Program: 143969
Project: 143969
Buggy source:
public static void main(String[] args){
  int numberOfExperimentRepetitions=2;
  int numberOfExamplesToPickPerIteration=1000000;
  int maximumNumberOfIterations=1000000000;
  boolean includeTitleInResultsPlot=true;
  boolean includeHorizontalAxisLabelInResultsPlot=true;
  boolean includeVerticalAxisLabelInResultsPlot=true;
  boolean includeLegendInResultsPlot=true;
  ScoringFunction[] scoringFunctions=new ScoringFunction[]{new RandomScoringFunction(),new EntropyScoringFunction(),new RandomScoringFunction(true),new EntropyScoringFunction(true),new ExpectedLabelsFixedScoringFunction(true),new ConstraintPropagationScoringFunction(true),new ConstraintPropagationScoringFunction(SurpriseFunction.NEGATIVE_LOGARITHM,true),new ConstraintPropagationScoringFunction(SurpriseFunction.ONE_MINUS_PROBABILITY,true)};
  Set<ResultType> resultTypes=new LinkedHashSet<>();
  resultTypes.add(ResultType.AVERAGE_AUC_FULL_DATA_SET);
  resultTypes.add(ResultType.AVERAGE_AUC_EVALUATION_DATA_SET);
  resultTypes.add(ResultType.NUMBER_OF_EXAMPLES_PICKED);
  boolean useLogicIntegrator=args[0].equals("String_Node_Str");
  boolean retrainClassifiers=args[1].equals("String_Node_Str");
  double negativeToPositiveTrainingExamplesRatio=1;
  ExamplePickingMethod examplePickingMethod=ExamplePickingMethod.PSEUDO_SEQUENTIAL;
  String experiment=args[2];
  String workingDirectory;
  String cplFeatureMapDirectory;
switch (execReadToString("String_Node_Str")) {
case "String_Node_Str":
    workingDirectory="String_Node_Str";
  cplFeatureMapDirectory="String_Node_Str";
break;
case "String_Node_Str":
case "String_Node_Str":
case "String_Node_Str":
workingDirectory="String_Node_Str";
cplFeatureMapDirectory="String_Node_Str";
break;
default :
throw new IllegalStateException("String_Node_Str");
}
ImportedDataSet dataSet;
Map<ScoringFunction,List<ExperimentResults>> results;
logger.info("String_Node_Str" + experiment + "String_Node_Str");
workingDirectory+="String_Node_Str" + experiment + "String_Node_Str";
numberOfExperimentRepetitions=10;
switch (experiment) {
case "String_Node_Str":
case "String_Node_Str":
case "String_Node_Str":
numberOfExamplesToPickPerIteration=1000;
maximumNumberOfIterations=1000;
dataSet=importNELLDataSet(cplFeatureMapDirectory,workingDirectory,negativeToPositiveTrainingExamplesRatio,0,0);
break;
case "String_Node_Str":
numberOfExamplesToPickPerIteration=1;
maximumNumberOfIterations=1000;
dataSet=importISOLETDataSet(workingDirectory,negativeToPositiveTrainingExamplesRatio,0.0,0.0);
break;
case "String_Node_Str":
numberOfExamplesToPickPerIteration=50;
maximumNumberOfIterations=1000;
dataSet=importLIBSVMDataSet(workingDirectory,true,negativeToPositiveTrainingExamplesRatio,0.0,0.0);
break;
case "String_Node_Str":
numberOfExamplesToPickPerIteration=1000;
maximumNumberOfIterations=100000;
dataSet=importLIBSVMDataSet(workingDirectory,true,negativeToPositiveTrainingExamplesRatio,0.0,0.0);
break;
case "String_Node_Str":
numberOfExamplesToPickPerIteration=100;
maximumNumberOfIterations=100000;
dataSet=importLIBSVMDataSet(workingDirectory,false,negativeToPositiveTrainingExamplesRatio,0.0,0.0);
break;
case "String_Node_Str":
numberOfExamplesToPickPerIteration=1000;
maximumNumberOfIterations=100000;
dataSet=importISOLETDataSet(workingDirectory,negativeToPositiveTrainingExamplesRatio,0.0,0.0);
break;
case "String_Node_Str":
numberOfExamplesToPickPerIteration=100;
maximumNumberOfIterations=2000;
dataSet=importLIBSVMDataSet(workingDirectory,false,negativeToPositiveTrainingExamplesRatio,0.0,0.0);
break;
case "String_Node_Str":
numberOfExamplesToPickPerIteration=100;
