/*
* 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 2 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, write to the Free Software
* Foundation, Inc., 675 Mass Ave, Cambridge, MA 02139, USA.
*/
/*
* HotSpot.java
* Copyright (C) 2008 University of Waikato, Hamilton, New Zealand
*
*/
package weka.associations;
import weka.core.*;
import weka.core.Capabilities.Capability;
import java.io.Serializable;
import java.util.*;
/**
<!-- globalinfo-start -->
* HotSpot learns a set of rules (displayed in a tree-like structure) that maximize/minimize a target variable/value of interest. With a nominal target, one might want to look for segments of the data where there is a high probability of a minority value occuring (given the constraint of a minimum support). For a numeric target, one might be interested in finding segments where this is higher on average than in the whole data set. For example, in a health insurance scenario, find which health insurance groups are at the highest risk (have the highest claim ratio), or, which groups have the highest average insurance payout.
* <p/>
<!-- globalinfo-end -->
*
<!-- options-start -->
* Valid options are: <p/>
*
* <pre> -c <num | first | last>
* The target index. (default = last)</pre>
*
* <pre> -V <num | first | last>
* The target value (nominal target only, default = first)</pre>
*
* <pre> -L
* Minimize rather than maximize.</pre>
*
* <pre> -S <num>
* Minimum value count (nominal target)/segment size (numeric target).
* Values between 0 and 1 are
* interpreted as a percentage of
* the total population; values > 1 are
* interpreted as an absolute number of
* instances (default = 0.3)</pre>
*
* <pre> -M <num>
* Maximum branching factor (default = 2)</pre>
*
* <pre> -I <num>
* Minimum improvement in target value in order
* to add a new branch/test (default = 0.01 (1%))</pre>
*
* <pre> -D
* Output debugging info (duplicate rule lookup
* hash table stats)</pre>
*
<!-- options-end -->
*
* @author Mark Hall (mhall{[at]}pentaho{[dot]}org
* @version $Revision: 6081 $
*/
public class HotSpot
implements Associator, OptionHandler, RevisionHandler,
CapabilitiesHandler, Drawable, Serializable {
static final long serialVersionUID = 42972325096347677L;
/** index of the target attribute */
protected SingleIndex m_targetSI = new SingleIndex("last");
protected int m_target;
/** Support as a fraction of the total training set */
protected double m_support;
/** Support as an instance count */
private int m_supportCount;
/** The global value of the attribute of interest (mean or probability) */
protected double m_globalTarget;
/** The minimum improvement necessary to justify adding a test */
protected double m_minImprovement;
/** Actual global support of the target value (discrete target only) */
protected int m_globalSupport;
/** For discrete target, the index of the value of interest */
protected SingleIndex m_targetIndexSI = new SingleIndex("first");
protected int m_targetIndex;
/** At each level of the tree consider at most this number extensions */
protected int m_maxBranchingFactor;
/** Number of instances in the full data */
protected int m_numInstances;
/** The head of the tree */
protected HotNode m_head;
/** Header of the training data */
protected Instances m_header;
/** Debugging stuff */
protected int m_lookups = 0;
protected int m_insertions = 0;
protected int m_hits = 0;
protected boolean m_debug;
/** Minimize, rather than maximize the target */
protected boolean m_minimize;
/** Error messages relating to too large/small support values */
protected String m_errorMessage;
/** Rule lookup table */
protected HashMap<HotSpotHashKey, String> m_ruleLookup;
/**
* Constructor
*/
public HotSpot() {
resetOptions();
}
/**
* Returns a string describing this classifier
* @return a description of the classifier suitable for
* displaying in the explorer/experimenter gui
*/
public String globalInfo() {
return "HotSpot learns a set of rules (displayed in a tree-like structure) "
+ "that maximize/minimize a target variable/value of interest. "
+ "With a nominal target, one might want to look for segments of the "
+ "data where there is a high probability of a minority value occuring ("
+ "given the constraint of a minimum support). For a numeric target, "
+ "one might be interested in finding segments where this is higher "
+ "on average than in the whole data set. For example, in a health "
+ "insurance scenario, find which health insurance groups are at "
+ "the highest risk (have the highest claim ratio), or, which groups "
+ "have the highest average insurance payout.";
}
/**
* Returns default capabilities of HotSpot
*
* @return the capabilities of HotSpot
*/
public Capabilities getCapabilities() {
Capabilities result = new Capabilities(this);
result.disableAll();
// attributes
result.enable(Capability.NOMINAL_ATTRIBUTES);
result.enable(Capability.NUMERIC_ATTRIBUTES);
result.enable(Capability.MISSING_VALUES);
// class
result.enable(Capability.NO_CLASS);
//result.enable(Capability.NUMERIC_CLASS);
// result.enable(Capability.NOMINAL_CLASS);
return result;
}
/**
* Hash key class for sets of attribute, value tests
*/
protected class HotSpotHashKey {
// split values, one for each attribute (0 indicates att not used).
