Weka中的不同分类结果：GUI与Java库

Question

当我将Weka GUI分类结果与我的Java程序进行比较时，我遇到了一些问题，用iris数据集执行树（J48）。如果你能帮助我，我将非常感激。

我正在使用iris数据集，并试图开发一个Java程序来对新实例进行分类。为此，我使用Weka GUI获得了一个模型（“iris_tree（CV）。模型”），它经过训练和验证（用10个折叠交叉验证）。Weka GUI的结果是好的和预期的：4个错误分类的实例。之后，我保存模型，以便以后由我的Java程序使用。

当我在Java程序中加载模型“iris_tree（CV）.model”，并尝试对新实例（测试数据集）进行分类时，结果是不同的：Java程序很好地分类了“setosa”和“virginica”，但没有分类“versicolor”。结果如下：

Classification: setosa
Classification: setosa
Classification: virginica
Classification: virginica
Classification: virginica
Classification: virginica

当我期望获得：

Classification: setosa
Classification: setosa
Classification: versicolour
Classification: versicolour
Classification: virginica
Classification: virginica

我读过一些相关帖子，但在使用Java而不是Weka GUI时，我找不到对这种奇怪行为的明确回应。

我将Java代码附加到两个类中，然后是培训和测试集。提前谢谢。

主要课程：

public static void main(String[] args) {

    try {


        Hashtable<String, String> values = new Hashtable<String, String>();

        //Loading the model
        String pathModel="";
        String pathTestSet="";
        JFileChooser chooserModel = new JFileChooser();
        chooserModel.setCurrentDirectory(new java.io.File("."));
        chooserModel.setDialogTitle("HoliDes: choose the model");
        chooserModel.setFileSelectionMode(JFileChooser.FILES_AND_DIRECTORIES);
        chooserModel.setAcceptAllFileFilterUsed(true);

        if (chooserModel.showOpenDialog(null) == JFileChooser.APPROVE_OPTION) {
            File filePathModel=chooserModel.getSelectedFile();
            pathModel=filePathModel.getPath();

            State irisModel = new State(pathModel);

            //Loading the model
            JFileChooser chooserTestSet = new JFileChooser();
            chooserTestSet.setDialogTitle("HoliDes: choose TEST SET");
            chooserTestSet.setFileSelectionMode(JFileChooser.FILES_AND_DIRECTORIES);
            chooserTestSet.setAcceptAllFileFilterUsed(true);

            //Loading the testing dataset
            if (chooserTestSet.showOpenDialog(null) == JFileChooser.APPROVE_OPTION) {
                File filePathTestSet=chooserTestSet.getSelectedFile();
                pathTestSet=filePathTestSet.getPath();

                //Transforming the data set into pairs attribute-value
                ConverterUtils.DataSource unlabeledSource = new ConverterUtils.DataSource(pathTestSet);
                Instances unlabeledData = unlabeledSource.getDataSet();
                if (unlabeledData.classIndex() == -1){
                    unlabeledData.setClassIndex(unlabeledData.numAttributes() - 1);
                }

                for (int i = 0; i < unlabeledData.numInstances(); i++) {
                    Instance ins=unlabeledData.instance(i);

                    for (int j = 0; j < ins.numAttributes(); j++) {

                        String attrib=ins.attribute(j).name();
                        double val=ins.value(ins.attribute(j));

                        values.put(attrib,String.valueOf(val));

                    }

                    System.out.println("Classification: " + irisModel.classifySpecies(values,pathModel));

                }

            }

        }

    } catch (Exception ex) {
        Logger.getLogger(PilotPatternClassifier.class.getName()).log(Level.SEVERE, null, ex);
    }

}

国家级：

public class State {

    //private String classModelFile = "/iris_tree.model";    
    private Classifier classModel;
    private Instances dataModel;

    /**
     *  Class constructor.
     */
    public State(String pathModel) throws Exception {
            //InputStream classModelStream;
            //  Create a stream object for the model file embedded within the JAR file.
            //classModelStream = getClass().getResourceAsStream(classModelFile);
            classModel=(Classifier) weka.core.SerializationHelper.read(pathModel);
    }

    /**
     *  Close the instance by setting both the model file string and
     *  the model object itself to null.  When the garbage collector
     *  runs, this should make clean up simpler.  However, the garbage
     *  collector is not called synchronously since that should be
     *  managed by the larger execution environment.
     */
    public void close() {
            classModel = null;
            //classModelFile=null;
    }

    /**
     * Evaluate the model on the data provided by @param measures.
     * This returns a string with the species name.
     *
     * @param measures object with petal and sepal measurements
     * @return string with the species name
     * @throws Exception
     */
    public String classifySpecies(Dictionary<String, String> measures, String pathTestSet) throws Exception {
            FastVector dataClasses = new FastVector();
            FastVector dataAttribs = new FastVector();
            Attribute species;
            double values[] = new double[measures.size() + 1];
            int i = 0, maxIndex = 0;

