生成在Eclipse（WEKA）ROC曲線

Question

我用下面的代碼來訓練/測試一些分類：

public class WekaTest { 
    public static BufferedReader readDataFile(String filename) 
    { 
    BufferedReader inputReader = null; 
    try 
    { 
     inputReader = new BufferedReader(new FileReader(filename)); 
    } 
    catch (FileNotFoundException ex) 
    { 
     System.err.println("File not found: " + filename); 
    } 

    return inputReader; 
} 

public static Evaluation classify(Classifier model, 
     Instances trainingSet, Instances testingSet) throws Exception { 
    Evaluation evaluation = new Evaluation(trainingSet); 

    model.buildClassifier(trainingSet); 
    evaluation.evaluateModel(model, testingSet); 

    return evaluation; 
} 

public static double calculateAccuracy(FastVector predictions) { 
    double correct = 0; 

    for (int i = 0; i < predictions.size(); i++) { 
     NominalPrediction np = (NominalPrediction) predictions.elementAt(i); 
     if (np.predicted() == np.actual()) { 
      correct++; 
     } 
    } 

    return 100 * correct/predictions.size(); 
} 

public static Instances[][] crossValidationSplit(Instances data, int numberOfFolds) { 
    Instances[][] split = new Instances[2][numberOfFolds]; 
    Random random = new Random(); 
    for (int i = 0; i < numberOfFolds; i++) 
    { 
     split[0][i] = data.trainCV(numberOfFolds, i, random); 
     split[1][i] = data.testCV(numberOfFolds, i); 
    } 

    return split; 
} 


    public static void main(String[] args) throws Exception 
    { 
     BufferedReader datafile = readDataFile("training_1.arff"); 

     Instances data = new Instances(datafile); 
     data.setClassIndex(data.numAttributes() - 1); 

     // Do 10-split cross validation 
     Instances[][] split = crossValidationSplit(data, 10); 

     // Separate split into training and testing arrays 
     Instances[] trainingSplits = split[0]; 
     Instances[] testingSplits = split[1]; 


     // Use a set of classifiers 
     Classifier[] models = { 
//    new J48(), // a decision tree 
//    new PART(), 
//     new DecisionTable(),//decision table majority classifier 
//   new DecisionStump(), //one-level decision tree 
       new NaiveBayes(), 
//     new AdaBoostM1() 
       new RandomForest() 
//     new LMT() 
     }; 

     // Run for each model 
     for (int j = 0; j < models.length; j++) 
     { 

      // Collect every group of predictions for current model in a FastVector 
      FastVector predictions = new FastVector(); 

      // For each training-testing split pair, train and test the classifier 

      for (int i = 0; i < trainingSplits.length; i++) 
      { 
       Evaluation validation = classify(models[j], trainingSplits[i], testingSplits[i]); 

       predictions.appendElements(validation.predictions()); 
       System.out.println(validation.toMatrixString()); 
       // Uncomment to see the summary for each training-testing pair. 
//    System.out.println(models[j].toString()); 
       // generate curve 
       ThresholdCurve tc = new ThresholdCurve(); 
       int classIndex = 0; 
       Instances result = tc.getCurve(validation.predictions(), classIndex); 
       System.out.println("tPR :"+validation.truePositiveRate(classIndex)); 
       System.out.println("fNR :"+validation.falseNegativeRate(classIndex)); 

       // plot curve 
       ThresholdVisualizePanel vmc = new ThresholdVisualizePanel(); 
       vmc.setROCString("(Area under ROC = " + 
        Utils.doubleToString(tc.getROCArea(result), 4) + ")"); 
       vmc.setName(result.relationName()); 
       PlotData2D tempd = new PlotData2D(result); 
       tempd.setPlotName(result.relationName()); 
       tempd.addInstanceNumberAttribute(); 
       // specify which points are connected 
       boolean[] cp = new boolean[result.numInstances()]; 
       for (int n = 1; n < cp.length; n++) 
        cp[n] = true; 
       tempd.setConnectPoints(cp); 
       // add plot 
       vmc.addPlot(tempd); 

       // display curve 
       String plotName = vmc.getName(); 
       final javax.swing.JFrame jf = 
        new javax.swing.JFrame("Weka Classifier Visualize: "+plotName); 
       jf.setSize(500,400); 
       jf.getContentPane().setLayout(new BorderLayout()); 
       jf.getContentPane().add(vmc, BorderLayout.CENTER); 
       jf.addWindowListener(new java.awt.event.WindowAdapter() { 
        public void windowClosing(java.awt.event.WindowEvent e) { 
        jf.dispose(); 
        } 
       }); 
       jf.setVisible(true); 

      } 

      // Calculate overall accuracy of current classifier on all splits 
      double accuracy = calculateAccuracy(predictions); 

      // Print current classifier's name and accuracy in a complicated, 
      // but nice-looking way. 
      System.out.println("Accuracy of " + models[j].getClass().getSimpleName() + ": " 
        + String.format("%.2f%%", accuracy) 
        + "\n---------------------------------"); 


     } 


    } 
} 

的ARFF文件包含描述後跟20點數據的屬性，接着一個「是」或'沒有'類標籤。 上的某些數據的TPR和FPR準確計算運行此並顯示對應於每個混淆矩陣;但是，ROC曲線下面積顯示爲'NaN'，曲線是直線或水平直線：

我在做什麼錯？任何幫助將非常感激。

Answer 1

這將是一個評論，但我新，不能評論。 我從內環路跑到你的代碼，並與我的數據它就像一個魅力。 所以它不是一個與印刷問題。

看起來像您的評估工作

上的某些數據的TPR和FPR準確計算和顯示對應於每個混淆矩陣運行此;

所以，你有沒有嘗試過的評價類的以下功能？

evaluation.areaUnderROC(int classIndex); 

要查看ROC曲線應該是什麼樣子？

是您的標籤類{ 「是」， 「否」}或{0,1}？ 我不認爲這是問題，但你可以嘗試的

Instances result = tc.getCurve(validation.predictions(), classIndex); 

Instances result = tc.getCurve(validation.predictions()); 

，而不是你能後的混淆矩陣和TPR/FPR的一些值。

歡呼聲