ServerRun 14084
Creatorzenogantner
ProgramMyMediaLite-matrix-factorization-k-60
Datasetmovielens100k
Task typeCollaborativeFiltering
Created6y110d ago
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Done! Flag_green
8m19s
432M
CollaborativeFiltering
0.638
0.454
1.02
0.808

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=90570 total examples, aiming for t=63399 training, but actually allocated u=63399
l=0 mandatory training examples
=== END program4: ./run split ../dataset6/train ../program1/cv.train ../program1/cv.test --- OK [3s]
===== 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 0.4
ratings range: [0, 5]
training data: 943 users, 1629 items, 63399 ratings, sparsity 95.87286
MatrixFactorization num_factors=60 regularization=0.01 learn_rate=0.005 num_iter=125 init_mean=0 init_stdev=0.1 training_time 00:01:28.0298720 
memory 2
Save model to model.txt
=== END _tune-hyperparameter0: ./run learn ../cv.train --- OK [91s]
=== START _tune-hyperparameter0: ./run predict ../cv.test /home/mlcomp/worker/scratch/program1/cvTestPredictions0
loading_time 0.54
ratings range: [0, 5]
training data: 943 users, 1629 items, 63399 ratings, sparsity 95.87286
test data:     940 users, 1480 items, 27171 ratings, sparsity 98.04694
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 1.13568 MAE 0.89007 NMAE 0.17801 testing_time 00:00:00.0865720
predicting_time 00:00:00.2053520
memory 3
=== END _tune-hyperparameter0: ./run predict ../cv.test /home/mlcomp/worker/scratch/program1/cvTestPredictions0 --- OK [2s]
=== 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 [1s]
CV error rate 1.28977089479738 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 0.35
ratings range: [0, 5]
training data: 943 users, 1629 items, 63399 ratings, sparsity 95.87286
MatrixFactorization num_factors=60 regularization=0.025 learn_rate=0.005 num_iter=125 init_mean=0 init_stdev=0.1 training_time 00:01:28.5337270 
memory 2
Save model to model.txt
=== END _tune-hyperparameter1: ./run learn ../cv.train --- OK [91s]
=== START _tune-hyperparameter1: ./run predict ../cv.test /home/mlcomp/worker/scratch/program1/cvTestPredictions1
loading_time 0.58
ratings range: [0, 5]
training data: 943 users, 1629 items, 63399 ratings, sparsity 95.87286
test data:     940 users, 1480 items, 27171 ratings, sparsity 98.04694
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 1.04917 MAE 0.82355 NMAE 0.16471 testing_time 00:00:00.0721900
predicting_time 00:00:00.2115380
memory 3
=== END _tune-hyperparameter1: ./run predict ../cv.test /home/mlcomp/worker/scratch/program1/cvTestPredictions1 --- OK [2s]
=== 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 [2s]
CV error rate 1.10076193403009 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 0.41
ratings range: [0, 5]
training data: 943 users, 1629 items, 63399 ratings, sparsity 95.87286
MatrixFactorization num_factors=60 regularization=0.05 learn_rate=0.005 num_iter=125 init_mean=0 init_stdev=0.1 training_time 00:01:28.5496670 
memory 2
Save model to model.txt
=== END _tune-hyperparameter2: ./run learn ../cv.train --- OK [91s]
=== START _tune-hyperparameter2: ./run predict ../cv.test /home/mlcomp/worker/scratch/program1/cvTestPredictions2
loading_time 0.56
ratings range: [0, 5]
training data: 943 users, 1629 items, 63399 ratings, sparsity 95.87286
test data:     940 users, 1480 items, 27171 ratings, sparsity 98.04694
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 0.99522 MAE 0.78261 NMAE 0.15652 testing_time 00:00:00.0562600
predicting_time 00:00:00.2143320
memory 3
=== END _tune-hyperparameter2: ./run predict ../cv.test /home/mlcomp/worker/scratch/program1/cvTestPredictions2 --- OK [2s]
=== 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 [2s]
CV error rate 0.99045914129899 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 0.34
ratings range: [0, 5]
training data: 943 users, 1629 items, 63399 ratings, sparsity 95.87286
MatrixFactorization num_factors=60 regularization=0.05 learn_rate=0.005 num_iter=125 init_mean=0 init_stdev=0.1 training_time 00:01:28.5322720 
memory 2
Save model to model.txt
=== END _tune-hyperparameter3: ./run learn ../cv.train --- OK [90s]
=== START _tune-hyperparameter3: ./run predict ../cv.test /home/mlcomp/worker/scratch/program1/cvTestPredictions3
loading_time 0.56
ratings range: [0, 5]
training data: 943 users, 1629 items, 63399 ratings, sparsity 95.87286
test data:     940 users, 1480 items, 27171 ratings, sparsity 98.04694
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 0.99133 MAE 0.78283 NMAE 0.15657 testing_time 00:00:00.0570860
predicting_time 00:00:00.1858630
memory 3
=== END _tune-hyperparameter3: ./run predict ../cv.test /home/mlcomp/worker/scratch/program1/cvTestPredictions3 --- OK [2s]
=== 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 [2s]
CV error rate 0.982738435966905 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 0.34
ratings range: [0, 5]
training data: 943 users, 1629 items, 63399 ratings, sparsity 95.87286
MatrixFactorization num_factors=60 regularization=0.05 learn_rate=0.005 num_iter=125 init_mean=0 init_stdev=0.1 training_time 00:01:28.3088980 
memory 2
Save model to model.txt
=== END _tune-hyperparameter4: ./run learn ../cv.train --- OK [90s]
=== START _tune-hyperparameter4: ./run predict ../cv.test /home/mlcomp/worker/scratch/program1/cvTestPredictions4
loading_time 0.58
ratings range: [0, 5]
training data: 943 users, 1629 items, 63399 ratings, sparsity 95.87286
test data:     940 users, 1480 items, 27171 ratings, sparsity 98.04694
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 0.9943 MAE 0.78242 NMAE 0.15648 testing_time 00:00:00.0571280
predicting_time 00:00:00.2083930
memory 3
=== END _tune-hyperparameter4: ./run predict ../cv.test /home/mlcomp/worker/scratch/program1/cvTestPredictions4 --- OK [3s]
=== 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 [2s]
CV error rate 0.988640591153157 with hyperparameter 100.0

