Examples of weka.classifiers.evaluation.output.prediction.AbstractOutput

weka.classifiers.evaluation.output.prediction.AbstractOutput

ned classifier Instances testset = ... // the test set to output the predictions for StringBuffer buffer = ... // the string buffer to add the output to AbstractOutput output = new FunkyOutput(); output.setHeader(...); output.printClassifications(classifier, testset); Basic use with a classifier and a data source:

 Classifier classifier = ... // trained classifier DataSource testset = ... // the data source to obtain the test set from to output the predictions for StringBuffer buffer = ... // the string buffer to add the output to AbstractOutput output = new FunkyOutput(); output.setHeader(...); output.printClassifications(classifier, testset);

In order to make the output generation easily integrate into GUI components, one can output the header, classifications and footer separately:

 Classifier classifier = ... // trained classifier Instances testset = ... // the test set to output the predictions for StringBuffer buffer = ... // the string buffer to add the output to AbstractOutput output = new FunkyOutput(); output.setHeader(...); // print the header output.printHeader(); // print the classifications one-by-one for (int i = 0; i < testset.numInstances(); i++) { output.printClassification(classifier, testset.instance(i), i); // output progress information if ((i+1) % 100 == 0) System.out.println((i+1) + "/" + testset.numInstances()); } // print the footer output.printFooter();

@author fracpete (fracpete at waikato dot ac dot nz) @version $Revision: 6804 $

                      weka.classifiers.pmml.consumer.PMMLClassifier)) {
                outBuff.append("NOTE - if test set is not compatible then results are "
                               + "unpredictable\n\n");
              }


              AbstractOutput classificationOutput = null;
              if (outputPredictionsText) {
          classificationOutput = (AbstractOutput) m_ClassificationOutputEditor.getValue();
          classificationOutput.setHeader(userTestStructure);
          classificationOutput.setBuffer(outBuff);
/*          classificationOutput.setAttributes("");
          classificationOutput.setOutputDistribution(false);*/
//          classificationOutput.printHeader();          
              }
              
              // make adjustments if the classifier is an InputMappedClassifier
              eval = setupEval(eval, classifierToUse, userTestStructure, costMatrix,
                  plotInstances, classificationOutput, false);
              eval.useNoPriors();
              
              if (outputPredictionsText) {
                printPredictionsHeader(outBuff, classificationOutput, "user test set");
              }


        Instance instance;
        int jj = 0;
        while (source.hasMoreElements(userTestStructure)) {
    instance = source.nextElement(userTestStructure);
    plotInstances.process(instance, classifierToUse, eval);
    if (outputPredictionsText) {
      classificationOutput.printClassification(classifierToUse, instance, jj);
    }
    if ((++jj % 100) == 0) {
      m_Log.statusMessage("Evaluating on test data. Processed "
          +jj+" instances...");
    }
        }


        if (outputPredictionsText)
    classificationOutput.printFooter();
              if (outputPredictionsText && classificationOutput.generatesOutput()) {
                outBuff.append("\n");
              } 
      
              if (outputSummary) {
                outBuff.append(eval.toSummaryString(outputEntropy) + "\n");

Examples of weka.classifiers.evaluation.output.prediction.AbstractOutput

Related Classes of weka.classifiers.evaluation.output.prediction.AbstractOutput