/*
* 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.
*/
/*
* CfsSubsetEval.java
* Copyright (C) 1999 Mark Hall
*
*/
package weka.attributeSelection;
import java.io.*;
import java.util.*;
import weka.core.*;
import weka.classifiers.*;
import weka.filters.supervised.attribute.Discretize;
import weka.filters.Filter;
/**
* CFS attribute subset evaluator.
* For more information see: <p>
*
* Hall, M. A. (1998). Correlation-based Feature Subset Selection for Machine
* Learning. Thesis submitted in partial fulfilment of the requirements of the
* degree of Doctor of Philosophy at the University of Waikato. <p>
*
* Valid options are:
*
* -M <br>
* Treat missing values as a seperate value. <p>
*
* -L <br>
* Include locally predictive attributes. <p>
*
* @author Mark Hall (mhall@cs.waikato.ac.nz)
* @version $Revision: 1.1.1.1 $
*/
public class CfsSubsetEval
extends SubsetEvaluator
implements OptionHandler
{
/** The training instances */
private Instances m_trainInstances;
/** Discretise attributes when class in nominal */
private Discretize m_disTransform;
/** The class index */
private int m_classIndex;
/** Is the class numeric */
private boolean m_isNumeric;
/** Number of attributes in the training data */
private int m_numAttribs;
/** Number of instances in the training data */
private int m_numInstances;
/** Treat missing values as seperate values */
private boolean m_missingSeperate;
/** Include locally predicitive attributes */
private boolean m_locallyPredictive;
/** Holds the matrix of attribute correlations */
private Matrix m_corr_matrix;
/** Standard deviations of attributes (when using pearsons correlation) */
private double[] m_std_devs;
/** Threshold for admitting locally predictive features */
private double m_c_Threshold;
/**
* Returns a string describing this attribute evaluator
* @return a description of the evaluator suitable for
* displaying in the explorer/experimenter gui
*/
public String globalInfo() {
return "CfsSubsetEval :\n\nEvaluates the worth of a subset of attributes "
+"by considering the individual predictive ability of each feature "
+"along with the degree of redundancy between them.\n\n"
+"Subsets of features that are highly correlated with the class "
+"while having low intercorrelation are preferred.\n";
}
/**
* Constructor
*/
public CfsSubsetEval () {
resetOptions();
}
/**
* 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("\tTreat missing values as a seperate"
+ "\n\tvalue.", "M", 0, "-M"));
newVector.addElement(new Option("\tInclude locally predictive attributes"
+ ".", "L", 0, "-L"));
return newVector.elements();
}
/**
* Parses and sets a given list of options. <p>
*
* Valid options are:
*
* -M <br>
* Treat missing values as a seperate value. <p>
*
* -L <br>
* Include locally predictive attributes. <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();
setMissingSeperate(Utils.getFlag('M', options));
setLocallyPredictive(Utils.getFlag('L', options));
}
/**
* Returns the tip text for this property
* @return tip text for this property suitable for
* displaying in the explorer/experimenter gui
*/
public String locallyPredictiveTipText() {
return "Identify locally predictive attributes. Iteratively adds "
+"attributes with the highest correlation with the class as long "
+"as there is not already an attribute in the subset that has a "
+"higher correlation with the attribute in question";
}
/**
* Include locally predictive attributes
*
* @param b true or false
*/
public void setLocallyPredictive (boolean b) {
m_locallyPredictive = b;
}
/**
* Return true if including locally predictive attributes
*
* @return true if locally predictive attributes are to be used
*/
public boolean getLocallyPredictive () {
return m_locallyPredictive;
}
/**
* Returns the tip text for this property
* @return tip text for this property suitable for
* displaying in the explorer/experimenter gui
*/
public String missingSeperateTipText() {
return "Treat missing as a separate value. Otherwise, counts for missing "
+"values are distributed across other values in proportion to their "
+"frequency.";
}
/**
* Treat missing as a seperate value
*
* @param b true or false
*/
public void setMissingSeperate (boolean b) {
m_missingSeperate = b;
}
/**
* Return true is missing is treated as a seperate value
*
* @return true if missing is to be treated as a seperate value
*/
public boolean getMissingSeperate () {
return m_missingSeperate;
}
/**
* Gets the current settings of CfsSubsetEval
*
* @return an array of strings suitable for passing to setOptions()
*/
public String[] getOptions () {
String[] options = new String[2];
int current = 0;
if (getMissingSeperate()) {
options[current++] = "-M";
}
if (getLocallyPredictive()) {
options[current++] = "-L";
}
while (current < options.length) {
options[current++] = "";
}
return options;
}
/**
* Generates a attribute evaluator. Has to initialize all fields of the
* evaluator that are not being set via options.
