@Override
public void addResult(Instance inst, double[] classVotes) {
double weight = inst.weight();
int trueClass = (int) inst.classValue();
if (weight > 0.0) {
if (TotalweightObserved == 0) {
reset(inst.dataset().numClasses());
}
this.TotalweightObserved += weight;
this.weightObserved.add(weight);
int predictedClass = Utils.maxIndex(classVotes);
if (predictedClass == trueClass) {
this.weightCorrect.add(weight);
} else {
this.weightCorrect.add(0);
}
// Add Kappa statistic information
for (int i = 0; i < this.numClasses; i++) {
this.rowKappa[i].add(i == predictedClass ? weight : 0);
this.columnKappa[i].add(i == trueClass ? weight : 0);
}
if (this.lastSeenClass == trueClass) {
this.weightCorrectNoChangeClassifier.add(weight);
} else {
this.weightCorrectNoChangeClassifier.add(0);
}
this.classAccuracy[trueClass].add(predictedClass == trueClass ? weight : 0.0);
this.lastSeenClass = trueClass;
}
}
/**
* Method for building an Id3 tree.
*
* @param data the training data
* @exception Exception if decision tree can't be built successfully
*/
private void makeTree(Instances data) throws Exception {
// Check if no instances have reached this node.
if (data.numInstances() == 0) {
m_Attribute = null;
m_ClassValue = Utils.missingValue();
m_Distribution = new double[data.numClasses()];
return;
}
// Compute attribute with maximum information gain.
double[] infoGains = new double[data.numAttributes()];
Enumeration attEnum = data.enumerateAttributes();
while (attEnum.hasMoreElements()) {
Attribute att = (Attribute) attEnum.nextElement();
infoGains[att.index()] = computeInfoGain(data, att);
}
m_Attribute = data.attribute(Utils.maxIndex(infoGains));
// Make leaf if information gain is zero.
// Otherwise create successors.
if (Utils.eq(infoGains[m_Attribute.index()], 0)) {
m_Attribute = null;
m_Distribution = new double[data.numClasses()];
Enumeration instEnum = data.enumerateInstances();
while (instEnum.hasMoreElements()) {
Instance inst = (Instance) instEnum.nextElement();
m_Distribution[(int) inst.classValue()]++;
}
Utils.normalize(m_Distribution);
m_ClassValue = Utils.maxIndex(m_Distribution);
m_ClassAttribute = data.classAttribute();
} else {
Instances[] splitData = splitData(data, m_Attribute);
m_Successors = new Id3[m_Attribute.numValues()];
for (int j = 0; j < m_Attribute.numValues(); j++) {
m_Successors[j] = new Id3();
m_Successors[j].makeTree(splitData[j]);
}
}
}
/**
* Evaluate Jacobian vector
*
* @param x the current values of variables
* @return the gradient vector
*/
protected double[] evaluateGradient(double[] x) {
double[] grad = new double[x.length];
int dim = m_NumPredictors + 1; // Number of variables per class
for (int i = 0; i < cls.length; i++) { // ith instance
double[] num = new double[m_NumClasses - 1]; // numerator of
// [-log(1+sum(exp))]'
int index;
for (int offset = 0; offset < m_NumClasses - 1; offset++) { // Which
// part of x
double exp = 0.0;
index = offset * dim;
for (int j = 0; j < dim; j++) {
exp += m_Data[i][j] * x[index + j];
}
num[offset] = exp;
}
double max = num[Utils.maxIndex(num)];
double denom = Math.exp(-max); // Denominator of [-log(1+sum(exp))]'
for (int offset = 0; offset < m_NumClasses - 1; offset++) {
num[offset] = Math.exp(num[offset] - max);
denom += num[offset];
}
Utils.normalize(num, denom);
// Update denominator of the gradient of -log(Posterior)
double firstTerm;
for (int offset = 0; offset < m_NumClasses - 1; offset++) { // Which
// part of x
index = offset * dim;
firstTerm = weights[i] * num[offset];
for (int q = 0; q < dim; q++) {
grad[index + q] += firstTerm * m_Data[i][q];
}
}
if (cls[i] != m_NumClasses - 1) { // Not the last class
for (int p = 0; p < dim; p++) {
grad[cls[i] * dim + p] -= weights[i] * m_Data[i][p];
}
}
}
// Ridge: note that intercepts NOT included
for (int offset = 0; offset < m_NumClasses - 1; offset++) {
for (int r = 1; r < dim; r++) {
grad[offset * dim + r] += 2 * m_Ridge * x[offset * dim + r];
}
}
return grad;
}
/**
* Classifies a given instance. Either this or distributionForInstance() needs to be implemented
* by subclasses.
