Examples of weka.classifiers.Evaluation.toSummaryString()

weka.classifiers.Evaluation.toSummaryString()
Calls toSummaryString() with no title and no complexity stats. @return a summary description of the classifier evaluation

              if (outputPredictionsText) {
                outBuff.append("\n");
              } 
      
              if (outputSummary) {
                outBuff.append(eval.toSummaryString(outputEntropy) + "\n");
              }
      
              if (userTestStructure.classAttribute().isNominal()) {
  
                if (outputPerClass) {

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    if(canMeasureCPUTime)
      testCPUTimeElapsed = thMonitor.getThreadUserTime(thID) - CPUStartTime;
    testTimeElapsed = System.currentTimeMillis() - testTimeStart;
    thMonitor = null;
    
    m_result = eval.toSummaryString();
    // The results stored are all per instance -- can be multiplied by the
    // number of instances to get absolute numbers
    int current = 0;
    result[current++] = new Double(train.numInstances());
    result[current++] = new Double(eval.numInstances());

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    if(canMeasureCPUTime)
      testCPUTimeElapsed = thMonitor.getThreadUserTime(thID) - CPUStartTime;
    testTimeElapsed = System.currentTimeMillis() - testTimeStart;
    thMonitor = null;
    
    m_result = eval.toSummaryString();
    // The results stored are all per instance -- can be multiplied by the
    // number of instances to get absolute numbers
    int current = 0;
    result[current++] = new Double(train.numInstances());
    result[current++] = new Double(eval.numInstances());

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    if(canMeasureCPUTime)
      testCPUTimeElapsed = thMonitor.getThreadUserTime(thID) - CPUStartTime;
    testTimeElapsed = System.currentTimeMillis() - testTimeStart;
    thMonitor = null;
    
    m_result = eval.toSummaryString();
    // The results stored are all per instance -- can be multiplied by the
    // number of instances to get absolute numbers
    int current = 0;
    result[current++] = new Double(train.numInstances());
    result[current++] = new Double(eval.numInstances());

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    if(canMeasureCPUTime)
      testCPUTimeElapsed = thMonitor.getThreadUserTime(thID) - CPUStartTime;
    testTimeElapsed = System.currentTimeMillis() - testTimeStart;
    thMonitor = null;
    
    m_result = eval.toSummaryString();
    // The results stored are all per instance -- can be multiplied by the
    // number of instances to get absolute numbers
    int current = 0;
    result[current++] = new Double(train.numInstances());
    result[current++] = new Double(eval.numInstances());

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        default:
        throw new Exception("Test mode not implemented");
      }
      
      if (outputSummary) {
        outBuff.append(eval.toSummaryString(outputEntropy) + "\n");
      }


      if (inst.attribute(classIndex).isNominal()) {


        if (outputPerClass) {

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              if (outputPredictionsText) {
                outBuff.append("\n");
              } 
      
              if (outputSummary) {
                outBuff.append(eval.toSummaryString(outputEntropy) + "\n");
              }
      
              if (userTestStructure.classAttribute().isNominal()) {
  
                if (outputPerClass) {

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    public void evaluate() throws Exception {
        readModel();
        _logger.info("Classifying with " + _config._classifier);
        Evaluation eval = new Evaluation(_testInstances);
        eval.evaluateModel(_cls, _testInstances);
        _logger.info("\n" + eval.toSummaryString());
        try {
            _logger.info("\n" + eval.toClassDetailsString());
        } catch (Exception e) {
            _logger.info("Can not create class details" + _config._classifier);
        }

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          scores[id - 1] = classification;
          probabilities[id - 1] = probability;
        }
                
        System.out.println(eval.toSummaryString());
      System.out.println(eval.toMatrixString());
      
      // Output classifications
      StringBuilder sb = new StringBuilder();
      for (String score : scores)

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        output.getBuffer().toString());
      
      // Output meta information
      sb = new StringBuilder();
      sb.append(classifier.toString() + LF);
      sb.append(eval.toSummaryString() + LF);
      sb.append(eval.toMatrixString() + LF);
      
      FileUtils.writeStringToFile(
        new File(OUTPUT_DIR + "/" + testDataset.toString() + "/" + wekaClassifier.toString() + "/" + testDataset.toString() + ".meta.txt"),
        sb.toString());

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