/*
* 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.
*/
/*
* ForwardSelection.java
* Copyright (C) 1999 Mark Hall
*
*/
package weka.attributeSelection;
import java.io.*;
import java.util.*;
import weka.core.*;
/**
* Class for performing a forward selection hill climbing search. <p>
*
* Valid options are: <p>
*
* -P <start set> <br>
* Specify a starting set of attributes. Eg 1,4,7-9. <p>
*
* -R <br>
* Produce a ranked list of attributes. <p>
*
* -T <threshold> <br>
* Specify a threshold by which the AttributeSelection module can. <br>
* discard attributes. Use in conjunction with -R <p>
*
* @author Mark Hall (mhall@cs.waikato.ac.nz)
* @version $Revision: 1.1.1.1 $
*/
public class ForwardSelection extends ASSearch
implements RankedOutputSearch, StartSetHandler, OptionHandler {
/** does the data have a class */
private boolean m_hasClass;
/** holds the class index */
private int m_classIndex;
/** number of attributes in the data */
private int m_numAttribs;
/** true if the user has requested a ranked list of attributes */
private boolean m_rankingRequested;
/**
* go from one side of the search space to the other in order to generate
* a ranking
*/
private boolean m_doRank;
/** used to indicate whether or not ranking has been performed */
private boolean m_doneRanking;
/**
* A threshold by which to discard attributes---used by the
* AttributeSelection module
*/
private double m_threshold;
/** The number of attributes to select. -1 indicates that all attributes
are to be retained. Has precedence over m_threshold */
private int m_numToSelect = -1;
private int m_calculatedNumToSelect;
/** the merit of the best subset found */
private double m_bestMerit;
/** a ranked list of attribute indexes */
private double [][] m_rankedAtts;
private int m_rankedSoFar;
/** the best subset found */
private BitSet m_best_group;
private ASEvaluation m_ASEval;
private Instances m_Instances;
/** holds the start set for the search as a Range */
private Range m_startRange;
/** holds an array of starting attributes */
private int [] m_starting;
/**
* Returns a string describing this search method
* @return a description of the search suitable for
* displaying in the explorer/experimenter gui
*/
public String globalInfo() {
return "ForwardSelection :\n\nPerforms a greedy forward search through "
+"the space of attribute subsets. May start with no attributes or from "
+"an arbitrary point in the space. Stops when the addition of any "
+"remaining attributes results in a decrease in evaluation. "
+"Can also produce a ranked list of "
+"attributes by traversing the space from one side to the other and "
+"recording the order that attributes are selected.\n";
}
public ForwardSelection () {
m_threshold = -Double.MAX_VALUE;
m_doneRanking = false;
m_startRange = new Range();
m_starting = null;
resetOptions();
}
/**
* Returns the tip text for this property
* @return tip text for this property suitable for
* displaying in the explorer/experimenter gui
*/
public String thresholdTipText() {
return "Set threshold by which attributes can be discarded. Default value "
+ "results in no attributes being discarded. Use in conjunction with "
+ "generateRanking";
}
/**
* Set the threshold by which the AttributeSelection module can discard
* attributes.
* @param threshold the threshold.
*/
public void setThreshold(double threshold) {
m_threshold = threshold;
}
/**
* Returns the threshold so that the AttributeSelection module can
* discard attributes from the ranking.
*/
public double getThreshold() {
return m_threshold;
}
/**
* Returns the tip text for this property
* @return tip text for this property suitable for
* displaying in the explorer/experimenter gui
*/
public String numToSelectTipText() {
return "Specify the number of attributes to retain. The default value "
+"(-1) indicates that all attributes are to be retained. Use either "
+"this option or a threshold to reduce the attribute set.";
}
/**
* Specify the number of attributes to select from the ranked list
* (if generating a ranking). -1
* indicates that all attributes are to be retained.
* @param n the number of attributes to retain
*/
public void setNumToSelect(int n) {
m_numToSelect = n;
}
/**
* Gets the number of attributes to be retained.
* @return the number of attributes to retain
*/
public int getNumToSelect() {
return m_numToSelect;
}
/**
* Gets the calculated number of attributes to retain. This is the
* actual number of attributes to retain. This is the same as
* getNumToSelect if the user specifies a number which is not less
* than zero. Otherwise it should be the number of attributes in the
* (potentially transformed) data.
*/
public int getCalculatedNumToSelect() {
if (m_numToSelect >= 0) {
m_calculatedNumToSelect = m_numToSelect;
}
return m_calculatedNumToSelect;
}
/**
* Returns the tip text for this property
* @return tip text for this property suitable for
* displaying in the explorer/experimenter gui
*/
public String generateRankingTipText() {
return "Set to true if a ranked list is required.";
}
/**
* Records whether the user has requested a ranked list of attributes.
* @param doRank true if ranking is requested
*/
public void setGenerateRanking(boolean doRank) {
m_rankingRequested = doRank;
}
/**
* Gets whether ranking has been requested. This is used by the
* AttributeSelection module to determine if rankedAttributes()
* should be called.
* @return true if ranking has been requested.
