/**
* Evaluates cluster assignments with respect to actual class labels. Assumes that m_Clusterer has
* been trained and tested on inst (minus the class).
*
* @param inst the instances (including class) to evaluate with respect to
* @exception Exception if something goes wrong
*/
private void evaluateClustersWithRespectToClass(Instances inst) throws Exception {
int numClasses = inst.classAttribute().numValues();
int[][] counts = new int[m_numClusters][numClasses];
int[] clusterTotals = new int[m_numClusters];
double[] best = new double[m_numClusters + 1];
double[] current = new double[m_numClusters + 1];
for (int i = 0; i < inst.numInstances(); i++) {
counts[(int) m_clusterAssignments[i]][(int) inst.instance(i).classValue()]++;
clusterTotals[(int) m_clusterAssignments[i]]++;
}
best[m_numClusters] = Double.MAX_VALUE;
mapClasses(0, counts, clusterTotals, current, best, 0);
m_clusteringResults.append("\n\nClass attribute: " + inst.classAttribute().name() + "\n");
m_clusteringResults.append("Classes to Clusters:\n");
String matrixString = toMatrixString(counts, clusterTotals, inst);
m_clusteringResults.append(matrixString).append("\n");
int Cwidth = 1 + (int) (Math.log(m_numClusters) / Math.log(10));
// add the minimum error assignment
for (int i = 0; i < m_numClusters; i++) {
if (clusterTotals[i] > 0) {
m_clusteringResults.append("Cluster " + Utils.doubleToString((double) i, Cwidth, 0));
m_clusteringResults.append(" <-- ");
if (best[i] < 0) {
m_clusteringResults.append("No class\n");
} else {
m_clusteringResults.append(inst.classAttribute().value((int) best[i])).append("\n");
}
}
}
m_clusteringResults.append(
"\nIncorrectly clustered instances :\t"
+ best[m_numClusters]
+ "\t"
+ (Utils.doubleToString((best[m_numClusters] / inst.numInstances() * 100.0), 8, 4))
+ " %\n");
// copy the class assignments
m_classToCluster = new int[m_numClusters];
for (int i = 0; i < m_numClusters; i++) {
m_classToCluster[i] = (int) best[i];
}
}
/**
* Returns a "confusion" style matrix of classes to clusters assignments
*
* @param counts the counts of classes for each cluster
* @param clusterTotals total number of examples in each cluster
* @param inst the training instances (with class)
* @exception Exception if matrix can't be generated
*/
private String toMatrixString(int[][] counts, int[] clusterTotals, Instances inst)
throws Exception {
StringBuffer ms = new StringBuffer();
int maxval = 0;
for (int i = 0; i < m_numClusters; i++) {
for (int j = 0; j < counts[i].length; j++) {
if (counts[i][j] > maxval) {
maxval = counts[i][j];
}
}
}
int Cwidth =
1
+ Math.max(
(int) (Math.log(maxval) / Math.log(10)),
(int) (Math.log(m_numClusters) / Math.log(10)));
ms.append("\n");
for (int i = 0; i < m_numClusters; i++) {
if (clusterTotals[i] > 0) {
ms.append(" ").append(Utils.doubleToString((double) i, Cwidth, 0));
}
}
ms.append(" <-- assigned to cluster\n");
for (int i = 0; i < counts[0].length; i++) {
for (int j = 0; j < m_numClusters; j++) {
if (clusterTotals[j] > 0) {
ms.append(" ").append(Utils.doubleToString((double) counts[j][i], Cwidth, 0));
}
}
ms.append(" | ").append(inst.classAttribute().value(i)).append("\n");
}
return ms.toString();
}
/**
* Performs a cross-validation for a DensityBasedClusterer clusterer on a set of instances.
