== 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 9.16 ratings range: [0, 6] training data: 30000 users, 1623 items, 2079628 ratings, sparsity 95.72884 test data: 29998 users, 1123 items, 29998 ratings, sparsity 99.91095 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 4.26413 MAE 4.12271 NMAE 0.68712 testing_time 00:00:00.1165410 predicting_time 00:00:00.1405510 memory 45 === END program1: ./run predict ../program0/evalTest.in ../program0/evalTest.out --- OK [17s] === START program4: ./run evaluate ../dataset2/test ../program0/evalTest.out === END program4: ./run evaluate ../dataset2/test ../program0/evalTest.out --- OK [1s] real 34m59.943s user 34m26.417s sys 0m10.165s
Run specification 
Results
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