 | Run 14089 |
| Creator | zenogantner |
| Program | MyMediaLite-matrix-factorization-k-20 |
| Dataset | movielens1m |
| Task type | CollaborativeFiltering |
| Created | 28d17h ago |
Done! 
| 32m56s | |
| 432M | |
| CollaborativeFiltering | |
| 32m11s | |
| 0.762 | |
| 0.598 | |
| 16s | |
| 0.910 | |
| 0.714 | |
Log file
Nothing to construct. ===== MAIN: learn based on training data ===== === START program1: ./run learn ../dataset6/train === START program4: ./run split ../dataset6/train ../program1/cv.train ../program1/cv.test n=830307 total examples, aiming for t=581215 training, but actually allocated u=581215 l=0 mandatory training examples === END program4: ./run split ../dataset6/train ../program1/cv.train ../program1/cv.test --- OK [12s] ===== Cross-validator: trying hyperparameter 0.01 ===== === START _tune-hyperparameter0: ./run setHyperparameter 0.01 === END _tune-hyperparameter0: ./run setHyperparameter 0.01 --- OK [0s] === START _tune-hyperparameter0: ./run learn ../cv.train loading_time 3.17 ratings range: [0, 5] training data: 5000 users, 3646 items, 581215 ratings, sparsity 96.81177 MatrixFactorization num_factors=20 regularization=0.01 learn_rate=0.005 num_iter=125 init_mean=0 init_stdev=0.1 training_time 00:06:05.5111230 memory 13 Save model to model.txt === END _tune-hyperparameter0: ./run learn ../cv.train --- OK [371s] === START _tune-hyperparameter0: ./run predict ../cv.test /home/mlcomp/worker/scratch/program1/cvTestPredictions0 loading_time 4.42 ratings range: [0, 5] training data: 5000 users, 3646 items, 581215 ratings, sparsity 96.81177 test data: 4998 users, 3510 items, 249092 ratings, sparsity 98.5801 Load model from model.txt Set num_factors to 20 MatrixFactorization num_factors=20 regularization=0.015 learn_rate=0.01 num_iter=30 init_mean=0 init_stdev=0.1 RMSE 0.92587 MAE 0.71369 NMAE 0.14274 testing_time 00:00:00.2541720 predicting_time 00:00:01.5837980 memory 14 === END _tune-hyperparameter0: ./run predict ../cv.test /home/mlcomp/worker/scratch/program1/cvTestPredictions0 --- OK [8s] === START program5: ./run evaluate ../program1/cv.test /home/mlcomp/worker/scratch/program1/cvTestPredictions0 === END program5: ./run evaluate ../program1/cv.test /home/mlcomp/worker/scratch/program1/cvTestPredictions0 --- OK [4s] CV error rate 0.857232295747676 with hyperparameter 0.01 ===== Cross-validator: trying hyperparameter 0.1 ===== === START _tune-hyperparameter1: ./run setHyperparameter 0.1 === END _tune-hyperparameter1: ./run setHyperparameter 0.1 --- OK [0s] === START _tune-hyperparameter1: ./run learn ../cv.train loading_time 3.19 ratings range: [0, 5] training data: 5000 users, 3646 items, 581215 ratings, sparsity 96.81177 MatrixFactorization num_factors=20 regularization=0.025 learn_rate=0.005 num_iter=125 init_mean=0 init_stdev=0.1 training_time 00:06:05.0630310 memory 13 Save model to model.txt === END _tune-hyperparameter1: ./run learn ../cv.train --- OK [371s] === START _tune-hyperparameter1: ./run predict ../cv.test /home/mlcomp/worker/scratch/program1/cvTestPredictions1 loading_time 4.7 ratings range: [0, 5] training data: 5000 users, 3646 items, 581215 ratings, sparsity 96.81177 test data: 4998 users, 3510 items, 249092 ratings, sparsity 98.5801 Load model from model.txt Set num_factors to 20 MatrixFactorization num_factors=20 regularization=0.015 learn_rate=0.01 num_iter=30 init_mean=0 init_stdev=0.1 RMSE 0.90057 MAE 0.69915 NMAE 0.13983 testing_time 00:00:00.3267680 predicting_time 00:00:01.5635000 memory 14 === END _tune-hyperparameter1: ./run predict ../cv.test /home/mlcomp/worker/scratch/program1/cvTestPredictions1 --- OK [8s] === START program5: ./run evaluate ../program1/cv.test /home/mlcomp/worker/scratch/program1/cvTestPredictions1 === END program5: ./run evaluate ../program1/cv.test /home/mlcomp/worker/scratch/program1/cvTestPredictions1 --- OK [4s] CV error rate 0.811026579303497 with hyperparameter 0.1 ===== Cross-validator: trying hyperparameter 1.0 ===== === START _tune-hyperparameter2: ./run setHyperparameter 1.0 Unknown hyperparameter. === END _tune-hyperparameter2: ./run setHyperparameter 1.0 --- OK [0s] === START _tune-hyperparameter2: ./run learn ../cv.train loading_time 3.41 ratings range: [0, 5] training data: 5000 users, 3646 items, 581215 ratings, sparsity 96.81177 MatrixFactorization num_factors=20 regularization=0.05 learn_rate=0.005 num_iter=125 init_mean=0 init_stdev=0.1 training_time 00:06:05.6373450 memory 13 Save model to model.txt === END _tune-hyperparameter2: ./run learn ../cv.train --- OK [372s] === START _tune-hyperparameter2: ./run predict ../cv.test /home/mlcomp/worker/scratch/program1/cvTestPredictions2 loading_time 4.51 ratings range: [0, 5] training data: 5000 users, 3646 items, 581215 ratings, sparsity 96.81177 test data: 4998 users, 3510 items, 249092 ratings, sparsity 98.5801 Load model from model.txt Set num_factors to 20 MatrixFactorization num_factors=20 regularization=0.015 learn_rate=0.01 num_iter=30 init_mean=0 init_stdev=0.1 RMSE 0.87461 MAE 0.68615 NMAE 0.13723 testing_time 00:00:00.2125200 predicting_time 00:00:01.5808940 memory 14 === END _tune-hyperparameter2: ./run predict ../cv.test /home/mlcomp/worker/scratch/program1/cvTestPredictions2 --- OK [8s] === START program5: ./run evaluate ../program1/cv.test /home/mlcomp/worker/scratch/program1/cvTestPredictions2 === END program5: ./run evaluate ../program1/cv.test /home/mlcomp/worker/scratch/program1/cvTestPredictions2 --- OK [4s] CV error rate 0.764939562260371 with hyperparameter 1.0 ===== Cross-validator: trying hyperparameter 10.0 ===== === START _tune-hyperparameter3: ./run setHyperparameter 10.0 Unknown hyperparameter. === END _tune-hyperparameter3: ./run setHyperparameter 10.0 --- OK [0s] === START _tune-hyperparameter3: ./run learn ../cv.train loading_time 3.15 ratings range: [0, 5] training data: 5000 users, 3646 items, 581215 ratings, sparsity 96.81177 MatrixFactorization num_factors=20 regularization=0.05 learn_rate=0.005 num_iter=125 init_mean=0 init_stdev=0.1 training_time 00:06:09.1883990 memory 13 Save model to model.txt === END _tune-hyperparameter3: ./run learn ../cv.train --- OK [374s] === START _tune-hyperparameter3: ./run predict ../cv.test /home/mlcomp/worker/scratch/program1/cvTestPredictions3 loading_time 4.5 ratings range: [0, 5] training data: 5000 users, 3646 items, 581215 ratings, sparsity 96.81177 test data: 4998 users, 3510 items, 249092 ratings, sparsity 98.5801 Load model from model.txt Set num_factors to 20 MatrixFactorization num_factors=20 regularization=0.015 learn_rate=0.01 num_iter=30 init_mean=0 init_stdev=0.1 RMSE 0.87364 MAE 0.68522 NMAE 0.13704 testing_time 00:00:00.2386500 predicting_time 00:00:01.4867910 memory 14 === END _tune-hyperparameter3: ./run predict ../cv.test /home/mlcomp/worker/scratch/program1/cvTestPredictions3 --- OK [8s] === START program5: ./run evaluate ../program1/cv.test /home/mlcomp/worker/scratch/program1/cvTestPredictions3 === END program5: ./run evaluate ../program1/cv.test /home/mlcomp/worker/scratch/program1/cvTestPredictions3 --- OK [4s] CV error rate 0.763238600388827 with hyperparameter 10.0 ===== Cross-validator: trying hyperparameter 100.0 ===== === START _tune-hyperparameter4: ./run setHyperparameter 100.0 Unknown hyperparameter. === END _tune-hyperparameter4: ./run setHyperparameter 100.0 --- OK [0s] === START _tune-hyperparameter4: ./run learn ../cv.train loading_time 3.19 ratings range: [0, 5] training data: 5000 users, 3646 items, 581215 ratings, sparsity 96.81177 MatrixFactorization num_factors=20 regularization=0.05 learn_rate=0.005 num_iter=125 init_mean=0 init_stdev=0.1 