/**
* Generate artificial training examples.
*
* @param artSize size of examples set to create
* @param data training data
* @return the set of unlabeled artificial examples
*/
protected Instances generateArtificialData(int artSize, Instances data) {
int numAttributes = data.numAttributes();
Instances artData = new Instances(data, artSize);
double[] att;
Instance artInstance;
for (int i = 0; i < artSize; i++) {
att = new double[numAttributes];
for (int j = 0; j < numAttributes; j++) {
if (data.attribute(j).isNominal()) {
// Select nominal value based on the frequency of occurence in the training data
double[] stats = (double[]) m_AttributeStats.get(j);
att[j] = (double) selectIndexProbabilistically(stats);
} else if (data.attribute(j).isNumeric()) {
// Generate numeric value from the Guassian distribution
// defined by the mean and std dev of the attribute
double[] stats = (double[]) m_AttributeStats.get(j);
att[j] = (m_Random.nextGaussian() * stats[1]) + stats[0];
} else System.err.println("Decorate can only handle numeric and nominal values.");
}
artInstance = new Instance(1.0, att);
artData.add(artInstance);
}
return artData;
}
/**
* Returns an enumeration of the additional measure names
*
* @return an enumeration of the measure names
*/
public Enumeration enumerateMeasures() {
Vector newVector = new Vector(3);
newVector.addElement("measureTreeSize");
newVector.addElement("measureNumLeaves");
newVector.addElement("measureNumRules");
return newVector.elements();
}
/**
* Compute and store statistics required for generating artificial data.
*
* @param data training instances
* @exception Exception if statistics could not be calculated successfully
*/
protected void computeStats(Instances data) throws Exception {
int numAttributes = data.numAttributes();
m_AttributeStats = new Vector(numAttributes); // use to map attributes to their stats
for (int j = 0; j < numAttributes; j++) {
if (data.attribute(j).isNominal()) {
// Compute the probability of occurence of each distinct value
int[] nomCounts = (data.attributeStats(j)).nominalCounts;
double[] counts = new double[nomCounts.length];
if (counts.length < 2)
throw new Exception("Nominal attribute has less than two distinct values!");
// Perform Laplace smoothing
for (int i = 0; i < counts.length; i++) counts[i] = nomCounts[i] + 1;
Utils.normalize(counts);
double[] stats = new double[counts.length - 1];
stats[0] = counts[0];
// Calculate cumulative probabilities
for (int i = 1; i < stats.length; i++) stats[i] = stats[i - 1] + counts[i];
m_AttributeStats.add(j, stats);
} else if (data.attribute(j).isNumeric()) {
// Get mean and standard deviation from the training data
double[] stats = new double[2];
stats[0] = data.meanOrMode(j);
stats[1] = Math.sqrt(data.variance(j));
m_AttributeStats.add(j, stats);
} else System.err.println("Decorate can only handle numeric and nominal values.");
}
}
/**
* Returns an enumeration describing the available options..
*
* <p>Valid options are:
*
* <p>-N <number of clusters> <br>
* Specify the number of clusters to generate. If omitted, FarthestFirst will use cross validation
* to select the number of clusters automatically.
*
* <p>-S <seed> <br>
* Specify random number seed.
*
* <p>
*
* @return an enumeration of all the available options.
*/
public Enumeration listOptions() {
Vector newVector = new Vector(2);
newVector.addElement(new Option("\tnumber of clusters. (default = 2).", "N", 1, "-N <num>"));
newVector.addElement(new Option("\trandom number seed.\n (default 10)", "S", 1, "-S <num>"));
return newVector.elements();
}
/**
* Returns description of the Decorate classifier.
*
* @return description of the Decorate classifier as a string
*/
public String toString() {
if (m_Committee == null) {
return "Decorate: No model built yet.";
}
StringBuffer text = new StringBuffer();
text.append("Decorate base classifiers: \n\n");
for (int i = 0; i < m_Committee.size(); i++)
text.append(((Classifier) m_Committee.get(i)).toString() + "\n\n");
text.append("Number of classifier in the ensemble: " + m_Committee.size() + "\n");
return text.toString();
}
/** The static initializer sets up the options vector */
static {
options.addElement(new Option("\tAlpha star. (default = 0.5).", "A", 1, "-A <0-1>"));
options.addElement(new Option("\tSigma. (default = 1.0).", "S", 1, "-S <num>"));
options.addElement(
new Option(
"\tR. All points that are far away more than this value have a zero similarity. (default = -1).",
"R",
1,
"-R <num>"));
options.addElement(
new Option("\tUse sparse matrix representation. (default = false).", "M", 0, "-M"));
}
/**
* Calculates the class membership probabilities for the given test instance.