            //  Assemble the potential species options.
            dataClasses.addElement("setosa");
            dataClasses.addElement("versicolour");
            dataClasses.addElement("virginica");
            species = new Attribute("species", dataClasses);

            //  Create the object to classify on.
            for (Enumeration<String> keys = measures.keys(); keys.hasMoreElements(); ) {

                    String key = keys.nextElement();
                    double val = Double.parseDouble(measures.get(key));         
                    dataAttribs.addElement(new Attribute(key));

                    values[i++] = val;

            }

            dataAttribs.addElement(species);
            dataModel = new Instances("iris-test", dataAttribs, 0);//"classify" is the name of the relationship of the test file. It is arbitrary
            dataModel.setClass(species);

            Instance ins=new DenseInstance(1, values);
            //dataModel.add(new Instance(1, values) {});            
            dataModel.add(ins);            
            dataModel.instance(0).setClassMissing();

            //  Find the class with the highest estimated likelihood
            double cl[] = classModel.distributionForInstance(dataModel.instance(0));
            for(i = 0; i < cl.length; i++){
                if(cl[i] > cl[maxIndex]){
                        maxIndex = i;
                }
            }
            return dataModel.classAttribute().value(maxIndex);


    }


}

这里是培训和测试集：

@RELATION iris-train

@ATTRIBUTE sepallength  REAL
@ATTRIBUTE sepalwidth   REAL
@ATTRIBUTE petallength  REAL
@ATTRIBUTE petalwidth   REAL
@ATTRIBUTE species  {setosa,versicolour,virginica}

