/*
* 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.
*/
/*
* Ranker.java
* Copyright (C) 1999 University of Waikato, Hamilton, New Zealand
*
*/
package weka.attributeSelection;
import weka.core.Instances;
import weka.core.Option;
import weka.core.OptionHandler;
import weka.core.Range;
import weka.core.RevisionUtils;
import weka.core.Utils;
import java.util.Enumeration;
import java.util.Vector;
/**
<!-- globalinfo-start -->
* Ranker : <br/>
* <br/>
* Ranks attributes by their individual evaluations. Use in conjunction with attribute evaluators (ReliefF, GainRatio, Entropy etc).<br/>
* <p/>
<!-- globalinfo-end -->
*
<!-- options-start -->
* Valid options are: <p/>
*
* <pre> -P <start set>
* Specify a starting set of attributes.
* Eg. 1,3,5-7.
* Any starting attributes specified are
* ignored during the ranking.</pre>
*
* <pre> -T <threshold>
* Specify a theshold by which attributes
* may be discarded from the ranking.</pre>
*
* <pre> -N <num to select>
* Specify number of attributes to select</pre>
*
<!-- options-end -->
*
* @author Mark Hall (mhall@cs.waikato.ac.nz)
* @version $Revision: 1.26 $
*/
public class Ranker
extends ASSearch
implements RankedOutputSearch, StartSetHandler, OptionHandler {
/** for serialization */
static final long serialVersionUID = -9086714848510751934L;
/** Holds the starting set as an array of attributes */
private int[] m_starting;
/** Holds the start set for the search as a range */
private Range m_startRange;
/** Holds the ordered list of attributes */
private int[] m_attributeList;
/** Holds the list of attribute merit scores */
private double[] m_attributeMerit;
/** Data has class attribute---if unsupervised evaluator then no class */
private boolean m_hasClass;
/** Class index of the data if supervised evaluator */
private int m_classIndex;
/** The number of attribtes */
private int m_numAttribs;
/**
* 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;
/** Used to compute the number to select */
private int m_calculatedNumToSelect = -1;
/**
* 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 "Ranker : \n\nRanks attributes by their individual evaluations. "
+"Use in conjunction with attribute evaluators (ReliefF, GainRatio, "
+"Entropy etc).\n";
}
/**
* Constructor
*/
public Ranker () {
resetOptions();
}
/**
* 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. -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 to select. This might be computed
* from a threshold, or if < 0 is set as the number to select then
* it is set to the number of attributes in the (transformed) data.
* @return the calculated number of attributes to select
*/
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 thresholdTipText() {
return "Set threshold by which attributes can be discarded. Default value "
+ "results in no attributes being discarded. Use either this option or "
+"numToSelect to reduce the attribute set.";
}
/**
* 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 generateRankingTipText() {
return "A constant option. Ranker is only capable of generating "
+" attribute rankings.";
}
/**
* This is a dummy set method---Ranker is ONLY capable of producing
* a ranked list of attributes for attribute evaluators.
* @param doRank this parameter is N/A and is ignored
*/
public void setGenerateRanking(boolean doRank) {
}
/**
* This is a dummy method. Ranker can ONLY be used with attribute
* evaluators and as such can only produce a ranked list of attributes
* @return true all the time.
*/
public boolean getGenerateRanking() {
return true;
}
/**
* 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 "Specify a set of attributes to ignore. "
+" When generating the ranking, Ranker will not evaluate the attributes "
+" in this list. "
+"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.
* @throws 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.\n"
+"\tAny starting attributes specified are\n"
+"\tignored during the ranking."
,"P",1
, "-P <start set>"));
newVector
.addElement(new Option("\tSpecify a theshold by which attributes\n"
+ "\tmay be discarded from the ranking.","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. <p/>
*
<!-- options-start -->
* Valid options are: <p/>
*
* <pre> -P <start set>
* Specify a starting set of attributes.
* Eg. 1,3,5-7.
* Any starting attributes specified are
* ignored during the ranking.</pre>
*
* <pre> -T <threshold>
* Specify a theshold by which attributes
* may be discarded from the ranking.</pre>
*
* <pre> -N <num to select>
* Specify number of attributes to select</pre>
*
<!-- options-end -->
*
* @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 {
String optionString;
resetOptions();
optionString = Utils.getOption('P', options);
if (optionString.length() != 0) {
setStartSet(optionString);
}
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[6];
int current = 0;
if (!(getStartSet().equals(""))) {
options[current++] = "-P";
options[current++] = ""+startSetToString();
}
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();
}
/**
* Kind of a dummy search algorithm. Calls a Attribute evaluator to
* evaluate each attribute not included in the startSet and then sorts
* them to produce a ranked list of attributes.
*
* @param ASEval the attribute evaluator to guide the search
* @param data the training instances.
