ServerRun 14082
Creatorzenogantner
ProgramMyMediaLite-matrix-factorization-k-60
Dataseteachmovie-1to5
Task typeCollaborativeFiltering
Created5y265d ago
Done! Flag_green
2h38m
438M
CollaborativeFiltering
0.678
0.506
0.929
0.721

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=2079628 total examples, aiming for t=1455740 training, but actually allocated u=1455740
l=0 mandatory training examples
=== END program4: ./run split ../dataset6/train ../program1/cv.train ../program1/cv.test --- OK [41s]
===== 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 7.05
ratings range: [0, 5]
training data: 30000 users, 1621 items, 1455740 ratings, sparsity 97.0065
MatrixFactorization num_factors=60 regularization=0.01 learn_rate=0.005 num_iter=125 init_mean=0 init_stdev=0.1 training_time 00:32:24.3800290 
memory 45
Save model to model.txt
=== END _tune-hyperparameter0: ./run learn ../cv.train --- OK [1962s]
=== START _tune-hyperparameter0: ./run predict ../cv.test /home/mlcomp/worker/scratch/program1/cvTestPredictions0
loading_time 10.01
ratings range: [0, 5]
training data: 30000 users, 1621 items, 1455740 ratings, sparsity 97.0065
test data:     29995 users, 1606 items, 623888 ratings, sparsity 98.70487
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 1.03419 MAE 0.78928 NMAE 0.15786 testing_time 00:00:00.9847220
predicting_time 00:00:03.6543990
memory 49
=== END _tune-hyperparameter0: ./run predict ../cv.test /home/mlcomp/worker/scratch/program1/cvTestPredictions0 --- OK [25s]
=== 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 [7s]
CV error rate 1.06954438112168 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 7
ratings range: [0, 5]
training data: 30000 users, 1621 items, 1455740 ratings, sparsity 97.0065
MatrixFactorization num_factors=60 regularization=0.025 learn_rate=0.005 num_iter=125 init_mean=0 init_stdev=0.1 training_time 00:28:02.4566030 
memory 45
Save model to model.txt
=== END _tune-hyperparameter1: ./run learn ../cv.train --- OK [1701s]
=== START _tune-hyperparameter1: ./run predict ../cv.test /home/mlcomp/worker/scratch/program1/cvTestPredictions1
loading_time 10.14
ratings range: [0, 5]
training data: 30000 users, 1621 items, 1455740 ratings, sparsity 97.0065
test data:     29995 users, 1606 items, 623888 ratings, sparsity 98.70487
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 0.95736 MAE 0.73365 NMAE 0.14673 testing_time 00:00:00.9943680
predicting_time 00:00:03.8588020
memory 49
=== END _tune-hyperparameter1: ./run predict ../cv.test /home/mlcomp/worker/scratch/program1/cvTestPredictions1 --- OK [25s]
=== 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 [7s]
CV error rate 0.91653337560817 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 7.21
ratings range: [0, 5]
training data: 30000 users, 1621 items, 1455740 ratings, sparsity 97.0065
MatrixFactorization num_factors=60 regularization=0.05 learn_rate=0.005 num_iter=125 init_mean=0 init_stdev=0.1 training_time 00:27:51.6550300 
memory 45
Save model to model.txt
=== END _tune-hyperparameter2: ./run learn ../cv.train --- OK [1690s]
=== START _tune-hyperparameter2: ./run predict ../cv.test /home/mlcomp/worker/scratch/program1/cvTestPredictions2
loading_time 10.12
ratings range: [0, 5]
training data: 30000 users, 1621 items, 1455740 ratings, sparsity 97.0065
test data:     29995 users, 1606 items, 623888 ratings, sparsity 98.70487
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 0.90798 MAE 0.70159 NMAE 0.14032 testing_time 00:00:00.9843940
predicting_time 00:00:03.6972090
memory 49
=== END _tune-hyperparameter2: ./run predict ../cv.test /home/mlcomp/worker/scratch/program1/cvTestPredictions2 --- OK [25s]
=== 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 [7s]
CV error rate 0.824421732686279 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 7.01
ratings range: [0, 5]
training data: 30000 users, 1621 items, 1455740 ratings, sparsity 97.0065
MatrixFactorization num_factors=60 regularization=0.05 learn_rate=0.005 num_iter=125 init_mean=0 init_stdev=0.1 training_time 00:35:46.0266140 
memory 45
Save model to model.txt
=== END _tune-hyperparameter3: ./run learn ../cv.train --- OK [2164s]
=== START _tune-hyperparameter3: ./run predict ../cv.test /home/mlcomp/worker/scratch/program1/cvTestPredictions3
loading_time 10.39
ratings range: [0, 5]
training data: 30000 users, 1621 items, 1455740 ratings, sparsity 97.0065
test data:     29995 users, 1606 items, 623888 ratings, sparsity 98.70487
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 0.90768 MAE 0.70123 NMAE 0.14025 testing_time 00:00:01.0562300
predicting_time 00:00:03.8053140
memory 49
=== END _tune-hyperparameter3: ./run predict ../cv.test /home/mlcomp/worker/scratch/program1/cvTestPredictions3 --- OK [26s]
=== 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 [6s]
CV error rate 0.823878951066424 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 7.07
ratings range: [0, 5]
training data: 30000 users, 1621 items, 1455740 ratings, sparsity 97.0065
MatrixFactorization num_factors=60 regularization=0.05 learn_rate=0.005 num_iter=125 init_mean=0 init_stdev=0.1 training_time 00:27:52.0774850 
memory 45
Save model to model.txt
=== END _tune-hyperparameter4: ./run learn ../cv.train --- OK [1691s]
=== START _tune-hyperparameter4: ./run predict ../cv.test /home/mlcomp/worker/scratch/program1/cvTestPredictions4
loading_time 9.94
ratings range: [0, 5]
training data: 30000 users, 1621 items, 1455740 ratings, sparsity 97.0065
test data:     29995 users, 1606 items, 623888 ratings, sparsity 98.70487
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 0.90808 MAE 0.70142 NMAE 0.14028 testing_time 00:00:00.9966310
predicting_time 00:00:03.8639840
memory 49
=== END _tune-hyperparameter4: ./run predict ../cv.test /home/mlcomp/worker/scratch/program1/cvTestPredictions4 --- OK [25s]
=== 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 [7s]
CV error rate 0.824604334378296 with hyperparameter 100.0

