weka属性选择使用

醉了～～～

package edu.dcy.weka;

import java.io.FileWriter;
import java.util.ArrayList;
import java.util.List;

import weka.classifiers.Classifier;
import weka.classifiers.Evaluation;
import weka.classifiers.misc.InputMappedClassifier;
import weka.core.Attribute;
import weka.core.Instance;
import weka.core.Instances;
import weka.core.SerializationHelper;
import weka.core.Utils;
import weka.core.converters.ConverterUtils.DataSource;

public class PredictModel {
    public static void main(String[] args) throws Exception {
        if(args.length!=3){
            System.err.println("Usage: <model file> <test file > < output dir>");
            System.exit(1);
        }
        // load data
        System.out.println("loading data.....");
        DataSource dstest = new DataSource(args[1]);
        Instances test = dstest.getDataSet();
        int attrnums=test.numAttributes();
        // lack of the class lable 'Y'
        if(attrnums==283){
            List<String> strname = new ArrayList<String>();
            strname.add("false");
            strname.add("true");
            Attribute attr = new Attribute("Y", strname);
            test.insertAttributeAt(attr, test.numAttributes());
        }
        test.setClassIndex(test.numAttributes() - 1);
        System.out.println("loading done.....");
        System.out.println("loading predict model.....");

    	// load classifier
		Object objs[]=SerializationHelper.readAll(args[0]);
		Classifier cls=(Classifier) objs[0];
		InputMappedClassifier mapper =new InputMappedClassifier();
		mapper.setClassifier((Classifier) objs[0]);
		mapper.setModelHeader((Instances) objs[1]);
		mapper.setTestStructure(test);
		cls=mapper;
        // output predictions
        System.out.println("execute ...");
        FileWriter fw=new FileWriter(args[2]);
        StringBuffer sb=new StringBuffer();
        sb.append("#,actual,predicted,error,p_false,p_true \n");
        for (int i = 0; i < test.numInstances(); i++) {
            Instance ins=test.instance(i);
            double pred = cls.classifyInstance(ins);
            double[] dist = cls.distributionForInstance(ins);

            sb.append(i+1).append(",")
            .append(ins.toString(test.classIndex()))
            .append(",")
            .append(test.classAttribute().value((int) pred))
            .append(",");

            if (pred != test.instance(i).classValue()) {
                sb.append("yes");
            } else {
                sb.append("no");
            }

            sb.append(",").append(Utils.arrayToString(dist)).append("\n");

//            System.out.println(sb.toString());
            fw.write(sb.toString());
            fw.flush();
            sb.delete(0, sb.length());
        }
        fw.close();

        System.out.println("finished,please check the outfile .....");
        // print the summary
        if(attrnums==284){
            Evaluation eval = new Evaluation(test);
            eval.evaluateModel(cls, test);
            System.out.println(eval.toClassDetailsString());
            System.out.println(eval.toSummaryString());
            System.out.println(eval.toMatrixString());
        }
    }
}