/**
* Inserts an instance into the hash table
*
* @param inst instance to be inserted
* @param instA to create the hash key from
* @throws Exception if the instance can't be inserted
*/
private void insertIntoTable(Instance inst, double[] instA) throws Exception {
double[] tempClassDist2;
double[] newDist;
DecisionTableHashKey thekey;
if (instA != null) {
thekey = new DecisionTableHashKey(instA);
} else {
thekey = new DecisionTableHashKey(inst, inst.numAttributes(), false);
}
// see if this one is already in the table
tempClassDist2 = (double[]) m_entries.get(thekey);
if (tempClassDist2 == null) {
if (m_classIsNominal) {
newDist = new double[m_theInstances.classAttribute().numValues()];
// Leplace estimation
for (int i = 0; i < m_theInstances.classAttribute().numValues(); i++) {
newDist[i] = 1.0;
}
newDist[(int) inst.classValue()] = inst.weight();
// add to the table
m_entries.put(thekey, newDist);
} else {
newDist = new double[2];
newDist[0] = inst.classValue() * inst.weight();
newDist[1] = inst.weight();
// add to the table
m_entries.put(thekey, newDist);
}
} else {
// update the distribution for this instance
if (m_classIsNominal) {
tempClassDist2[(int) inst.classValue()] += inst.weight();
// update the table
m_entries.put(thekey, tempClassDist2);
} else {
tempClassDist2[0] += (inst.classValue() * inst.weight());
tempClassDist2[1] += inst.weight();
// update the table
m_entries.put(thekey, tempClassDist2);
}
}
}
/**
* Computes the error in classification on the given data.
*
* @param data the instances to be classified
* @return classification error
* @exception Exception if error can not be computed successfully
*/
protected double computeError(Instances data) throws Exception {
double error = 0.0;
int numInstances = data.numInstances();
Instance curr;
for (int i = 0; i < numInstances; i++) {
curr = data.instance(i);
// Check if the instance has been misclassified
if (curr.classValue() != ((int) classifyInstance(curr))) error++;
}
return (error / numInstances);
}
/**
* Calculates the accuracy on a test fold for internal cross validation of feature sets
*
* @param fold set of instances to be "left out" and classified
* @param fs currently selected feature set
* @return the accuracy for the fold
* @throws Exception if something goes wrong
*/
double evaluateFoldCV(Instances fold, int[] fs) throws Exception {
int i;
int ruleCount = 0;
int numFold = fold.numInstances();
int numCl = m_theInstances.classAttribute().numValues();
double[][] class_distribs = new double[numFold][numCl];
double[] instA = new double[fs.length];
double[] normDist;
DecisionTableHashKey thekey;
double acc = 0.0;
int classI = m_theInstances.classIndex();
Instance inst;
if (m_classIsNominal) {
normDist = new double[numCl];
} else {
normDist = new double[2];
}
// first *remove* instances
for (i = 0; i < numFold; i++) {
inst = fold.instance(i);
for (int j = 0; j < fs.length; j++) {
if (fs[j] == classI) {
instA[j] = Double.MAX_VALUE; // missing for the class
} else if (inst.isMissing(fs[j])) {
instA[j] = Double.MAX_VALUE;
} else {
instA[j] = inst.value(fs[j]);
}
}
thekey = new DecisionTableHashKey(instA);
if ((class_distribs[i] = (double[]) m_entries.get(thekey)) == null) {
throw new Error("This should never happen!");
} else {
if (m_classIsNominal) {
class_distribs[i][(int) inst.classValue()] -= inst.weight();
} else {
class_distribs[i][0] -= (inst.classValue() * inst.weight());
class_distribs[i][1] -= inst.weight();
}
ruleCount++;
}
m_classPriorCounts[(int) inst.classValue()] -= inst.weight();
}
double[] classPriors = m_classPriorCounts.clone();
Utils.normalize(classPriors);
// now classify instances
for (i = 0; i < numFold; i++) {
inst = fold.instance(i);
System.arraycopy(class_distribs[i], 0, normDist, 0, normDist.length);
if (m_classIsNominal) {
boolean ok = false;
for (int j = 0; j < normDist.length; j++) {
