 | Run 14770 |
| Creator | internal |
| Program | MyMediaLite-matrix-factorization-k-40 |
| Dataset | movielens100k |
| Task type | CollaborativeFiltering |
| Created | 1y340d ago |
| Download | Login required! |
Done! 
| 1m34s | |
| 415M | |
| CollaborativeFiltering | |
| 0.522 | |
| 0.409 | |
| 0.986 | |
| 0.778 | |
Log file
Nothing to construct. ===== MAIN: learn based on training data ===== === START program1: ./run learn ../dataset2/train loading_time 0.4 ratings range: [0, 5] training data: 943 users, 1680 items, 90570 ratings, sparsity 94.28306 MatrixFactorization num_factors=40 regularization=0.05 learn_rate=0.005 num_iter=125 init_mean=0 init_stdev=0.1 training_time 00:01:23.1891000 memory 3 Save model to model.txt === END program1: ./run learn ../dataset2/train --- OK [84s] ===== 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 [2s] === START program1: ./run predict ../program0/evalTrain.in ../program0/evalTrain.out loading_time 0.75 ratings range: [0, 5] training data: 943 users, 1680 items, 90570 ratings, sparsity 94.28306 test data: 943 users, 1680 items, 90570 ratings, sparsity 94.28306 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 3.61883 MAE 3.52624 NMAE 0.70525 testing_time 00:00:00.0863160 predicting_time 00:00:00.4662480 memory 4 === END program1: ./run predict ../program0/evalTrain.in ../program0/evalTrain.out --- OK [2s] === 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 0.5 ratings range: [0, 5] training data: 943 users, 1680 items, 90570 ratings, sparsity 94.28306 test data: 943 users, 1129 items, 9430 ratings, sparsity 99.11426 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 3.64632 MAE 3.58933 NMAE 0.71787 testing_time 00:00:00.0062070 predicting_time 00:00:00.0713410 memory 2 === END program1: ./run predict ../program0/evalTest.in ../program0/evalTest.out --- OK [2s] === START program4: ./run evaluate ../dataset2/test ../program0/evalTest.out === END program4: ./run evaluate ../dataset2/test ../program0/evalTest.out --- OK [1s] real 1m36.451s user 1m4.796s sys 0m1.372s
Run specification 
Results
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