*/
public boolean getGenerateRanking() {
return m_rankingRequested;
}
/**
* Returns the tip text for this property
* @return tip text for this property suitable for
* displaying in the explorer/experimenter gui
*/
public String startSetTipText() {
return "Set the start point for the search. This is specified as a comma "
+"seperated list off attribute indexes starting at 1. It can include "
+"ranges. Eg. 1,2,5-9,17.";
}
/**
* Sets a starting set of attributes for the search. It is the
* search method's responsibility to report this start set (if any)
* in its toString() method.
* @param startSet a string containing a list of attributes (and or ranges),
* eg. 1,2,6,10-15.
* @exception Exception if start set can't be set.
*/
public void setStartSet (String startSet) throws Exception {
m_startRange.setRanges(startSet);
}
/**
* Returns a list of attributes (and or attribute ranges) as a String
* @return a list of attributes (and or attribute ranges)
*/
public String getStartSet () {
return m_startRange.getRanges();
}
/**
* Returns an enumeration describing the available options.
* @return an enumeration of all the available options.
**/
public Enumeration listOptions () {
Vector newVector = new Vector(3);
newVector
.addElement(new Option("\tSpecify a starting set of attributes."
+ "\n\tEg. 1,3,5-7."
,"P",1
, "-P <start set>"));
newVector.addElement(new Option("\tProduce a ranked list of attributes."
,"R",0,"-R"));
newVector
.addElement(new Option("\tSpecify a theshold by which attributes"
+ "\n\tmay be discarded from the ranking."
+"\n\tUse in conjuction with -R","T",1
, "-T <threshold>"));
newVector
.addElement(new Option("\tSpecify number of attributes to select"
,"N",1
, "-N <num to select>"));
return newVector.elements();
}
/**
* Parses a given list of options.
*
* Valid options are: <p>
*
* -P <start set> <br>
* Specify a starting set of attributes. Eg 1,4,7-9. <p>
*
* -R <br>
* Produce a ranked list of attributes. <p>
*
* -T <threshold> <br>
* Specify a threshold by which the AttributeSelection module can <br>
* discard attributes. Use in conjunction with -R <p>
*
* -N <number to retain> <br>
* Specify the number of attributes to retain. Overides any threshold. <br>
* <p>
*
* @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 {
String optionString;
resetOptions();
optionString = Utils.getOption('P', options);
if (optionString.length() != 0) {
setStartSet(optionString);
}
setGenerateRanking(Utils.getFlag('R', options));
optionString = Utils.getOption('T', options);
if (optionString.length() != 0) {
Double temp;
temp = Double.valueOf(optionString);
setThreshold(temp.doubleValue());
}
optionString = Utils.getOption('N', options);
if (optionString.length() != 0) {
setNumToSelect(Integer.parseInt(optionString));
}
}
/**
* Gets the current settings of ReliefFAttributeEval.
*
* @return an array of strings suitable for passing to setOptions()
*/
public String[] getOptions () {
String[] options = new String[7];
int current = 0;
if (!(getStartSet().equals(""))) {
options[current++] = "-P";
options[current++] = ""+startSetToString();
}
if (getGenerateRanking()) {
options[current++] = "-R";
}
options[current++] = "-T";
options[current++] = "" + getThreshold();
options[current++] = "-N";
options[current++] = ""+getNumToSelect();
while (current < options.length) {
options[current++] = "";
}
return options;
}
/**
* converts the array of starting attributes to a string. This is
* used by getOptions to return the actual attributes specified
* as the starting set. This is better than using m_startRanges.getRanges()
* as the same start set can be specified in different ways from the
* command line---eg 1,2,3 == 1-3. This is to ensure that stuff that
* is stored in a database is comparable.
* @return a comma seperated list of individual attribute numbers as a String
*/
private String startSetToString() {
StringBuffer FString = new StringBuffer();
boolean didPrint;
if (m_starting == null) {
return getStartSet();
}
for (int i = 0; i < m_starting.length; i++) {
didPrint = false;
if ((m_hasClass == false) ||
(m_hasClass == true && i != m_classIndex)) {
FString.append((m_starting[i] + 1));
didPrint = true;
}
if (i == (m_starting.length - 1)) {
FString.append("");
}
else {
if (didPrint) {
FString.append(",");
}
}
}
return FString.toString();
}
/**
* returns a description of the search.
* @return a description of the search as a String.
*/
public String toString() {
StringBuffer FString = new StringBuffer();
FString.append("\tForward Selection.\n\tStart set: ");
if (m_starting == null) {
FString.append("no attributes\n");
}
else {
FString.append(startSetToString()+"\n");
}
if (!m_doneRanking) {
FString.append("\tMerit of best subset found: "
+Utils.doubleToString(Math.abs(m_bestMerit),8,3)+"\n");
}
if ((m_threshold != -Double.MAX_VALUE) && (m_doneRanking)) {
FString.append("\tThreshold for discarding attributes: "
+ Utils.doubleToString(m_threshold,8,4)+"\n");
}
return FString.toString();
}
/**
* Searches the attribute subset space by forward selection.
*
* @param ASEvaluator the attribute evaluator to guide the search
* @param data the training instances.