// for nominal indexes, 1 is added so that 0 can indicate not used.
protected double[] m_splitValues;
// 0 = not used, 1 = "<=", 2 = "=", 3 = ">"
protected byte[] m_testTypes;
protected boolean m_computed = false;
protected int m_key;
public HotSpotHashKey(double[] splitValues, byte[] testTypes) {
m_splitValues = splitValues.clone();
m_testTypes = testTypes.clone();
}
public boolean equals(Object b) {
if ((b == null) || !(b.getClass().equals(this.getClass()))) {
return false;
}
HotSpotHashKey comp = (HotSpotHashKey)b;
boolean ok = true;
for (int i = 0; i < m_splitValues.length; i++) {
if (m_splitValues[i] != comp.m_splitValues[i] ||
m_testTypes[i] != comp.m_testTypes[i]) {
ok = false;
break;
}
}
return ok;
}
public int hashCode() {
if (m_computed) {
return m_key;
} else {
int hv = 0;
for (int i = 0; i < m_splitValues.length; i++) {
hv += (m_splitValues[i] * 5 * i);
hv += (m_testTypes[i] * i * 3);
}
m_computed = true;
m_key = hv;
}
return m_key;
}
}
/**
* Build the tree
*
* @param instances the training instances
* @throws Exception if something goes wrong
*/
public void buildAssociations(Instances instances) throws Exception {
// can associator handle the data?
getCapabilities().testWithFail(instances);
m_errorMessage = null;
m_targetSI.setUpper(instances.numAttributes() - 1);
m_target = m_targetSI.getIndex();
Instances inst = new Instances(instances);
inst.setClassIndex(m_target);
inst.deleteWithMissingClass();
if (inst.attribute(m_target).isNominal()) {
m_targetIndexSI.setUpper(inst.attribute(m_target).numValues() - 1);
m_targetIndex = m_targetIndexSI.getIndex();
} else {
m_targetIndexSI.setUpper(1); // just to stop this SingleIndex from moaning
}
if (m_support <= 0) {
throw new Exception("Support must be greater than zero.");
}
m_numInstances = inst.numInstances();
if (m_support >= 1) {
m_supportCount = (int)m_support;
m_support = m_support / (double)m_numInstances;
}
m_supportCount = (int)Math.floor((m_support * m_numInstances) + 0.5d);
// m_supportCount = (int)(m_support * m_numInstances);
if (m_supportCount < 1) {
m_supportCount = 1;
}
m_header = new Instances(inst, 0);
if (inst.attribute(m_target).isNumeric()) {
if (m_supportCount > m_numInstances) {
m_errorMessage = "Error: support set to more instances than there are in the data!";
return;
}
m_globalTarget = inst.meanOrMode(m_target);
} else {
double[] probs = new double[inst.attributeStats(m_target).nominalCounts.length];
for (int i = 0; i < probs.length; i++) {
probs[i] = (double)inst.attributeStats(m_target).nominalCounts[i];
}
m_globalSupport = (int)probs[m_targetIndex];
// check that global support is greater than min support
if (m_globalSupport < m_supportCount) {
m_errorMessage = "Error: minimum support " + m_supportCount
+ " is too high. Target value "
+ m_header.attribute(m_target).value(m_targetIndex) + " has support "
+ m_globalSupport + ".";
}
Utils.normalize(probs);
m_globalTarget = probs[m_targetIndex];
/* System.err.println("Global target " + m_globalTarget);
System.err.println("Min support count " + m_supportCount); */
}
m_ruleLookup = new HashMap<HotSpotHashKey, String>();
double[] splitVals = new double[m_header.numAttributes()];
byte[] tests = new byte[m_header.numAttributes()];
m_head = new HotNode(inst, m_globalTarget, splitVals, tests);
// m_head = new HotNode(inst, m_globalTarget);
}
/**
* Return the tree as a string
*
* @return a String containing the tree
*/
public String toString() {
StringBuffer buff = new StringBuffer();
buff.append("\nHot Spot\n========");
if (m_errorMessage != null) {
buff.append("\n\n" + m_errorMessage + "\n\n");
return buff.toString();
}
if (m_head == null) {
buff.append("No model built!");
return buff.toString();
}
buff.append("\nTotal population: ");
buff.append("" + m_numInstances + " instances");
buff.append("\nTarget attribute: " + m_header.attribute(m_target).name());