*
* CFS also discretises attributes (if necessary) and initializes
* the correlation matrix.
*
* @param data set of instances serving as training data
* @exception Exception if the evaluator has not been
* generated successfully
*/
public void buildEvaluator (Instances data)
throws Exception {
if (data.checkForStringAttributes()) {
throw new UnsupportedAttributeTypeException("Can't handle string attributes!");
}
m_trainInstances = data;
m_trainInstances.deleteWithMissingClass();
m_classIndex = m_trainInstances.classIndex();
m_numAttribs = m_trainInstances.numAttributes();
m_numInstances = m_trainInstances.numInstances();
m_isNumeric = m_trainInstances.attribute(m_classIndex).isNumeric();
if (!m_isNumeric) {
m_disTransform = new Discretize();
m_disTransform.setUseBetterEncoding(true);
m_disTransform.setInputFormat(m_trainInstances);
m_trainInstances = Filter.useFilter(m_trainInstances, m_disTransform);
}
m_std_devs = new double[m_numAttribs];
m_corr_matrix = new Matrix(m_numAttribs, m_numAttribs);
for (int i = 0; i < m_corr_matrix.numRows(); i++) {
m_corr_matrix.setElement(i, i, 1.0);
m_std_devs[i] = 1.0;
}
for (int i = 0; i < m_numAttribs; i++) {
for (int j = i + 1; j < m_numAttribs; j++) {
m_corr_matrix.setElement(i, j, -999);
m_corr_matrix.setElement(j, i, -999);
}
}
}
/**
* evaluates a subset of attributes
*
* @param subset a bitset representing the attribute subset to be
* evaluated
* @exception Exception if the subset could not be evaluated
*/
public double evaluateSubset (BitSet subset)
throws Exception {
double num = 0.0;
double denom = 0.0;
double corr;
// do numerator
for (int i = 0; i < m_numAttribs; i++) {
if (i != m_classIndex) {
if (subset.get(i)) {
if (m_corr_matrix.getElement(i, m_classIndex) == -999) {
corr = correlate(i, m_classIndex);
m_corr_matrix.setElement(i, m_classIndex, corr);
m_corr_matrix.setElement(m_classIndex, i, corr);
num += (m_std_devs[i] * corr);
}
else {num += (m_std_devs[i] *
m_corr_matrix.getElement(i, m_classIndex));
}
}
}
}
// do denominator
for (int i = 0; i < m_numAttribs; i++) {
if (i != m_classIndex) {
if (subset.get(i)) {
denom += (1.0 * m_std_devs[i] * m_std_devs[i]);
for (int j = i + 1; j < m_numAttribs; j++) {if (subset.get(j)) {
if (m_corr_matrix.getElement(i, j) == -999) {
corr = correlate(i, j);
m_corr_matrix.setElement(i, j, corr);
m_corr_matrix.setElement(j, i, corr);
denom += (2.0 * m_std_devs[i] * m_std_devs[j] * corr);
}
else {denom += (2.0 * m_std_devs[i] * m_std_devs[j] *
m_corr_matrix.getElement(i, j));
}
}
}
}
}
}
if (denom < 0.0) {
denom *= -1.0;
}
if (denom == 0.0) {
return (0.0);
}
double merit = (num/Math.sqrt(denom));
if (merit < 0.0) {
merit *= -1.0;
}
return merit;
}
private double correlate (int att1, int att2) {
if (!m_isNumeric) {
return symmUncertCorr(att1, att2);
}
boolean att1_is_num = (m_trainInstances.attribute(att1).isNumeric());
boolean att2_is_num = (m_trainInstances.attribute(att2).isNumeric());
if (att1_is_num && att2_is_num) {
return num_num(att1, att2);
}
else {if (att2_is_num) {
return num_nom2(att1, att2);
}
else {if (att1_is_num) {
return num_nom2(att2, att1);
}
}
}
return nom_nom(att1, att2);
}
private double symmUncertCorr (int att1, int att2) {
int i, j, k, ii, jj;
int nnj, nni, ni, nj;
double sum = 0.0;
double sumi[], sumj[];
double counts[][];
Instance inst;
double corr_measure;
boolean flag = false;
double temp = 0.0;
if (att1 == m_classIndex || att2 == m_classIndex) {
flag = true;
}
ni = m_trainInstances.attribute(att1).numValues() + 1;
nj = m_trainInstances.attribute(att2).numValues() + 1;
