MLcomp version alpha Statistics: 1208 users, 401 datasets, 328 programs, 9937 runs (0 queued, 0 running), 0 workers

Run 5389

Creator	internal
Program	ThresholdSelector_weka_nominal
Dataset	Dexter_train
Task type	MulticlassClassification
Created	1y122d ago
Download	Login required!

Status: Failed!

Note: this page autoupdates while a run is in progress

Reason	(see end of log file)
Total Time	12s
Max Memory Usage	21M
Domain:	BinaryClassification
Learn time
Train error
Predict train time
Test error
Predict test time

Log file

./run:3: warning: parenthesize argument(s) for future version
Note: Some input files use unchecked or unsafe operations.
Note: Recompile with -Xlint:unchecked for details.
MulticlassClassification
===== MAIN: learn based on training data =====
=== START program1: ./run learn ../dataset3/train
=== START program2: ./run learn ../program1/data
./run:3: warning: parenthesize argument(s) for future version
Exception in thread "main" java.lang.OutOfMemoryError: Java heap space
	at weka.core.matrix.Matrix.<init>(Matrix.java:111)
	at weka.core.Matrix.<init>(Matrix.java:61)
	at weka.core.Optimization.findArgmin(Optimization.java:843)
	at weka.classifiers.functions.Logistic.buildClassifier(Logistic.java:665)
	at weka.classifiers.evaluation.EvaluationUtils.getTrainTestPredictions(EvaluationUtils.java:97)
	at weka.classifiers.meta.ThresholdSelector.getPredictions(ThresholdSelector.java:288)
	at weka.classifiers.meta.ThresholdSelector.buildClassifier(ThresholdSelector.java:732)
	at MainScript.main(MainScript.java:27)
MulticlassClassification
=== END program2: ./run learn ../program1/data --- OK [3s]
=== END program1: ./run learn ../dataset3/train --- OK [3s]

===== MAIN: predict/evaluate on train data =====
=== START program4: ./run stripLabels ../dataset3/train ../program0/evalTrain.in
=== END program4: ./run stripLabels ../dataset3/train ../program0/evalTrain.in --- OK [0s]
=== START program1: ./run predict ../program0/evalTrain.in ../program0/evalTrain.out
=== START program2: ./run predict ../program0/evalTrain.in ../program0/evalTrain.out.multiclass-output
./run:3: warning: parenthesize argument(s) for future version
java.io.FileNotFoundException: model_file (No such file or directory)
	at java.io.FileInputStream.open(Native Method)
	at java.io.FileInputStream.<init>(FileInputStream.java:106)
	at java.io.FileInputStream.<init>(FileInputStream.java:66)
	at weka.core.SerializationHelper.read(SerializationHelper.java:232)
	at MainScript.main(MainScript.java:47)
MulticlassClassification
./general.rb:89:in `foreach': No such file or directory - ../program0/evalTrain.out.multiclass-output (Errno::ENOENT)
	from ./general.rb:89:in `foreachClean'
	from ./run:40
=== END program2: ./run predict ../program0/evalTrain.in ../program0/evalTrain.out.multiclass-output --- OK [1s]
=== END program1: ./run predict ../program0/evalTrain.in ../program0/evalTrain.out --- FAILED [1s]

Run specification

• supervised-learning: Main entry for supervised learning for training and testing a program on a dataset.
  • (learner:Program) binary-to-multi: Allows multiclass classification to be run on binary classification datasets (trivial reduction).
    • (multiclassLearner:Program[MulticlassClassification]) ThresholdSelector_weka_nominal: This programs is part of the WEKA classifier library. The code used to generate this program is from the java class 'weka/classifiers/meta/ThresholdSelector.java' from WEKA's libraries. The following description was taken from this classes JavaDoc information:
      
      ---------------------
      A metaclassifier that selecting a mid-point threshold on the probability output by a Classifier. The midpoint threshold is set so that a given performance measure is optimized. Currently this is the F-measure. Performance is measured either on the training data, a hold-out set or using cross-validation. In addition, the probabilities returned by the base learner can have their range expanded so that the output probabilities will reside between 0 and 1 (this is useful if the scheme normally produces probabilities in a very narrow range).
      ---------------------
      
      NOTE: This algorithm has no parameter tuning, it is using the default WEKA parameters
      
      NOTE: WEKA's Classifiers read a data in the .arff format. For Multiclass datasets, the SVMlight format converted to .arff multiclass format so they can be read by WEKA programs
  • (dataset:Dataset) Dexter_train: This is the training set of the Dexter dataset used in the feature selection challenge, see http://www.nipsfsc.ecs.soton.ac.uk/
  • (stripper:Program[Strip]) binary-utils: Validates and inspects a dataset in BinaryClassification format.
  • (evaluator:Program[Evaluate]) classification-evaluator: Evaluates predictions of classification datasets (discrete outputs).

Results

• doTrain:
  • predict:
    • predict:
      • success: true
      • time: 1
    • success: false
    • time: 1
  • strip:
    • success: true
    • time: 0
• exitCode: 1
• learn:
  • learn:
    • success: true
    • time: 3
  • success: true
  • time: 3
• success: false

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