miboost.java

代码是一个分类器的实现,其中使用了部分weka的源代码。可以将项目导入eclipse运行

    extends Optimization {
    
    private double[] weights, errs;

    public void setWeights(double[] w){
      weights = w;
    }

    public void setErrs(double[] e){
      errs = e;
    }

    /** 
     * Evaluate objective function
     * @param x the current values of variables
     * @return the value of the objective function 
     * @throws Exception if result is NaN
     */
    protected double objectiveFunction(double[] x) throws Exception{
      double obj=0;
      for(int i=0; i<weights.length; i++){
        obj += weights[i]*Math.exp(x[0]*(2.0*errs[i]-1.0));
        if(Double.isNaN(obj))
          throw new Exception("Objective function value is NaN!");

      }
      return obj;
    }

    /** 
     * Evaluate Jacobian vector
     * @param x the current values of variables
     * @return the gradient vector 
     * @throws Exception if gradient is NaN
     */
    protected double[] evaluateGradient(double[] x)  throws Exception{
      double[] grad = new double[1];
      for(int i=0; i<weights.length; i++){
        grad[0] += weights[i]*(2.0*errs[i]-1.0)*Math.exp(x[0]*(2.0*errs[i]-1.0));
        if(Double.isNaN(grad[0]))
          throw new Exception("Gradient is NaN!");

      }
      return grad;
    }
  }

  /**
   * Returns default capabilities of the classifier.
   *
   * @return      the capabilities of this classifier
   */
  public Capabilities getCapabilities() {
    Capabilities result = super.getCapabilities();

    // attributes
    result.enable(Capability.NOMINAL_ATTRIBUTES);
    result.enable(Capability.RELATIONAL_ATTRIBUTES);
    result.enable(Capability.MISSING_VALUES);

    // class
    result.disableAllClasses();
    result.disableAllClassDependencies();
    if (super.getCapabilities().handles(Capability.BINARY_CLASS))
      result.enable(Capability.BINARY_CLASS);
    result.enable(Capability.MISSING_CLASS_VALUES);
    
    // other
    result.enable(Capability.ONLY_MULTIINSTANCE);
    
    return result;
  }

  /**
   * Returns the capabilities of this multi-instance classifier for the
   * relational data.
   *
   * @return            the capabilities of this object
   * @see               Capabilities
   */
  public Capabilities getMultiInstanceCapabilities() {
    Capabilities result = super.getCapabilities();
    
    // class
    result.disableAllClasses();
    result.enable(Capability.NO_CLASS);
    
    return result;
  }

  /**
   * Builds the classifier
   *
   * @param exps the training data to be used for generating the
   * boosted classifier.
   * @throws Exception if the classifier could not be built successfully
   */
  public void buildClassifier(Instances exps) throws Exception {

    // can classifier handle the data?
    getCapabilities().testWithFail(exps);

    // remove instances with missing class
    Instances train = new Instances(exps);
    train.deleteWithMissingClass();

    m_NumClasses = train.numClasses();
    m_NumIterations = m_MaxIterations;

    if (m_Classifier == null)
      throw new Exception("A base classifier has not been specified!");
    if(!(m_Classifier instanceof WeightedInstancesHandler))
      throw new Exception("Base classifier cannot handle weighted instances!");

    m_Models = Classifier.makeCopies(m_Classifier, getMaxIterations());
    if(m_Debug)
      System.err.println("Base classifier: "+m_Classifier.getClass().getName());

    m_Beta = new double[m_NumIterations];

    /* modified by Lin Dong. (use MIToSingleInstance filter to convert the MI datasets) */

    //Initialize the bags' weights
    double N = (double)train.numInstances(), sumNi=0;
    for(int i=0; i<N; i++)
      sumNi += train.instance(i).relationalValue(1).numInstances();	
    for(int i=0; i<N; i++){
      train.instance(i).setWeight(sumNi/N);
    }

    //convert the training dataset into single-instance dataset
    m_ConvertToSI.setInputFormat(train);
    Instances data = Filter.useFilter( train, m_ConvertToSI);
    data.deleteAttributeAt(0); //remove the bagIndex attribute;


    // Assume the order of the instances are preserved in the Discretize filter
    if(m_DiscretizeBin > 0){
      m_Filter = new Discretize();
      m_Filter.setInputFormat(new Instances(data, 0));
      m_Filter.setBins(m_DiscretizeBin);
      data = Filter.useFilter(data, m_Filter);
    }

    // Main algorithm
    int dataIdx;
iterations:
    for(int m=0; m < m_MaxIterations; m++){
      if(m_Debug)
        System.err.println("\nIteration "+m); 


      // Build a model
      m_Models[m].buildClassifier(data);

      // Prediction of each bag
      double[] err=new double[(int)N], weights=new double[(int)N];
      boolean perfect = true, tooWrong=true;
      dataIdx = 0;
      for(int n=0; n<N; n++){
        Instance exn = train.instance(n);
        // Prediction of each instance and the predicted class distribution
        // of the bag		
        double nn = (double)exn.relationalValue(1).numInstances();
        for(int p=0; p<nn; p++){
          Instance testIns = data.instance(dataIdx++);			
          if((int)m_Models[m].classifyInstance(testIns) 
              != (int)exn.classValue()) // Weighted instance-wise 0-1 errors
            err[n] ++;		       		       
        }
        weights[n] = exn.weight();
        err[n] /= nn;
        if(err[n] > 0.5)
          perfect = false;
        if(err[n] < 0.5)
          tooWrong = false;
      }

      if(perfect || tooWrong){ // No or 100% classification error, cannot find beta
        if (m == 0)
          m_Beta[m] = 1.0;
        else		    
          m_Beta[m] = 0;		
        m_NumIterations = m+1;
        if(m_Debug)  System.err.println("No errors");
        break iterations;
      }

      double[] x = new double[1];
      x[0] = 0;
      double[][] b = new double[2][x.length];
      b[0][0] = Double.NaN;
      b[1][0] = Double.NaN;

