ServerRun 14727
Creatorinternal
ProgramMyMediaLite-matrix-factorization-k-60
Datasetmovielens1m
Task typeCollaborativeFiltering
Created5y257d ago
Done! Flag_green
22m23s
423M
CollaborativeFiltering
0.644
0.508
0.889
0.699

Log file

Nothing to construct.
===== MAIN: learn based on training data =====
=== START program1: ./run learn ../dataset2/train
loading_time 4.25
ratings range: [0, 5]
training data: 5000 users, 3692 items, 830307 ratings, sparsity 95.50213
MatrixFactorization num_factors=60 regularization=0.05 learn_rate=0.005 num_iter=125 init_mean=0 init_stdev=0.1 training_time 00:21:27.2242000 
memory 20
Save model to model.txt
=== END program1: ./run learn ../dataset2/train --- OK [1297s]

===== MAIN: predict/evaluate on train data =====
=== START program3: ./run stripLabels ../dataset2/train ../program0/evalTrain.in
=== END program3: ./run stripLabels ../dataset2/train ../program0/evalTrain.in --- OK [4s]
=== START program1: ./run predict ../program0/evalTrain.in ../program0/evalTrain.out
loading_time 8.69
ratings range: [0, 5]
training data: 5000 users, 3692 items, 830307 ratings, sparsity 95.50213
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 3.65743 MAE 3.58231 NMAE 0.71646 testing_time 00:00:01.8091290
predicting_time 00:00:05.9106970
memory 30
=== END program1: ./run predict ../program0/evalTrain.in ../program0/evalTrain.out --- OK [21s]
=== START program4: ./run evaluate ../dataset2/train ../program0/evalTrain.out
=== END program4: ./run evaluate ../dataset2/train ../program0/evalTrain.out --- OK [10s]

===== MAIN: predict/evaluate on test data =====
=== START program3: ./run stripLabels ../dataset2/test ../program0/evalTest.in
=== END program3: ./run stripLabels ../dataset2/test ../program0/evalTest.in --- OK [1s]
=== START program1: ./run predict ../program0/evalTest.in ../program0/evalTest.out
loading_time 4.35
ratings range: [0, 5]
training data: 5000 users, 3692 items, 830307 ratings, sparsity 95.50213
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 3.76861 MAE 3.71309 NMAE 0.74262 testing_time 00:00:00.0258110
predicting_time 00:00:00.0591990
memory 17
=== END program1: ./run predict ../program0/evalTest.in ../program0/evalTest.out --- OK [9s]
=== START program4: ./run evaluate ../dataset2/test ../program0/evalTest.out
=== END program4: ./run evaluate ../dataset2/test ../program0/evalTest.out --- OK [1s]


real	22m26.059s
user	15m7.097s
sys	0m3.932s

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