adtree.java

为了下东西 随便发了个 datamining 的源代码

    for (int i = 0; i < instances.numInstances() - (numMissing + 1); i++) {
      Instance inst = instances.instance(i);
      Instance instPlusOne = instances.instance(i + 1);
      distribution[0][(int)inst.classValue()] += inst.weight();
      distribution[1][(int)inst.classValue()] -= inst.weight();
      if (Utils.sm(inst.value(attIndex), instPlusOne.value(attIndex))) {
	currCutPoint = (inst.value(attIndex) + instPlusOne.value(attIndex)) / 2.0;
	currVal = conditionedZOnRows(distribution);
	if (currVal < bestVal) {
	  splitPoint = currCutPoint;
	  bestVal = currVal;
	}
      }
    }
	
    double[] splitAndZ = new double[2];
    splitAndZ[0] = splitPoint;
    splitAndZ[1] = bestVal;
    return splitAndZ;
  }

  /**
   * Calculates the Z-value from the rows of a weight distribution array.
   *
   * @param distribution the weight distribution
   * @return the Z-value
   */
  private double conditionedZOnRows(double [][] distribution) {
    
    double w1 = distribution[0][0] + 1.0;
    double w2 = distribution[0][1] + 1.0;
    double w3 = distribution[1][0] + 1.0; 
    double w4 = distribution[1][1] + 1.0;
    double wRemainder = m_trainTotalWeight + 4.0 - (w1 + w2 + w3 + w4);
    return (2.0 * (Math.sqrt(w1 * w2) + Math.sqrt(w3 * w4))) + wRemainder;
  }

  /**
   * Returns the class probability distribution for an instance.
   *
   * @param instance the instance to be classified
   * @return the distribution the tree generates for the instance
   */
  public double[] distributionForInstance(Instance instance) {
    
    double predVal = predictionValueForInstance(instance, m_root, 0.0);
    
    double[] distribution = new double[2];
    distribution[0] = 1.0 / (1.0 + Math.pow(Math.E, predVal));
    distribution[1] = 1.0 / (1.0 + Math.pow(Math.E, -predVal));

    return distribution;
  }

  /**
   * Returns the class prediction value (vote) for an instance.
   *
   * @param inst the instance
   * @param currentNode the root of the tree to get the values from
   * @param currentValue the current value before adding the value contained in the
   * subtree
   * @return the class prediction value (vote)
   */
  protected double predictionValueForInstance(Instance inst, PredictionNode currentNode,
					    double currentValue) {
    
    currentValue += currentNode.getValue();
    for (Enumeration e = currentNode.children(); e.hasMoreElements(); ) {
      Splitter split = (Splitter) e.nextElement();
      int branch = split.branchInstanceGoesDown(inst);
      if (branch >= 0)
	currentValue = predictionValueForInstance(inst, split.getChildForBranch(branch),
						  currentValue);
    }
    return currentValue;
  }

  /**
   * Returns a description of the classifier.
   *
   * @return a string containing a description of the classifier
   */
  public String toString() {
    
    if (m_root == null)
      return ("ADTree not built yet");
    else {
      return ("Alternating decision tree:\n\n" + toString(m_root, 1) +
	      "\nLegend: " + legend() +
	      "\nTree size (total number of nodes): " + numOfAllNodes(m_root) + 
	      "\nLeaves (number of predictor nodes): " + numOfPredictionNodes(m_root)
	      );
    }
  }

  /**
   * Traverses the tree, forming a string that describes it.
   *
   * @param currentNode the current node under investigation
   * @param level the current level in the tree
   * @return the string describing the subtree
   */      
  protected String toString(PredictionNode currentNode, int level) {
    