*
* @param clustererString a string naming the class of the clusterer
* @param data the data on which the cross-validation is to be performed
* @param numFolds the number of folds for the cross-validation
* @param options the options to the clusterer
* @param random a random number generator
* @return a string containing the cross validated log likelihood
* @exception Exception if a clusterer could not be generated
*/
public static String crossValidateModel(
String clustererString, Instances data, int numFolds, String[] options, Random random)
throws Exception {
Clusterer clusterer = null;
Instances train, test;
String[] savedOptions = null;
double foldAv;
double CvAv = 0.0;
double[] tempDist;
StringBuffer CvString = new StringBuffer();
if (options != null) {
savedOptions = new String[options.length];
}
data = new Instances(data);
// create clusterer
try {
clusterer = (Clusterer) Class.forName(clustererString).newInstance();
} catch (Exception e) {
throw new Exception("Can't find class with name " + clustererString + '.');
}
if (!(clusterer instanceof DensityBasedClusterer)) {
throw new Exception(clustererString + " must be a distrinbution " + "clusterer.");
}
// Save options
if (options != null) {
System.arraycopy(options, 0, savedOptions, 0, options.length);
}
// Parse options
if (clusterer instanceof OptionHandler) {
try {
((OptionHandler) clusterer).setOptions(savedOptions);
Utils.checkForRemainingOptions(savedOptions);
} catch (Exception e) {
throw new Exception("Can't parse given options in " + "cross-validation!");
}
}
CvAv = crossValidateModel((DensityBasedClusterer) clusterer, data, numFolds, random);
CvString.append(
"\n" + numFolds + " fold CV Log Likelihood: " + Utils.doubleToString(CvAv, 6, 4) + "\n");
return CvString.toString();
}
/**
* Returns description of the bagged classifier.
*
* @return description of the bagged classifier as a string
*/
@Override
public String toString() {
if (m_Classifiers == null) {
return "Bagging: No model built yet.";
}
StringBuffer text = new StringBuffer();
text.append("All the base classifiers: \n\n");
for (int i = 0; i < m_Classifiers.length; i++)
text.append(m_Classifiers[i].toString() + "\n\n");
if (m_CalcOutOfBag) {
text.append("Out of bag error: " + Utils.doubleToString(m_OutOfBagError, 4) + "\n\n");
}
return text.toString();
}
private static String numToString(double num) {
int precision = 1;
int whole = (int) Math.abs(num);
double decimal = Math.abs(num) - whole;
int nondecimal;
nondecimal = (whole > 0) ? (int) (Math.log(whole) / Math.log(10)) : 1;
precision = (decimal > 0) ? (int) Math.abs(((Math.log(Math.abs(num)) / Math.log(10)))) + 2 : 1;
if (precision > 5) {
precision = 1;
}
String numString =
reconcile.weka.core.Utils.doubleToString(num, nondecimal + 1 + precision, precision);
return numString;
}
/**
* Print the cluster statistics for either the training or the testing data.
*
* @param clusterer the clusterer to use for generating statistics.
* @return a string containing cluster statistics.
* @exception if statistics can't be generated.
*/
private static String printClusterStats(Clusterer clusterer, String fileName) throws Exception {
StringBuffer text = new StringBuffer();
int i = 0;
int cnum;
double loglk = 0.0;
double[] dist;
double temp;
int cc = clusterer.numberOfClusters();
double[] instanceStats = new double[cc];
int unclusteredInstances = 0;
if (fileName.length() != 0) {
BufferedReader inStream = null;
try {
inStream = new BufferedReader(new FileReader(fileName));
} catch (Exception e) {
throw new Exception("Can't open file " + e.getMessage() + '.');
}
Instances inst = new Instances(inStream, 1);
while (inst.readInstance(inStream)) {
try {
cnum = clusterer.clusterInstance(inst.instance(0));
if (clusterer instanceof DensityBasedClusterer) {
loglk += ((DensityBasedClusterer) clusterer).logDensityForInstance(inst.instance(0));
// temp = Utils.sum(dist);
}
instanceStats[cnum]++;
} catch (Exception e) {
unclusteredInstances++;
}
inst.delete(0);
i++;
}
/*
// count the actual number of used clusters
int count = 0;
for (i = 0; i < cc; i++) {
if (instanceStats[i] > 0) {
count++;
}
}
