调用weka模拟实现 “主动学习“ 算法

主动学习：

主动学习的过程：需要分类器与标记专家进行交互。一个典型的过程：

（1）基于少量已标记样本构建模型

（2）从未标记样本中选出信息量最大的样本，交给专家进行标记

（3）将这些样本与之前样本进行融合，并构建模型

（4）重复执行步骤（2）和步骤（3），直到stopping criterion（不存在未标记样本或其他条件）满足为止

模拟思路：

1. 将数据分为label 和 unlabel数据集

2. 将 unlabel 分为100个一组，每组样本数组分别求出熵值，按照熵值排序，取前5个样本，添加到 label样本之中

package demo;


import java.io.FileReader;
import java.util.ArrayList;
import java.util.Collections;
import java.util.Random;
import weka.classifiers.Evaluation;
import weka.classifiers.bayes.NaiveBayes;
import weka.core.Instance;
import weka.core.Instances;
import weka.core.converters.ConverterUtils.DataSource;

//将测试用例，按照熵值进行排序
class InstanceSort implements Comparable<InstanceSort>{
    public Instance instance;
    public double entropy;
    
    public InstanceSort( Instance instance, double entropy){
        this.instance = instance;
        this.entropy = entropy;
    }
    @Override
    public int compareTo(InstanceSort o) {
        // TODO Auto-generated method stub
        if (this.entropy < o.entropy){
            return 1;
        }else if ( this.entropy > o.entropy){
            return -1;
        }
        
        return 0;
    }
}

public class ActiveLearning {

    public static Instances getInstances( String fileName) throws Exception{
        Instances data = new Instances (new FileReader(fileName));
        data.setClassIndex(data.numAttributes()-1);
        return data;
    }
    
    //计算熵
    public static double computeEntropy(double predictValue){
        double entropy = 0.0;
        if ( 1-predictValue < 0.000000001d || predictValue < 0.000000001d){
            return 0;
        }else {
            return -predictValue*(Math.log(predictValue)/Math.log(2.0d))-(1-predictValue)*(Math.log(1-predictValue)/Math.log(2.0d));
        }
    }
    
    public static void classify(Instances train, Instances test) throws Exception{
        NaiveBayes classifier = new NaiveBayes();
        //训练模型
        classifier.buildClassifier(train);
        
        //评价模型
        Evaluation eval = new Evaluation(test);
        eval.evaluateModel(classifier, test);
        System.out.println(eval.toClassDetailsString());
    }
    
    //不确定采样
    public static Instances uncertaintySample(Instances labeled, Instances unlabeled, int start, int end) throws Exception{
        //用有标签的先训练模型
        NaiveBayes classifier = new NaiveBayes();
        classifier.buildClassifier(labeled);
        //按照熵进行排序
        ArrayList <InstanceSort> l = new ArrayList<InstanceSort>();
        
        for (int i = start; i < end; i++) {
            double result = classifier.classifyInstance(unlabeled.instance(i));
            double entropy =  computeEntropy (result);
            InstanceSort is = new InstanceSort(unlabeled.instance(i), entropy);
            l.add(is);
        }
        //按照熵值进行排序
        Collections.sort(l);
        
        DataSource source = new DataSource("NASA//pc1.arff");
        Instances A = source.getDataSet();
        Instances chosenInstances = new Instances(A, 0);
        //每100个里面选择5个熵值最小的实例
        for(int i = 0; i < 5; i++){
            chosenInstances.add(l.get(i).instance);
        }
        
        return chosenInstances;
    }
    
    //采样
    public static void sample( Instances instances, Instances test) throws Exception{
        Random rand = new Random(1023);
        instances.randomize(rand);
        instances.stratify(10);
        Instances unlabeled = instances.trainCV(10, 0);
        Instances labeled = instances.testCV(10, 0);
        
        int iterations = unlabeled.numInstances() / 100 +1;
        
        for ( int i=0; i< iterations-1 ; i++){
            //每100个里面选择5个熵值最小的实例
            //100个一组
            Instances resultInstances = uncertaintySample(labeled, unlabeled, i*100, (i+1)*100);
            for (int j = 0; j < resultInstances.numInstances(); j++){
                labeled.add(resultInstances.instance(j));
            }
            classify(labeled, test);
        }
        
        Instances resultInstances = uncertaintySample(labeled, unlabeled, (iterations-1)*100, unlabeled.numInstances());
        
        for (int j = 0; j < resultInstances.numInstances(); j++){
            labeled.add(resultInstances.instance(j));
        }
        
        classify(labeled, test);    
    
    }
    
    public static void main(String[] args)  throws Exception{
        // TODO Auto-generated method stub
        Instances instances = getInstances("NASA//pc1.arff");
        
        //10-fold cross validation
        Random rand = new Random(1023);
        instances.randomize(rand);
        instances.stratify(10);
        Instances train = instances.trainCV(10, 0);
        Instances test = instances.testCV(10, 0);
//        System.out.println(train.numInstances());
//        System.out.println(test.numInstances());
        
        sample(train,test);

    }

}