/*
* This program is free software: you can redistribute it and/or modify
* it under the terms of the GNU General Public License as published by
* the Free Software Foundation, either version 3 of the License, or
* (at your option) any later version.
*
* This program is distributed in the hope that it will be useful,
* but WITHOUT ANY WARRANTY; without even the implied warranty of
* MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
* GNU General Public License for more details.
*
* You should have received a copy of the GNU General Public License
* along with this program. If not, see <http://www.gnu.org/licenses/>.
*/
/*
* M5P.java
* Copyright (C) 2001-2012 University of Waikato, Hamilton, New Zealand
*
*/
package weka.classifiers.trees;
import java.util.Enumeration;
import java.util.Vector;
import weka.classifiers.trees.m5.M5Base;
import weka.classifiers.trees.m5.Rule;
import weka.core.Drawable;
import weka.core.Option;
import weka.core.RevisionUtils;
import weka.core.Utils;
/**
<!-- globalinfo-start -->
* M5Base. Implements base routines for generating M5 Model trees and rules<br/>
* The original algorithm M5 was invented by R. Quinlan and Yong Wang made improvements.<br/>
* <br/>
* For more information see:<br/>
* <br/>
* Ross J. Quinlan: Learning with Continuous Classes. In: 5th Australian Joint Conference on Artificial Intelligence, Singapore, 343-348, 1992.<br/>
* <br/>
* Y. Wang, I. H. Witten: Induction of model trees for predicting continuous classes. In: Poster papers of the 9th European Conference on Machine Learning, 1997.
* <p/>
<!-- globalinfo-end -->
*
<!-- technical-bibtex-start -->
* BibTeX:
* <pre>
* @inproceedings{Quinlan1992,
* address = {Singapore},
* author = {Ross J. Quinlan},
* booktitle = {5th Australian Joint Conference on Artificial Intelligence},
* pages = {343-348},
* publisher = {World Scientific},
* title = {Learning with Continuous Classes},
* year = {1992}
* }
*
* @inproceedings{Wang1997,
* author = {Y. Wang and I. H. Witten},
* booktitle = {Poster papers of the 9th European Conference on Machine Learning},
* publisher = {Springer},
* title = {Induction of model trees for predicting continuous classes},
* year = {1997}
* }
* </pre>
* <p/>
<!-- technical-bibtex-end -->
*
<!-- options-start -->
* Valid options are: <p/>
*
* <pre> -N
* Use unpruned tree/rules</pre>
*
* <pre> -U
* Use unsmoothed predictions</pre>
*
* <pre> -R
* Build regression tree/rule rather than a model tree/rule</pre>
*
* <pre> -M <minimum number of instances>
* Set minimum number of instances per leaf
* (default 4)</pre>
*
* <pre> -L
* Save instances at the nodes in
* the tree (for visualization purposes)</pre>
*
<!-- options-end -->
*
* @author <a href="mailto:mhall@cs.waikato.ac.nz">Mark Hall</a>
* @version $Revision: 8034 $
*/
public class M5P
extends M5Base
implements Drawable {
/** for serialization */
static final long serialVersionUID = -6118439039768244417L;
/**
* Creates a new <code>M5P</code> instance.
*/
public M5P() {
super();
setGenerateRules(false);
}
/**
* Returns the type of graph this classifier
* represents.
* @return Drawable.TREE
*/
public int graphType() {
return Drawable.TREE;
}
/**
* Return a dot style String describing the tree.
*
* @return a <code>String</code> value
* @throws Exception if an error occurs
*/
public String graph() throws Exception {
StringBuffer text = new StringBuffer();
text.append("digraph M5Tree {\n");
Rule temp = (Rule)m_ruleSet.elementAt(0);
temp.topOfTree().graph(text);
text.append("}\n");
return text.toString();
}
/**
* Returns the tip text for this property
*
* @return tip text for this property suitable for
* displaying in the explorer/experimenter gui
*/
public String saveInstancesTipText() {
return
"Whether to save instance data at each node in the tree for "
+ "visualization purposes.";
}
/**
* Set whether to save instance data at each node in the
* tree for visualization purposes
*
* @param save a <code>boolean</code> value
*/
public void setSaveInstances(boolean save) {
m_saveInstances = save;
}
/**
* Get whether instance data is being save.
*
* @return a <code>boolean</code> value
*/
public boolean getSaveInstances() {
return m_saveInstances;
}
/**
* Returns an enumeration describing the available options
*
* @return an enumeration of all the available options
*/
public Enumeration listOptions() {
Enumeration superOpts = super.listOptions();
Vector newVector = new Vector();
while (superOpts.hasMoreElements()) {
newVector.addElement((Option)superOpts.nextElement());
}
newVector.addElement(new Option("\tSave instances at the nodes in\n"
+"\tthe tree (for visualization purposes)",
"L", 0, "-L"));
return newVector.elements();
}
/**
* Parses a given list of options. <p/>
*
<!-- options-start -->
* Valid options are: <p/>
*
* <pre> -N
* Use unpruned tree/rules</pre>
*
* <pre> -U
* Use unsmoothed predictions</pre>
*
* <pre> -R
* Build regression tree/rule rather than a model tree/rule</pre>
*
* <pre> -M <minimum number of instances>
* Set minimum number of instances per leaf
* (default 4)</pre>
*
* <pre> -L
* Save instances at the nodes in
* the tree (for visualization purposes)</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 {
setSaveInstances(Utils.getFlag('L', options));
super.setOptions(options);
}
/**
* Gets the current settings of the classifier.
*
* @return an array of strings suitable for passing to setOptions
*/
public String [] getOptions() {
String[] superOpts = super.getOptions();
String [] options = new String [superOpts.length+1];
int current = superOpts.length;
for (int i = 0; i < current; i++) {
options[i] = superOpts[i];
}
if (getSaveInstances()) {
options[current++] = "-L";
}
while (current < options.length) {
options[current++] = "";
}
return options;
}
/**
* Returns the revision string.
*
* @return the revision
*/
public String getRevision() {
return RevisionUtils.extract("$Revision: 8034 $");
}
/**
* Main method by which this class can be tested
*
* @param args an array of options
*/
public static void main(String[] args) {
runClassifier(new M5P(), args);
}
}