if (m_header.attribute(m_target).isNominal()) {
buff.append("\nTarget value: " + m_header.attribute(m_target).value(m_targetIndex));
buff.append(" [value count in total population: " + m_globalSupport + " instances ("
+ Utils.doubleToString((m_globalTarget * 100.0), 2) + "%)]");
buff.append("\nMinimum value count for segments: ");
} else {
buff.append("\nMinimum segment size: ");
}
buff.append("" + m_supportCount + " instances ("
+ Utils.doubleToString((m_support * 100.0), 2)
+ "% of total population)");
buff.append("\nMaximum branching factor: " + m_maxBranchingFactor);
buff.append("\nMinimum improvement in target: "
+ Utils.doubleToString((m_minImprovement * 100.0), 2) + "%");
buff.append("\n\n");
buff.append(m_header.attribute(m_target).name());
if (m_header.attribute(m_target).isNumeric()) {
buff.append(" (" + Utils.doubleToString(m_globalTarget, 4) + ")");
} else {
buff.append("=" + m_header.attribute(m_target).value(m_targetIndex) + " (");
buff.append("" + Utils.doubleToString((m_globalTarget * 100.0), 2) + "% [");
buff.append("" + m_globalSupport
+ "/" + m_numInstances + "])");
}
m_head.dumpTree(0, buff);
buff.append("\n");
if (m_debug) {
buff.append("\n=== Duplicate rule lookup hashtable stats ===\n");
buff.append("Insertions: "+ m_insertions);
buff.append("\nLookups : "+ m_lookups);
buff.append("\nHits: "+ m_hits);
buff.append("\n");
}
return buff.toString();
}
public String graph() throws Exception {
System.err.println("Here");
m_head.assignIDs(-1);
StringBuffer text = new StringBuffer();
text.append("digraph HotSpot {\n");
text.append("rankdir=LR;\n");
text.append("N0 [label=\""
+ m_header.attribute(m_target).name());
if (m_header.attribute(m_target).isNumeric()) {
text.append("\\n(" + Utils.doubleToString(m_globalTarget, 4) + ")");
} else {
text.append("=" + m_header.attribute(m_target).value(m_targetIndex) + "\\n(");
text.append("" + Utils.doubleToString((m_globalTarget * 100.0), 2) + "% [");
text.append("" + m_globalSupport
+ "/" + m_numInstances + "])");
}
text.append("\" shape=plaintext]\n");
m_head.graphHotSpot(text);
text.append("}\n");
return text.toString();
}
/**
* Inner class representing a node/leaf in the tree
*/
protected class HotNode implements Serializable {
/**
* An inner class holding data on a particular attribute value test
*/
protected class HotTestDetails
implements Comparable<HotTestDetails>,
Serializable {
public double m_merit;
public int m_support;
public int m_subsetSize;
public int m_splitAttIndex;
public double m_splitValue;
public boolean m_lessThan;
public HotTestDetails(int attIndex,
double splitVal,
boolean lessThan,
int support,
int subsetSize,
double merit) {
m_merit = merit;
m_splitAttIndex = attIndex;
m_splitValue = splitVal;
m_lessThan = lessThan;
m_support = support;
m_subsetSize = subsetSize;
}
// reverse order for maximize as PriorityQueue has the least element at the head
public int compareTo(HotTestDetails comp) {
int result = 0;
if (m_minimize) {
if (m_merit == comp.m_merit) {
// larger support is better
if (m_support == comp.m_support) {
} else if (m_support > comp.m_support) {
result = -1;
} else {
result = 1;
}
} else if (m_merit < comp.m_merit) {
result = -1;
} else {
result = 1;
}
} else {
if (m_merit == comp.m_merit) {
// larger support is better
if (m_support == comp.m_support) {
} else if (m_support > comp.m_support) {
result = -1;
} else {
result = 1;
}
} else if (m_merit < comp.m_merit) {
result = 1;
} else {
result = -1;
}
}
return result;
}
}
// the instances at this node
protected Instances m_insts;
// the value (to beat) of the target for these instances
protected double m_targetValue;
// child nodes
protected HotNode[] m_children;
protected HotTestDetails[] m_testDetails;
public int m_id;
/**
* Constructor
*
* @param insts the instances at this node
* @param targetValue the target value