counts = new double[ni][nj];
sumi = new double[ni];
sumj = new double[nj];
for (i = 0; i < ni; i++) {
sumi[i] = 0.0;
for (j = 0; j < nj; j++) {
sumj[j] = 0.0;
counts[i][j] = 0.0;
}
}
// Fill the contingency table
for (i = 0; i < m_numInstances; i++) {
inst = m_trainInstances.instance(i);
if (inst.isMissing(att1)) {
ii = ni - 1;
}
else {
ii = (int)inst.value(att1);
}
if (inst.isMissing(att2)) {
jj = nj - 1;
}
else {
jj = (int)inst.value(att2);
}
counts[ii][jj]++;
}
// get the row totals
for (i = 0; i < ni; i++) {
sumi[i] = 0.0;
for (j = 0; j < nj; j++) {
sumi[i] += counts[i][j];
sum += counts[i][j];
}
}
// get the column totals
for (j = 0; j < nj; j++) {
sumj[j] = 0.0;
for (i = 0; i < ni; i++) {
sumj[j] += counts[i][j];
}
}
// distribute missing counts
if (!m_missingSeperate &&
(sumi[ni-1] < m_numInstances) &&
(sumj[nj-1] < m_numInstances)) {
double[] i_copy = new double[sumi.length];
double[] j_copy = new double[sumj.length];
double[][] counts_copy = new double[sumi.length][sumj.length];
for (i = 0; i < ni; i++) {
System.arraycopy(counts[i], 0, counts_copy[i], 0, sumj.length);
}
System.arraycopy(sumi, 0, i_copy, 0, sumi.length);
System.arraycopy(sumj, 0, j_copy, 0, sumj.length);
double total_missing =
(sumi[ni - 1] + sumj[nj - 1] - counts[ni - 1][nj - 1]);
// do the missing i's
if (sumi[ni - 1] > 0.0) {
for (j = 0; j < nj - 1; j++) {
if (counts[ni - 1][j] > 0.0) {
for (i = 0; i < ni - 1; i++) {
temp = ((i_copy[i]/(sum - i_copy[ni - 1]))*counts[ni - 1][j]);
counts[i][j] += temp;
sumi[i] += temp;
}
counts[ni - 1][j] = 0.0;
}
}
}
sumi[ni - 1] = 0.0;
// do the missing j's
if (sumj[nj - 1] > 0.0) {
for (i = 0; i < ni - 1; i++) {
if (counts[i][nj - 1] > 0.0) {
for (j = 0; j < nj - 1; j++) {
temp = ((j_copy[j]/(sum - j_copy[nj - 1]))*counts[i][nj - 1]);
counts[i][j] += temp;
sumj[j] += temp;
}
counts[i][nj - 1] = 0.0;
}
}
}
sumj[nj - 1] = 0.0;
// do the both missing
if (counts[ni - 1][nj - 1] > 0.0 && total_missing != sum) {
for (i = 0; i < ni - 1; i++) {
for (j = 0; j < nj - 1; j++) {
temp = (counts_copy[i][j]/(sum - total_missing)) *
counts_copy[ni - 1][nj - 1];
counts[i][j] += temp;
sumi[i] += temp;
sumj[j] += temp;
}
}
counts[ni - 1][nj - 1] = 0.0;
}
}
// corr_measure = Correlate.symm_uncert(counts,sumi,sumj,sum,ni,nj,flag);
corr_measure = ContingencyTables.symmetricalUncertainty(counts);
// corr_measure = ContingencyTables.gainRatio(counts);
if (Utils.eq(corr_measure, 0.0)) {
if (flag == true) {
return (0.0);
}
else {
return (1.0);
}
}
else {
return (corr_measure);
}
}
private double num_num (int att1, int att2) {
int i;
Instance inst;
double r, diff1, diff2, num = 0.0, sx = 0.0, sy = 0.0;
double mx = m_trainInstances.meanOrMode(m_trainInstances.attribute(att1));
double my = m_trainInstances.meanOrMode(m_trainInstances.attribute(att2));
for (i = 0; i < m_numInstances; i++) {
inst = m_trainInstances.instance(i);
diff1 = (inst.isMissing(att1))? 0.0 : (inst.value(att1) - mx);
diff2 = (inst.isMissing(att2))? 0.0 : (inst.value(att2) - my);
num += (diff1*diff2);
sx += (diff1*diff1);
sy += (diff2*diff2);
}
if (sx != 0.0) {
if (m_std_devs[att1] == 1.0) {
m_std_devs[att1] = Math.sqrt((sx/m_numInstances));
}
}
if (sy != 0.0) {
if (m_std_devs[att2] == 1.0) {
m_std_devs[att2] = Math.sqrt((sy/m_numInstances));
}
}
if ((sx*sy) > 0.0) {
r = (num/(Math.sqrt(sx*sy)));
return ((r < 0.0)? -r : r);
}
else {
if (att1 != m_classIndex && att2 != m_classIndex) {
return 1.0;
}
else {
return 0.0;
}
}
}
private double num_nom2 (int att1, int att2) {
int i, ii, k;
double temp;
Instance inst;
int mx = (int)m_trainInstances.