*
* @param instance the instance to be classified
* @return predicted class probability distribution
* @exception Exception if distribution can't be computed successfully
*/
public double[] distributionForInstance(Instance instance) throws Exception {
if (instance.classAttribute().isNumeric()) {
throw new UnsupportedClassTypeException("Decorate can't handle a numeric class!");
}
double[] sums = new double[instance.numClasses()], newProbs;
Classifier curr;
for (int i = 0; i < m_Committee.size(); i++) {
curr = (Classifier) m_Committee.get(i);
newProbs = curr.distributionForInstance(instance);
for (int j = 0; j < newProbs.length; j++) sums[j] += newProbs[j];
}
if (Utils.eq(Utils.sum(sums), 0)) {
return sums;
} else {
Utils.normalize(sums);
return sums;
}
}
/**
* Returns an enumeration describing the available options.
*
* @return an enumeration of all the available options.
*/
public Enumeration listOptions() {
Vector newVector = new Vector(7);
newVector.addElement(
new Option(
"\tFull class name of search method, followed\n"
+ "\tby its options.\n"
+ "\teg: \"weka.attributeSelection.BestFirst -D 1\"\n"
+ "\t(default weka.attributeSelection.BestFirst)",
"S",
1,
"-S <search method specification>"));
newVector.addElement(
new Option(
"\tUse cross validation to evaluate features.\n"
+ "\tUse number of folds = 1 for leave one out CV.\n"
+ "\t(Default = leave one out CV)",
"X",
1,
"-X <number of folds>"));
newVector.addElement(
new Option(
"\tPerformance evaluation measure to use for selecting attributes.\n"
+ "\t(Default = accuracy for discrete class and rmse for numeric class)",
"E",
1,
"-E <acc | rmse | mae | auc>"));
newVector.addElement(
new Option("\tUse nearest neighbour instead of global table majority.", "I", 0, "-I"));
newVector.addElement(new Option("\tDisplay decision table rules.\n", "R", 0, "-R"));
newVector.addElement(
new Option(
"",
"",
0,
"\nOptions specific to search method " + m_search.getClass().getName() + ":"));
Enumeration enu = ((OptionHandler) m_search).listOptions();
while (enu.hasMoreElements()) {
newVector.addElement(enu.nextElement());
}
return newVector.elements();
}
/**
* Returns an enumeration describing the available options
*
* @return an enumeration of all the available options
*/
public Enumeration listOptions() {
Vector newVector = new Vector(8);
newVector.addElement(
new Option("\tDesired size of ensemble.\n" + "\t(default 10)", "E", 1, "-E"));
newVector.addElement(
new Option(
"\tFactor that determines number of artificial examples to generate.\n"
+ "\tSpecified proportional to training set size.\n"
+ "\t(default 1.0)",
"R",
1,
"-R"));
Enumeration enu = super.listOptions();
while (enu.hasMoreElements()) {
newVector.addElement(enu.nextElement());
}
return newVector.elements();
}
/**
* Returns an enumeration describing the available options.
*
* @return an enumeration of all the available options.
*/
public Enumeration listOptions() {
Vector newVector = new Vector(4);
newVector.addElement(new Option("\tTurn on debugging output.", "D", 0, "-D"));
newVector.addElement(
new Option(
"\tFull class name of classifier to include, followed\n"
+ "\tby scheme options. May be specified multiple times,\n"
+ "\trequired at least twice.\n"
+ "\teg: \"weka.classifiers.bayes.NaiveBayes -D\"",
"B",
1,
"-B <classifier specification>"));
newVector.addElement(
new Option(
"\tSets the random number seed (default 1).", "S", 1, "-S <random number seed>"));
newVector.addElement(
new Option(
"\tUse cross validation for model selection using the\n"
+ "\tgiven number of folds. (default 0, is to\n"
+ "\tuse training error)",
"X",
1,
"-X <number of folds>"));
return newVector.elements();
}
// this method MajorityVoting to decide the probs of the Instance;
//
protected double[] distributionForInstanceMajorityVoting(Instance instance) throws Exception {
double[] probs = new double[instance.classAttribute().numValues()];
double[] votes = new double[probs.length];
for (int i = 0; i < class_Array.length; i++) {
probs = class_Array[i].distributionForInstance(instance);
int maxIndex = 0;
for (int j = 0; j < probs.length; j++) {
if (probs[j] > probs[maxIndex]) maxIndex = j;
}
// Consider the cases when multiple classes happen to have the same probability
for (int j = 0; j < probs.length; j++) {
if (probs[j] == probs[maxIndex]) votes[j]++;
}
}
int tmpMajorityIndex = 0;
for (int k = 1; k < votes.length; k++) {
if (votes[k] > votes[tmpMajorityIndex]) tmpMajorityIndex = k;
}
// Consider the cases when multiple classes receive the same amount of votes
Vector<Integer> majorityIndexes = new Vector<Integer>();
for (int k = 0; k < votes.length; k++) {
if (votes[k] == votes[tmpMajorityIndex]) majorityIndexes.add(k);
}
// System.out.println("forth");
// Resolve the ties according to a uniform random distribution
int majorityIndex = majorityIndexes.get(m_Random.nextInt(majorityIndexes.size()));
// set the probs of the classes which have not been voted to 0
for (int k = 0; k < probs.length; k++) probs[k] = 0;
// the class that have been voted the most receives 1