* @param splitVals the values of attributes split on so far down this branch
* @param tests the types of tests corresponding to the split values (<=, =, >)
*/
public HotNode(Instances insts,
double targetValue,
double[] splitVals,
byte[] tests) {
m_insts = insts;
m_targetValue = targetValue;
PriorityQueue<HotTestDetails> splitQueue = new PriorityQueue<HotTestDetails>();
// Consider each attribute
for (int i = 0; i < m_insts.numAttributes(); i++) {
if (i != m_target) {
if (m_insts.attribute(i).isNominal()) {
evaluateNominal(i, splitQueue);
} else {
evaluateNumeric(i, splitQueue);
}
}
}
if (splitQueue.size() > 0) {
int queueSize = splitQueue.size();
// count how many of the potential children are unique
ArrayList<HotTestDetails> newCandidates = new ArrayList<HotTestDetails>();
ArrayList<HotSpotHashKey> keyList = new ArrayList<HotSpotHashKey>();
for (int i = 0; i < queueSize; i++) {
if (newCandidates.size() < m_maxBranchingFactor) {
HotTestDetails temp = splitQueue.poll();
double[] newSplitVals = splitVals.clone();
byte[] newTests = tests.clone();
newSplitVals[temp.m_splitAttIndex] = temp.m_splitValue + 1;
newTests[temp.m_splitAttIndex] =
(m_header.attribute(temp.m_splitAttIndex).isNominal())
? (byte)2 // ==
: (temp.m_lessThan)
? (byte)1
: (byte)3;
HotSpotHashKey key = new HotSpotHashKey(newSplitVals, newTests);
m_lookups++;
if (!m_ruleLookup.containsKey(key)) {
// insert it into the hash table
m_ruleLookup.put(key, "");
newCandidates.add(temp);
keyList.add(key);
m_insertions++;
} else {
m_hits++;
}
} else {
break;
}
}
m_children = new HotNode[(newCandidates.size() < m_maxBranchingFactor)
? newCandidates.size()
: m_maxBranchingFactor];
// save the details of the tests at this node
m_testDetails = new HotTestDetails[m_children.length];
for (int i = 0; i < m_children.length; i++) {
m_testDetails[i] = newCandidates.get(i);
}
// save memory
splitQueue = null;
newCandidates = null;
m_insts = new Instances(m_insts, 0);
// process the children
for (int i = 0; i < m_children.length; i++) {
Instances subset = subset(insts, m_testDetails[i]);
HotSpotHashKey tempKey = keyList.get(i);
m_children[i] = new HotNode(subset, m_testDetails[i].m_merit,
tempKey.m_splitValues, tempKey.m_testTypes);
}
}
}
/**
* Create a subset of instances that correspond to the supplied test details
*
* @param insts the instances to create the subset from
* @param test the details of the split
*/
private Instances subset(Instances insts, HotTestDetails test) {
Instances sub = new Instances(insts, insts.numInstances());
for (int i = 0; i < insts.numInstances(); i++) {
Instance temp = insts.instance(i);
if (!temp.isMissing(test.m_splitAttIndex)) {
if (insts.attribute(test.m_splitAttIndex).isNominal()) {
if (temp.value(test.m_splitAttIndex) == test.m_splitValue) {
sub.add(temp);
}
} else {
if (test.m_lessThan) {
if (temp.value(test.m_splitAttIndex) <= test.m_splitValue) {
sub.add(temp);
}
} else {
if (temp.value(test.m_splitAttIndex) > test.m_splitValue) {
sub.add(temp);
}
}
}
}
}
sub.compactify();
return sub;
}
/**
* Evaluate a numeric attribute for a potential split
*
* @param attIndex the index of the attribute
* @param pq the priority queue of candidtate splits
*/
private void evaluateNumeric(int attIndex, PriorityQueue<HotTestDetails> pq) {
Instances tempInsts = m_insts;
tempInsts.sort(attIndex);
// target sums/counts
double targetLeft = 0;
double targetRight = 0;
int numMissing = 0;
// count missing values and sum/counts for the initial right subset
for (int i = tempInsts.numInstances() - 1; i >= 0; i--) {
if (!tempInsts.instance(i).isMissing(attIndex)) {
targetRight += (tempInsts.attribute(m_target).isNumeric())
? (tempInsts.instance(i).value(m_target))
: ((tempInsts.instance(i).value(m_target) == m_targetIndex)
? 1
: 0);
} else {
numMissing++;
}
}
// are there still enough instances?
