/*
* 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/>.
*/
/*
* M5Base.java
* Copyright (C) 2000-2012 University of Waikato, Hamilton, New Zealand
*
*/
package weka.classifiers.trees.m5;
import java.util.Enumeration;
import java.util.Random;
import java.util.Vector;
import weka.classifiers.AbstractClassifier;
import weka.classifiers.functions.LinearRegression;
import weka.core.AdditionalMeasureProducer;
import weka.core.Capabilities;
import weka.core.FastVector;
import weka.core.Instance;
import weka.core.Instances;
import weka.core.Option;
import weka.core.TechnicalInformation;
import weka.core.TechnicalInformation.Field;
import weka.core.TechnicalInformation.Type;
import weka.core.TechnicalInformationHandler;
import weka.core.Utils;
import weka.filters.Filter;
import weka.filters.supervised.attribute.NominalToBinary;
import weka.filters.unsupervised.attribute.RemoveUseless;
import weka.filters.unsupervised.attribute.ReplaceMissingValues;
/**
* M5Base. Implements base routines
* for generating M5 Model trees and rules. <p>
*
* The original algorithm M5 was invented by Quinlan: <br/>
*
* Quinlan J. R. (1992). Learning with continuous classes. Proceedings of
* the Australian Joint Conference on Artificial Intelligence. 343--348.
* World Scientific, Singapore. <p/>
*
* Yong Wang made improvements and created M5': <br/>
*
* Wang, Y and Witten, I. H. (1997). Induction of model trees for
* predicting continuous classes. Proceedings of the poster papers of the
* European Conference on Machine Learning. University of Economics,
* Faculty of Informatics and Statistics, Prague. <p/>
*
* Valid options are:<p>
*
* -U <br>
* Use unsmoothed predictions. <p>
*
* -R <br>
* Build regression tree/rule rather than model tree/rule
*
* @author Mark Hall (mhall@cs.waikato.ac.nz)
* @version $Revision: 8034 $
*/
public abstract class M5Base
extends AbstractClassifier
implements AdditionalMeasureProducer,
TechnicalInformationHandler {
/** for serialization */
private static final long serialVersionUID = -4022221950191647679L;
/**
* the instances covered by the tree/rules
*/
private Instances m_instances;
/**
* the rule set
*/
protected FastVector m_ruleSet;
/**
* generate a decision list instead of a single tree.
*/
private boolean m_generateRules;
/**
* use unsmoothed predictions
*/
private boolean m_unsmoothedPredictions;
/**
* filter to fill in missing values
*/
private ReplaceMissingValues m_replaceMissing;
/**
* filter to convert nominal attributes to binary
*/
private NominalToBinary m_nominalToBinary;
/**
* for removing useless attributes
*/
private RemoveUseless m_removeUseless;
/**
* Save instances at each node in an M5 tree for visualization purposes.
*/
protected boolean m_saveInstances = false;
/**
* Make a regression tree/rule instead of a model tree/rule
*/
protected boolean m_regressionTree;
/**
* Do not prune tree/rules
*/
protected boolean m_useUnpruned = false;
/**
* The minimum number of instances to allow at a leaf node
*/
protected double m_minNumInstances = 4;
/**
* Constructor
*/
public M5Base() {
m_generateRules = false;
m_unsmoothedPredictions = false;
m_useUnpruned = false;
m_minNumInstances = 4;
}
/**
* returns information about the classifier
* @return a description suitable for
* displaying in the explorer/experimenter gui
*/
public String globalInfo() {
return
"M5Base. Implements base routines for generating M5 Model trees and "
+ "rules\n"
+ "The original algorithm M5 was invented by R. Quinlan and Yong Wang "
+ "made improvements.\n\n"
+ "For more information see:\n\n"
+ getTechnicalInformation().toString();
}
/**
* Returns an instance of a TechnicalInformation object, containing
* detailed information about the technical background of this class,
* e.g., paper reference or book this class is based on.