meanOrMode(m_trainInstances.attribute(att1));
double my = m_trainInstances.
meanOrMode(m_trainInstances.attribute(att2));
double stdv_num = 0.0;
double diff1, diff2;
double r = 0.0, rr, max_corr = 0.0;
int nx = (!m_missingSeperate)
? m_trainInstances.attribute(att1).numValues()
: m_trainInstances.attribute(att1).numValues() + 1;
double[] prior_nom = new double[nx];
double[] stdvs_nom = new double[nx];
double[] covs = new double[nx];
for (i = 0; i < nx; i++) {
stdvs_nom[i] = covs[i] = prior_nom[i] = 0.0;
}
// calculate frequencies (and means) of the values of the nominal
// attribute
for (i = 0; i < m_numInstances; i++) {
inst = m_trainInstances.instance(i);
if (inst.isMissing(att1)) {
if (!m_missingSeperate) {
ii = mx;
}
else {
ii = nx - 1;
}
}
else {
ii = (int)inst.value(att1);
}
// increment freq for nominal
prior_nom[ii]++;
}
for (k = 0; k < m_numInstances; k++) {
inst = m_trainInstances.instance(k);
// std dev of numeric attribute
diff2 = (inst.isMissing(att2))? 0.0 : (inst.value(att2) - my);
stdv_num += (diff2*diff2);
//
for (i = 0; i < nx; i++) {
if (inst.isMissing(att1)) {
if (!m_missingSeperate) {
temp = (i == mx)? 1.0 : 0.0;
}
else {
temp = (i == (nx - 1))? 1.0 : 0.0;
}
}
else {
temp = (i == inst.value(att1))? 1.0 : 0.0;
}
diff1 = (temp - (prior_nom[i]/m_numInstances));
stdvs_nom[i] += (diff1*diff1);
covs[i] += (diff1*diff2);
}
}
// calculate weighted correlation
for (i = 0, temp = 0.0; i < nx; i++) {
// calculate the weighted variance of the nominal
temp += ((prior_nom[i]/m_numInstances)*(stdvs_nom[i]/m_numInstances));
if ((stdvs_nom[i]*stdv_num) > 0.0) {
//System.out.println("Stdv :"+stdvs_nom[i]);
rr = (covs[i]/(Math.sqrt(stdvs_nom[i]*stdv_num)));
if (rr < 0.0) {
rr = -rr;
}
r += ((prior_nom[i]/m_numInstances)*rr);
}
/* if there is zero variance for the numeric att at a specific
level of the catergorical att then if neither is the class then
make this correlation at this level maximally bad i.e. 1.0.
If either is the class then maximally bad correlation is 0.0 */
else {if (att1 != m_classIndex && att2 != m_classIndex) {
r += ((prior_nom[i]/m_numInstances)*1.0);
}
}
}
// set the standard deviations for these attributes if necessary
// if ((att1 != classIndex) && (att2 != classIndex)) // =============
if (temp != 0.0) {
if (m_std_devs[att1] == 1.0) {
m_std_devs[att1] = Math.sqrt(temp);
}
}
if (stdv_num != 0.0) {
if (m_std_devs[att2] == 1.0) {
m_std_devs[att2] = Math.sqrt((stdv_num/m_numInstances));
}
}
if (r == 0.0) {
if (att1 != m_classIndex && att2 != m_classIndex) {
r = 1.0;
}
}
return r;
}
private double nom_nom (int att1, int att2) {
int i, j, ii, jj, z;
double temp1, temp2;
Instance inst;
int mx = (int)m_trainInstances.