if (tempInsts.numInstances() - numMissing <= m_supportCount) {
return;
}
double bestMerit = 0.0;
double bestSplit = 0.0;
double bestSupport = 0.0;
double bestSubsetSize = 0;
boolean lessThan = true;
// denominators
double leftCount = 0;
double rightCount = tempInsts.numInstances() - numMissing;
/* targetRight = (tempInsts.attribute(m_target).isNumeric())
? tempInsts.attributeStats(m_target).numericStats.sum
: tempInsts.attributeStats(m_target).nominalCounts[m_targetIndex]; */
// targetRight = tempInsts.attributeStats(attIndexnominalCounts[m_targetIndex];
// consider all splits
for (int i = 0; i < tempInsts.numInstances() - numMissing; i++) {
Instance inst = tempInsts.instance(i);
if (tempInsts.attribute(m_target).isNumeric()) {
targetLeft += inst.value(m_target);
targetRight -= inst.value(m_target);
} else {
if ((int)inst.value(m_target) == m_targetIndex) {
targetLeft++;
targetRight--;
}
}
leftCount++;
rightCount--;
// move to the end of any ties
if (i < tempInsts.numInstances() - 1 &&
inst.value(attIndex) == tempInsts.instance(i + 1).value(attIndex)) {
continue;
}
// evaluate split
if (tempInsts.attribute(m_target).isNominal()) {
if (targetLeft >= m_supportCount) {
double delta = (m_minimize)
? (bestMerit - (targetLeft / leftCount))
: ((targetLeft / leftCount) - bestMerit);
// if (targetLeft / leftCount > bestMerit) {
if (delta > 0) {
bestMerit = targetLeft / leftCount;
bestSplit = inst.value(attIndex);
bestSupport = targetLeft;
bestSubsetSize = leftCount;
lessThan = true;
// } else if (targetLeft / leftCount == bestMerit) {
} else if (delta == 0) {
// break ties in favour of higher support
if (targetLeft > bestSupport) {
bestMerit = targetLeft / leftCount;
bestSplit = inst.value(attIndex);
bestSupport = targetLeft;
bestSubsetSize = leftCount;
lessThan = true;
}
}
}
if (targetRight >= m_supportCount) {
double delta = (m_minimize)
? (bestMerit - (targetRight / rightCount))
: ((targetRight / rightCount) - bestMerit);
// if (targetRight / rightCount > bestMerit) {
if (delta > 0) {
bestMerit = targetRight / rightCount;
bestSplit = inst.value(attIndex);
bestSupport = targetRight;
bestSubsetSize = rightCount;
lessThan = false;
// } else if (targetRight / rightCount == bestMerit) {
} else if (delta == 0) {
// break ties in favour of higher support
if (targetRight > bestSupport) {
bestMerit = targetRight / rightCount;
bestSplit = inst.value(attIndex);
bestSupport = targetRight;
bestSubsetSize = rightCount;
lessThan = false;
}
}
}
} else {
if (leftCount >= m_supportCount) {
double delta = (m_minimize)
? (bestMerit - (targetLeft / leftCount))
: ((targetLeft / leftCount) - bestMerit);
// if (targetLeft / leftCount > bestMerit) {
if (delta > 0) {
bestMerit = targetLeft / leftCount;
bestSplit = inst.value(attIndex);
bestSupport = leftCount;
bestSubsetSize = leftCount;
lessThan = true;
// } else if (targetLeft / leftCount == bestMerit) {
} else if (delta == 0) {
// break ties in favour of higher support
if (leftCount > bestSupport) {
bestMerit = targetLeft / leftCount;
bestSplit = inst.value(attIndex);
bestSupport = leftCount;
bestSubsetSize = leftCount;
lessThan = true;
}
}
}
if (rightCount >= m_supportCount) {
double delta = (m_minimize)
? (bestMerit - (targetRight / rightCount))
: ((targetRight / rightCount) - bestMerit);
// if (targetRight / rightCount > bestMerit) {
if (delta > 0) {
bestMerit = targetRight / rightCount;
bestSplit = inst.value(attIndex);
bestSupport = rightCount;
bestSubsetSize = rightCount;
lessThan = false;
// } else if (targetRight / rightCount == bestMerit) {
} else if (delta == 0) {
// break ties in favour of higher support
if (rightCount > bestSupport) {
bestMerit = targetRight / rightCount;
bestSplit = inst.value(attIndex);
bestSupport = rightCount;
bestSubsetSize = rightCount;
lessThan = false;
}
}
}
}
}
double delta = (m_minimize)
? m_targetValue - bestMerit
: bestMerit - m_targetValue;
// Have we found a candidate split?
