Run 14732 |

Creator | internal |

Program | MyMediaLite-matrix-factorization-k-60 |

Dataset | BanditMulticlassData |

Task type | CollaborativeFiltering |

Created | 5y352d ago |

Download | Login required! |

Done!

19s | |

418M | |

CollaborativeFiltering | |

0.069 | |

0.063 | |

0.340 | |

0.253 | |

#### Log file

Nothing to construct. ===== MAIN: learn based on training data ===== === START program1: ./run learn ../dataset2/train loading_time 0.02 ratings range: [0, 0.984446878422782] training data: 4 users, 3 items, 9 ratings, sparsity 25 MatrixFactorization num_factors=60 regularization=0.05 learn_rate=0.005 num_iter=125 init_mean=0 init_stdev=0.1 training_time 00:00:00.0460150 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 0.06 ratings range: [0, 0.984446878422782] training data: 4 users, 3 items, 9 ratings, sparsity 25 test data: 4 users, 3 items, 9 ratings, sparsity 25 Load model from model.txt Set num_factors to 60 MatrixFactorization num_factors=60 regularization=0.015 learn_rate=0.01 num_iter=30 init_mean=0 init_stdev=0.1 RMSE 0.69438 MAE 0.67249 NMAE 0.68312 testing_time 00:00:00.0022480 predicting_time 00:00:00.0033490 memory 0 === END program1: ./run predict ../program0/evalTrain.in ../program0/evalTrain.out --- OK [1s] === START program4: ./run evaluate ../dataset2/train ../program0/evalTrain.out === END program4: ./run evaluate ../dataset2/train ../program0/evalTrain.out --- OK [1s] ===== 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 0.05 ratings range: [0, 0.984446878422782] training data: 4 users, 3 items, 9 ratings, sparsity 25 test data: 3 users, 2 items, 3 ratings, sparsity 50 Load model from model.txt Set num_factors to 60 MatrixFactorization num_factors=60 regularization=0.015 learn_rate=0.01 num_iter=30 init_mean=0 init_stdev=0.1 RMSE 0.50361 MAE 0.4327 NMAE 0.43954 testing_time 00:00:00.0026150 predicting_time 00:00:00.0034420 memory 0 === END program1: ./run predict ../program0/evalTest.in ../program0/evalTest.out --- OK [1s] === START program4: ./run evaluate ../dataset2/test ../program0/evalTest.out === END program4: ./run evaluate ../dataset2/test ../program0/evalTest.out --- OK [1s] real 0m9.672s user 0m5.068s sys 0m1.172s

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