if (Utils.gr(normDist[j], 1.0)) {
ok = true;
break;
}
}
if (!ok) { // majority class
normDist = classPriors.clone();
}
// if (ok) {
Utils.normalize(normDist);
if (m_evaluationMeasure == EVAL_AUC) {
m_evaluation.evaluateModelOnceAndRecordPrediction(normDist, inst);
} else {
m_evaluation.evaluateModelOnce(normDist, inst);
}
/* } else {
normDist[(int)m_majority] = 1.0;
if (m_evaluationMeasure == EVAL_AUC) {
m_evaluation.evaluateModelOnceAndRecordPrediction(normDist, inst);
} else {
m_evaluation.evaluateModelOnce(normDist, inst);
}
} */
} else {
if (Utils.eq(normDist[1], 0.0)) {
double[] temp = new double[1];
temp[0] = m_majority;
m_evaluation.evaluateModelOnce(temp, inst);
} else {
double[] temp = new double[1];
temp[0] = normDist[0] / normDist[1];
m_evaluation.evaluateModelOnce(temp, inst);
}
}
}
// now re-insert instances
for (i = 0; i < numFold; i++) {
inst = fold.instance(i);
m_classPriorCounts[(int) inst.classValue()] += inst.weight();
if (m_classIsNominal) {
class_distribs[i][(int) inst.classValue()] += inst.weight();
} else {
class_distribs[i][0] += (inst.classValue() * inst.weight());
class_distribs[i][1] += inst.weight();
}
}
return acc;
}
/**
* Classifies an instance for internal leave one out cross validation of feature sets
*
* @param instance instance to be "left out" and classified
* @param instA feature values of the selected features for the instance
* @return the classification of the instance
* @throws Exception if something goes wrong
*/
double evaluateInstanceLeaveOneOut(Instance instance, double[] instA) throws Exception {
DecisionTableHashKey thekey;
double[] tempDist;
double[] normDist;
thekey = new DecisionTableHashKey(instA);
if (m_classIsNominal) {
// if this one is not in the table
if ((tempDist = (double[]) m_entries.get(thekey)) == null) {
throw new Error("This should never happen!");
} else {
normDist = new double[tempDist.length];
System.arraycopy(tempDist, 0, normDist, 0, tempDist.length);
normDist[(int) instance.classValue()] -= instance.weight();
// update the table
// first check to see if the class counts are all zero now
boolean ok = false;
for (int i = 0; i < normDist.length; i++) {
if (Utils.gr(normDist[i], 1.0)) {
ok = true;
break;
}
}
// downdate the class prior counts
m_classPriorCounts[(int) instance.classValue()] -= instance.weight();
double[] classPriors = m_classPriorCounts.clone();
Utils.normalize(classPriors);
if (!ok) { // majority class
normDist = classPriors;
}
m_classPriorCounts[(int) instance.classValue()] += instance.weight();
// if (ok) {
Utils.normalize(normDist);
if (m_evaluationMeasure == EVAL_AUC) {
m_evaluation.evaluateModelOnceAndRecordPrediction(normDist, instance);
} else {
m_evaluation.evaluateModelOnce(normDist, instance);
}
return Utils.maxIndex(normDist);
/*} else {
normDist = new double [normDist.length];
normDist[(int)m_majority] = 1.0;
if (m_evaluationMeasure == EVAL_AUC) {
m_evaluation.evaluateModelOnceAndRecordPrediction(normDist, instance);
} else {
m_evaluation.evaluateModelOnce(normDist, instance);
}
return m_majority;
} */
}
// return Utils.maxIndex(tempDist);
} else {
// see if this one is already in the table
if ((tempDist = (double[]) m_entries.get(thekey)) != null) {
normDist = new double[tempDist.length];
System.arraycopy(tempDist, 0, normDist, 0, tempDist.length);
normDist[0] -= (instance.classValue() * instance.weight());
normDist[1] -= instance.weight();
if (Utils.eq(normDist[1], 0.0)) {
double[] temp = new double[1];
temp[0] = m_majority;
m_evaluation.evaluateModelOnce(temp, instance);
return m_majority;
} else {
double[] temp = new double[1];
temp[0] = normDist[0] / normDist[1];
m_evaluation.evaluateModelOnce(temp, instance);
return temp[0];
}
} else {
throw new Error("This should never happen!");
}
}
// shouldn't get here
// return 0.0;
}
/**
* Generates the classifier.
