/*
* 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/>.
*/
/*
* ClassifierSplitModel.java
* Copyright (C) 1999-2012 University of Waikato, Hamilton, New Zealand
*
*/
package weka.classifiers.trees.j48;
import java.io.Serializable;
import weka.core.Instance;
import weka.core.Instances;
import weka.core.RevisionHandler;
import weka.core.Utils;
/**
* Abstract class for classification models that can be used
* recursively to split the data.
*
* @author Eibe Frank (eibe@cs.waikato.ac.nz)
* @version $Revision: 8034 $
*/
public abstract class ClassifierSplitModel
implements Cloneable, Serializable, RevisionHandler {
/** for serialization */
private static final long serialVersionUID = 4280730118393457457L;
/** Distribution of class values. */
protected Distribution m_distribution;
/** Number of created subsets. */
protected int m_numSubsets;
/**
* Allows to clone a model (shallow copy).
*/
public Object clone() {
Object clone = null;
try {
clone = super.clone();
} catch (CloneNotSupportedException e) {
}
return clone;
}
/**
* Builds the classifier split model for the given set of instances.
*
* @exception Exception if something goes wrong
*/
public abstract void buildClassifier(Instances instances) throws Exception;
/**
* Checks if generated model is valid.
*/
public final boolean checkModel() {
if (m_numSubsets > 0)
return true;
else
return false;
}
/**
* Classifies a given instance.
*
* @exception Exception if something goes wrong
*/
public final double classifyInstance(Instance instance)
throws Exception {
int theSubset;
theSubset = whichSubset(instance);
if (theSubset > -1)
return (double)m_distribution.maxClass(theSubset);
else
return (double)m_distribution.maxClass();
}
/**
* Gets class probability for instance.
*
* @exception Exception if something goes wrong
*/
public double classProb(int classIndex, Instance instance, int theSubset)
throws Exception {
if (theSubset > -1) {
return m_distribution.prob(classIndex,theSubset);
} else {
double [] weights = weights(instance);
if (weights == null) {
return m_distribution.prob(classIndex);
} else {
double prob = 0;
for (int i = 0; i < weights.length; i++) {
prob += weights[i] * m_distribution.prob(classIndex, i);
}
return prob;
}
}
}
/**
* Gets class probability for instance.
*
* @exception Exception if something goes wrong
*/
public double classProbLaplace(int classIndex, Instance instance,
int theSubset) throws Exception {
if (theSubset > -1) {
return m_distribution.laplaceProb(classIndex, theSubset);
} else {
double [] weights = weights(instance);
if (weights == null) {
return m_distribution.laplaceProb(classIndex);
} else {
double prob = 0;
for (int i = 0; i < weights.length; i++) {
prob += weights[i] * m_distribution.laplaceProb(classIndex, i);
}
return prob;
}
}
}
/**
* Returns coding costs of model. Returns 0 if not overwritten.
*/
public double codingCost() {
return 0;
}
/**
* Returns the distribution of class values induced by the model.
*/
public final Distribution distribution() {
return m_distribution;
}
/**
* Prints left side of condition satisfied by instances.
*
* @param data the data.
*/
public abstract String leftSide(Instances data);
/**
* Prints left side of condition satisfied by instances in subset index.
*/
public abstract String rightSide(int index,Instances data);
/**
* Prints label for subset index of instances (eg class).
*
* @exception Exception if something goes wrong
*/
public final String dumpLabel(int index,Instances data) throws Exception {
StringBuffer text;
text = new StringBuffer();
text.append(((Instances)data).classAttribute().
value(m_distribution.maxClass(index)));
text.append(" ("+Utils.roundDouble(m_distribution.perBag(index),2));
if (Utils.gr(m_distribution.numIncorrect(index),0))
text.append("/"+Utils.roundDouble(m_distribution.numIncorrect(index),2));
text.append(")");
return text.toString();
}
public final String sourceClass(int index, Instances data) throws Exception {
System.err.println("sourceClass");
return (new StringBuffer(m_distribution.maxClass(index))).toString();
}
public abstract String sourceExpression(int index, Instances data);
/**
* Prints the split model.
*
* @exception Exception if something goes wrong
*/
public final String dumpModel(Instances data) throws Exception {
StringBuffer text;
int i;
text = new StringBuffer();
for (i=0;i<m_numSubsets;i++) {
text.append(leftSide(data)+rightSide(i,data)+": ");
text.append(dumpLabel(i,data)+"\n");
}
return text.toString();
}
/**
* Returns the number of created subsets for the split.
*/
public final int numSubsets() {
return m_numSubsets;
}
/**
* Sets distribution associated with model.
*/
public void resetDistribution(Instances data) throws Exception {
m_distribution = new Distribution(data, this);
}
/**
* Splits the given set of instances into subsets.
*
* @exception Exception if something goes wrong
*/
public final Instances [] split(Instances data)
throws Exception {
Instances [] instances = new Instances [m_numSubsets];
double [] weights;
double newWeight;
Instance instance;
int subset, i, j;
for (j=0;j<m_numSubsets;j++)
instances[j] = new Instances((Instances)data,
data.numInstances());
for (i = 0; i < data.numInstances(); i++) {
instance = ((Instances) data).instance(i);
weights = weights(instance);
subset = whichSubset(instance);
if (subset > -1)
instances[subset].add(instance);
else
for (j = 0; j < m_numSubsets; j++)
if (Utils.gr(weights[j],0)) {
newWeight = weights[j]*instance.weight();
instances[j].add(instance);
instances[j].lastInstance().setWeight(newWeight);
}
}
for (j = 0; j < m_numSubsets; j++)
instances[j].compactify();
return instances;
}
/**
* Returns weights if instance is assigned to more than one subset.
* Returns null if instance is only assigned to one subset.
*/
public abstract double [] weights(Instance instance);
/**
* Returns index of subset instance is assigned to.
* Returns -1 if instance is assigned to more than one subset.
*
* @exception Exception if something goes wrong
*/
public abstract int whichSubset(Instance instance) throws Exception;
}