    StringBuffer text = new StringBuffer();
    
    text.append(": " + Utils.doubleToString(currentNode.getValue(),3));
    
    for (Enumeration e = currentNode.children(); e.hasMoreElements(); ) {
      Splitter split = (Splitter) e.nextElement();
	    
      for (int j=0; j<split.getNumOfBranches(); j++) {
	PredictionNode child = split.getChildForBranch(j);
	if (child != null) {
	  text.append("\n");
	  for (int k = 0; k < level; k++) {
	    text.append("|  ");
	  }
	  text.append("(" + split.orderAdded + ")");
	  text.append(split.attributeString(m_trainInstances) + " "
		      + split.comparisonString(j, m_trainInstances));
	  text.append(toString(child, level + 1));
	}
      }
    }
    return text.toString();
  }

  /**
   *  Returns the type of graph this classifier
   *  represents.
   *  @return Drawable.TREE
   */   
  public int graphType() {
      return Drawable.TREE;
  }

  /**
   * Returns graph describing the tree.
   *
   * @return the graph of the tree in dotty format
   * @exception Exception if something goes wrong
   */
  public String graph() throws Exception {
    
    StringBuffer text = new StringBuffer();
    text.append("digraph ADTree {\n");
    graphTraverse(m_root, text, 0, 0, m_trainInstances);
    return text.toString() +"}\n";
  }

  /**
   * Traverses the tree, graphing each node.
   *
   * @param currentNode the currentNode under investigation
   * @param text the string built so far
   * @param splitOrder the order the parent splitter was added to the tree
   * @param predOrder the order this predictor was added to the split
   * @exception Exception if something goes wrong
   */       
  protected void graphTraverse(PredictionNode currentNode, StringBuffer text,
			       int splitOrder, int predOrder, Instances instances)
    throws Exception {
    
    text.append("S" + splitOrder + "P" + predOrder + " [label=\"");
    text.append(Utils.doubleToString(currentNode.getValue(),3));
    if (splitOrder == 0) // show legend in root
      text.append(" (" + legend() + ")");
    text.append("\" shape=box style=filled");
    if (instances.numInstances() > 0) text.append(" data=\n" + instances + "\n,\n");
    text.append("]\n");
    for (Enumeration e = currentNode.children(); e.hasMoreElements(); ) {
      Splitter split = (Splitter) e.nextElement();
      text.append("S" + splitOrder + "P" + predOrder + "->" + "S" + split.orderAdded +
		  " [style=dotted]\n");
      text.append("S" + split.orderAdded + " [label=\"" + split.orderAdded + ": " +
		  split.attributeString(m_trainInstances) + "\"]\n");

      for (int i=0; i<split.getNumOfBranches(); i++) {
	PredictionNode child = split.getChildForBranch(i);
	if (child != null) {
	  text.append("S" + split.orderAdded + "->" + "S" + split.orderAdded + "P" + i +
		      " [label=\"" + split.comparisonString(i, m_trainInstances) + "\"]\n");
	  graphTraverse(child, text, split.orderAdded, i,
			split.instancesDownBranch(i, instances));
	}
      }
    }  
  }

  /**
   * Returns the legend of the tree, describing how results are to be interpreted.
   *
   * @return a string containing the legend of the classifier
   */
  public String legend() {
    
    Attribute classAttribute = null;
    if (m_trainInstances == null) return "";
    try {classAttribute = m_trainInstances.classAttribute();} catch (Exception x){};
    return ("-ve = " + classAttribute.value(0) +
	    ", +ve = " + classAttribute.value(1));
  }

  /**
   * @return tip text for this property suitable for
   * displaying in the explorer/experimenter gui
   */
  public String numOfBoostingIterationsTipText() {

    return "Sets the number of boosting iterations to perform. You will need to manually "
      + "tune this parameter to suit the dataset and the desired complexity/accuracy "
      + "tradeoff. More boosting iterations will result in larger (potentially more "
      + " accurate) trees, but will make learning slower. Each iteration will add 3 nodes "
      + "(1 split + 2 prediction) to the tree unless merging occurs.";
  }