* @return an array (not necessarily ordered) of selected attribute indexes
* @throws Exception if the search can't be completed
*/
public int[] search (ASEvaluation ASEval, Instances data)
throws Exception {
int i, j;
if (!(ASEval instanceof AttributeEvaluator)) {
throw new Exception(ASEval.getClass().getName()
+ " is not a"
+ "Attribute evaluator!");
}
m_numAttribs = data.numAttributes();
if (ASEval instanceof UnsupervisedAttributeEvaluator) {
m_hasClass = false;
}
else {
m_classIndex = data.classIndex();
if (m_classIndex >= 0) {
m_hasClass = true;
} else {
m_hasClass = false;
}
}
// get the transformed data and check to see if the transformer
// preserves a class index
if (ASEval instanceof AttributeTransformer) {
data = ((AttributeTransformer)ASEval).transformedHeader();
if (m_classIndex >= 0 && data.classIndex() >= 0) {
m_classIndex = data.classIndex();
m_hasClass = true;
}
}
m_startRange.setUpper(m_numAttribs - 1);
if (!(getStartSet().equals(""))) {
m_starting = m_startRange.getSelection();
}
int sl=0;
if (m_starting != null) {
sl = m_starting.length;
}
if ((m_starting != null) && (m_hasClass == true)) {
// see if the supplied list contains the class index
boolean ok = false;
for (i = 0; i < sl; i++) {
if (m_starting[i] == m_classIndex) {
ok = true;
break;
}
}
if (ok == false) {
sl++;
}
}
else {
if (m_hasClass == true) {
sl++;
}
}
m_attributeList = new int[m_numAttribs - sl];
m_attributeMerit = new double[m_numAttribs - sl];
// add in those attributes not in the starting (omit list)
for (i = 0, j = 0; i < m_numAttribs; i++) {
if (!inStarting(i)) {
m_attributeList[j++] = i;
}
}
AttributeEvaluator ASEvaluator = (AttributeEvaluator)ASEval;
for (i = 0; i < m_attributeList.length; i++) {
m_attributeMerit[i] = ASEvaluator.evaluateAttribute(m_attributeList[i]);
}
double[][] tempRanked = rankedAttributes();
int[] rankedAttributes = new int[m_attributeList.length];
for (i = 0; i < m_attributeList.length; i++) {
rankedAttributes[i] = (int)tempRanked[i][0];
}
return rankedAttributes;
}
/**
* Sorts the evaluated attribute list
*
* @return an array of sorted (highest eval to lowest) attribute indexes
* @throws Exception of sorting can't be done.
*/
public double[][] rankedAttributes ()
throws Exception {
int i, j;
if (m_attributeList == null || m_attributeMerit == null) {
throw new Exception("Search must be performed before a ranked "
+ "attribute list can be obtained");
}
int[] ranked = Utils.sort(m_attributeMerit);
// reverse the order of the ranked indexes
double[][] bestToWorst = new double[ranked.length][2];
for (i = ranked.length - 1, j = 0; i >= 0; i--) {
bestToWorst[j++][0] = ranked[i];
}
// convert the indexes to attribute indexes
for (i = 0; i < bestToWorst.length; i++) {
int temp = ((int)bestToWorst[i][0]);
bestToWorst[i][0] = m_attributeList[temp];
bestToWorst[i][1] = m_attributeMerit[temp];
}
if (m_numToSelect > bestToWorst.length) {
throw new Exception("More attributes requested than exist in the data");
}
if (m_numToSelect <= 0) {
if (m_threshold == -Double.MAX_VALUE) {
m_calculatedNumToSelect = bestToWorst.length;
} else {
determineNumToSelectFromThreshold(bestToWorst);
}
}
/* if (m_numToSelect > 0) {
determineThreshFromNumToSelect(bestToWorst);
} */
return bestToWorst;
}
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;
}
private void determineThreshFromNumToSelect(double [][] ranking)
throws Exception {
if (m_numToSelect > ranking.length) {
throw new Exception("More attributes requested than exist in the data");
}
if (m_numToSelect == ranking.length) {
return;
}
m_threshold = (ranking[m_numToSelect-1][1] +
ranking[m_numToSelect][1]) / 2.0;
}
/**
* returns a description of the search as a String
* @return a description of the search
*/
public String toString () {
StringBuffer BfString = new StringBuffer();
BfString.append("\tAttribute ranking.\n");
if (m_starting != null) {
BfString.append("\tIgnored attributes: ");
BfString.append(startSetToString());
BfString.append("\n");
}
if (m_threshold != -Double.MAX_VALUE) {
BfString.append("\tThreshold for discarding attributes: "
+ Utils.doubleToString(m_threshold,8,4)+"\n");
}
return BfString.toString();
}
/**
* Resets stuff to default values
*/
protected void resetOptions () {
m_starting = null;
m_startRange = new Range();
m_attributeList = null;
m_attributeMerit = null;
m_threshold = -Double.MAX_VALUE;
}
private boolean inStarting (int feat) {
// omit the class from the evaluation
if ((m_hasClass == true) && (feat == m_classIndex)) {
return true;
}
if (m_starting == null) {
return false;
}
for (int i = 0; i < m_starting.length; i++) {
if (m_starting[i] == feat) {
return true;
}
}
return false;
}
/**
* Returns the revision string.
*
* @return the revision
*/
public String getRevision() {
return RevisionUtils.extract("$Revision: 1.26 $");
}
}