j48graft.java

来自「Weka」· Java 代码 · 共 823 行 · 第 1/2 页

   * <pre> -A
   *  Laplace smoothing for predicted probabilities.
   *  (note: this option only affects initial tree; grafting process always uses laplace). </pre>
   *
   * <pre> -E
   * Allow relabelling when performing grafting.</pre>
   *
   <!-- options-end -->
   *
   * @param options the list of options as an array of strings
   * @throws Exception if an option is not supported
   */
  public void setOptions(String[] options) throws Exception {
 
    // Other options
    String minNumString = Utils.getOption('M', options);
    if (minNumString.length() != 0) {
      m_minNumObj = Integer.parseInt(minNumString);
    } else {
      m_minNumObj = 2;
    }
    m_binarySplits = Utils.getFlag('B', options);
    m_useLaplace = Utils.getFlag('A', options);

    // Pruning options
    m_unpruned = Utils.getFlag('U', options);
    m_subtreeRaising = !Utils.getFlag('S', options);
    m_noCleanup = Utils.getFlag('L', options);
		if ((m_unpruned) && (!m_subtreeRaising)) {
      throw new Exception("Subtree raising doesn't need to be unset for unpruned tree!");
    }
    m_relabel = Utils.getFlag('E', options);
    String confidenceString = Utils.getOption('C', options);
    if (confidenceString.length() != 0) {
      if (m_unpruned) {
	throw new Exception("Doesn't make sense to change confidence for unpruned "
			    +"tree!");
      } else {
	m_CF = (new Float(confidenceString)).floatValue();
	if ((m_CF <= 0) || (m_CF >= 1)) {
	  throw new Exception("Confidence has to be greater than zero and smaller " +
			      "than one!");
	}
      }
    } else {
      m_CF = 0.25f;
    }
  }

  /**
   * Gets the current settings of the Classifier.
   *
   * @return an array of strings suitable for passing to setOptions
   */
  public String [] getOptions() {

    String [] options = new String [10];
    int current = 0;

    if (m_noCleanup) {
      options[current++] = "-L";
    }
    if (m_unpruned) {
      options[current++] = "-U";
    } else {
      if (!m_subtreeRaising) {
	options[current++] = "-S";
      }
      options[current++] = "-C"; options[current++] = "" + m_CF;
    }
    if (m_binarySplits) {
      options[current++] = "-B";
    }
    options[current++] = "-M"; options[current++] = "" + m_minNumObj;
    if (m_useLaplace) {
      options[current++] = "-A";
    }

    if(m_relabel) {
       options[current++] = "-E";
    }

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

  /**
   * Returns the tip text for this property
   * @return tip text for this property suitable for
   * displaying in the explorer/experimenter gui
   */
  public String useLaplaceTipText() {
    return "Whether counts at leaves are smoothed based on Laplace.";
  }

  /**
   * Get the value of useLaplace.
   *
   * @return Value of useLaplace.
   */
  public boolean getUseLaplace() {
    
    return m_useLaplace;
  }
  
  /**
   * Set the value of useLaplace.
   *
   * @param newuseLaplace Value to assign to useLaplace.
   */
  public void setUseLaplace(boolean newuseLaplace) {
    
    m_useLaplace = newuseLaplace;
  }
  
  /**
   * Returns a description of the classifier.
   *
   * @return a description of the classifier
   */
  public String toString() {

    if (m_root == null) {
      return "No classifier built";
    }
    if (m_unpruned)
      return "J48graft unpruned tree\n------------------\n" + m_root.toString();
    else
      return "J48graft pruned tree\n------------------\n" + m_root.toString();
  }

  /**
   * Returns a superconcise version of the model
   *
   * @return a summary of the model
   */
  public String toSummaryString() {

    return "Number of leaves: " + m_root.numLeaves() + "\n"
         + "Size of the tree: " + m_root.numNodes() + "\n";
  }

  /**
   * Returns the size of the tree
   * @return the size of the tree
   */
  public double measureTreeSize() {
    return m_root.numNodes();
  }

  /**
   * Returns the number of leaves
   * @return the number of leaves
   */
  public double measureNumLeaves() {
    return m_root.numLeaves();
  }

  /**
   * Returns the number of rules (same as number of leaves)
   * @return the number of rules
   */
  public double measureNumRules() {
    return m_root.numLeaves();
  }

  /**
   * Returns an enumeration of the additional measure names
   * @return an enumeration of the measure names
   */
  public Enumeration enumerateMeasures() {
    Vector newVector = new Vector(3);
    newVector.addElement("measureTreeSize");
    newVector.addElement("measureNumLeaves");
    newVector.addElement("measureNumRules");
    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
   * @throws IllegalArgumentException if the named measure is not supported
   */
  public double getMeasure(String additionalMeasureName) {
    if (additionalMeasureName.compareTo("measureNumRules") == 0) {
      return measureNumRules();
    } else if (additionalMeasureName.compareTo("measureTreeSize") == 0) {
      return measureTreeSize();
    } else if (additionalMeasureName.compareTo("measureNumLeaves") == 0) {
      return measureNumLeaves();
    } else {
      throw new IllegalArgumentException(additionalMeasureName
			  + " not supported (j48)");
    }
  }

