MLcomp version alpha Statistics: 1403 users, 461 datasets, 360 programs, 16577 runs (0 queued, 0 running), 0 workers

Run 11971

Creator	pmc8p
Program	RBFNetwork_weka_nominal
Dataset	Bing Data
Task type	MulticlassClassification
Created	28d16h ago
Download	Login required!

Status: Failed!

Note: this page autoupdates while a run is in progress

Reason	(see end of log file)
Total Time	27s
Max Memory Usage	424M
Domain	MulticlassClassification
Learn time	18s
Train error	0.023
Predict train time	2s
Test error
Predict test time	1s

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 ../dataset2/train
./run:3: warning: parenthesize argument(s) for future version
MulticlassClassification
=== END program1: ./run learn ../dataset2/train --- OK [18s]

===== MAIN: predict/evaluate on train data =====
=== START program3: ./run stripLabels ../dataset2/train ../program0/evalTrain.in
=== END program3: ./run stripLabels ../dataset2/train ../program0/evalTrain.in --- OK [0s]
=== START program1: ./run predict ../program0/evalTrain.in ../program0/evalTrain.out
./run:3: warning: parenthesize argument(s) for future version
MulticlassClassification
=== END program1: ./run predict ../program0/evalTrain.in ../program0/evalTrain.out --- OK [2s]
=== START program4: ./run evaluate ../dataset2/train ../program0/evalTrain.out
=== END program4: ./run evaluate ../dataset2/train ../program0/evalTrain.out --- OK [0s]

===== MAIN: predict/evaluate on test data =====
=== START program3: ./run stripLabels ../dataset2/test ../program0/evalTest.in
=== END program3: ./run stripLabels ../dataset2/test ../program0/evalTest.in --- OK [0s]
=== START program1: ./run predict ../program0/evalTest.in ../program0/evalTest.out
./run:3: warning: parenthesize argument(s) for future version
java.lang.IllegalArgumentException: Src and Dest differ in # of attributes!!
	at weka.core.RelationalLocator.copyRelationalValues(RelationalLocator.java:88)
	at weka.filters.Filter.copyValues(Filter.java:359)
	at weka.filters.Filter.push(Filter.java:276)
	at weka.filters.unsupervised.attribute.Standardize.convertInstance(Standardize.java:260)
	at weka.filters.unsupervised.attribute.Standardize.input(Standardize.java:142)
	at weka.classifiers.functions.RBFNetwork.distributionForInstance(RBFNetwork.java:218)
	at weka.classifiers.Classifier.classifyInstance(Classifier.java:84)
	at MainScript.main(MainScript.java:55)
MulticlassClassification
=== END program1: ./run predict ../program0/evalTest.in ../program0/evalTest.out --- OK [1s]
=== START program4: ./run evaluate ../dataset2/test ../program0/evalTest.out
Failed: True file has 57 examples but prediction file has 0 examples
=== END program4: ./run evaluate ../dataset2/test ../program0/evalTest.out --- FAILED [0s]

Run specification

• supervised-learning: Main entry for supervised learning for training and testing a program on a dataset.
  • (learner:Program) 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) Bing Data: Classifying Side-Channels for Bing Search Suggestions
  • (stripper:Program[Strip]) multiclass-utils: Validates and inspects a dataset in MulticlassClassification format.
  • (evaluator:Program[Evaluate]) classification-evaluator: Evaluates predictions of classification datasets (discrete outputs).

Results

• doTest:
  • evaluate:
    • message: True file has 57 examples but prediction file has 0 examples
    • success: false
    • time: 0
  • predict:
    • success: true
    • time: 1
  • strip:
    • success: true
    • time: 0
• doTrain:
  • evaluate:
    • errorRate: 0.0227272727272727
    • numErrors: 3
    • numExamples: 132
    • success: true
    • time: 0
  • predict:
    • success: true
    • time: 2
  • strip:
    • success: true
    • time: 0
• exitCode: 1
• learn:
  • success: true
  • time: 18
• success: false

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