if (bestSupport > 0 && (delta / m_targetValue >= m_minImprovement)) {
/* System.err.println("Evaluating " + tempInsts.attribute(attIndex).name());
System.err.println("Merit : " + bestMerit);
System.err.println("Support : " + bestSupport); */
// double suppFraction = bestSupport / m_numInstances;
HotTestDetails newD = new HotTestDetails(attIndex, bestSplit,
lessThan, (int)bestSupport,
(int)bestSubsetSize,
bestMerit);
pq.add(newD);
}
}
/**
* Evaluate a nominal attribute for a potential split
*
* @param attIndex the index of the attribute
* @param pq the priority queue of candidtate splits
*/
private void evaluateNominal(int attIndex, PriorityQueue<HotTestDetails> pq) {
int[] counts = m_insts.attributeStats(attIndex).nominalCounts;
boolean ok = false;
// only consider attribute values that result in subsets that meet/exceed min support
for (int i = 0; i < m_insts.attribute(attIndex).numValues(); i++) {
if (counts[i] >= m_supportCount) {
ok = true;
break;
}
}
if (ok) {
double[] subsetMerit =
new double[m_insts.attribute(attIndex).numValues()];
for (int i = 0; i < m_insts.numInstances(); i++) {
Instance temp = m_insts.instance(i);
if (!temp.isMissing(attIndex)) {
int attVal = (int)temp.value(attIndex);
if (m_insts.attribute(m_target).isNumeric()) {
subsetMerit[attVal] += temp.value(m_target);
} else {
subsetMerit[attVal] +=
((int)temp.value(m_target) == m_targetIndex)
? 1.0
: 0;
}
}
}
// add to queue if it meets min support and exceeds the merit for the full set
for (int i = 0; i < m_insts.attribute(attIndex).numValues(); i++) {
// does the subset based on this value have enough instances, and, furthermore,
// does the target value (nominal only) occur enough times to exceed min support
if (counts[i] >= m_supportCount &&
((m_insts.attribute(m_target).isNominal())
? (subsetMerit[i] >= m_supportCount) // nominal only test
: true)) {
double merit = subsetMerit[i] / counts[i]; //subsetMerit[i][1];
double delta = (m_minimize)
? m_targetValue - merit
: merit - m_targetValue;
if (delta / m_targetValue >= m_minImprovement) {
double support =
(m_insts.attribute(m_target).isNominal())
? subsetMerit[i]
: counts[i];
HotTestDetails newD = new HotTestDetails(attIndex, (double)i,
false, (int)support,
counts[i],
merit);
pq.add(newD);
}
}
}
}
}
public int assignIDs(int lastID) {
int currentLastID = lastID + 1;
m_id = currentLastID;
if (m_children != null) {
for (int i = 0; i < m_children.length; i++) {
currentLastID = m_children[i].assignIDs(currentLastID);
}
}
return currentLastID;
}
private void addNodeDetails(StringBuffer buff, int i, String spacer) {
buff.append(m_header.attribute(m_testDetails[i].m_splitAttIndex).name());
if (m_header.attribute(m_testDetails[i].m_splitAttIndex).isNumeric()) {
if (m_testDetails[i].m_lessThan) {
buff.append(" <= ");
} else {
buff.append(" > ");
}
buff.append(Utils.doubleToString(m_testDetails[i].m_splitValue, 4));
} else {
buff.append(" = " + m_header.
attribute(m_testDetails[i].m_splitAttIndex).