maximumNumberOfIterations=100000;
dataSet=importLIBSVMDataSet(workingDirectory,false,negativeToPositiveTrainingExamplesRatio,0.0,0.0);
break;
case "String_Node_Str":
numberOfExamplesToPickPerIteration=10;
maximumNumberOfIterations=100000;
dataSet=importLIBSVMDataSet(workingDirectory,false,negativeToPositiveTrainingExamplesRatio,0.0,0.0);
break;
case "String_Node_Str":
numberOfExamplesToPickPerIteration=1000;
maximumNumberOfIterations=100000;
dataSet=importLIBSVMDataSet(workingDirectory,false,negativeToPositiveTrainingExamplesRatio,0.0,0.0);
break;
case "String_Node_Str":
numberOfExamplesToPickPerIteration=1000;
maximumNumberOfIterations=100000;
dataSet=importLIBSVMDataSet(workingDirectory,false,negativeToPositiveTrainingExamplesRatio,0.0,0.0);
break;
case "String_Node_Str":
numberOfExamplesToPickPerIteration=1000;
maximumNumberOfIterations=10000;
dataSet=importLIBSVMDataSet(workingDirectory,false,negativeToPositiveTrainingExamplesRatio,0.0,0.0);
break;
case "String_Node_Str":
numberOfExamplesToPickPerIteration=1;
maximumNumberOfIterations=1000;
dataSet=importLIBSVMDataSet(workingDirectory,false,negativeToPositiveTrainingExamplesRatio,0.0,0.0);
break;
case "String_Node_Str":
numberOfExamplesToPickPerIteration=10;
maximumNumberOfIterations=10000000;
dataSet=importLIBSVMDataSet(workingDirectory,false,negativeToPositiveTrainingExamplesRatio,0.0,0.0);
break;
case "String_Node_Str":
numberOfExamplesToPickPerIteration=10;
maximumNumberOfIterations=1000;
dataSet=importLIBSVMDataSet(workingDirectory,false,negativeToPositiveTrainingExamplesRatio,0.0,0.0);
break;
case "String_Node_Str":
numberOfExamplesToPickPerIteration=10000;
maximumNumberOfIterations=100000;
dataSet=importLIBSVMDataSet(workingDirectory,true,negativeToPositiveTrainingExamplesRatio,0.0,0.0);
break;
case "String_Node_Str":
numberOfExamplesToPickPerIteration=100;
maximumNumberOfIterations=1000;
dataSet=importLIBSVMDataSet(workingDirectory,false,negativeToPositiveTrainingExamplesRatio,0.0,0.0);
break;
case "String_Node_Str":
numberOfExamplesToPickPerIteration=10000;
maximumNumberOfIterations=1000000;
dataSet=importLIBSVMDataSet(workingDirectory,true,negativeToPositiveTrainingExamplesRatio,0.0,0.0);
break;
case "String_Node_Str":
numberOfExamplesToPickPerIteration=10000;
maximumNumberOfIterations=1000000;
dataSet=importLIBSVMDataSet(workingDirectory,true,negativeToPositiveTrainingExamplesRatio,0.0,0.0);
break;
default :
throw new IllegalStateException("String_Node_Str");
}
String resultsDirectoryName;
if (retrainClassifiers && useLogicIntegrator) resultsDirectoryName="String_Node_Str" + examplePickingMethod.name() + "String_Node_Str";
 else if (retrainClassifiers) resultsDirectoryName="String_Node_Str" + examplePickingMethod.name() + "String_Node_Str";
 else if (useLogicIntegrator) resultsDirectoryName="String_Node_Str" + examplePickingMethod.name() + "String_Node_Str";
 else resultsDirectoryName="String_Node_Str" + examplePickingMethod.name() + "String_Node_Str";
dataSet.exportStatistics(workingDirectory + "String_Node_Str");
results=runExperiments(numberOfExperimentRepetitions,numberOfExamplesToPickPerIteration,maximumNumberOfIterations,scoringFunctions,examplePickingMethod,useLogicIntegrator,retrainClassifiers,dataSet,resultTypes);
ConstrainedLearningExperiment.exportResults(numberOfExperimentRepetitions,numberOfExamplesToPickPerIteration,maximumNumberOfIterations,results,workingDirectory + resultsDirectoryName,resultTypes,includeTitleInResultsPlot,includeHorizontalAxisLabelInResultsPlot,includeVerticalAxisLabelInResultsPlot,includeLegendInResultsPlot);
logger.info("String_Node_Str");
}

Fixed version (ground truth):
