lmt.java

MacroWeka扩展了著名数据挖掘工具weka

	
    if (getErrorOnProbabilities()) {
      options[current++] = "-P";
    } 
	
    options[current++] = "-I"; 
    options[current++] = ""+getNumBoostingIterations();

    options[current++] = "-M"; 
    options[current++] = ""+getMinNumInstances();
	
    while (current < options.length) {
      options[current++] = "";
    } 
    return options;
  } 

  /**
   * Get the value of convertNominal.
   *
   * @return Value of convertNominal.
   */
  public boolean getConvertNominal(){
    return m_convertNominal;
  }

  /**
   * Get the value of splitOnResiduals.
   *
   * @return Value of splitOnResiduals.
   */
  public boolean getSplitOnResiduals(){
    return m_splitOnResiduals;
  }

  /**
   * Get the value of fastRegression.
   *
   * @return Value of fastRegression.
   */
  public boolean getFastRegression(){
    return m_fastRegression;
  }
    
  /**
   * Get the value of errorOnProbabilities.
   *
   * @return Value of errorOnProbabilities.
   */
  public boolean getErrorOnProbabilities(){
    return m_errorOnProbabilities;
  }

  /**
   * Get the value of numBoostingIterations.
   *
   * @return Value of numBoostingIterations.
   */
  public int getNumBoostingIterations(){
    return m_numBoostingIterations;
  }
    
  /**
   * Get the value of minNumInstances.
   *
   * @return Value of minNumInstances.
   */
  public int getMinNumInstances(){
    return m_minNumInstances;
  }
    
  /**
   * Set the value of convertNominal.
   *
   * @param c Value to assign to convertNominal.
   */
  public void setConvertNominal(boolean c){
    m_convertNominal = c;
  }

  /**
   * Set the value of splitOnResiduals.
   *
   * @param c Value to assign to splitOnResiduals.
   */
  public void setSplitOnResiduals(boolean c){
    m_splitOnResiduals = c;
  }

  /**
   * Set the value of fastRegression.
   *
   * @param c Value to assign to fastRegression.
   */
  public void setFastRegression(boolean c){
    m_fastRegression = c;
  }

  /**
   * Set the value of errorOnProbabilities.
   *
   * @param c Value to assign to errorOnProbabilities.
   */
  public void setErrorOnProbabilities(boolean c){
    m_errorOnProbabilities = c;
  }

  /**
   * Set the value of numBoostingIterations.
   *
   * @param c Value to assign to numBoostingIterations.
   */
  public void setNumBoostingIterations(int c){
    m_numBoostingIterations = c;
  } 

  /**
   * Set the value of minNumInstances.
   *
   * @param c Value to assign to minNumInstances.
   */
  public void setMinNumInstances(int c){
    m_minNumInstances = c;
  }
    
  /**
   *  Returns the type of graph this classifier
   *  represents.
   *  @return Drawable.TREE
   */   
  public int graphType() {
    return Drawable.TREE;
  }

  /**
   * Returns graph describing the tree.
   *
   * @exception Exception if graph can't be computed
   */
  public String graph() throws Exception {

    return m_tree.graph();
  }

  /**
   * Returns the size of the tree
   * @return the size of the tree
   */
  public int measureTreeSize(){
    return m_tree.numNodes();
  }
    
  /**
   * Returns the number of leaves in the tree
   * @return the number of leaves in the tree
   */
  public int measureNumLeaves(){
    return m_tree.numLeaves();
  }
     
