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

Run 13339

Creator	yorinasub17
Program	BayesianLogisticRegression_weka_nominal
Dataset	Synthetic 75% Density, Large, Few Labels
Task type	MulticlassClassification
Created	28d17h ago
Download	Login required!

Status: Failed!

Note: this page autoupdates while a run is in progress

Reason	On ./run predict <in> <out>, your program (../program1) did not write to <out>.
Total Time	10m18s
Max Memory Usage	420M
Domain	MulticlassClassification
Learn time	5m13s
Train error
Predict train time	4m58s
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 ../dataset2/train
./run:3: warning: parenthesize argument(s) for future version
weka.core.UnsupportedAttributeTypeException: weka.classifiers.bayes.BayesianLogisticRegression: Cannot handle multi-valued nominal class!
	at weka.core.Capabilities.test(Capabilities.java:929)
	at weka.core.Capabilities.test(Capabilities.java:1098)
	at weka.core.Capabilities.test(Capabilities.java:1011)
	at weka.core.Capabilities.testWithFail(Capabilities.java:1290)
	at weka.classifiers.bayes.BayesianLogisticRegression.buildClassifier(BayesianLogisticRegression.java:330)
	at MainScript.main(MainScript.java:27)
MulticlassClassification
=== END program1: ./run learn ../dataset2/train --- OK [313s]

===== 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 [7s]
=== START program1: ./run predict ../program0/evalTrain.in ../program0/evalTrain.out
./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:137)
	at java.io.FileInputStream.<init>(FileInputStream.java:96)
	at weka.core.SerializationHelper.read(SerializationHelper.java:232)
	at MainScript.main(MainScript.java:47)
MulticlassClassification
=== END program1: ./run predict ../program0/evalTrain.in ../program0/evalTrain.out --- OK [298s]
Failed: On ./run predict <in> <out>, your program (../program1) did not write to <out>.

Run specification

• supervised-learning: Main entry for supervised learning for training and testing a program on a dataset.
  • (learner:Program) BayesianLogisticRegression_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/BayesianLogisticRegression.java' from WEKA's libraries. The following description was taken from this classes JavaDoc information:
    
    ---------------------
    Implements Bayesian Logistic Regression for both Gaussian and Laplace Priors. For more information, see Alexander Genkin, David D. Lewis, David Madigan (2004). Large-scale bayesian logistic regression for text categorization. URL http://www.stat.rutgers.edu/~madigan/PAPERS/shortFat-v3a.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) Synthetic 75% Density, Large, Few Labels : A synthetically generated data set Attributes: =label(i) = argmax_j w(j)'*x(i) for randomly generated weight vectors. =weight vectors elements are independently sampled from Normal distribution. =density is what percentage of weight vector's elements were not set to 0 =x(i) normally distributed according to multivariate Gaussian, random parameters
  • (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

• doTrain:
  • predict:
    • success: true
    • time: 298
  • strip:
    • success: true
    • time: 7
• exitCode: 1
• learn:
  • success: true
  • time: 313
• message: On ./run predict , your program (../program1) did not write to .
• success: false

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