if (count > 0) {
double [] tempStats = new double [count];
count=0;
for (i=0;i<cc;i++) {
if (instanceStats[i] > 0) {
tempStats[count++] = instanceStats[i];
}
}
instanceStats = tempStats;
cc = instanceStats.length;
} */
int clustFieldWidth = (int) ((Math.log(cc) / Math.log(10)) + 1);
int numInstFieldWidth = (int) ((Math.log(i) / Math.log(10)) + 1);
double sum = Utils.sum(instanceStats);
loglk /= sum;
text.append("Clustered Instances\n");
for (i = 0; i < cc; i++) {
if (instanceStats[i] > 0) {
text.append(
Utils.doubleToString((double) i, clustFieldWidth, 0)
+ " "
+ Utils.doubleToString(instanceStats[i], numInstFieldWidth, 0)
+ " ("
+ Utils.doubleToString((instanceStats[i] / sum * 100.0), 3, 0)
+ "%)\n");
}
}
if (unclusteredInstances > 0) {
text.append("\nUnclustered Instances : " + unclusteredInstances);
}
if (clusterer instanceof DensityBasedClusterer) {
text.append("\n\nLog likelihood: " + Utils.doubleToString(loglk, 1, 5) + "\n");
}
}
return text.toString();
}
/**
* Evaluate the clusterer on a set of instances. Calculates clustering statistics and stores
* cluster assigments for the instances in m_clusterAssignments
*
* @param test the set of instances to cluster
* @exception Exception if something goes wrong
*/
public void evaluateClusterer(Instances test) throws Exception {
int i = 0;
int cnum;
double loglk = 0.0;
double[] dist;
double temp;
int cc = m_Clusterer.numberOfClusters();
m_numClusters = cc;
int numInstFieldWidth = (int) ((Math.log(test.numInstances()) / Math.log(10)) + 1);
double[] instanceStats = new double[cc];
m_clusterAssignments = new double[test.numInstances()];
Instances testCopy = test;
boolean hasClass = (testCopy.classIndex() >= 0);
int unclusteredInstances = 0;
// If class is set then do class based evaluation as well
if (hasClass) {
if (testCopy.classAttribute().isNumeric()) {
throw new Exception("ClusterEvaluation: Class must be nominal!");
}
Remove removeClass = new Remove();
removeClass.setAttributeIndices("" + (testCopy.classIndex() + 1));
removeClass.setInvertSelection(false);
removeClass.setInputFormat(testCopy);
testCopy = Filter.useFilter(testCopy, removeClass);
}
for (i = 0; i < testCopy.numInstances(); i++) {
cnum = -1;
try {
if (m_Clusterer instanceof DensityBasedClusterer) {
loglk +=
((DensityBasedClusterer) m_Clusterer).logDensityForInstance(testCopy.instance(i));
// temp = Utils.sum(dist);
// Utils.normalize(dist);
cnum = m_Clusterer.clusterInstance(testCopy.instance(i));
// Utils.maxIndex(dist);
m_clusterAssignments[i] = (double) cnum;
} else {
cnum = m_Clusterer.clusterInstance(testCopy.instance(i));
m_clusterAssignments[i] = (double) cnum;
}
} catch (Exception e) {
unclusteredInstances++;
}
if (cnum != -1) {
instanceStats[cnum]++;
}
}
/* // count the actual number of used clusters
int count = 0;
for (i = 0; i < cc; i++) {
if (instanceStats[i] > 0) {
count++;
}
}
if (count > 0) {
double [] tempStats = new double [count];
double [] map = new double [m_clusterAssignments.length];
count=0;
for (i=0;i<cc;i++) {
if (instanceStats[i] > 0) {
tempStats[count] = instanceStats[i];
map[i] = count;
count++;
}
}
instanceStats = tempStats;
cc = instanceStats.length;
for (i=0;i<m_clusterAssignments.length;i++) {
m_clusterAssignments[i] = map[(int)m_clusterAssignments[i]];
}
} */
double sum = Utils.sum(instanceStats);
loglk /= sum;
m_logL = loglk;
m_clusteringResults.append(m_Clusterer.toString());
m_clusteringResults.append("Clustered Instances\n\n");
int clustFieldWidth = (int) ((Math.log(cc) / Math.log(10)) + 1);
for (i = 0; i < cc; i++) {
if (instanceStats[i] > 0) {
m_clusteringResults.append(
Utils.doubleToString((double) i, clustFieldWidth, 0)
+ " "
+ Utils.doubleToString(instanceStats[i], numInstFieldWidth, 0)
+ " ("
+ Utils.doubleToString((instanceStats[i] / sum * 100.0), 3, 0)
+ "%)\n");
}
}
if (unclusteredInstances > 0) {
m_clusteringResults.append("\nUnclustered instances : " + unclusteredInstances);
}
if (m_Clusterer instanceof DensityBasedClusterer) {
m_clusteringResults.append("\n\nLog likelihood: " + Utils.doubleToString(loglk, 1, 5) + "\n");
}
if (hasClass) {
evaluateClustersWithRespectToClass(test);
}
}