training_time 00:06:04.7054270 memory 13 Save model to model.txt === END _tune-hyperparameter4: ./run learn ../cv.train --- OK [370s] === START _tune-hyperparameter4: ./run predict ../cv.test /home/mlcomp/worker/scratch/program1/cvTestPredictions4 loading_time 4.51 ratings range: [0, 5] training data: 5000 users, 3646 items, 581215 ratings, sparsity 96.81177 test data: 4998 users, 3510 items, 249092 ratings, sparsity 98.5801 Load model from model.txt Set num_factors to 20 MatrixFactorization num_factors=20 regularization=0.015 learn_rate=0.01 num_iter=30 init_mean=0 init_stdev=0.1 RMSE 0.87556 MAE 0.68669 NMAE 0.13734 testing_time 00:00:00.3955380 predicting_time 00:00:01.5992650 memory 14 === END _tune-hyperparameter4: ./run predict ../cv.test /home/mlcomp/worker/scratch/program1/cvTestPredictions4 --- OK [9s] === START program5: ./run evaluate ../program1/cv.test /home/mlcomp/worker/scratch/program1/cvTestPredictions4 === END program5: ./run evaluate ../program1/cv.test /home/mlcomp/worker/scratch/program1/cvTestPredictions4 --- OK [4s] CV error rate 0.766600996256164 with hyperparameter 100.0 Best hyperparameter value is 10.0; got CV error rate 0.763238600388827 === END program1: ./run learn ../dataset6/train --- OK [1931s] ===== MAIN: predict/evaluate on train data ===== === START program7: ./run stripLabels ../dataset6/train ../program0/evalTrain.in === END program7: ./run stripLabels ../dataset6/train ../program0/evalTrain.in --- OK [4s] === START program1: ./run predict ../program0/evalTrain.in ../program0/evalTrain.out === START _tune-hyperparameter-best: ./run predict ../../program0/evalTrain.in ../../program0/evalTrain.out loading_time 7.21 ratings range: [0, 5] training data: 5000 users, 3646 items, 581215 ratings, sparsity 96.81177 test data: 5000 users, 3692 items, 830307 ratings, sparsity 95.50213 Load model from model.txt Set num_factors to 20 MatrixFactorization num_factors=20 regularization=0.015 learn_rate=0.01 num_iter=30 init_mean=0 init_stdev=0.1 RMSE 3.65411 MAE 3.58312 NMAE 0.71662 testing_time 00:00:00.8744320 predicting_time 00:00:04.9161500 memory 23 === END _tune-hyperparameter-best: ./run predict ../../program0/evalTrain.in ../../program0/evalTrain.out --- OK [16s] === END program1: ./run predict ../program0/evalTrain.in ../program0/evalTrain.out --- OK [16s] === START program8: ./run evaluate ../dataset6/train ../program0/evalTrain.out === END program8: ./run evaluate ../dataset6/train ../program0/evalTrain.out --- OK [9s] ===== MAIN: predict/evaluate on test data ===== === START program7: ./run stripLabels ../dataset6/test ../program0/evalTest.in === END program7: ./run stripLabels ../dataset6/test ../program0/evalTest.in --- OK [1s] === START program1: ./run predict ../program0/evalTest.in ../program0/evalTest.out === START _tune-hyperparameter-best: ./run predict ../../program0/evalTest.in ../../program0/evalTest.out loading_time 3.43 ratings range: [0, 5] training data: 5000 users, 3646 items, 581215 ratings, sparsity 96.81177 test data: 5000 users, 1648 items, 5000 ratings, sparsity 99.93932 Load model from model.txt Set num_factors to 20 MatrixFactorization num_factors=20 regularization=0.015 learn_rate=0.01 num_iter=30 init_mean=0 init_stdev=0.1 RMSE 3.77264 MAE 3.7135 NMAE 0.7427 testing_time 00:00:00.0059080 predicting_time 00:00:00.0170980 memory 11 === END _tune-hyperparameter-best: ./run predict ../../program0/evalTest.in ../../program0/evalTest.out --- OK [6s] === END program1: ./run predict ../program0/evalTest.in ../program0/evalTest.out --- OK [6s] === START program8: ./run evaluate ../dataset6/test ../program0/evalTest.out === END program8: ./run evaluate ../dataset6/test ../program0/evalTest.out --- OK [1s] real 33m0.151s user 21m10.191s sys 0m9.053s
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