/**
* 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 "J48 unpruned tree\n------------------\n" + m_root.toString();
else return "J48 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 tree as an if-then statement.
*
* @param className the name of the Java class
* @return the tree as a Java if-then type statement
* @throws Exception if something goes wrong
*/
public String toSource(String className) throws Exception {
StringBuffer[] source = m_root.toSource(className);
return "class "
+ className
+ " {\n\n"
+ " public static double classify(Object[] i)\n"
+ " throws Exception {\n\n"
+ " double p = Double.NaN;\n"
+ source[0] // Assignment code
+ " return p;\n"
+ " }\n"
+ source[1] // Support code
+ "}\n";
}
/**
* Generates the classifier.
*
* @param instances the data to train the classifier with
* @throws Exception if classifier can't be built successfully
*/
public void buildClassifier(Instances instances) throws Exception {
ModelSelection modSelection;
if (m_binarySplits)
modSelection = new BinC45ModelSelection(m_minNumObj, instances, m_useMDLcorrection);
else modSelection = new C45ModelSelection(m_minNumObj, instances, m_useMDLcorrection);
if (!m_reducedErrorPruning)
m_root =
new C45PruneableClassifierTree(
modSelection, !m_unpruned, m_CF, m_subtreeRaising, !m_noCleanup, m_collapseTree);
else
m_root =
new PruneableClassifierTree(modSelection, !m_unpruned, m_numFolds, !m_noCleanup, m_Seed);
m_root.buildClassifier(instances);
if (m_binarySplits) {
((BinC45ModelSelection) modSelection).cleanup();
} else {
((C45ModelSelection) modSelection).cleanup();
}
}
/**
* Returns the number of rules (same as number of leaves)
*
* @return the number of rules
*/
public double measureNumRules() {
return m_root.numLeaves();
}
/**
* Returns the size of the tree
*
* @return the size of the tree
*/
public double measureTreeSize() {
return m_root.numNodes();
}
/**
* Returns tree in prefix order.
*
* @return the tree in prefix order
* @throws Exception if something goes wrong
*/
public String prefix() throws Exception {
return m_root.prefix();
}
/**
* Returns graph describing the tree.
*
* @return the graph describing the tree
* @throws Exception if graph can't be computed
*/
public String graph() throws Exception {
return m_root.graph();
}
/**
* Returns class probabilities for an instance.
*
* @param instance the instance to calculate the class probabilities for
* @return the class probabilities
* @throws Exception if distribution can't be computed successfully
*/
public final double[] distributionForInstance(Instance instance) throws Exception {
return m_root.distributionForInstance(instance, m_useLaplace);
}
/**
* Classifies an instance.
*
* @param instance the instance to classify
* @return the classification for the instance
* @throws Exception if instance can't be classified successfully
*/
public double classifyInstance(Instance instance) throws Exception {
return m_root.classifyInstance(instance);
}