ServerRun 14083
Creatorzenogantner
ProgramMyMediaLite-matrix-factorization-k-60
Dataseteachmovie
Task typeCollaborativeFiltering
Created5y265d ago
Done! Flag_green
2h36m
438M
CollaborativeFiltering
0.814
0.590
1.18
0.909

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 [38s]
===== 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 6.97
ratings range: [0, 6]
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 00:31:48.6604990 
memory 45
Save model to model.txt
=== END _tune-hyperparameter0: ./run learn ../cv.train --- OK [1927s]
=== START _tune-hyperparameter0: ./run predict ../cv.test /home/mlcomp/worker/scratch/program1/cvTestPredictions0
loading_time 10.25
ratings range: [0, 6]
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 1.31656 MAE 1.00246 NMAE 0.16708 testing_time 00:00:01.0928110
predicting_time 00:00:03.6572080
memory 49
=== END _tune-hyperparameter0: ./run predict ../cv.test /home/mlcomp/worker/scratch/program1/cvTestPredictions0 --- OK [26s]
=== 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 [6s]
CV error rate 1.73332653934084 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 6.94
ratings range: [0, 6]
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 00:35:40.2450920 
memory 45
Save model to model.txt
=== END _tune-hyperparameter1: ./run learn ../cv.train --- OK [2159s]
=== START _tune-hyperparameter1: ./run predict ../cv.test /home/mlcomp/worker/scratch/program1/cvTestPredictions1
loading_time 10.32
ratings range: [0, 6]
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 1.2213 MAE 0.93435 NMAE 0.15572 testing_time 00:00:01.0725210
predicting_time 00:00:03.6088110
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 1.49156194615226 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 6.9
ratings range: [0, 6]
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 00:27:26.1431730 
memory 45
Save model to model.txt
=== END _tune-hyperparameter2: ./run learn ../cv.train --- OK [1664s]
=== START _tune-hyperparameter2: ./run predict ../cv.test /home/mlcomp/worker/scratch/program1/cvTestPredictions2
loading_time 10.06
ratings range: [0, 6]
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 1.15376 MAE 0.88849 NMAE 0.14808 testing_time 00:00:01.3956600
predicting_time 00:00:03.7022010
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 1.33116534243248 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
ratings range: [0, 6]
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 00:27:33.6328770 
memory 45
Save model to model.txt
=== END _tune-hyperparameter3: ./run learn ../cv.train --- OK [1672s]
=== START _tune-hyperparameter3: ./run predict ../cv.test /home/mlcomp/worker/scratch/program1/cvTestPredictions3
loading_time 10.05
ratings range: [0, 6]
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 1.15421 MAE 0.88878 NMAE 0.14813 testing_time 00:00:00.9442420
predicting_time 00:00:03.5715030
memory 49
=== END _tune-hyperparameter3: ./run predict ../cv.test /home/mlcomp/worker/scratch/program1/cvTestPredictions3 --- OK [25s]
=== 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 [7s]
CV error rate 1.33220505081797 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 6.89
ratings range: [0, 6]
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 00:27:30.1806410 
memory 45
Save model to model.txt
=== END _tune-hyperparameter4: ./run learn ../cv.train --- OK [1668s]
=== START _tune-hyperparameter4: ./run predict ../cv.test /home/mlcomp/worker/scratch/program1/cvTestPredictions4
loading_time 10.18
ratings range: [0, 6]
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 1.15398 MAE 0.88856 NMAE 0.14809 testing_time 00:00:01.0026810
predicting_time 00:00:03.8362780
memory 49
=== END _tune-hyperparameter4: ./run predict ../cv.test /home/mlcomp/worker/scratch/program1/cvTestPredictions4 --- OK [26s]
=== 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 [6s]
CV error rate 1.33167109745988 with hyperparameter 100.0

Best hyperparameter value is 1.0; got CV error rate 1.33116534243248
=== END program1: ./run learn ../dataset6/train --- OK [9289s]

===== 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 15.52
ratings range: [0, 6]
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 4.18944 MAE 4.01372 NMAE 0.66895 testing_time 00:00:03.1630230
predicting_time 00:00:11.5552480
memory 71
=== END _tune-hyperparameter-best: ./run predict ../../program0/evalTrain.in ../../program0/evalTrain.out --- OK [41s]
=== END program1: ./run predict ../program0/evalTrain.in ../program0/evalTrain.out --- OK [41s]
=== START program8: ./run evaluate ../dataset6/train ../program0/evalTrain.out
=== END program8: ./run evaluate ../dataset6/train ../program0/evalTrain.out --- OK [36s]

===== 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 7.26
ratings range: [0, 6]
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 4.24518 MAE 4.11729 NMAE 0.68622 testing_time 00:00:00.0508650
predicting_time 00:00:00.1845670
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	156m38.173s
user	101m39.737s
sys	0m17.009s

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