使用 Java WEKA 库时正确标记预测类

Question

我有一个程序用 2-class 分类结果训练算法，然后运行s 并写出预测（2 classes 中每一个的概率）对于未标记的数据集。

针对此程序的所有数据集运行将具有与结果相同的 2 classes。考虑到这一点，我运行进行了预测，并使用了一些 post-hoc 统计数据来确定结果的哪一列描述了哪个结果，并继续对其进行硬编码：

public class runPredictions {
public static void runPredictions(ArrayList al2) throws IOException, Exception{
    // Retrieve objects
    Instances newTest = (Instances) al2.get(0);
    Classifier clf = (Classifier) al2.get(1);

    // Print status
    System.out.println("Generating predictions...");

    // create copy
    Instances labeled = new Instances(newTest);

    BufferedWriter outFile = new BufferedWriter(new FileWriter("silverbullet_rro_output.csv"));
    StringBuilder builder = new StringBuilder();

    builder.append("Prob_Retain"+","+"Prob_Attrite"+"\n");
    for (int i = 0; i < labeled.size(); i++)      
    {
        double[] clsLabel = clf.distributionForInstance(newTest.instance(i));
        for(int j=0;j<2;j++){
           builder.append(clsLabel[j]+""); 
           if(j < clsLabel.length - 1)
               builder.append(",");
        }
        builder.append("\n");
    }
    outFile.write(builder.toString());//save the string representation
    System.out.println("Output file written.");
    System.out.println("Completed successfully!");
    outFile.close();    
}    
}

问题在于，结果是 2 列中的哪一列描述了 2 个结果类别中的哪一个是不固定的。这似乎与训练数据集中哪个类别首先出现有关，这完全是任意的。因此，当其他数据集与此程序一起使用时，硬编码标签是倒退的。

所以，我需要一种更好的方法来标记它们，但是查看 Classifier 和 distributionForInstance 的文档，我没有看到任何有用的东西。

更新：

我想出了如何将它打印到屏幕上（感谢 this），但在将它写入 csv 时仍然遇到问题：

for (int i = 0; i < labeled.size(); i++)      
    {
        // Discreet prediction
        double predictionIndex = 
            clf.classifyInstance(newTest.instance(i)); 

        // Get the predicted class label from the predictionIndex.
        String predictedClassLabel =
            newTest.classAttribute().value((int) predictionIndex);

        // Get the prediction probability distribution.
        double[] predictionDistribution = 
            clf.distributionForInstance(newTest.instance(i)); 

        // Print out the true predicted label, and the distribution
        System.out.printf("%5d: predicted=%-10s, distribution=", 
                          i, predictedClassLabel); 

        // Loop over all the prediction labels in the distribution.
        for (int predictionDistributionIndex = 0; 
             predictionDistributionIndex < predictionDistribution.length; 
             predictionDistributionIndex++)
        {
            // Get this distribution index's class label.
            String predictionDistributionIndexAsClassLabel = 
                newTest.classAttribute().value(
                    predictionDistributionIndex);

            // Get the probability.
            double predictionProbability = 
                predictionDistribution[predictionDistributionIndex];

            System.out.printf("[%10s : %6.3f]", 
                              predictionDistributionIndexAsClassLabel, 
                              predictionProbability );

            // Attempt to write to CSV
            builder.append(i+","+predictedClassLabel+","+
                    predictionDistributionIndexAsClassLabel+","+predictionProbability);
                            //.charAt(0)+','+predictionProbability.charAt(0));

        }

        System.out.printf("\n");
        builder.append("\n");

Answer 1

我从这个 answer and this answer 改编了下面的代码。基本上，您可以查询 class 属性的测试数据，然后获取每个可能的 class.

的具体值

for (int i = 0; i < labeled.size(); i++)      
{
// Discreet prediction

double predictionIndex = 
    clf.classifyInstance(newTest.instance(i)); 

// Get the predicted class label from the predictionIndex.
String predictedClassLabel =
    newTest.classAttribute().value((int) predictionIndex);

// Get the prediction probability distribution.
double[] predictionDistribution = 
    clf.distributionForInstance(newTest.instance(i)); 

// Print out the true predicted label, and the distribution
System.out.printf("%5d: predicted=%-10s, distribution=", 
                  i, predictedClassLabel); 

// Loop over all the prediction labels in the distribution.
for (int predictionDistributionIndex = 0; 
     predictionDistributionIndex < predictionDistribution.length; 
     predictionDistributionIndex++)
{
    // Get this distribution index's class label.
    String predictionDistributionIndexAsClassLabel = 
        newTest.classAttribute().value(
            predictionDistributionIndex);

    // Get the probability.
    double predictionProbability = 
        predictionDistribution[predictionDistributionIndex];

    System.out.printf("[%10s : %6.3f]", 
                      predictionDistributionIndexAsClassLabel, 
                      predictionProbability );

    // Write to CSV
    builder.append(i+","+
            predictionDistributionIndexAsClassLabel+","+predictionProbability);


}

System.out.printf("\n");
builder.append("\n");

}


// Save results in .csv file
outFile.write(builder.toString());//save the string representation

使用 Java WEKA 库时正确标记预测类

Correctly labeling predicted classes when using the Java WEKA library

java

weka

使用 Java WEKA 库时正确标记预测 类

Correctly labeling predicted classes when using the Java WEKA library

java

weka

使用 Java WEKA 库时正确标记预测类