simplemi.java

Java 编写的多种数据挖掘算法 包括聚类、分类、预处理等

    return new SelectedTag(m_TransformMethod, TAGS_TRANSFORMMETHOD);
  }

  /** 
   * Implements MITransform (3 type of transformation) 1.arithmatic average;
   * 2.geometric centor; 3.merge minima and maxima attribute value together
   *
   * @param train the multi-instance dataset (with relational attribute)  
   * @return the transformed dataset with each bag contain mono-instance
   * (without relational attribute) so that any classifier not for MI dataset
   * can be applied on it.
   * @throws Exception if the transformation fails
   */
  public Instances transform(Instances train) throws Exception{

    Attribute classAttribute = (Attribute) train.classAttribute().copy();
    Attribute bagLabel = (Attribute) train.attribute(0);
    double labelValue;

    Instances newData = train.attribute(1).relation().stringFreeStructure();

    //insert a bag label attribute at the begining
    newData.insertAttributeAt(bagLabel, 0);

    //insert a class attribute at the end
    newData.insertAttributeAt(classAttribute, newData.numAttributes());
    newData.setClassIndex(newData.numAttributes()-1);

    Instances mini_data = newData.stringFreeStructure();
    Instances max_data = newData.stringFreeStructure();

    Instance newInst = new Instance (newData.numAttributes()); 
    Instance mini_Inst = new Instance (mini_data.numAttributes());
    Instance max_Inst = new Instance (max_data.numAttributes());
    newInst.setDataset(newData);
    mini_Inst.setDataset(mini_data);
    max_Inst.setDataset(max_data);

    double N= train.numInstances( );//number of bags   
    for(int i=0; i<N; i++){	
      int attIdx =1;
      Instance bag = train.instance(i); //retrieve the bag instance
      labelValue= bag.value(0);
      if (m_TransformMethod != TRANSFORMMETHOD_MINIMAX)	    
        newInst.setValue(0, labelValue);
      else {
        mini_Inst.setValue(0, labelValue);
        max_Inst.setValue(0, labelValue);
      }

      Instances data = bag.relationalValue(1); // retrieve relational value for each bag 
      for(int j=0; j<data.numAttributes( ); j++){ 	
        double value;
        if(m_TransformMethod == TRANSFORMMETHOD_ARITHMETIC){
          value = data.meanOrMode(j); 
          newInst.setValue(attIdx++, value);
        }
        else if (m_TransformMethod == TRANSFORMMETHOD_GEOMETRIC){
          double[] minimax = minimax(data, j);
          value = (minimax[0]+minimax[1])/2.0;
          newInst.setValue(attIdx++, value);
        }
        else {  //m_TransformMethod == TRANSFORMMETHOD_MINIMAX
          double[] minimax = minimax(data, j);
          mini_Inst.setValue(attIdx, minimax[0]);//minima value
          max_Inst.setValue(attIdx, minimax[1]);//maxima value
          attIdx++;
        }
      }

      if (m_TransformMethod == TRANSFORMMETHOD_MINIMAX) {
        if (!bag.classIsMissing())
          max_Inst.setClassValue(bag.classValue()); //set class value
        mini_data.add(mini_Inst); 
        max_data.add(max_Inst);
      }
      else{
        if (!bag.classIsMissing())
          newInst.setClassValue(bag.classValue()); //set class value
        newData.add(newInst);		
      }  
    }

    if (m_TransformMethod == TRANSFORMMETHOD_MINIMAX) {
      mini_data.setClassIndex(-1);
      mini_data.deleteAttributeAt(mini_data.numAttributes()-1); //delete class attribute for the minima data
      max_data.deleteAttributeAt(0); // delete the bag label attribute for the maxima data

      newData = Instances.mergeInstances(mini_data, max_data); //merge minima and maxima data
      newData.setClassIndex(newData.numAttributes()-1);

    }	

    return newData;
  }

  /**
   * Get the minimal and maximal value of a certain attribute in a certain data
   *
   * @param data the data
   * @param attIndex the index of the attribute
   * @return the double array containing in entry 0 for min and 1 for max.
   */
  public static double[] minimax(Instances data, int attIndex){
    double[] rt = {Double.POSITIVE_INFINITY, Double.NEGATIVE_INFINITY};
    for(int i=0; i<data.numInstances(); i++){
      double val = data.instance(i).value(attIndex);
      if(val > rt[1])
        rt[1] = val;
      if(val < rt[0])
        rt[0] = val;
    }

    for(int j=0; j<2; j++)
      if(Double.isInfinite(rt[j]))
        rt[j] = Double.NaN;

    return rt;
  }

  /**
   * 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.NOMINAL_CLASS))
      result.enable(Capability.NOMINAL_CLASS);
    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();
    
    // attributes
    result.enable(Capability.NOMINAL_ATTRIBUTES);
    result.enable(Capability.NUMERIC_ATTRIBUTES);
    result.enable(Capability.DATE_ATTRIBUTES);
    result.enable(Capability.MISSING_VALUES);

    // class
    result.disableAllClasses();
    result.enable(Capability.NO_CLASS);
    
    return result;
  }

  /**
   * Builds the classifier
   *
   * @param train 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 train) throws Exception {

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

    // remove instances with missing class
    train = new Instances(train);
    train.deleteWithMissingClass();
    
    if (m_Classifier == null) {
      throw new Exception("A base classifier has not been specified!");
    }

    if (getDebug())
      System.out.println("Start training ...");
    Instances data = transform(train); 

    data.deleteAttributeAt(0); // delete the bagID attribute
    m_Classifier.buildClassifier(data);

    if (getDebug())
      System.out.println("Finish building model");
  }		

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

    double [] distribution = new double[2];
    Instances test = new Instances (newBag.dataset(), 0);	
    test.add(newBag);	

    test = transform(test);
    test.deleteAttributeAt(0);
    Instance newInst=test.firstInstance();

    distribution = m_Classifier.distributionForInstance(newInst);

    return distribution;	   
  }

  /**
   * Gets a string describing the classifier.
   *
   * @return a string describing the classifer built.
   */
  public String toString() {	
    return "SimpleMI with base classifier: \n"+m_Classifier.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) {
    try {
      System.out.println(Evaluation.evaluateModel(new SimpleMI(), argv));
    } catch (Exception e) {
      e.printStackTrace();
      System.err.println(e.getMessage());
    }
  }
}