ServerRun 14080
Creatorzenogantner
ProgramMyMediaLite-matrix-factorization-k-60
Datasetcollaborativefiltering-sample
Task typeCollaborativeFiltering
Created5y325d ago
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Done! Flag_green
6s
423M
CollaborativeFiltering
0.136
0.117
1.08
0.879

Log file

Nothing to construct.
===== MAIN: learn based on training data =====
=== START program1: ./run learn ../dataset2/train
loading_time 0.11
ratings range: [0, 4]
training data: 3 users, 3 items, 5 ratings, sparsity 44.44444
MatrixFactorization num_factors=60 regularization=0.05 learn_rate=0.005 num_iter=125 init_mean=0 init_stdev=0.1 training_time 00:00:00.0081330 
memory 0
Save model to model.txt
=== END program1: ./run learn ../dataset2/train --- OK [1s]

===== 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 [1s]
=== START program1: ./run predict ../program0/evalTrain.in ../program0/evalTrain.out
loading_time 0.03
ratings range: [0, 4]
training data: 3 users, 3 items, 5 ratings, sparsity 44.44444
test data:     3 users, 3 items, 5 ratings, sparsity 44.44444
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 3.10934 MAE 2.95363 NMAE 0.73841 testing_time 00:00:00.0024840
predicting_time 00:00:00.0033240
memory 0
=== END program1: ./run predict ../program0/evalTrain.in ../program0/evalTrain.out --- OK [1s]
=== START program4: ./run evaluate ../dataset2/train ../program0/evalTrain.out
=== END program4: ./run evaluate ../dataset2/train ../program0/evalTrain.out --- OK [1s]

===== 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 0.03
ratings range: [0, 4]
training data: 3 users, 3 items, 5 ratings, sparsity 44.44444
test data:     3 users, 3 items, 4 ratings, sparsity 55.55556
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 2.86378 MAE 2.86042 NMAE 0.71511 testing_time 00:00:00.0026400
predicting_time 00:00:00.0033570
memory 0
=== END program1: ./run predict ../program0/evalTest.in ../program0/evalTest.out --- OK [1s]
=== START program4: ./run evaluate ../dataset2/test ../program0/evalTest.out
=== END program4: ./run evaluate ../dataset2/test ../program0/evalTest.out --- OK [1s]


real	0m9.226s
user	0m6.620s
sys	0m2.136s

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