Best hyperparameter value is 10.0; got CV error rate 0.982738435966905
=== END program1: ./run learn ../dataset6/train --- OK [476s]

===== 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 [1s]
=== START program1: ./run predict ../program0/evalTrain.in ../program0/evalTrain.out
=== START _tune-hyperparameter-best: ./run predict ../../program0/evalTrain.in ../../program0/evalTrain.out
loading_time 0.85
ratings range: [0, 5]
training data: 943 users, 1629 items, 63399 ratings, sparsity 95.87286
test data:     943 users, 1680 items, 90570 ratings, sparsity 94.28306
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 3.62602 MAE 3.53873 NMAE 0.70775 testing_time 00:00:00.2252590
predicting_time 00:00:00.5926460
memory 4
=== END _tune-hyperparameter-best: ./run predict ../../program0/evalTrain.in ../../program0/evalTrain.out --- OK [3s]
=== END program1: ./run predict ../program0/evalTrain.in ../program0/evalTrain.out --- OK [3s]
=== START program8: ./run evaluate ../dataset6/train ../program0/evalTrain.out
=== END program8: ./run evaluate ../dataset6/train ../program0/evalTrain.out --- OK [3s]

===== 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 0.39
ratings range: [0, 5]
training data: 943 users, 1629 items, 63399 ratings, sparsity 95.87286
test data:     943 users, 1129 items, 9430 ratings, sparsity 99.11426
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 3.64185 MAE 3.59727 NMAE 0.71945 testing_time 00:00:00.0111730
predicting_time 00:00:00.1210370
memory 2
=== END _tune-hyperparameter-best: ./run predict ../../program0/evalTest.in ../../program0/evalTest.out --- OK [2s]
=== END program1: ./run predict ../program0/evalTest.in ../program0/evalTest.out --- OK [2s]
=== START program8: ./run evaluate ../dataset6/test ../program0/evalTest.out
=== END program8: ./run evaluate ../dataset6/test ../program0/evalTest.out --- OK [2s]


real	8m19.080s
user	5m19.876s
sys	0m4.624s

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