如何使用 weka 实现决策树？

Question

我正在使用 java、eclipse 和 weka，我想显示包含每个规则的树和一组数据的预测来测试我的决策树。

我正在尝试使用这段代码做一些事情，但它没有做我需要的事情，即显示所有可能的规则的树。我只能看到树的一部分而不是全部。

而且我还没有能够通过数据测试和数据训练来测试它，我认为这与我在文本和训练文件中使用的格式有关。

所以问题是，我怎样才能展示每一个可能的决策树然后测试它？？

这是我目前拥有的：

public class Test {
    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 void main(String[] args) throws Exception {

        //Get File
        BufferedReader reader = readDataFile("maitre.txt");

       //Get the data
       Instances data = new Instances(reader);
       reader.close();

       //Setting class attribute 
       data.setClassIndex(data.numAttributes() - 1);

      //Make tree
      J48 tree = new J48();
      String[] options = new String[1];
      options[0] = "-U"; 
      tree.setOptions(options);
      tree.buildClassifier(data);

      //Print tree
      System.out.println(tree);

      //Predictions with test and training set of data

      BufferedReader datafile = readDataFile("maitre.txt");
      BufferedReader testfile = readDataFile("maitretest.txt");

      Instances train = new Instances(datafile);
      data.setClassIndex(data.numAttributes() - 1);  // from somewhere
      Instances test = new Instances(testfile);
      data.setClassIndex(data.numAttributes() - 1);    // from somewhere
      // train classifier
      Classifier cls = new J48();
      cls.buildClassifier(train);
      // evaluate classifier and print some statistics
      Evaluation eval = new Evaluation(train);
      eval.evaluateModel(cls, test);
      System.out.println(eval.toSummaryString("\nResults\n======\n", false));
   }
}

错误：

Exception in thread "main" weka.core.UnassignedClassException: weka.classifiers.trees.j48.C45PruneableClassifierTree: Class attribute not set!
    at weka.core.Capabilities.test(Capabilities.java:1284)
    at weka.core.Capabilities.test(Capabilities.java:1208)
    at weka.core.Capabilities.testWithFail(Capabilities.java:1506)
    at weka.classifiers.trees.j48.C45PruneableClassifierTree.buildClassifier(C45PruneableClassifierTree.java:120)
    at weka.classifiers.trees.J48.buildClassifier(J48.java:293)
    at Test.main(Test.java:60)

maitre.txt 和 maitretest.txt 是这样的：

@relation maitre

@attribute patrons {none, some, full}
@attribute waitEstation {0-10,10-30,30-60,>60}
@attribute reservation {TRUE, FALSE}
@attribute bar {TRUE, FALSE}
@attribute alternative {TRUE, FALSE}
@attribute sit {yes, no}

@data
some,0-10,TRUE,FALSE,TRUE,yes
full,30-60,FALSE,FALSE,TRUE,no
some,0-10,FALSE,TRUE,FALSE,yes
full,10-30,FALSE,FALSE,TRUE,yes
full,>60,TRUE,FALSE,TRUE,no
some,0-10,TRUE,TRUE,FALSE,yes
none,0-10,FALSE,TRUE,FALSE,no
some,0-10,TRUE,FALSE,FALSE,yes
full,>60,FALSE,TRUE,FALSE,no
full,10-30,TRUE,TRUE,TRUE,yes
none,0-10,FALSE,FALSE,FALSE,no
full,30-60,FALSE,TRUE,TRUE,no

Answer 1

是的，当然你的文件格式有问题。将这两个文件保存为 .arff 文件，然后重复该过程。是的，为此您需要使用 arffloader 读取 arff 文件....下面的代码将帮助您...

        ArffLoader loader= new ArffLoader();
        loader.setSource(new File("C:/Users/..../Desktop/maitre.arff"));
        Instances data= loader.getDataSet();

祝你好运:)

Answer 2

此代码可能会有所帮助...

for(int i=0;i<Train.numInstances();i++)
        {
              double value=cls.classifyInstance(originalTrain.instance(i));
              String prediction=Train.classAttribute().value((int)value); 
       System.out.println(Train.instance(i)+"............Prediction.......... "+prediction); 
        }

以上代码用于预测值。以下是您的示例 maitretest.arff 文件。希望对您有所帮助:)

@relation maitretest

@attribute patrons {none, some, full}
@attribute waitEstation {0-10,10-30,30-60,>60}
@attribute reservation {TRUE, FALSE}
@attribute bar {TRUE, FALSE}
@attribute alternative {TRUE, FALSE}
@attribute sit {yes, no}

@data
some,0-10,TRUE,FALSE,TRUE,?
full,30-60,FALSE,FALSE,TRUE,?

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