*
* @param data set of instances serving as training data
* @throws Exception if the classifier has not been generated successfully
*/
public void buildClassifier(Instances data) throws Exception {
// can classifier handle the data?
getCapabilities().testWithFail(data);
// remove instances with missing class
m_theInstances = new Instances(data);
m_theInstances.deleteWithMissingClass();
m_rr = new Random(1);
if (m_theInstances.classAttribute().isNominal()) { // Set up class priors
m_classPriorCounts = new double[data.classAttribute().numValues()];
Arrays.fill(m_classPriorCounts, 1.0);
for (int i = 0; i < data.numInstances(); i++) {
Instance curr = data.instance(i);
m_classPriorCounts[(int) curr.classValue()] += curr.weight();
}
m_classPriors = m_classPriorCounts.clone();
Utils.normalize(m_classPriors);
}
setUpEvaluator();
if (m_theInstances.classAttribute().isNumeric()) {
m_disTransform = new weka.filters.unsupervised.attribute.Discretize();
m_classIsNominal = false;
// use binned discretisation if the class is numeric
((weka.filters.unsupervised.attribute.Discretize) m_disTransform).setBins(10);
((weka.filters.unsupervised.attribute.Discretize) m_disTransform).setInvertSelection(true);
// Discretize all attributes EXCEPT the class
String rangeList = "";
rangeList += (m_theInstances.classIndex() + 1);
// System.out.println("The class col: "+m_theInstances.classIndex());
((weka.filters.unsupervised.attribute.Discretize) m_disTransform)
.setAttributeIndices(rangeList);
} else {
m_disTransform = new weka.filters.supervised.attribute.Discretize();
((weka.filters.supervised.attribute.Discretize) m_disTransform).setUseBetterEncoding(true);
m_classIsNominal = true;
}
m_disTransform.setInputFormat(m_theInstances);
m_theInstances = Filter.useFilter(m_theInstances, m_disTransform);
m_numAttributes = m_theInstances.numAttributes();
m_numInstances = m_theInstances.numInstances();
m_majority = m_theInstances.meanOrMode(m_theInstances.classAttribute());
// Perform the search
int[] selected = m_search.search(m_evaluator, m_theInstances);
m_decisionFeatures = new int[selected.length + 1];
System.arraycopy(selected, 0, m_decisionFeatures, 0, selected.length);
m_decisionFeatures[m_decisionFeatures.length - 1] = m_theInstances.classIndex();
// reduce instances to selected features
m_delTransform = new Remove();
m_delTransform.setInvertSelection(true);
// set features to keep
m_delTransform.setAttributeIndicesArray(m_decisionFeatures);
m_delTransform.setInputFormat(m_theInstances);
m_dtInstances = Filter.useFilter(m_theInstances, m_delTransform);
// reset the number of attributes
m_numAttributes = m_dtInstances.numAttributes();
// create hash table
m_entries = new Hashtable((int) (m_dtInstances.numInstances() * 1.5));
// insert instances into the hash table
for (int i = 0; i < m_numInstances; i++) {
Instance inst = m_dtInstances.instance(i);
insertIntoTable(inst, null);
}
// Replace the global table majority with nearest neighbour?
if (m_useIBk) {
m_ibk = new IBk();
m_ibk.buildClassifier(m_theInstances);
}
// Save memory
if (m_saveMemory) {
m_theInstances = new Instances(m_theInstances, 0);
m_dtInstances = new Instances(m_dtInstances, 0);
}
m_evaluation = null;
}