 | Run 17765 |
| Creator | internal |
| Program | MyMediaLite-biased-matrix-factorization-k-60 |
| Dataset | WordSegmentationData |
| Task type | CollaborativeFiltering |
| Created | 319d15h ago |
| Download | Login required! |
Done! 
| CollaborativeFiltering | |
| 1s | |
| 0.049 | |
| 0.042 | |
| 0s | |
| 0.096 | |
| 0.096 | |
Log file
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0.990873922286952 loss 0.0496085006073503 learn_rate 1.0404176184013 loss 0.0496082942758752 learn_rate 1.09243849932136 loss 0.0496081152363708 learn_rate 1.14706042428743 loss 0.0496079652105863 learn_rate 1.2044134455018 loss 0.0496078464610933 learn_rate 1.26463411777689 loss 0.0496077618515089 learn_rate 1.32786582366574 loss 0.0496077149077667 learn_rate 1.39425911484903 loss 0.0496077098814585 learn_rate 1.46397207059148 loss 0.0496077518164933 learn_rate 0.731986035295739 loss 0.0496073892253371 learn_rate 0.768585337060526 loss 0.0496070510015751 learn_rate 0.807014603913552 loss 0.0496067368352277 learn_rate 0.84736533410923 loss 0.049606446533146 learn_rate 0.889733600814692 training_time 00:00:00.1531630 memory 0 Save model to model.txt === END program1: ./run learn ../dataset2/train --- OK [1s] ===== 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 [1s] === START program1: ./run predict ../program0/evalTrain.in ../program0/evalTrain.out loading_time 0.12 ratings range: [0, 0.984386975548248] training data: 4 users, 2 items, 5 ratings, sparsity 37.5 test data: 4 users, 2 items, 5 ratings, sparsity 37.5 Load model from model.txt Set num_factors to 4 BiasedMatrixFactorization num_factors=60 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 0.74521 MAE 0.68532 NMAE 0.69619 testing_time 00:00:00.0029980 predicting_time 00:00:00.0034200 memory 0 === END program1: ./run predict ../program0/evalTrain.in ../program0/evalTrain.out --- OK [0s] === START program4: ./run evaluate ../dataset2/train ../program0/evalTrain.out === END program4: ./run evaluate ../dataset2/train ../program0/evalTrain.out --- OK [2s] ===== 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 0.09 ratings range: [0, 0.984386975548248] training data: 4 users, 2 items, 5 ratings, sparsity 37.5 test data: 1 users, 1 items, 1 ratings, sparsity 0 Load model from model.txt Set num_factors to 4 BiasedMatrixFactorization num_factors=60 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 0.98142 MAE 0.98142 NMAE 0.99699 testing_time 00:00:00.0933450 predicting_time 00:00:00.0036730 memory 0 === END program1: ./run predict ../program0/evalTest.in ../program0/evalTest.out --- OK [0s] === START program4: ./run evaluate ../dataset2/test ../program0/evalTest.out === END program4: ./run evaluate ../dataset2/test ../program0/evalTest.out --- OK [2s] real 0m9.355s user 0m6.940s sys 0m2.112s
Run specification 
Results
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