Run 14730 |

Creator | internal |

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

Dataset | eachmovie-1to5 |

Task type | CollaborativeFiltering |

Created | 6y281d ago |

Done!

54m29s | |

433M | |

CollaborativeFiltering | |

0.609 | |

0.469 | |

0.898 | |

0.694 | |

#### Log file

Nothing to construct. ===== MAIN: learn based on training data ===== === START program1: ./run learn ../dataset2/train loading_time 11.28 ratings range: [0, 5] training data: 30000 users, 1623 items, 2079628 ratings, sparsity 95.72884 MatrixFactorization num_factors=60 regularization=0.05 learn_rate=0.005 num_iter=125 init_mean=0 init_stdev=0.1 training_time 00:51:48.1883230 memory 57 Save model to model.txt === END program1: ./run learn ../dataset2/train --- OK [3134s] ===== 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 [10s] === START program1: ./run predict ../program0/evalTrain.in ../program0/evalTrain.out loading_time 21.15 ratings range: [0, 5] 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 60 MatrixFactorization num_factors=60 regularization=0.015 learn_rate=0.01 num_iter=30 init_mean=0 init_stdev=0.1 RMSE 3.53408 MAE 3.41163 NMAE 0.68233 testing_time 00:00:04.3713970 predicting_time 00:00:14.7996430 memory 81 === END program1: ./run predict ../program0/evalTrain.in ../program0/evalTrain.out --- OK [53s] === START program4: ./run evaluate ../dataset2/train ../program0/evalTrain.out === END program4: ./run evaluate ../dataset2/train ../program0/evalTrain.out --- OK [47s] ===== 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 [2s] === START program1: ./run predict ../program0/evalTest.in ../program0/evalTest.out loading_time 11.45 ratings range: [0, 5] 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 60 MatrixFactorization num_factors=60 regularization=0.015 learn_rate=0.01 num_iter=30 init_mean=0 init_stdev=0.1 RMSE 3.59346 MAE 3.49901 NMAE 0.6998 testing_time 00:00:00.0598530 predicting_time 00:00:00.1974750 memory 49 === END program1: ./run predict ../program0/evalTest.in ../program0/evalTest.out --- OK [23s] === START program4: ./run evaluate ../dataset2/test ../program0/evalTest.out === END program4: ./run evaluate ../dataset2/test ../program0/evalTest.out --- OK [2s] real 54m35.001s user 36m26.105s sys 0m7.208s

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