* @return an array (not necessarily ordered) of selected attribute indexes
* @exception Exception if the search can't be completed
*/
public int[] search (ASEvaluation ASEval, Instances data)
throws Exception {
int i;
double best_merit = -Double.MAX_VALUE;
double temp_best,temp_merit;
int temp_index=0;
BitSet temp_group;
if (data != null) { // this is a fresh run so reset
resetOptions();
m_Instances = data;
}
m_ASEval = ASEval;
m_numAttribs = m_Instances.numAttributes();
if (m_best_group == null) {
m_best_group = new BitSet(m_numAttribs);
}
if (!(m_ASEval instanceof SubsetEvaluator)) {
throw new Exception(m_ASEval.getClass().getName()
+ " is not a "
+ "Subset evaluator!");
}
m_startRange.setUpper(m_numAttribs-1);
if (!(getStartSet().equals(""))) {
m_starting = m_startRange.getSelection();
}
if (m_ASEval instanceof UnsupervisedSubsetEvaluator) {
m_hasClass = false;
}
else {
m_hasClass = true;
m_classIndex = m_Instances.classIndex();
}
SubsetEvaluator ASEvaluator = (SubsetEvaluator)m_ASEval;
if (m_rankedAtts == null) {
m_rankedAtts = new double[m_numAttribs][2];
m_rankedSoFar = 0;
}
// If a starting subset has been supplied, then initialise the bitset
if (m_starting != null) {
for (i = 0; i < m_starting.length; i++) {
if ((m_starting[i]) != m_classIndex) {
m_best_group.set(m_starting[i]);
}
}
}
// Evaluate the initial subset
best_merit = ASEvaluator.evaluateSubset(m_best_group);
// main search loop
boolean done = false;
boolean addone = false;
while (!done) {
temp_group = (BitSet)m_best_group.clone();
temp_best = best_merit;
if (m_doRank) {
temp_best = -Double.MAX_VALUE;
}
done = true;
addone = false;
for (i=0;i<m_numAttribs;i++) {
if ((i != m_classIndex) && (!temp_group.get(i))) {
// set the bit
temp_group.set(i);
temp_merit = ASEvaluator.evaluateSubset(temp_group);
if (temp_merit > temp_best) {
temp_best = temp_merit;
temp_index = i;
addone = true;
done = false;
}
// unset the bit
temp_group.clear(i);
if (m_doRank) {
done = false;
}
}
}
if (addone) {
m_best_group.set(temp_index);
best_merit = temp_best;
m_rankedAtts[m_rankedSoFar][0] = temp_index;
m_rankedAtts[m_rankedSoFar][1] = best_merit;
m_rankedSoFar++;
}
}
m_bestMerit = best_merit;
return attributeList(m_best_group);
}
/**
* Produces a ranked list of attributes. Search must have been performed
* prior to calling this function. Search is called by this function to
* complete the traversal of the the search space. A list of
* attributes and merits are returned. The attributes a ranked by the
* order they are added to the subset during a forward selection search.
* Individual merit values reflect the merit associated with adding the
* corresponding attribute to the subset; because of this, merit values
* may initially increase but then decrease as the best subset is
* "passed by" on the way to the far side of the search space.
*
* @return an array of attribute indexes and associated merit values
* @exception Exception if something goes wrong.
*/
public double [][] rankedAttributes() throws Exception {
if (m_rankedAtts == null || m_rankedSoFar == -1) {
throw new Exception("Search must be performed before attributes "
+"can be ranked.");
}
m_doRank = true;
search (m_ASEval, null);
double [][] final_rank = new double [m_rankedSoFar][2];
for (int i=0;i<m_rankedSoFar;i++) {
final_rank[i][0] = m_rankedAtts[i][0];
final_rank[i][1] = m_rankedAtts[i][1];
}
resetOptions();
m_doneRanking = true;
if (m_numToSelect > final_rank.length) {
throw new Exception("More attributes requested than exist in the data");
}
if (m_numToSelect <= 0) {
if (m_threshold == -Double.MAX_VALUE) {
m_calculatedNumToSelect = final_rank.length;
} else {
determineNumToSelectFromThreshold(final_rank);
}
}
return final_rank;
}
private void determineNumToSelectFromThreshold(double [][] ranking) {
int count = 0;
for (int i = 0; i < ranking.length; i++) {
if (ranking[i][1] > m_threshold) {
count++;
}
}
m_calculatedNumToSelect = count;
}
/**
* converts a BitSet into a list of attribute indexes
* @param group the BitSet to convert
* @return an array of attribute indexes
**/
private int[] attributeList (BitSet group) {
int count = 0;
// count how many were selected
for (int i = 0; i < m_numAttribs; i++) {
if (group.get(i)) {
count++;
}
}
int[] list = new int[count];
count = 0;
for (int i = 0; i < m_numAttribs; i++) {
if (group.get(i)) {
list[count++] = i;
}
}
return list;
}
/**
* Resets options
*/
private void resetOptions() {
m_doRank = false;
m_best_group = null;
m_ASEval = null;
m_Instances = null;
m_rankedSoFar = -1;
m_rankedAtts = null;
}
}