value((int)m_testDetails[i].m_splitValue));
}
if (m_header.attribute(m_target).isNumeric()) {
buff.append(spacer + "(" + Utils.doubleToString(m_testDetails[i].m_merit, 4) + " ["
+ m_testDetails[i].m_support + "])");
} else {
buff.append(spacer + "(" + Utils.doubleToString((m_testDetails[i].m_merit * 100.0), 2) + "% ["
+ m_testDetails[i].m_support
+ "/" + m_testDetails[i].m_subsetSize + "])");
}
}
private void graphHotSpot(StringBuffer text) {
if (m_children != null) {
for (int i = 0; i < m_children.length; i++) {
text.append("N" + m_children[i].m_id);
text.append(" [label=\"");
addNodeDetails(text, i, "\\n");
text.append("\" shape=plaintext]\n");
m_children[i].graphHotSpot(text);
text.append("N" + m_id + "->" + "N" + m_children[i].m_id + "\n");
}
}
}
/**
* Traverse the tree to create a string description
*
* @param depth the depth at this point in the tree
* @param buff the string buffer to append node details to
*/
protected void dumpTree(int depth, StringBuffer buff) {
if (m_children == null) {
// buff.append("\n");
} else {
for (int i = 0; i < m_children.length; i++) {
buff.append("\n ");
for (int j = 0; j < depth; j++) {
buff.append("| ");
}
addNodeDetails(buff, i, " ");
m_children[i].dumpTree(depth + 1, buff);
}
}
}
}
/**
* Returns the tip text for this property
* @return tip text for this property suitable for
* displaying in the explorer/experimenter gui
*/
public String targetTipText() {
return "The target attribute of interest.";
}
/**
* Set the target index
*
* @param target the target index as a string (1-based)
*/
public void setTarget(String target) {
m_targetSI.setSingleIndex(target);
}
/**
* Get the target index as a string
*
* @return the target index (1-based)
*/
public String getTarget() {
return m_targetSI.getSingleIndex();
}
/**
* Returns the tip text for this property
* @return tip text for this property suitable for
* displaying in the explorer/experimenter gui
*/
public String targetIndexTipText() {
return "The value of the target (nominal attributes only) of interest.";
}
/**
* For a nominal target, set the index of the value of interest (1-based)
*
* @param index the index of the nominal value of interest
*/
public void setTargetIndex(String index) {
m_targetIndexSI.setSingleIndex(index);
}
/**
* For a nominal target, get the index of the value of interest (1-based)
*
* @return the index of the nominal value of interest
*/
public String getTargetIndex() {
return m_targetIndexSI.getSingleIndex();
}
/**
* Returns the tip text for this property
* @return tip text for this property suitable for
* displaying in the explorer/experimenter gui
*/
public String minimizeTargetTipText() {
return "Minimize rather than maximize the target.";
}
/**
* Set whether to minimize the target rather than maximize
*
* @param m true if target is to be minimized
*/
public void setMinimizeTarget(boolean m) {
m_minimize = m;
}
/**
* Get whether to minimize the target rather than maximize
*
* @return true if target is to be minimized
*/
public boolean getMinimizeTarget() {
return m_minimize;
}
/**
* Returns the tip text for this property
* @return tip text for this property suitable for
* displaying in the explorer/experimenter gui
*/
public String supportTipText() {
return "The minimum support. Values between 0 and 1 are interpreted "
+ "as a percentage of the total population; values > 1 are "
+ "interpreted as an absolute number of instances";
}
/**
* Get the minimum support
*
* @return the minimum support
*/
public double getSupport() {
return m_support;
}
/**
* Set the minimum support
*
* @param s the minimum support
*/
public void setSupport(double s) {
m_support = s;
}
/**
* Returns the tip text for this property
* @return tip text for this property suitable for
* displaying in the explorer/experimenter gui
*/
public String maxBranchingFactorTipText() {
return "Maximum branching factor. The maximum number of children "
+ "to consider extending each node with.";
}
/**
* Set the maximum branching factor
*
* @param b the maximum branching factor
*/
public void setMaxBranchingFactor(int b) {
m_maxBranchingFactor = b;
}
/**
* Get the maximum branching factor
*
* @return the maximum branching factor
*/
public int getMaxBranchingFactor() {
return m_maxBranchingFactor;
}
/**
* Returns the tip text for this property
* @return tip text for this property suitable for
* displaying in the explorer/experimenter gui
*/
public String minImprovementTipText() {
return "Minimum improvement in target value in order to "
+ "consider adding a new branch/test";
}
/**
* Set the minimum improvement in the target necessary to add a test
*
* @param i the minimum improvement
*/
public void setMinImprovement(double i) {
m_minImprovement = i;
}
/**
* Get the minimum improvement in the target necessary to add a test
*
* @return the minimum improvement
*/
public double getMinImprovement() {
return m_minImprovement;
}
/**
* Returns the tip text for this property
* @return tip text for this property suitable for
* displaying in the explorer/experimenter gui
*/
public String debugTipText() {
return "Output debugging info (duplicate rule lookup hash table stats).";
}
/**
* Set whether debugging info is output
*
* @param d true to output debugging info
*/
public void setDebug(boolean d) {
m_debug = d;
}
/**
* Get whether debugging info is output
*
* @return true if outputing debugging info
*/
public boolean getDebug() {
return m_debug;
}
/**
* Returns an enumeration describing the available options.
*
* @return an enumeration of all the available options.
