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

Run 3226

Creator	internal
Program	RBFNetwork_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	1s
Max Memory Usage	247M
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: ./weka/classifiers/functions/LinearRegression.java uses or overrides a deprecated API.
Note: Recompile with -Xlint:deprecation for details.
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.clusterers.SimpleKMeans.moveCentroid(SimpleKMeans.java:397)
	at weka.clusterers.SimpleKMeans.buildClusterer(SimpleKMeans.java:247)
	at weka.clusterers.MakeDensityBasedClusterer.buildClusterer(MakeDensityBasedClusterer.java:206)
	at weka.filters.unsupervised.attribute.ClusterMembership.batchFinished(ClusterMembership.java:236)
	at weka.filters.Filter.useFilter(Filter.java:657)
	at weka.classifiers.functions.RBFNetwork.buildClassifier(RBFNetwork.java:185)
	at MainScript.main(MainScript.java:27)
MulticlassClassification
=== END program2: ./run learn ../program1/data --- OK [6s]
=== END program1: ./run learn ../dataset3/train --- OK [6s]

===== 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]) RBFNetwork_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/functions/RBFNetwork.java' from WEKA's libraries. The following description was taken from this classes JavaDoc information:
      
      ---------------------
      Class that implements a normalized Gaussian radial basisbasis function network. It uses the k-means clustering algorithm to provide the basis functions and learns either a logistic regression (discrete class problems) or linear regression (numeric class problems) on top of that. Symmetric multivariate Gaussians are fit to the data from each cluster. If the class is nominal it uses the given number of clusters per class.It standardizes all numeric attributes to zero mean and unit variance.
      ---------------------
      
      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: 6
  • success: true
  • time: 6
• success: false

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