Best hyperparameter value is 10.0; got CV error rate 0.823878951066424
=== END program1: ./run learn ../dataset6/train --- OK [9409s]

===== 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 [8s]
=== START program1: ./run predict ../program0/evalTrain.in ../program0/evalTrain.out
=== START _tune-hyperparameter-best: ./run predict ../../program0/evalTrain.in ../../program0/evalTrain.out
loading_time 15.87
ratings range: [0, 5]
training data: 30000 users, 1621 items, 1455740 ratings, sparsity 97.0065
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 3.53229 MAE 3.41154 NMAE 0.68231 testing_time 00:00:03.2108210
predicting_time 00:00:11.7086550
memory 71
=== END _tune-hyperparameter-best: ./run predict ../../program0/evalTrain.in ../../program0/evalTrain.out --- OK [42s]
=== END program1: ./run predict ../program0/evalTrain.in ../program0/evalTrain.out --- OK [42s]
=== START program8: ./run evaluate ../dataset6/train ../program0/evalTrain.out
=== END program8: ./run evaluate ../dataset6/train ../program0/evalTrain.out --- OK [38s]

===== 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 7.28
ratings range: [0, 5]
training data: 30000 users, 1621 items, 1455740 ratings, sparsity 97.0065
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 3.58643 MAE 3.4961 NMAE 0.69922 testing_time 00:00:00.0768150
predicting_time 00:00:00.1664390
memory 40
=== END _tune-hyperparameter-best: ./run predict ../../program0/evalTest.in ../../program0/evalTest.out --- OK [18s]
=== END program1: ./run predict ../program0/evalTest.in ../program0/evalTest.out --- OK [18s]
=== START program8: ./run evaluate ../dataset6/test ../program0/evalTest.out
=== END program8: ./run evaluate ../dataset6/test ../program0/evalTest.out --- OK [2s]


real	158m41.562s
user	103m12.443s
sys	0m16.973s

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