*/
public Enumeration listOptions() {
Vector newVector = new Vector();
newVector.addElement(new Option("\tThe target index. (default = last)",
"c", 1,
"-c <num | first | last>"));
newVector.addElement(new Option("\tThe target value (nominal target only, default = first)",
"V", 1,
"-V <num | first | last>"));
newVector.addElement(new Option("\tMinimize rather than maximize.", "L", 0, "-L"));
newVector.addElement(new Option("\tMinimum value count (nominal target)/segment size "
+ "(numeric target)."
+"\n\tValues between 0 and 1 are "
+ "\n\tinterpreted as a percentage of "
+ "\n\tthe total population; values > 1 are "
+ "\n\tinterpreted as an absolute number of "
+"\n\tinstances (default = 0.3)",
"-S", 1,
"-S <num>"));
newVector.addElement(new Option("\tMaximum branching factor (default = 2)",
"-M", 1,
"-M <num>"));
newVector.addElement(new Option("\tMinimum improvement in target value in order "
+ "\n\tto add a new branch/test (default = 0.01 (1%))",
"-I", 1,
"-I <num>"));
newVector.addElement(new Option("\tOutput debugging info (duplicate rule lookup "
+ "\n\thash table stats)", "-D", 0, "-D"));
return newVector.elements();
}
/**
* Reset options to their defaults
*/
public void resetOptions() {
m_support = 0.33;
m_minImprovement = 0.01; // 1%
m_maxBranchingFactor = 2;
m_minimize = false;
m_debug = false;
setTarget("last");
setTargetIndex("first");
m_errorMessage = null;
}
/**
* Parses a given list of options. <p/>
*
<!-- options-start -->
* Valid options are: <p/>
*
* <pre> -c <num | first | last>
* The target index. (default = last)</pre>
*
* <pre> -V <num | first | last>
* The target value (nominal target only, default = first)</pre>
*
* <pre> -L
* Minimize rather than maximize.</pre>
*
* <pre> -S <num>
* Minimum value count (nominal target)/segment size (numeric target).
* Values between 0 and 1 are
* interpreted as a percentage of
* the total population; values > 1 are
* interpreted as an absolute number of
* instances (default = 0.3)</pre>
*
* <pre> -M <num>
* Maximum branching factor (default = 2)</pre>
*
* <pre> -I <num>
* Minimum improvement in target value in order
* to add a new branch/test (default = 0.01 (1%))</pre>
*
* <pre> -D
* Output debugging info (duplicate rule lookup
* hash table stats)</pre>
*
<!-- options-end -->
*
* @param options the list of options as an array of strings
* @exception Exception if an option is not supported
*/
public void setOptions(String[] options) throws Exception {
resetOptions();
String tempString = Utils.getOption('c', options);
if (tempString.length() != 0) {
setTarget(tempString);
}
tempString = Utils.getOption('V', options);
if (tempString.length() != 0) {
setTargetIndex(tempString);
}
setMinimizeTarget(Utils.getFlag('L', options));
tempString = Utils.getOption('S', options);
if (tempString.length() != 0) {
setSupport(Double.parseDouble(tempString));
}
tempString = Utils.getOption('M', options);
if (tempString.length() != 0) {
setMaxBranchingFactor(Integer.parseInt(tempString));
}
tempString = Utils.getOption('I', options);
if (tempString.length() != 0) {
setMinImprovement(Double.parseDouble(tempString));
}
setDebug(Utils.getFlag('D', options));
}
/**
* Gets the current settings of HotSpot.
*
* @return an array of strings suitable for passing to setOptions
*/
public String [] getOptions() {
String[] options = new String[12];
int current = 0;
options[current++] = "-c"; options[current++] = getTarget();
options[current++] = "-V"; options[current++] = getTargetIndex();
if (getMinimizeTarget()) {
options[current++] = "-L";
}
options[current++] = "-S"; options[current++] = "" + getSupport();
options[current++] = "-M"; options[current++] = "" + getMaxBranchingFactor();
options[current++] = "-I"; options[current++] = "" + getMinImprovement();
if (getDebug()) {
options[current++] = "-D";
}
while (current < options.length) {
options[current++] = "";
}
return options;
}
/**
* Returns the revision string.
*
* @return the revision
*/
public String getRevision() {
return RevisionUtils.extract("$Revision: 6081 $");
}
/**
* Returns the type of graph this scheme
* represents.
* @return Drawable.TREE
*/
public int graphType() {
return Drawable.TREE;
}
/**
* Main method for testing this class.
*
* @param args the options
*/
public static void main(String[] args) {
try {
HotSpot h = new HotSpot();
AbstractAssociator.runAssociator(new HotSpot(), args);
} catch (Exception ex) {
ex.printStackTrace();
}
}
}