      OptEng opt = new OptEng();	
      opt.setWeights(weights);
      opt.setErrs(err);
      //opt.setDebug(m_Debug);
      if (m_Debug)
        System.out.println("Start searching for c... ");
      x = opt.findArgmin(x, b);
      while(x==null){
        x = opt.getVarbValues();
        if (m_Debug)
          System.out.println("200 iterations finished, not enough!");
        x = opt.findArgmin(x, b);
      }	
      if (m_Debug)
        System.out.println("Finished.");    
      m_Beta[m] = x[0];

      if(m_Debug)
        System.err.println("c = "+m_Beta[m]);

      // Stop if error too small or error too big and ignore this model
      if (Double.isInfinite(m_Beta[m]) 
          || Utils.smOrEq(m_Beta[m], 0)
         ) {
        if (m == 0)
          m_Beta[m] = 1.0;
        else		    
          m_Beta[m] = 0;
        m_NumIterations = m+1;
        if(m_Debug)
          System.err.println("Errors out of range!");
        break iterations;
         }

      // Update weights of data and class label of wfData
      dataIdx=0;
      double totWeights=0;
      for(int r=0; r<N; r++){		
        Instance exr = train.instance(r);
        exr.setWeight(weights[r]*Math.exp(m_Beta[m]*(2.0*err[r]-1.0)));
        totWeights += exr.weight();
      }

      if(m_Debug)
        System.err.println("Total weights = "+totWeights);

      for(int r=0; r<N; r++){		
        Instance exr = train.instance(r);
        double num = (double)exr.relationalValue(1).numInstances();
        exr.setWeight(sumNi*exr.weight()/totWeights);
        //if(m_Debug)
        //    System.err.print("\nExemplar "+r+"="+exr.weight()+": \t");
        for(int s=0; s<num; s++){
          Instance inss = data.instance(dataIdx);	
          inss.setWeight(exr.weight()/num);		   
          //    if(m_Debug)
          //  System.err.print("instance "+s+"="+inss.weight()+
          //			 "|ew*iw*sumNi="+data.instance(dataIdx).weight()+"\t");
          if(Double.isNaN(inss.weight()))
            throw new Exception("instance "+s+" in bag "+r+" has weight NaN!"); 
          dataIdx++;
        }
        //if(m_Debug)
        //    System.err.println();
      }	       
    }
  }		

  /**
   * Computes the distribution for a given exemplar
   *
   * @param exmp the exemplar for which distribution is computed
   * @return the classification
   * @throws Exception if the distribution can't be computed successfully
   */
  public double[] distributionForInstance(Instance exmp) 
    throws Exception { 

    double[] rt = new double[m_NumClasses];

    Instances insts = new Instances(exmp.dataset(), 0);
    insts.add(exmp);

    // convert the training dataset into single-instance dataset
    insts = Filter.useFilter( insts, m_ConvertToSI);
    insts.deleteAttributeAt(0); //remove the bagIndex attribute	

    double n = insts.numInstances();

    if(m_DiscretizeBin > 0)
      insts = Filter.useFilter(insts, m_Filter);

    for(int y=0; y<n; y++){
      Instance ins = insts.instance(y);	
      for(int x=0; x<m_NumIterations; x++){ 
        rt[(int)m_Models[x].classifyInstance(ins)] += m_Beta[x]/n;
      }
    }

    for(int i=0; i<rt.length; i++)
      rt[i] = Math.exp(rt[i]);

    Utils.normalize(rt);
    return rt;
  }

  /**
   * Gets a string describing the classifier.
   *
   * @return a string describing the classifer built.
   */
  public String toString() {

    if (m_Models == null) {
      return "No model built yet!";
    }
    StringBuffer text = new StringBuffer();
    text.append("MIBoost: number of bins in discretization = "+m_DiscretizeBin+"\n");
    if (m_NumIterations == 0) {
      text.append("No model built yet.\n");
    } else if (m_NumIterations == 1) {
      text.append("No boosting possible, one classifier used: Weight = " 
          + Utils.roundDouble(m_Beta[0], 2)+"\n");
      text.append("Base classifiers:\n"+m_Models[0].toString());
    } else {
      text.append("Base classifiers and their weights: \n");
      for (int i = 0; i < m_NumIterations ; i++) {
        text.append("\n\n"+i+": Weight = " + Utils.roundDouble(m_Beta[i], 2)
            +"\nBase classifier:\n"+m_Models[i].toString() );
      }
    }

    text.append("\n\nNumber of performed Iterations: " 
        + m_NumIterations + "\n");

    return text.toString();
  }

  /**
   * Main method for testing this class.
   *
   * @param argv should contain the command line arguments to the
   * scheme (see Evaluation)
   */
  public static void main(String[] argv) {
    runClassifier(new MIBoost(), argv);
  }
}