ServerRun 14081
Creatorzenogantner
ProgramMyMediaLite-matrix-factorization-k-60
Datasetmovielens1m
Task typeCollaborativeFiltering
Created5y360d ago
Done! Flag_green
1h3m
430M
CollaborativeFiltering
0.689
0.532
0.918
0.720

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 [11s]
===== 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 2.73
ratings range: [0, 5]
training data: 5000 users, 3646 items, 581215 ratings, sparsity 96.81177
MatrixFactorization num_factors=60 regularization=0.01 learn_rate=0.005 num_iter=125 init_mean=0 init_stdev=0.1 training_time 00:12:09.4041080 
memory 15
Save model to model.txt
=== END _tune-hyperparameter0: ./run learn ../cv.train --- OK [736s]
=== START _tune-hyperparameter0: ./run predict ../cv.test /home/mlcomp/worker/scratch/program1/cvTestPredictions0
loading_time 3.93
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 60
MatrixFactorization num_factors=60 regularization=0.015 learn_rate=0.01 num_iter=30 init_mean=0 init_stdev=0.1 RMSE 1.0163 MAE 0.78448 NMAE 0.1569 testing_time 00:00:00.3872640
predicting_time 00:00:01.5078140
memory 17
=== END _tune-hyperparameter0: ./run predict ../cv.test /home/mlcomp/worker/scratch/program1/cvTestPredictions0 --- OK [9s]
=== 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 1.03286562501915 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 2.83
ratings range: [0, 5]
training data: 5000 users, 3646 items, 581215 ratings, sparsity 96.81177
MatrixFactorization num_factors=60 regularization=0.025 learn_rate=0.005 num_iter=125 init_mean=0 init_stdev=0.1 training_time 00:12:12.4353050 
memory 15
Save model to model.txt
=== END _tune-hyperparameter1: ./run learn ../cv.train --- OK [738s]
=== START _tune-hyperparameter1: ./run predict ../cv.test /home/mlcomp/worker/scratch/program1/cvTestPredictions1
loading_time 4.04
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 60
MatrixFactorization num_factors=60 regularization=0.015 learn_rate=0.01 num_iter=30 init_mean=0 init_stdev=0.1 RMSE 0.93644 MAE 0.72824 NMAE 0.14565 testing_time 00:00:00.4498510
predicting_time 00:00:01.3968350
memory 17
=== END _tune-hyperparameter1: ./run predict ../cv.test /home/mlcomp/worker/scratch/program1/cvTestPredictions1 --- OK [10s]
=== 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 [3s]
CV error rate 0.876923033273357 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 2.75
ratings range: [0, 5]
training data: 5000 users, 3646 items, 581215 ratings, sparsity 96.81177
MatrixFactorization num_factors=60 regularization=0.05 learn_rate=0.005 num_iter=125 init_mean=0 init_stdev=0.1 training_time 00:12:19.4395340 
memory 15
Save model to model.txt
=== END _tune-hyperparameter2: ./run learn ../cv.train --- OK [746s]
=== START _tune-hyperparameter2: ./run predict ../cv.test /home/mlcomp/worker/scratch/program1/cvTestPredictions2
loading_time 4.06
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 60
MatrixFactorization num_factors=60 regularization=0.015 learn_rate=0.01 num_iter=30 init_mean=0 init_stdev=0.1 RMSE 0.88302 MAE 0.69344 NMAE 0.13869 testing_time 00:00:00.3190320
predicting_time 00:00:01.5719470
memory 17
=== END _tune-hyperparameter2: ./run predict ../cv.test /home/mlcomp/worker/scratch/program1/cvTestPredictions2 --- OK [9s]
=== 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 0.779730709552324 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 2.83
ratings range: [0, 5]
training data: 5000 users, 3646 items, 581215 ratings, sparsity 96.81177
MatrixFactorization num_factors=60 regularization=0.05 learn_rate=0.005 num_iter=125 init_mean=0 init_stdev=0.1 training_time 00:12:09.5500980 
memory 15
Save model to model.txt
=== END _tune-hyperparameter3: ./run learn ../cv.train --- OK [736s]
=== START _tune-hyperparameter3: ./run predict ../cv.test /home/mlcomp/worker/scratch/program1/cvTestPredictions3
loading_time 3.95
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 60
MatrixFactorization num_factors=60 regularization=0.015 learn_rate=0.01 num_iter=30 init_mean=0 init_stdev=0.1 RMSE 0.88198 MAE 0.69331 NMAE 0.13866 testing_time 00:00:00.3992180
predicting_time 00:00:01.6209700
memory 17
=== END _tune-hyperparameter3: ./run predict ../cv.test /home/mlcomp/worker/scratch/program1/cvTestPredictions3 --- OK [9s]
=== 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 0.777883487040078 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 2.8
ratings range: [0, 5]
training data: 5000 users, 3646 items, 581215 ratings, sparsity 96.81177
MatrixFactorization num_factors=60 regularization=0.05 learn_rate=0.005 num_iter=125 init_mean=0 init_stdev=0.1 training_time 00:12:09.8408430 
memory 15
Save model to model.txt
=== END _tune-hyperparameter4: ./run learn ../cv.train --- OK [736s]
=== START _tune-hyperparameter4: ./run predict ../cv.test /home/mlcomp/worker/scratch/program1/cvTestPredictions4
loading_time 3.97
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 60
MatrixFactorization num_factors=60 regularization=0.015 learn_rate=0.01 num_iter=30 init_mean=0 init_stdev=0.1 RMSE 0.88201 MAE 0.69301 NMAE 0.1386 testing_time 00:00:00.3773940
predicting_time 00:00:01.6426630
memory 17
=== 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 0.777941949733638 with hyperparameter 100.0

Best hyperparameter value is 10.0; got CV error rate 0.777883487040078
=== END program1: ./run learn ../dataset6/train --- OK [3768s]

===== 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 6.18
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 60
MatrixFactorization num_factors=60 regularization=0.015 learn_rate=0.01 num_iter=30 init_mean=0 init_stdev=0.1 RMSE 3.66015 MAE 3.58436 NMAE 0.71687 testing_time 00:00:01.2241650
predicting_time 00:00:04.6603800
memory 26
=== 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 [8s]

===== 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 2.84
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 60
MatrixFactorization num_factors=60 regularization=0.015 learn_rate=0.01 num_iter=30 init_mean=0 init_stdev=0.1 RMSE 3.75762 MAE 3.70113 NMAE 0.74023 testing_time 00:00:00.0083110
predicting_time 00:00:00.0771220
memory 13
=== 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 [2s]


real	63m29.662s
user	62m16.153s
sys	0m15.313s

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