*
* @param instance the instance to be assigned to a cluster
* @return the number of the assigned cluster as an integer
* @exception Exception if instance could not be clustered successfully
*/
@Override
public int clusterInstance(Instance instance) throws Exception {
double[] dist = distributionForInstance(instance);
if (dist == null) {
throw new Exception("Null distribution predicted");
}
if (Utils.sum(dist) <= 0) {
throw new Exception("Unable to cluster instance");
}
return Utils.maxIndex(dist);
}
/**
* Generates a clusterer by the mean of spectral clustering algorithm.
*
* @param data set of instances serving as training data
* @exception Exception if the clusterer has not been generated successfully
*/
public void buildClusterer(Instances data) throws java.lang.Exception {
m_Sequences = new Instances(data);
int n = data.numInstances();
int k = data.numAttributes();
DoubleMatrix2D w;
if (useSparseMatrix) w = DoubleFactory2D.sparse.make(n, n);
else w = DoubleFactory2D.dense.make(n, n);
double[][] v1 = new double[n][];
for (int i = 0; i < n; i++) v1[i] = data.instance(i).toDoubleArray();
v = DoubleFactory2D.dense.make(v1);
double sigma_sq = sigma * sigma;
// Sets up similarity matrix
for (int i = 0; i < n; i++)
for (int j = i; j < n; j++) {
/*double dist = distnorm2(v.viewRow(i), v.viewRow(j));
if((r == -1) || (dist < r)) {
double sim = Math.exp(- (dist * dist) / (2 * sigma_sq));
w.set(i, j, sim);
w.set(j, i, sim);
}*/
/* String [] key = {data.instance(i).stringValue(0), data.instance(j).stringValue(0)};
System.out.println(key[0]);
System.out.println(key[1]);
System.out.println(simScoreMap.containsKey(key));
Double simValue = simScoreMap.get(key);*/
double sim = sim_matrix[i][j];
w.set(i, j, sim);
w.set(j, i, sim);
}
// Partitions points
int[][] p = partition(w, alpha_star);
// Deploys results
numOfClusters = p.length;
cluster = new int[n];
for (int i = 0; i < p.length; i++) for (int j = 0; j < p[i].length; j++) cluster[p[i][j]] = i;
// System.out.println("Final partition:");
// UtilsJS.printMatrix(p);
// System.out.println("Cluster:\n");
// UtilsJS.printArray(cluster);
this.numOfClusters = cluster[Utils.maxIndex(cluster)] + 1;
// System.out.println("Num clusters:\t"+this.numOfClusters);
}
// use the TriTrainer Classifier to classify Instance;
public double classifyInstance(Instance instance) throws Exception {
double result;
double[] dist;
int index;
dist = distributionForInstance(instance); // 分类概率
if (instance.classAttribute().isNominal()) {
index = Utils.maxIndex(dist); // 返回概率最大的
if (dist[index] == 0) result = Instance.missingValue();
else result = dist[index];
} else if (instance.classAttribute().isNumeric()) {
result = dist[0];
} else {
result = Instance.missingValue();
}
return result;
}
/**
* Calculates the class membership probabilities for the given test instance.
*
* @param instance the instance to be classified
* @return predicted class probability distribution
* @throws Exception if there is a problem generating the prediction
*/
public double[] distributionForInstance(Instance instance) throws Exception {
double[] probOfClassGivenDoc = new double[m_numClasses];
// calculate the array of log(Pr[D|C])
double[] logDocGivenClass = new double[m_numClasses];
for (int h = 0; h < m_numClasses; h++) logDocGivenClass[h] = probOfDocGivenClass(instance, h);
double max = logDocGivenClass[Utils.maxIndex(logDocGivenClass)];
double probOfDoc = 0.0;
for (int i = 0; i < m_numClasses; i++) {
probOfClassGivenDoc[i] = Math.exp(logDocGivenClass[i] - max) * m_probOfClass[i];
probOfDoc += probOfClassGivenDoc[i];
}
Utils.normalize(probOfClassGivenDoc, probOfDoc);
return probOfClassGivenDoc;
}
/**
* Evaluate objective function
*
* @param x the current values of variables
* @return the value of the objective function
*/
protected double objectiveFunction(double[] x) {
double nll = 0; // -LogLikelihood
int dim = m_NumPredictors + 1; // Number of variables per class
for (int i = 0; i < cls.length; i++) { // ith instance
double[] exp = new double[m_NumClasses - 1];
int index;
for (int offset = 0; offset < m_NumClasses - 1; offset++) {
index = offset * dim;
for (int j = 0; j < dim; j++) {
exp[offset] += m_Data[i][j] * x[index + j];
}
}
double max = exp[Utils.maxIndex(exp)];
double denom = Math.exp(-max);
double num;
if (cls[i] == m_NumClasses - 1) { // Class of this instance
num = -max;
} else {
num = exp[cls[i]] - max;
}
for (int offset = 0; offset < m_NumClasses - 1; offset++) {
denom += Math.exp(exp[offset] - max);
}
nll -= weights[i] * (num - Math.log(denom)); // Weighted NLL
}
// Ridge: note that intercepts NOT included
for (int offset = 0; offset < m_NumClasses - 1; offset++) {
for (int r = 1; r < dim; r++) {
nll += m_Ridge * x[offset * dim + r] * x[offset * dim + r];
}
}
return nll;
}
/**
* Calculates the class membership probabilities for the given test instance.