  /**
   * Returns an enumeration of the additional measure names
   * @return an enumeration of the measure names
   */
  public Enumeration enumerateMeasures() {
    Vector newVector = new Vector(2);
    newVector.addElement("measureTreeSize");
    newVector.addElement("measureNumLeaves");
	
    return newVector.elements();
  }
    

  /**
   * Returns the value of the named measure
   * @param additionalMeasureName the name of the measure to query for its value
   * @return the value of the named measure
   * @exception IllegalArgumentException if the named measure is not supported
   */
  public double getMeasure(String additionalMeasureName) {
    if (additionalMeasureName.compareToIgnoreCase("measureTreeSize") == 0) {
      return measureTreeSize();
    } else if (additionalMeasureName.compareToIgnoreCase("measureNumLeaves") == 0) {
      return measureNumLeaves();
    } else {
      throw new IllegalArgumentException(additionalMeasureName 
					 + " not supported (LMT)");
    }
  }    
    
  /**
   * Returns a string describing classifier
   * @return a description suitable for
   * displaying in the explorer/experimenter gui
   */
  public String globalInfo() {
    return "Classifier for building 'logistic model trees', which are classification trees with "
      +"logistic regression functions at the leaves. The algorithm can deal with binary and multi-class "
      +"target variables, numeric and nominal attributes and missing values. "
      +"For more information see: N.Landwehr, M.Hall, E. Frank 'Logistic Model Trees' (ECML 2003).";	    
  }


  /**
   * Returns the tip text for this property
   * @return tip text for this property suitable for
   * displaying in the explorer/experimenter gui
   */
  public String convertNominalTipText() {
    return "Convert all nominal attributes to binary ones before building the tree. "
      +"This means that all splits in the final tree will be binary.";
  }
    
  /**
   * Returns the tip text for this property
   * @return tip text for this property suitable for
   * displaying in the explorer/experimenter gui
   */
  public String splitOnResidualsTipText() {
    return "Set splitting criterion based on the residuals of LogitBoost. "
      +"There are two possible splitting criteria for LMT: the default is to use the C4.5 "
      +"splitting criterion that uses information gain on the class variable. The other splitting "
      +"criterion tries to improve the purity in the residuals produces when fitting the logistic "
      +"regression functions. The choice of the splitting criterion does not usually affect classification "
      +"accuracy much, but can produce different trees.";
  }  

  /**
   * Returns the tip text for this property
   * @return tip text for this property suitable for
   * displaying in the explorer/experimenter gui
   */
  public String fastRegressionTipText() {
    return "Use heuristic that avoids cross-validating the number of Logit-Boost iterations at every node. "
      +"When fitting the logistic regression functions at a node, LMT has to determine the number of LogitBoost "
      +"iterations to run. Originally, this number was cross-validated at every node in the tree. "
      +"To save time, this heuristic cross-validates the number only once and then uses that number at every "
      +"node in the tree. Usually this does not decrease accuracy but improves runtime considerably.";
  }  


  /**
   * Returns the tip text for this property
   * @return tip text for this property suitable for
   * displaying in the explorer/experimenter gui
   */
  public String errorOnProbabilitiesTipText() {
    return "Minimize error on probabilities instead of misclassification error when cross-validating the number "
      +"of LogitBoost iterations. When set, the number of LogitBoost iterations is chosen that minimizes "
      +"the root mean squared error instead of the misclassification error.";	   
  }  

  /**
   * Returns the tip text for this property
   * @return tip text for this property suitable for
   * displaying in the explorer/experimenter gui
   */
  public String numBoostingIterationsTipText() {
    return "Set a fixed number of iterations for LogitBoost. If >= 0, this sets a fixed number of LogitBoost "
      +"iterations that is used everywhere in the tree. If < 0, the number is cross-validated.";
  }  

  /**
   * Returns the tip text for this property
   * @return tip text for this property suitable for
   * displaying in the explorer/experimenter gui
   */
  public String minNumInstancesTipText() {
    return "Set the minimum number of instances at which a node is considered for splitting. "
      +"The default value is 15.";
  }  

  /**
   * Main method for testing this class
   *
   * @param String options 
   */
  public static void main (String [] argv) throws Exception{	
    System.out.println(Evaluation.evaluateModel(new LMT(), argv));

  }  

}