ServerRun 14728
Creatorinternal
ProgramMyMediaLite-matrix-factorization-k-60
Datasetmovielens100k
Task typeCollaborativeFiltering
Created6y102d ago
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Done! Flag_green
1m52s
419M
CollaborativeFiltering
0.465
0.364
0.982
0.775

Log file

Nothing to construct.
===== MAIN: learn based on training data =====
=== START program1: ./run learn ../dataset2/train
loading_time 0.41
ratings range: [0, 5]
training data: 943 users, 1680 items, 90570 ratings, sparsity 94.28306
MatrixFactorization num_factors=60 regularization=0.05 learn_rate=0.005 num_iter=125 init_mean=0 init_stdev=0.1 training_time 00:01:43.4656880 
memory 3
Save model to model.txt
=== END program1: ./run learn ../dataset2/train --- OK [106s]

===== 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 [2s]
=== START program1: ./run predict ../program0/evalTrain.in ../program0/evalTrain.out
loading_time 0.71
ratings range: [0, 5]
training data: 943 users, 1680 items, 90570 ratings, sparsity 94.28306
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 3.62163 MAE 3.52559 NMAE 0.70512 testing_time 00:00:00.1133910
predicting_time 00:00:00.5430540
memory 4
=== END program1: ./run predict ../program0/evalTrain.in ../program0/evalTrain.out --- OK [2s]
=== START program4: ./run evaluate ../dataset2/train ../program0/evalTrain.out
=== END program4: ./run evaluate ../dataset2/train ../program0/evalTrain.out --- OK [2s]

===== 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 [2s]
=== START program1: ./run predict ../program0/evalTest.in ../program0/evalTest.out
loading_time 0.43
ratings range: [0, 5]
training data: 943 users, 1680 items, 90570 ratings, sparsity 94.28306
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 3.64544 MAE 3.59416 NMAE 0.71883 testing_time 00:00:00.0086670
predicting_time 00:00:00.0257020
memory 3
=== 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	1m59.521s
user	1m55.059s
sys	0m2.492s

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