@DATA
5.1,3.5,1.4,0.2,setosa
4.9,3.0,1.4,0.2,setosa
4.7,3.2,1.3,0.2,setosa
4.6,3.1,1.5,0.2,setosa
5.0,3.6,1.4,0.2,setosa
5.4,3.9,1.7,0.4,setosa
4.6,3.4,1.4,0.3,setosa
5.0,3.4,1.5,0.2,setosa
4.4,2.9,1.4,0.2,setosa
4.9,3.1,1.5,0.1,setosa
5.4,3.7,1.5,0.2,setosa
4.8,3.4,1.6,0.2,setosa
4.8,3.0,1.4,0.1,setosa
4.3,3.0,1.1,0.1,setosa
5.8,4.0,1.2,0.2,setosa
5.7,4.4,1.5,0.4,setosa
5.4,3.9,1.3,0.4,setosa
5.1,3.5,1.4,0.3,setosa
5.7,3.8,1.7,0.3,setosa
5.1,3.8,1.5,0.3,setosa
5.4,3.4,1.7,0.2,setosa
5.1,3.7,1.5,0.4,setosa
4.6,3.6,1.0,0.2,setosa
5.1,3.3,1.7,0.5,setosa
4.8,3.4,1.9,0.2,setosa
5.0,3.0,1.6,0.2,setosa
5.0,3.4,1.6,0.4,setosa
5.2,3.5,1.5,0.2,setosa
5.2,3.4,1.4,0.2,setosa
4.7,3.2,1.6,0.2,setosa
4.8,3.1,1.6,0.2,setosa
5.4,3.4,1.5,0.4,setosa
5.2,4.1,1.5,0.1,setosa
5.5,4.2,1.4,0.2,setosa
4.9,3.1,1.5,0.1,setosa
5.0,3.2,1.2,0.2,setosa
5.5,3.5,1.3,0.2,setosa
4.9,3.1,1.5,0.1,setosa
4.4,3.0,1.3,0.2,setosa
5.1,3.4,1.5,0.2,setosa
5.0,3.5,1.3,0.3,setosa
4.5,2.3,1.3,0.3,setosa
4.4,3.2,1.3,0.2,setosa
5.0,3.5,1.6,0.6,setosa
5.1,3.8,1.9,0.4,setosa
4.8,3.0,1.4,0.3,setosa
5.1,3.8,1.6,0.2,setosa
4.6,3.2,1.4,0.2,setosa
5.3,3.7,1.5,0.2,setosa
5.0,3.3,1.4,0.2,setosa
7.0,3.2,4.7,1.4,versicolour
6.4,3.2,4.5,1.5,versicolour
6.9,3.1,4.9,1.5,versicolour
5.5,2.3,4.0,1.3,versicolour
6.5,2.8,4.6,1.5,versicolour
5.7,2.8,4.5,1.3,versicolour
6.3,3.3,4.7,1.6,versicolour
4.9,2.4,3.3,1.0,versicolour
6.6,2.9,4.6,1.3,versicolour
5.2,2.7,3.9,1.4,versicolour
5.0,2.0,3.5,1.0,versicolour
5.9,3.0,4.2,1.5,versicolour
6.0,2.2,4.0,1.0,versicolour
6.1,2.9,4.7,1.4,versicolour
5.6,2.9,3.6,1.3,versicolour
6.7,3.1,4.4,1.4,versicolour
5.6,3.0,4.5,1.5,versicolour
5.8,2.7,4.1,1.0,versicolour
6.2,2.2,4.5,1.5,versicolour
5.6,2.5,3.9,1.1,versicolour
5.9,3.2,4.8,1.8,versicolour
6.1,2.8,4.0,1.3,versicolour
6.3,2.5,4.9,1.5,versicolour
6.1,2.8,4.7,1.2,versicolour
6.4,2.9,4.3,1.3,versicolour
6.6,3.0,4.4,1.4,versicolour
6.8,2.8,4.8,1.4,versicolour
6.7,3.0,5.0,1.7,versicolour
6.0,2.9,4.5,1.5,versicolour
5.7,2.6,3.5,1.0,versicolour
5.5,2.4,3.8,1.1,versicolour
5.5,2.4,3.7,1.0,versicolour
5.8,2.7,3.9,1.2,versicolour
6.0,2.7,5.1,1.6,versicolour
5.4,3.0,4.5,1.5,versicolour
6.0,3.4,4.5,1.6,versicolour
6.7,3.1,4.7,1.5,versicolour
6.3,2.3,4.4,1.3,versicolour
5.6,3.0,4.1,1.3,versicolour
5.5,2.5,4.0,1.3,versicolour
5.5,2.6,4.4,1.2,versicolour
6.1,3.0,4.6,1.4,versicolour
5.8,2.6,4.0,1.2,versicolour
5.0,2.3,3.3,1.0,versicolour
5.6,2.7,4.2,1.3,versicolour
5.7,3.0,4.2,1.2,versicolour
5.7,2.9,4.2,1.3,versicolour
6.2,2.9,4.3,1.3,versicolour
5.1,2.5,3.0,1.1,versicolour
5.7,2.8,4.1,1.3,versicolour
6.3,3.3,6.0,2.5,virginica
5.8,2.7,5.1,1.9,virginica
7.1,3.0,5.9,2.1,virginica
6.3,2.9,5.6,1.8,virginica
6.5,3.0,5.8,2.2,virginica
7.6,3.0,6.6,2.1,virginica
4.9,2.5,4.5,1.7,virginica
7.3,2.9,6.3,1.8,virginica
6.7,2.5,5.8,1.8,virginica
7.2,3.6,6.1,2.5,virginica
6.5,3.2,5.1,2.0,virginica
6.4,2.7,5.3,1.9,virginica
6.8,3.0,5.5,2.1,virginica
5.7,2.5,5.0,2.0,virginica
5.8,2.8,5.1,2.4,virginica
6.4,3.2,5.3,2.3,virginica
6.5,3.0,5.5,1.8,virginica
7.7,3.8,6.7,2.2,virginica
7.7,2.6,6.9,2.3,virginica
6.0,2.2,5.0,1.5,virginica
6.9,3.2,5.7,2.3,virginica
5.6,2.8,4.9,2.0,virginica
7.7,2.8,6.7,2.0,virginica
6.3,2.7,4.9,1.8,virginica
6.7,3.3,5.7,2.1,virginica
7.2,3.2,6.0,1.8,virginica
6.2,2.8,4.8,1.8,virginica
6.1,3.0,4.9,1.8,virginica
6.4,2.8,5.6,2.1,virginica
7.2,3.0,5.8,1.6,virginica
7.4,2.8,6.1,1.9,virginica
7.9,3.8,6.4,2.0,virginica
6.4,2.8,5.6,2.2,virginica
6.3,2.8,5.1,1.5,virginica
6.1,2.6,5.6,1.4,virginica
7.7,3.0,6.1,2.3,virginica
6.3,3.4,5.6,2.4,virginica
6.4,3.1,5.5,1.8,virginica
6.0,3.0,4.8,1.8,virginica
6.9,3.1,5.4,2.1,virginica
6.7,3.1,5.6,2.4,virginica
6.9,3.1,5.1,2.3,virginica
5.8,2.7,5.1,1.9,virginica
6.8,3.2,5.9,2.3,virginica
6.7,3.3,5.7,2.5,virginica
6.7,3.0,5.2,2.3,virginica
6.3,2.5,5.0,1.9,virginica
6.5,3.0,5.2,2.0,virginica
6.2,3.4,5.4,2.3,virginica
5.9,3.0,5.1,1.8,virginica

和

@RELATION iris-test

@ATTRIBUTE sepallength  REAL
@ATTRIBUTE sepalwidth   REAL
@ATTRIBUTE petallength  REAL
@ATTRIBUTE petalwidth   REAL

@DATA
5.1,3.5,1.4,0.2
4.9,3.0,1.4,0.2
6.6,3.0,4.4,1.4
6.8,2.8,4.8,1.4
6.4,3.1,5.5,1.8
6.0,3.0,4.8,1.8

非常感谢你的帮助。

Answer 1

我认为，与使用训练集特征文件进行检查时相比，将分类器模型应用到测试集时，精确度较低是正常的。尝试在这个测试集中使用Weka GUI，也许你会得到同样的结果。这不是GUI和Java的问题

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