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

Run 1312

Creator	lester
Program	cberner-KNNImproved-r
Dataset	eachmovie-1to5
Task type	CollaborativeFiltering
Created	1y120d ago

Status: Failed!

Note: this page autoupdates while a run is in progress

Reason	(see end of log file)
Total Time	24m27s
Max Memory Usage	2010M
Domain:	CollaborativeFiltering
Learn time
Train RMSE
Train MAE
Predict train time
Test RMSE
Test MAE
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'
[1] "starting to build R"
   user  system elapsed 
  5.220   0.340   5.579 
[1] "done building R"
   user  system elapsed 
394.768   1.168 397.281 
[1] "done building B"
[1] "done building S"
[1] 2079628
[1] "building new R"
    user   system  elapsed 
1412.536    4.336 1447.062 
Error: cannot allocate vector of size 377.2 Mb
Execution halted
=== Finished: cd /home/mlcomp/worker1/scratch/program0/../program1 && ./run learn '/home/mlcomp/worker1/scratch/program0/../dataset2/train' --- OK [1449s]
=== 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 [6s]
=== 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"
Error in readChar(con, 5L, useBytes = TRUE) : cannot open the connection
Calls: load -> readChar
In addition: Warning message:
In readChar(con, 5L, useBytes = TRUE) :
  cannot open compressed file 'KNN-bestK.Rdata', probable reason 'No such file or directory'
Execution halted
=== 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 [6s]
=== Starting: cd /home/mlcomp/worker1/scratch/program0/../program4 && ./run evaluate '/home/mlcomp/worker1/scratch/program0/../dataset2/train' '/home/mlcomp/worker1/scratch/program0/evalTrain.out'
java.lang.RuntimeException: Unable to open file: /home/mlcomp/worker1/scratch/program0/evalTrain.out
	at collaborativefiltering.Utils$.foreachLine(Utils.scala:30)
	at collaborativefiltering.Utils$.readLines(Utils.scala:64)
	at collaborativefiltering.Utils$.evaluate(Utils.scala:174)
	at collaborativefiltering.Utils$.main(Utils.scala:199)
	at collaborativefiltering.Utils.main(Utils.scala)
	at sun.reflect.NativeMethodAccessorImpl.invoke0(Native Method)
	at sun.reflect.NativeMethodAccessorImpl.invoke(NativeMethodAccessorImpl.java:39)
	at sun.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:25)
	at java.lang.reflect.Method.invoke(Method.java:597)
	at scala.tools.nsc.ObjectRunner$$anonfun$run$1.apply(ObjectRunner.scala:75)
	at scala.tools.nsc.ObjectRunner$.withContextClassLoader(ObjectRunner.scala:49)
	at scala.tools.nsc.ObjectRunner$.run(ObjectRunner.scala:74)
	at scala.tools.nsc.MainGenericRunner$.main(MainGenericRunner.scala:154)
	at scala.tools.nsc.MainGenericRunner.main(MainGenericRunner.scala)
=== Finished: cd /home/mlcomp/worker1/scratch/program0/../program4 && ./run evaluate '/home/mlcomp/worker1/scratch/program0/../dataset2/train' '/home/mlcomp/worker1/scratch/program0/evalTrain.out' --- FAILED [10s]

Run specification

• supervised-learning: Main entry for supervised learning for training and testing a program on a dataset.
  • (learner:Program) cberner-KNNImproved-r: I implemented KNN with learning to find the best K, using cosine similarity and similarity weighting for prediction. Also the prediction is clamped to the range [1,5] since we know that ratings are in that range.
  • (dataset:Dataset) eachmovie-1to5: Modified EachMovie movie ratings dataset from HP/Compaq (more information on http://www.grouplens.org/). The original EachMovie dataset contained 2,811,983 ratings (1-6 stars) entered by 72,916 users for 1628 different movies. This sub-dataset includes the ratings of 30,000 randomly selected users with 20 or more ratings. A single rating from each user was withheld to form the test set. Ratings values outside of the 1-6 range have been discarded. Finally, ratings in the range 1-6 were mapped to the range 1-5 via r = 1 + (r-1)*(4/5).
  • (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

• doTrain:
  • evaluate:
    • success: false
    • time: 10
  • predict:
    • success: true
    • time: 6
  • strip:
    • success: true
    • time: 6
• exitCode: 1
• learn:
  • success: true
  • time: 1449
• success: false

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