*
* @return the technical information about this class
*/
public TechnicalInformation getTechnicalInformation() {
TechnicalInformation result;
TechnicalInformation additional;
result = new TechnicalInformation(Type.INPROCEEDINGS);
result.setValue(Field.AUTHOR, "Ross J. Quinlan");
result.setValue(Field.TITLE, "Learning with Continuous Classes");
result.setValue(Field.BOOKTITLE, "5th Australian Joint Conference on Artificial Intelligence");
result.setValue(Field.YEAR, "1992");
result.setValue(Field.PAGES, "343-348");
result.setValue(Field.PUBLISHER, "World Scientific");
result.setValue(Field.ADDRESS, "Singapore");
additional = result.add(Type.INPROCEEDINGS);
additional.setValue(Field.AUTHOR, "Y. Wang and I. H. Witten");
additional.setValue(Field.TITLE, "Induction of model trees for predicting continuous classes");
additional.setValue(Field.BOOKTITLE, "Poster papers of the 9th European Conference on Machine Learning");
additional.setValue(Field.YEAR, "1997");
additional.setValue(Field.PUBLISHER, "Springer");
return result;
}
/**
* Returns an enumeration describing the available options
*
* @return an enumeration of all the available options
*/
public Enumeration listOptions() {
Vector newVector = new Vector(4);
newVector.addElement(new Option("\tUse unpruned tree/rules",
"N", 0, "-N"));
newVector.addElement(new Option("\tUse unsmoothed predictions",
"U", 0, "-U"));
newVector.addElement(new Option("\tBuild regression tree/rule rather "
+"than a model tree/rule",
"R", 0, "-R"));
newVector.addElement(new Option("\tSet minimum number of instances "
+"per leaf\n\t(default 4)",
"M",1,"-M <minimum number of instances>"));
return newVector.elements();
}
/**
* Parses a given list of options. <p/>
*
* Valid options are:<p>
*
* -U <br>
* Use unsmoothed predictions. <p>
*
* -R <br>
* Build a regression tree rather than a model tree. <p>
*
* @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 {
setUnpruned(Utils.getFlag('N', options));
setUseUnsmoothed(Utils.getFlag('U', options));
setBuildRegressionTree(Utils.getFlag('R', options));
String optionString = Utils.getOption('M', options);
if (optionString.length() != 0) {
setMinNumInstances((new Double(optionString)).doubleValue());
}
Utils.checkForRemainingOptions(options);
}
/**
* Gets the current settings of the classifier.
*
* @return an array of strings suitable for passing to setOptions
*/
public String[] getOptions() {
String[] options = new String[5];
int current = 0;
if (getUnpruned()) {
options[current++] = "-N";
}
if (getUseUnsmoothed()) {
options[current++] = "-U";
}
if (getBuildRegressionTree()) {
options[current++] = "-R";
}
options[current++] = "-M";
options[current++] = ""+getMinNumInstances();
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 unprunedTipText() {
return "Whether unpruned tree/rules are to be generated.";
}
/**
* Use unpruned tree/rules
*
* @param unpruned true if unpruned tree/rules are to be generated
*/
public void setUnpruned(boolean unpruned) {
m_useUnpruned = unpruned;
}
/**
* Get whether unpruned tree/rules are being generated
*
* @return true if unpruned tree/rules are to be generated
*/
public boolean getUnpruned() {
return m_useUnpruned;
}
/**
* Returns the tip text for this property
*
* @return tip text for this property suitable for
* displaying in the explorer/experimenter gui
*/
public String generateRulesTipText() {
return "Whether to generate rules (decision list) rather than a tree.";
}
/**
* Generate rules (decision list) rather than a tree
*
* @param u true if rules are to be generated
*/
protected void setGenerateRules(boolean u) {
m_generateRules = u;
}
/**
* get whether rules are being generated rather than a tree
*
* @return true if rules are to be generated
*/
protected boolean getGenerateRules() {
return m_generateRules;
}
/**
* Returns the tip text for this property
*
* @return tip text for this property suitable for
* displaying in the explorer/experimenter gui
*/
public String useUnsmoothedTipText() {
return "Whether to use unsmoothed predictions.";
}
/**
* Use unsmoothed predictions
*
* @param s true if unsmoothed predictions are to be used
*/
public void setUseUnsmoothed(boolean s) {
m_unsmoothedPredictions = s;
}
/**
* Get whether or not smoothing is being used
*
* @return true if unsmoothed predictions are to be used
*/
public boolean getUseUnsmoothed() {
return m_unsmoothedPredictions;
}
/**
* Returns the tip text for this property
*
* @return tip text for this property suitable for
* displaying in the explorer/experimenter gui
*/
public String buildRegressionTreeTipText() {
return "Whether to generate a regression tree/rule instead of a model tree/rule.";
}
/**
* Get the value of regressionTree.
*
* @return Value of regressionTree.
*/
public boolean getBuildRegressionTree() {
return m_regressionTree;
}
/**
* Set the value of regressionTree.
*
* @param newregressionTree Value to assign to regressionTree.
*/
public void setBuildRegressionTree(boolean newregressionTree) {
m_regressionTree = newregressionTree;
}
/**
* 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 "The minimum number of instances to allow at a leaf node.";
}
/**
* Set the minimum number of instances to allow at a leaf node
*
* @param minNum the minimum number of instances
*/
public void setMinNumInstances(double minNum) {
m_minNumInstances = minNum;
}
/**
* Get the minimum number of instances to allow at a leaf node
*
* @return a <code>double</code> value
*/
public double getMinNumInstances() {
return m_minNumInstances;
}
/**
* Returns default capabilities of the classifier, i.e., of LinearRegression.
*
* @return the capabilities of this classifier
*/
public Capabilities getCapabilities() {
return new LinearRegression().getCapabilities();
}
/**
* Generates the classifier.