probs[majorityIndex] = 1;
return probs;
}
/**
* Build Decorate classifier
*
* @param data the training data to be used for generating the classifier
* @exception Exception if the classifier could not be built successfully
*/
public void buildClassifier(Instances data) throws Exception {
if (m_Classifier == null) {
throw new Exception("A base classifier has not been specified!");
}
if (data.checkForStringAttributes()) {
throw new UnsupportedAttributeTypeException("Cannot handle string attributes!");
}
if (data.classAttribute().isNumeric()) {
throw new UnsupportedClassTypeException("Decorate can't handle a numeric class!");
}
if (m_NumIterations < m_DesiredSize)
throw new Exception("Max number of iterations must be >= desired ensemble size!");
// initialize random number generator
if (m_Seed == -1) m_Random = new Random();
else m_Random = new Random(m_Seed);
int i = 1; // current committee size
int numTrials = 1; // number of Decorate iterations
Instances divData = new Instances(data); // local copy of data - diversity data
divData.deleteWithMissingClass();
Instances artData = null; // artificial data
// compute number of artficial instances to add at each iteration
int artSize = (int) (Math.abs(m_ArtSize) * divData.numInstances());
if (artSize == 0) artSize = 1; // atleast add one random example
computeStats(data); // Compute training data stats for creating artificial examples
// initialize new committee
m_Committee = new Vector();
Classifier newClassifier = m_Classifier;
newClassifier.buildClassifier(divData);
m_Committee.add(newClassifier);
double eComm = computeError(divData); // compute ensemble error
if (m_Debug)
System.out.println(
"Initialize:\tClassifier " + i + " added to ensemble. Ensemble error = " + eComm);
// repeat till desired committee size is reached OR the max number of iterations is exceeded
while (i < m_DesiredSize && numTrials < m_NumIterations) {
// Generate artificial training examples
artData = generateArtificialData(artSize, data);
// Label artificial examples
labelData(artData);
addInstances(divData, artData); // Add new artificial data
// Build new classifier
Classifier tmp[] = Classifier.makeCopies(m_Classifier, 1);
newClassifier = tmp[0];
newClassifier.buildClassifier(divData);
// Remove all the artificial data
removeInstances(divData, artSize);
// Test if the new classifier should be added to the ensemble
m_Committee.add(newClassifier); // add new classifier to current committee
double currError = computeError(divData);
if (currError <= eComm) { // adding the new member did not increase the error
i++;
eComm = currError;
if (m_Debug)
System.out.println(
"Iteration: "
+ (1 + numTrials)
+ "\tClassifier "
+ i
+ " added to ensemble. Ensemble error = "
+ eComm);
} else { // reject the current classifier because it increased the ensemble error
m_Committee.removeElementAt(m_Committee.size() - 1); // pop the last member
}
numTrials++;
}
}
/**
* Returns an enumeration describing the available options.
*
* <p>
*
* @return an enumeration of all the available options
*/
public Enumeration listOptions() {
return options.elements();
}
/**
* Returns an enumeration describing the available options.
*
* <p>Valid options are:
*
* <p>-U <br>
* Use unpruned tree.
*
* <p>-C confidence <br>
* Set confidence threshold for pruning. (Default: 0.25)
*
* <p>-M number <br>
* Set minimum number of instances per leaf. (Default: 2)
*
* <p>-R <br>
* Use reduced error pruning. No subtree raising is performed.
*
* <p>-N number <br>
* Set number of folds for reduced error pruning. One fold is used as the pruning set. (Default:
* 3)
*
* <p>-B <br>
* Use binary splits for nominal attributes.
*
* <p>-S <br>
* Don't perform subtree raising.
*
* <p>-L <br>
* Do not clean up after the tree has been built.
*
* <p>-A <br>
* If set, Laplace smoothing is used for predicted probabilites.
*
* <p>
*
* @return an enumeration of all the available options.
*/
public Enumeration listOptions() {
Vector newVector = new Vector(9);
newVector.addElement(new Option("\tUse unpruned tree.", "U", 0, "-U"));
newVector.addElement(
new Option(
"\tSet confidence threshold for pruning.\n" + "\t(default 0.25)",
"C",
1,
"-C <pruning confidence>"));
newVector.addElement(
new Option(
"\tSet minimum number of instances per leaf.\n" + "\t(default 2)",
"M",
1,
"-M <minimum number of instances>"));
newVector.addElement(new Option("\tUse reduced error pruning.", "R", 0, "-R"));
newVector.addElement(
new Option(
"\tSet number of folds for reduced error\n"
+ "\tpruning. One fold is used as pruning set.\n"
+ "\t(default 3)",
"N",
1,
"-N <number of folds>"));
newVector.addElement(new Option("\tUse binary splits only.", "B", 0, "-B"));
newVector.addElement(new Option("\tDon't perform subtree raising.", "S", 0, "-S"));
newVector.addElement(
new Option("\tDo not clean up after the tree has been built.", "L", 0, "-L"));
newVector.addElement(
new Option("\tLaplace smoothing for predicted probabilities.", "A", 0, "-A"));
return newVector.elements();
}