public static void main(String[] args){
  int numberOfExperimentRepetitions=2;
  int numberOfExamplesToPickPerIteration=1000000;
  int maximumNumberOfIterations=1000000000;
  boolean includeTitleInResultsPlot=true;
  boolean includeHorizontalAxisLabelInResultsPlot=true;
  boolean includeVerticalAxisLabelInResultsPlot=true;
  boolean includeLegendInResultsPlot=true;
  ScoringFunction[] scoringFunctions=new ScoringFunction[]{new RandomScoringFunction(),new EntropyScoringFunction(),new RandomScoringFunction(true),new EntropyScoringFunction(true),new ExpectedLabelsFixedScoringFunction(true),new ConstraintPropagationScoringFunction(true),new ConstraintPropagationScoringFunction(SurpriseFunction.NEGATIVE_LOGARITHM,true),new ConstraintPropagationScoringFunction(SurpriseFunction.ONE_MINUS_PROBABILITY,true)};
  Set<ResultType> resultTypes=new LinkedHashSet<>();
  resultTypes.add(ResultType.AVERAGE_AUC_FULL_DATA_SET);
  resultTypes.add(ResultType.AVERAGE_AUC_EVALUATION_DATA_SET);
  resultTypes.add(ResultType.NUMBER_OF_EXAMPLES_PICKED);
  boolean useLogicIntegrator=args[0].equals("String_Node_Str");
  boolean retrainClassifiers=args[1].equals("String_Node_Str");
  double negativeToPositiveTrainingExamplesRatio=1;
  ExamplePickingMethod examplePickingMethod=ExamplePickingMethod.PSEUDO_SEQUENTIAL;
  String experiment=args[2];
  String workingDirectory;
  String cplFeatureMapDirectory;
  String hostname=execReadToString("String_Node_Str");
switch (hostname) {
case "String_Node_Str":
    workingDirectory="String_Node_Str";
  cplFeatureMapDirectory="String_Node_Str";
break;
case "String_Node_Str":
case "String_Node_Str":
case "String_Node_Str":
workingDirectory="String_Node_Str";
cplFeatureMapDirectory="String_Node_Str";
break;
default :
throw new IllegalStateException("String_Node_Str" + hostname + "String_Node_Str");
}
ImportedDataSet dataSet;
Map<ScoringFunction,List<ExperimentResults>> results;
logger.info("String_Node_Str" + experiment + "String_Node_Str");
workingDirectory+="String_Node_Str" + experiment + "String_Node_Str";
numberOfExperimentRepetitions=10;
switch (experiment) {
case "String_Node_Str":
case "String_Node_Str":
case "String_Node_Str":
numberOfExamplesToPickPerIteration=1000;
maximumNumberOfIterations=1000;
dataSet=importNELLDataSet(cplFeatureMapDirectory,workingDirectory,negativeToPositiveTrainingExamplesRatio,0,0);
break;
case "String_Node_Str":
numberOfExamplesToPickPerIteration=1;
maximumNumberOfIterations=1000;
dataSet=importISOLETDataSet(workingDirectory,negativeToPositiveTrainingExamplesRatio,0.0,0.0);
break;
case "String_Node_Str":
numberOfExamplesToPickPerIteration=50;
maximumNumberOfIterations=1000;
dataSet=importLIBSVMDataSet(workingDirectory,true,negativeToPositiveTrainingExamplesRatio,0.0,0.0);
break;
case "String_Node_Str":
numberOfExamplesToPickPerIteration=1000;
maximumNumberOfIterations=100000;
dataSet=importLIBSVMDataSet(workingDirectory,true,negativeToPositiveTrainingExamplesRatio,0.0,0.0);
break;
case "String_Node_Str":
numberOfExamplesToPickPerIteration=100;
maximumNumberOfIterations=100000;
dataSet=importLIBSVMDataSet(workingDirectory,false,negativeToPositiveTrainingExamplesRatio,0.0,0.0);
break;
case "String_Node_Str":
numberOfExamplesToPickPerIteration=1000;
maximumNumberOfIterations=100000;
dataSet=importISOLETDataSet(workingDirectory,negativeToPositiveTrainingExamplesRatio,0.0,0.0);
break;
case "String_Node_Str":
numberOfExamplesToPickPerIteration=100;
maximumNumberOfIterations=2000;
dataSet=importLIBSVMDataSet(workingDirectory,false,negativeToPositiveTrainingExamplesRatio,0.0,0.0);
break;
case "String_Node_Str":
