MLcomp version alpha Statistics: 2043 users, 665 datasets, 389 programs, 18920 runs (0 queued, 4 running), 0 workers

Run 25044

Creator	internal
Program	BayesNet_weka_nominal
Dataset	qqq-1
Task type	MulticlassClassification
Created	321d10h ago

Status: Failed!

Note: this page autoupdates while a run is in progress

Reason	(see end of log file)
Total Time
Max Memory Usage
Domain	BinaryClassification
Learn time	6s
Train error
Predict train time	4s
Test error
Predict test time

Log file

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
./svmlight2arff.rb:2:in `max': undefined method `<=>' for nil:NilClass (NoMethodError)
	from ./svmlight2arff.rb:30:in `each'
	from ./svmlight2arff.rb:30:in `max'
	from ./svmlight2arff.rb:30
java.io.IOException: No source has been specified
	at weka.core.converters.ArffLoader.getDataSet(ArffLoader.java:1000)
	at MainScript.loadData(MainScript.java:83)
	at MainScript.main(MainScript.java:22)
MulticlassClassification
=== END program2: ./run learn ../program1/data --- OK [4s]
=== 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 [2s]
=== START program1: ./run predict ../program0/evalTrain.in ../program0/evalTrain.out
=== START program2: ./run predict ../program0/evalTrain.in ../program0/evalTrain.out.multiclass-output
./svmlight2arff.rb:2:in `max': undefined method `<=>' for nil:NilClass (NoMethodError)
	from ./svmlight2arff.rb:30:in `each'
	from ./svmlight2arff.rb:30:in `max'
	from ./svmlight2arff.rb:30
java.io.FileNotFoundException: model_file (No such file or directory)
	at java.io.FileInputStream.open(Native Method)
	at java.io.FileInputStream.<init>(FileInputStream.java:137)
	at java.io.FileInputStream.<init>(FileInputStream.java:96)
	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 [4s]
=== END program1: ./run predict ../program0/evalTrain.in ../program0/evalTrain.out --- FAILED [4s]

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]) BayesNet_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/bayes/BayesNet.java' from WEKA's libraries. The following description was taken from this classes JavaDoc information:
      
      ---------------------
      Bayes Network learning using various search algorithms and quality measures. Base class for a Bayes Network classifier. Provides datastructures (network structure, conditional probability distributions, etc.) and facilities common to Bayes Network learning algorithms like K2 and B. For more information see: http://www.cs.waikato.ac.nz/~remco/weka.pdf
      ---------------------
      
      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) qqq-1:
  • (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: 4
    • success: false
    • time: 4
  • strip:
    • success: true
    • time: 2
• exitCode: 0
• learn:
  • learn:
    • success: true
    • time: 4
  • success: true
  • time: 6
• success: false

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