meanOrMode(m_trainInstances.attribute(att1));
int my = (int)m_trainInstances.
meanOrMode(m_trainInstances.attribute(att2));
double diff1, diff2;
double r = 0.0, rr, max_corr = 0.0;
int nx = (!m_missingSeperate)
? m_trainInstances.attribute(att1).numValues()
: m_trainInstances.attribute(att1).numValues() + 1;
int ny = (!m_missingSeperate)
? m_trainInstances.attribute(att2).numValues()
: m_trainInstances.attribute(att2).numValues() + 1;
double[][] prior_nom = new double[nx][ny];
double[] sumx = new double[nx];
double[] sumy = new double[ny];
double[] stdvsx = new double[nx];
double[] stdvsy = new double[ny];
double[][] covs = new double[nx][ny];
for (i = 0; i < nx; i++) {
sumx[i] = stdvsx[i] = 0.0;
}
for (j = 0; j < ny; j++) {
sumy[j] = stdvsy[j] = 0.0;
}
for (i = 0; i < nx; i++) {
for (j = 0; j < ny; j++) {
covs[i][j] = prior_nom[i][j] = 0.0;
}
}
// calculate frequencies (and means) of the values of the nominal
// attribute
for (i = 0; i < m_numInstances; i++) {
inst = m_trainInstances.instance(i);
if (inst.isMissing(att1)) {
if (!m_missingSeperate) {
ii = mx;
}
else {
ii = nx - 1;
}
}
else {
ii = (int)inst.value(att1);
}
if (inst.isMissing(att2)) {
if (!m_missingSeperate) {
jj = my;
}
else {
jj = ny - 1;
}
}
else {
jj = (int)inst.value(att2);
}
// increment freq for nominal
prior_nom[ii][jj]++;
sumx[ii]++;
sumy[jj]++;
}
for (z = 0; z < m_numInstances; z++) {
inst = m_trainInstances.instance(z);
for (j = 0; j < ny; j++) {
if (inst.isMissing(att2)) {
if (!m_missingSeperate) {
temp2 = (j == my)? 1.0 : 0.0;
}
else {
temp2 = (j == (ny - 1))? 1.0 : 0.0;
}
}
else {
temp2 = (j == inst.value(att2))? 1.0 : 0.0;
}
diff2 = (temp2 - (sumy[j]/m_numInstances));
stdvsy[j] += (diff2*diff2);
}
//
for (i = 0; i < nx; i++) {
if (inst.isMissing(att1)) {
if (!m_missingSeperate) {
temp1 = (i == mx)? 1.0 : 0.0;
}
else {
temp1 = (i == (nx - 1))? 1.0 : 0.0;
}
}
else {
temp1 = (i == inst.value(att1))? 1.0 : 0.0;
}
diff1 = (temp1 - (sumx[i]/m_numInstances));
stdvsx[i] += (diff1*diff1);
for (j = 0; j < ny; j++) {
if (inst.isMissing(att2)) {
if (!m_missingSeperate) {
temp2 = (j == my)? 1.0 : 0.0;
}
else {
temp2 = (j == (ny - 1))? 1.0 : 0.0;
}
}
else {
temp2 = (j == inst.value(att2))? 1.0 : 0.0;
}
diff2 = (temp2 - (sumy[j]/m_numInstances));
covs[i][j] += (diff1*diff2);
}
}
}
// calculate weighted correlation
for (i = 0; i < nx; i++) {
for (j = 0; j < ny; j++) {
if ((stdvsx[i]*stdvsy[j]) > 0.0) {
//System.out.println("Stdv :"+stdvs_nom[i]);
rr = (covs[i][j]/(Math.sqrt(stdvsx[i]*stdvsy[j])));
if (rr < 0.0) {
rr = -rr;
}
r += ((prior_nom[i][j]/m_numInstances)*rr);
}
// if there is zero variance for either of the categorical atts then if
// neither is the class then make this
// correlation at this level maximally bad i.e. 1.0. If either is
// the class then maximally bad correlation is 0.0
else {if (att1 != m_classIndex && att2 != m_classIndex) {
r += ((prior_nom[i][j]/m_numInstances)*1.0);
}
}
}
}
// calculate weighted standard deviations for these attributes
// (if necessary)
for (i = 0, temp1 = 0.0; i < nx; i++) {
temp1 += ((sumx[i]/m_numInstances)*(stdvsx[i]/m_numInstances));
}
if (temp1 != 0.0) {
if (m_std_devs[att1] == 1.0) {
m_std_devs[att1] = Math.sqrt(temp1);
}
}
for (j = 0, temp2 = 0.0; j < ny; j++) {
temp2 += ((sumy[j]/m_numInstances)*(stdvsy[j]/m_numInstances));