  /**
   * Returns the tip text for this property
   * @return tip text for this property suitable for
   * displaying in the explorer/experimenter gui
   */
  public String unprunedTipText() {
    return "Whether pruning is performed.";
  }

  /**
   * Get the value of unpruned.
   *
   * @return Value of unpruned.
   */
  public boolean getUnpruned() {

    return m_unpruned;
  }

  /**
   * Set the value of unpruned.
   * @param v  Value to assign to unpruned.
   */
  public void setUnpruned(boolean v) {

    m_unpruned = v;
  }

  /**
   * Returns the tip text for this property
   * @return tip text for this property suitable for
   * displaying in the explorer/experimenter gui
   */
  public String relabelTipText() {
    return "Whether relabelling is allowed during grafting.";
  }

  /**
   * Get the value of relabelling
   *
   * @return Value of relabelling.
   */
  public boolean getRelabel() {

    return m_relabel;
  }

  /**
   * Set the value of relabelling. 
   *
   * @param v  Value to assign to relabelling flag.
   */
  public void setRelabel(boolean v) {
    m_relabel = v;
  }

  /**
   * Returns the tip text for this property
   * @return tip text for this property suitable for
   * displaying in the explorer/experimenter gui
   */
  public String confidenceFactorTipText() {
    return "The confidence factor used for pruning (smaller values incur "
      + "more pruning).";
  }

  /**
   * Get the value of CF.
   *
   * @return Value of CF.
   */
  public float getConfidenceFactor() {
    
    return m_CF;
  }
  
  /**
   * Set the value of CF.
   *
   * @param v  Value to assign to CF.
   */
  public void setConfidenceFactor(float v) {
    
    m_CF = v;
  }

  /**
   * Returns the tip text for this property
   * @return tip text for this property suitable for
   * displaying in the explorer/experimenter gui
   */
  public String minNumObjTipText() {
    return "The minimum number of instances per leaf.";
  }

  /**
   * Get the value of minNumObj.
   *
   * @return Value of minNumObj.
   */
  public int getMinNumObj() {
    
    return m_minNumObj;
  }
  
  /**
   * Set the value of minNumObj.
   *
   * @param v  Value to assign to minNumObj.
   */
  public void setMinNumObj(int v) {
    
    m_minNumObj = v;
  }

  /**
   * Returns the tip text for this property
   * @return tip text for this property suitable for
   * displaying in the explorer/experimenter gui
   */
  public String binarySplitsTipText() {
    return "Whether to use binary splits on nominal attributes when "
      + "building the trees.";
  }
  
  /**
   * Get the value of binarySplits.
   *
   * @return Value of binarySplits.
   */
  public boolean getBinarySplits() {

    return m_binarySplits;
  }

  /**
   * Set the value of binarySplits.
   *
   * @param v  Value to assign to binarySplits.
   */
  public void setBinarySplits(boolean v) {
    
    m_binarySplits = v;
  }
  
  /**
   * Returns the tip text for this property
   * @return tip text for this property suitable for
   * displaying in the explorer/experimenter gui
   */
  public String subtreeRaisingTipText() {
    return "Whether to consider the subtree raising operation when pruning.";
  }
 
  /**
   * Get the value of subtreeRaising.
   *
   * @return Value of subtreeRaising.
   */
  public boolean getSubtreeRaising() {
    
    return m_subtreeRaising;
  }
  
  /**
   * Set the value of subtreeRaising.
   *
   * @param v  Value to assign to subtreeRaising.
   */
  public void setSubtreeRaising(boolean v) {
    
    m_subtreeRaising = v;
  }

  /**
   * Returns the tip text for this property
   * @return tip text for this property suitable for
   * displaying in the explorer/experimenter gui
   */
  public String saveInstanceDataTipText() {
    return "Whether to save the training data for visualization.";
  }

  /**
   * Check whether instance data is to be saved.
   *
   * @return true if instance data is saved
   */
  public boolean getSaveInstanceData() {
    
    return m_noCleanup;
  }
  
  /**
   * Set whether instance data is to be saved.
   * @param v true if instance data is to be saved
   */
  public void setSaveInstanceData(boolean v) {

    m_noCleanup = v;
  }
 
  /**
   * Main method for testing this class
   *
   * @param argv the commandline options
   */
  public static void main(String [] argv){
    runClassifier(new J48graft(), argv);
  }
}