numberOfExamplesToPickPerIteration=100;
maximumNumberOfIterations=100000;
dataSet=importLIBSVMDataSet(workingDirectory,false,negativeToPositiveTrainingExamplesRatio,0.0,0.0);
break;
case "String_Node_Str":
numberOfExamplesToPickPerIteration=10;
maximumNumberOfIterations=100000;
dataSet=importLIBSVMDataSet(workingDirectory,false,negativeToPositiveTrainingExamplesRatio,0.0,0.0);
break;
case "String_Node_Str":
numberOfExamplesToPickPerIteration=1000;
maximumNumberOfIterations=100000;
dataSet=importLIBSVMDataSet(workingDirectory,false,negativeToPositiveTrainingExamplesRatio,0.0,0.0);
break;
case "String_Node_Str":
numberOfExamplesToPickPerIteration=1000;
maximumNumberOfIterations=100000;
dataSet=importLIBSVMDataSet(workingDirectory,false,negativeToPositiveTrainingExamplesRatio,0.0,0.0);
break;
case "String_Node_Str":
numberOfExamplesToPickPerIteration=1000;
maximumNumberOfIterations=10000;
dataSet=importLIBSVMDataSet(workingDirectory,false,negativeToPositiveTrainingExamplesRatio,0.0,0.0);
break;
case "String_Node_Str":
numberOfExamplesToPickPerIteration=1;
maximumNumberOfIterations=1000;
dataSet=importLIBSVMDataSet(workingDirectory,false,negativeToPositiveTrainingExamplesRatio,0.0,0.0);
break;
case "String_Node_Str":
numberOfExamplesToPickPerIteration=10;
maximumNumberOfIterations=10000000;
dataSet=importLIBSVMDataSet(workingDirectory,false,negativeToPositiveTrainingExamplesRatio,0.0,0.0);
break;
case "String_Node_Str":
numberOfExamplesToPickPerIteration=10;
maximumNumberOfIterations=1000;
dataSet=importLIBSVMDataSet(workingDirectory,false,negativeToPositiveTrainingExamplesRatio,0.0,0.0);
break;
case "String_Node_Str":
numberOfExamplesToPickPerIteration=10000;
maximumNumberOfIterations=100000;
dataSet=importLIBSVMDataSet(workingDirectory,true,negativeToPositiveTrainingExamplesRatio,0.0,0.0);
break;
case "String_Node_Str":
numberOfExamplesToPickPerIteration=100;
maximumNumberOfIterations=1000;
dataSet=importLIBSVMDataSet(workingDirectory,false,negativeToPositiveTrainingExamplesRatio,0.0,0.0);
break;
case "String_Node_Str":
numberOfExamplesToPickPerIteration=10000;
maximumNumberOfIterations=1000000;
dataSet=importLIBSVMDataSet(workingDirectory,true,negativeToPositiveTrainingExamplesRatio,0.0,0.0);
break;
case "String_Node_Str":
numberOfExamplesToPickPerIteration=10000;
maximumNumberOfIterations=1000000;
dataSet=importLIBSVMDataSet(workingDirectory,true,negativeToPositiveTrainingExamplesRatio,0.0,0.0);
break;
default :
throw new IllegalStateException("String_Node_Str");
}
String resultsDirectoryName;
if (retrainClassifiers && useLogicIntegrator) resultsDirectoryName="String_Node_Str" + examplePickingMethod.name() + "String_Node_Str";
 else if (retrainClassifiers) resultsDirectoryName="String_Node_Str" + examplePickingMethod.name() + "String_Node_Str";
 else if (useLogicIntegrator) resultsDirectoryName="String_Node_Str" + examplePickingMethod.name() + "String_Node_Str";
 else resultsDirectoryName="String_Node_Str" + examplePickingMethod.name() + "String_Node_Str";
dataSet.exportStatistics(workingDirectory + "String_Node_Str");
results=runExperiments(numberOfExperimentRepetitions,numberOfExamplesToPickPerIteration,maximumNumberOfIterations,scoringFunctions,examplePickingMethod,useLogicIntegrator,retrainClassifiers,dataSet,resultTypes);
ConstrainedLearningExperiment.exportResults(numberOfExperimentRepetitions,numberOfExamplesToPickPerIteration,maximumNumberOfIterations,results,workingDirectory + resultsDirectoryName,resultTypes,includeTitleInResultsPlot,includeHorizontalAxisLabelInResultsPlot,includeVerticalAxisLabelInResultsPlot,includeLegendInResultsPlot);
logger.info("String_Node_Str");
}