  /**
   * Gets the number of boosting iterations.
   *
   * @return the number of boosting iterations
   */
  public int getNumOfBoostingIterations() {
    
    return m_boostingIterations;
  }

  /**
   * Sets the number of boosting iterations.
   *
   * @param b the number of boosting iterations to use
   */
  public void setNumOfBoostingIterations(int b) {
    
    m_boostingIterations = b; 
  }

  /**
   * @return tip text for this property suitable for
   * displaying in the explorer/experimenter gui
   */
  public String searchPathTipText() {

    return "Sets the type of search to perform when building the tree. The default option"
      + " (Expand all paths) will do an exhaustive search. The other search methods are"
      + " heuristic, so they are not guaranteed to find an optimal solution but they are"
      + " much faster. Expand the heaviest path: searches the path with the most heavily"
      + " weighted instances. Expand the best z-pure path: searches the path determined"
      + " by the best z-pure estimate. Expand a random path: the fastest method, simply"
      + " searches down a single random path on each iteration.";
  }

  /**
   * Gets the method of searching the tree for a new insertion. Will be one of
   * SEARCHPATH_ALL, SEARCHPATH_HEAVIEST, SEARCHPATH_ZPURE, SEARCHPATH_RANDOM.
   *
   * @return the tree searching mode
   */
  public SelectedTag getSearchPath() {

    return new SelectedTag(m_searchPath, TAGS_SEARCHPATH);
  }
  
  /**
   * Sets the method of searching the tree for a new insertion. Will be one of
   * SEARCHPATH_ALL, SEARCHPATH_HEAVIEST, SEARCHPATH_ZPURE, SEARCHPATH_RANDOM.
   *
   * @param newMethod the new tree searching mode
   */
  public void setSearchPath(SelectedTag newMethod) {
    
    if (newMethod.getTags() == TAGS_SEARCHPATH) {
      m_searchPath = newMethod.getSelectedTag().getID();
    }
  }

  /**
   * @return tip text for this property suitable for
   * displaying in the explorer/experimenter gui
   */
  public String randomSeedTipText() {

    return "Sets the random seed to use for a random search.";
  }

  /**
   * Gets random seed for a random walk.
   *
   * @return the random seed
   */
  public int getRandomSeed() {
    
    return m_randomSeed;
  }

  /**
   * Sets random seed for a random walk.
   *
   * @param s the random seed
   */
  public void setRandomSeed(int seed) {
    
    // the actual random object is created when the tree is initialized
    m_randomSeed = seed; 
  }  

  /**
   * @return tip text for this property suitable for
   * displaying in the explorer/experimenter gui
   */
  public String saveInstanceDataTipText() {

    return "Sets whether the tree is to save instance data - the model will take up more"
      + " memory if it does. If enabled you will be able to visualize the instances at"
      + " the prediction nodes when visualizing the tree.";
  }

  /**
   * Gets whether the tree is to save instance data.
   *
   * @return the random seed
   */
  public boolean getSaveInstanceData() {
    
    return m_saveInstanceData;
  }

  /**
   * Sets whether the tree is to save instance data.
   *
   * @param s the random seed
   */
  public void setSaveInstanceData(boolean v) {
    
    m_saveInstanceData = v;
  }

  /**
   * Returns an enumeration describing the available options..
   *
   * @return an enumeration of all the available options.
   */
  public Enumeration listOptions() {
    
    Vector newVector = new Vector(3);
    newVector.addElement(new Option(
				    "\tNumber of boosting iterations.\n"
				    +"\t(Default = 10)",
				    "B", 1,"-B <number of boosting iterations>"));
    newVector.addElement(new Option(
				    "\tExpand nodes: -3(all), -2(weight), -1(z_pure), "
				    +">=0 seed for random walk\n"
				    +"\t(Default = -3)",
				    "E", 1,"-E <-3|-2|-1|>=0>"));
    newVector.addElement(new Option(
				    "\tSave the instance data with the model",
				    "D", 0,"-D"));
    return newVector.elements();
  }