*
* @param data set of instances serving as training data
* @throws Exception if the classifier has not been generated
* successfully
*/
public void buildClassifier(Instances data) throws Exception {
// can classifier handle the data?
getCapabilities().testWithFail(data);
// remove instances with missing class
data = new Instances(data);
data.deleteWithMissingClass();
m_instances = new Instances(data);
m_replaceMissing = new ReplaceMissingValues();
m_replaceMissing.setInputFormat(m_instances);
m_instances = Filter.useFilter(m_instances, m_replaceMissing);
m_nominalToBinary = new NominalToBinary();
m_nominalToBinary.setInputFormat(m_instances);
m_instances = Filter.useFilter(m_instances, m_nominalToBinary);
m_removeUseless = new RemoveUseless();
m_removeUseless.setInputFormat(m_instances);
m_instances = Filter.useFilter(m_instances, m_removeUseless);
m_instances.randomize(new Random(1));
m_ruleSet = new FastVector();
Rule tempRule;
if (m_generateRules) {
Instances tempInst = m_instances;
do {
tempRule = new Rule();
tempRule.setSmoothing(!m_unsmoothedPredictions);
tempRule.setRegressionTree(m_regressionTree);
tempRule.setUnpruned(m_useUnpruned);
tempRule.setSaveInstances(false);
tempRule.setMinNumInstances(m_minNumInstances);
tempRule.buildClassifier(tempInst);
m_ruleSet.addElement(tempRule);
// System.err.println("Built rule : "+tempRule.toString());
tempInst = tempRule.notCoveredInstances();
tempRule.freeNotCoveredInstances();
} while (tempInst.numInstances() > 0);
} else {
// just build a single tree
tempRule = new Rule();
tempRule.setUseTree(true);
// tempRule.setGrowFullTree(true);
tempRule.setSmoothing(!m_unsmoothedPredictions);
tempRule.setSaveInstances(m_saveInstances);
tempRule.setRegressionTree(m_regressionTree);
tempRule.setUnpruned(m_useUnpruned);
tempRule.setMinNumInstances(m_minNumInstances);
Instances temp_train;
temp_train = m_instances;
tempRule.buildClassifier(temp_train);
m_ruleSet.addElement(tempRule);
// System.err.print(tempRule.m_topOfTree.treeToString(0));
}
// save space
m_instances = new Instances(m_instances, 0);
}
/**
* Calculates a prediction for an instance using a set of rules
* or an M5 model tree
*
* @param inst the instance whos class value is to be predicted
* @return the prediction
* @throws Exception if a prediction can't be made.
*/
public double classifyInstance(Instance inst) throws Exception {
Rule temp;
double prediction = 0;
boolean success = false;
m_replaceMissing.input(inst);
inst = m_replaceMissing.output();
m_nominalToBinary.input(inst);
inst = m_nominalToBinary.output();
m_removeUseless.input(inst);
inst = m_removeUseless.output();
if (m_ruleSet == null) {
throw new Exception("Classifier has not been built yet!");
}
if (!m_generateRules) {
temp = (Rule) m_ruleSet.elementAt(0);
return temp.classifyInstance(inst);
}
boolean cont;
int i;
for (i = 0; i < m_ruleSet.size(); i++) {
cont = false;
temp = (Rule) m_ruleSet.elementAt(i);
try {
prediction = temp.classifyInstance(inst);
success = true;
} catch (Exception e) {
cont = true;
}
if (!cont) {
break;
}
}
if (!success) {
System.out.println("Error in predicting (DecList)");
}
return prediction;
}
/**
* Returns a description of the classifier
*
* @return a description of the classifier as a String
*/
public String toString() {
StringBuffer text = new StringBuffer();
Rule temp;
if (m_ruleSet == null) {
return "Classifier hasn't been built yet!";
}
if (m_generateRules) {
text.append("M5 "
+ ((m_useUnpruned == true)
? "unpruned "
: "pruned ")
+ ((m_regressionTree == true)
? "regression "
: "model ")
+ "rules ");
if (!m_unsmoothedPredictions) {
text.append("\n(using smoothed linear models) ");
}
text.append(":\n");
text.append("Number of Rules : " + m_ruleSet.size() + "\n\n");
for (int j = 0; j < m_ruleSet.size(); j++) {
temp = (Rule) m_ruleSet.elementAt(j);
text.append("Rule: " + (j + 1) + "\n");
text.append(temp.toString());
}
} else {
temp = (Rule) m_ruleSet.elementAt(0);
text.append(temp.toString());
}
return text.toString();
}
/**
* Returns an enumeration of the additional measure names
* @return an enumeration of the measure names
*/
public Enumeration enumerateMeasures() {
Vector newVector = new Vector(1);
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 Exception if the named measure is not supported
*/
public double getMeasure(String additionalMeasureName)
{
if (additionalMeasureName.compareToIgnoreCase("measureNumRules") == 0) {
return measureNumRules();
} else {
throw new IllegalArgumentException(additionalMeasureName
+ " not supported (M5)");
}
}
/**
* return the number of rules
* @return the number of rules (same as # linear models &
* # leaves in the tree)
*/
public double measureNumRules() {
if (m_generateRules) {
return m_ruleSet.size();
}
return ((Rule)m_ruleSet.elementAt(0)).m_topOfTree.numberOfLinearModels();
}
public RuleNode getM5RootNode() {
Rule temp = (Rule) m_ruleSet.elementAt(0);
return temp.getM5RootNode();
}
}