}
if (temp2 != 0.0) {
if (m_std_devs[att2] == 1.0) {
m_std_devs[att2] = Math.sqrt(temp2);
}
}
if (r == 0.0) {
if (att1 != m_classIndex && att2 != m_classIndex) {
r = 1.0;
}
}
return r;
}
/**
* returns a string describing CFS
*
* @return the description as a string
*/
public String toString () {
StringBuffer text = new StringBuffer();
if (m_trainInstances == null) {
text.append("CFS subset evaluator has not been built yet\n");
}
else {
text.append("\tCFS Subset Evaluator\n");
if (m_missingSeperate) {
text.append("\tTreating missing values as a seperate value\n");
}
if (m_locallyPredictive) {
text.append("\tIncluding locally predictive attributes\n");
}
}
return text.toString();
}
private void addLocallyPredictive (BitSet best_group) {
int i, j;
boolean done = false;
boolean ok = true;
double temp_best = -1.0;
double corr;
j = 0;
BitSet temp_group = (BitSet)best_group.clone();
while (!done) {
temp_best = -1.0;
// find best not already in group
for (i = 0; i < m_numAttribs; i++) {
if ((!temp_group.get(i)) && (i != m_classIndex)) {
if (m_corr_matrix.getElement(i, m_classIndex) == -999) {
corr = correlate(i, m_classIndex);
m_corr_matrix.setElement(i, m_classIndex, corr);
m_corr_matrix.setElement(m_classIndex, i, corr);
}
if (m_corr_matrix.getElement(i, m_classIndex) > temp_best) {
temp_best = m_corr_matrix.getElement(i, m_classIndex);
j = i;
}
}
}
if (temp_best == -1.0) {
done = true;
}
else {
ok = true;
temp_group.set(j);
// check the best against correlations with others already
// in group
for (i = 0; i < m_numAttribs; i++) {
if (best_group.get(i)) {
if (m_corr_matrix.getElement(i, j) == -999) {
corr = correlate(i, j);
m_corr_matrix.setElement(i, j, corr);
m_corr_matrix.setElement(j, i, corr);
}
if (m_corr_matrix.getElement(i, j) > temp_best - m_c_Threshold) {
ok = false;
break;
}
}
}
// if ok then add to best_group
if (ok) {
best_group.set(j);
}
}
}
}
/**
* Calls locallyPredictive in order to include locally predictive
* attributes (if requested).
*
* @param attributeSet the set of attributes found by the search
* @return a possibly ranked list of postprocessed attributes
* @exception Exception if postprocessing fails for some reason
*/
public int[] postProcess (int[] attributeSet)
throws Exception {
int j = 0;
if (!m_locallyPredictive) {
// m_trainInstances = new Instances(m_trainInstances,0);
return attributeSet;
}
BitSet bestGroup = new BitSet(m_numAttribs);
for (int i = 0; i < attributeSet.length; i++) {
bestGroup.set(attributeSet[i]);
}
addLocallyPredictive(bestGroup);
// count how many are set
for (int i = 0; i < m_numAttribs; i++) {
if (bestGroup.get(i)) {
j++;
}
}
int[] newSet = new int[j];
j = 0;
for (int i = 0; i < m_numAttribs; i++) {
if (bestGroup.get(i)) {
newSet[j++] = i;
}
}
// m_trainInstances = new Instances(m_trainInstances,0);
return newSet;
}
protected void resetOptions () {
m_trainInstances = null;
m_missingSeperate = false;
m_locallyPredictive = false;
m_c_Threshold = 0.0;
}
/**
* Main method for testing this class.
*
* @param args the options
*/
public static void main (String[] args) {
try {
System.out.println(AttributeSelection.
SelectAttributes(new CfsSubsetEval(), args));
}
catch (Exception e) {
e.printStackTrace();
System.out.println(e.getMessage());
}
}
}