MLcomp version alpha Statistics: 1202 users, 398 datasets, 328 programs, 9930 runs (0 queued, 0 running), 0 workers

Run 831

Creator	cberner
Program	cberner-SVD-r
Dataset	movielens100k
Task type	CollaborativeFiltering
Created	1y120d ago

Status: Done!

Total Time	6m11s
Max Memory Usage	203M
Domain:	CollaborativeFiltering
Learn time
Train RMSE	0
Train MAE	0
Predict train time
Test RMSE	1.18
Test MAE	0.920
Predict test time

Log file

Usage: "run learn trainingFileName" OR "run predict testFileName predictionFileName"
=== Starting: cd /home/mlcomp/worker1/scratch/program0/../program1 && ./run learn '/home/mlcomp/worker1/scratch/program0/../dataset2/train'
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=== Finished: cd /home/mlcomp/worker1/scratch/program0/../program1 && ./run learn '/home/mlcomp/worker1/scratch/program0/../dataset2/train' --- OK [350s]
=== Starting: cd /home/mlcomp/worker1/scratch/program0/../program3 && ./run stripLabels '/home/mlcomp/worker1/scratch/program0/../dataset2/train' '/home/mlcomp/worker1/scratch/program0/evalTrain.in'
=== Finished: cd /home/mlcomp/worker1/scratch/program0/../program3 && ./run stripLabels '/home/mlcomp/worker1/scratch/program0/../dataset2/train' '/home/mlcomp/worker1/scratch/program0/evalTrain.in' --- OK [1s]
=== Starting: cd /home/mlcomp/worker1/scratch/program0/../program1 && ./run predict '/home/mlcomp/worker1/scratch/program0/evalTrain.in' '/home/mlcomp/worker1/scratch/program0/evalTrain.out'
[1] "/home/mlcomp/worker1/scratch/program0/evalTrain.in"
[1] "/home/mlcomp/worker1/scratch/program0/evalTrain.out"
[1] 3.699376
=== Finished: cd /home/mlcomp/worker1/scratch/program0/../program1 && ./run predict '/home/mlcomp/worker1/scratch/program0/evalTrain.in' '/home/mlcomp/worker1/scratch/program0/evalTrain.out' --- OK [16s]
=== Starting: cd /home/mlcomp/worker1/scratch/program0/../program4 && ./run evaluate '/home/mlcomp/worker1/scratch/program0/../dataset2/train' '/home/mlcomp/worker1/scratch/program0/evalTrain.out'
=== Finished: cd /home/mlcomp/worker1/scratch/program0/../program4 && ./run evaluate '/home/mlcomp/worker1/scratch/program0/../dataset2/train' '/home/mlcomp/worker1/scratch/program0/evalTrain.out' --- OK [1s]
=== Starting: cd /home/mlcomp/worker1/scratch/program0/../program3 && ./run stripLabels '/home/mlcomp/worker1/scratch/program0/../dataset2/test' '/home/mlcomp/worker1/scratch/program0/evalTest.in'
=== Finished: cd /home/mlcomp/worker1/scratch/program0/../program3 && ./run stripLabels '/home/mlcomp/worker1/scratch/program0/../dataset2/test' '/home/mlcomp/worker1/scratch/program0/evalTest.in' --- OK [1s]
=== Starting: cd /home/mlcomp/worker1/scratch/program0/../program1 && ./run predict '/home/mlcomp/worker1/scratch/program0/evalTest.in' '/home/mlcomp/worker1/scratch/program0/evalTest.out'
[1] "/home/mlcomp/worker1/scratch/program0/evalTest.in"
[1] "/home/mlcomp/worker1/scratch/program0/evalTest.out"
[1] 3.325032
=== Finished: cd /home/mlcomp/worker1/scratch/program0/../program1 && ./run predict '/home/mlcomp/worker1/scratch/program0/evalTest.in' '/home/mlcomp/worker1/scratch/program0/evalTest.out' --- OK [2s]
=== Starting: cd /home/mlcomp/worker1/scratch/program0/../program4 && ./run evaluate '/home/mlcomp/worker1/scratch/program0/../dataset2/test' '/home/mlcomp/worker1/scratch/program0/evalTest.out'
=== Finished: cd /home/mlcomp/worker1/scratch/program0/../program4 && ./run evaluate '/home/mlcomp/worker1/scratch/program0/../dataset2/test' '/home/mlcomp/worker1/scratch/program0/evalTest.out' --- OK [1s]

real	6m12.688s
user	5m43.945s
sys	0m6.656s

Run specification

• supervised-learning: Main entry for supervised learning for training and testing a program on a dataset.
  • (learner:Program) cberner-SVD-r: I've used SVD with missing values, and K = 9. The EM iteration is limited to 20 steps, for performance reasons
  • (dataset:Dataset) movielens100k: 100K MovieLens movie ratings dataset from http://www.grouplens.org/. 100,000 ratings (1-5) from 943 users on 1682 movies. Test set contains exactly 10 ratings per user. See included README.txt for more information.
  • (stripper:Program[Strip]) collaborativefiltering-utils: Validates, inspects, and evaluates a dataset in CollaborativeFiltering format.
  • (evaluator:Program[Evaluate]) collaborativefiltering-utils: Validates, inspects, and evaluates a dataset in CollaborativeFiltering format.

Results

• doTest:
  • evaluate:
    • meanAbsoluteError: 0.919999292184517
    • meanSquaredError: 1.3864927333421
    • numExamples: 9430
    • rootMeanSquaredError: 1.17749426042852
    • success: true
    • time: 1
  • predict:
    • success: true
    • time: 2
  • strip:
    • success: true
    • time: 1
• doTrain:
  • evaluate:
    • meanAbsoluteError: 0.0
    • meanSquaredError: 0.0
    • numExamples: 90570
    • rootMeanSquaredError: 0.0
    • success: true
    • time: 1
  • predict:
    • success: true
    • time: 16
  • strip:
    • success: true
    • time: 1
• exitCode: 0
• learn:
  • success: true
  • time: 350
• success: true

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