*
* @param instance the instance to be classified
* @return predicted class probability distribution
* @throws Exception if there is a problem generating the prediction
*/
@Override
public double[] distributionForInstance(Instance instance) throws Exception {
tokenizeInstance(instance, false);
double[] probOfClassGivenDoc = new double[m_data.numClasses()];
double[] logDocGivenClass = new double[m_data.numClasses()];
for (int i = 0; i < m_data.numClasses(); i++) {
logDocGivenClass[i] += Math.log(m_probOfClass[i]);
LinkedHashMap<String, Count> dictForClass = m_probOfWordGivenClass.get(i);
int allWords = 0;
// for document normalization (if in use)
double iNorm = 0;
double fv = 0;
if (m_normalize) {
for (Map.Entry<String, Count> feature : m_inputVector.entrySet()) {
String word = feature.getKey();
Count c = feature.getValue();
// check the word against all the dictionaries (all classes)
boolean ok = false;
for (int clss = 0; clss < m_data.numClasses(); clss++) {
if (m_probOfWordGivenClass.get(clss).get(word) != null) {
ok = true;
break;
}
}
// only normalize with respect to those words that we've seen during
// training
// (i.e. dictionary over all classes)
if (ok) {
// word counts or bag-of-words?
fv = (m_wordFrequencies) ? c.m_count : 1.0;
iNorm += Math.pow(Math.abs(fv), m_lnorm);
}
}
iNorm = Math.pow(iNorm, 1.0 / m_lnorm);
}
// System.out.println("---- " + m_inputVector.size());
for (Map.Entry<String, Count> feature : m_inputVector.entrySet()) {
String word = feature.getKey();
Count dictCount = dictForClass.get(word);
// System.out.print(word + " ");
/*
* if (dictCount != null) { System.out.println(dictCount.m_count); }
* else { System.out.println("*1"); }
*/
// check the word against all the dictionaries (all classes)
boolean ok = false;
for (int clss = 0; clss < m_data.numClasses(); clss++) {
if (m_probOfWordGivenClass.get(clss).get(word) != null) {
ok = true;
break;
}
}
// ignore words we haven't seen in the training data
if (ok) {
double freq = (m_wordFrequencies) ? feature.getValue().m_count : 1.0;
// double freq = (feature.getValue().m_count / iNorm * m_norm);
if (m_normalize) {
freq /= iNorm * m_norm;
}
allWords += freq;
if (dictCount != null) {
logDocGivenClass[i] += freq * Math.log(dictCount.m_count);
} else {
// leplace for zero frequency
logDocGivenClass[i] += freq * Math.log(m_leplace);
}
}
}
if (m_wordsPerClass[i] > 0) {
logDocGivenClass[i] -= allWords * Math.log(m_wordsPerClass[i]);
}
}
double max = logDocGivenClass[Utils.maxIndex(logDocGivenClass)];
for (int i = 0; i < m_data.numClasses(); i++) {
probOfClassGivenDoc[i] = Math.exp(logDocGivenClass[i] - max);
}
Utils.normalize(probOfClassGivenDoc);
return probOfClassGivenDoc;
}
/**
* Return the argmax on #distribution(Instance, double[]).
*
* @return argmax_{k in 0,1,...} p( y_j = k | x , y_pred )
*/
public double classify(Instance x, double ypred[]) throws Exception {
Instance x_ = transform(x, ypred);
return Utils.maxIndex(h.distributionForInstance(x_));
}
/**
* Accepts and processes a classifier encapsulated in an incremental classifier event
*
* @param ce an <code>IncrementalClassifierEvent</code> value
*/
@Override
public void acceptClassifier(final IncrementalClassifierEvent ce) {
try {
if (ce.getStatus() == IncrementalClassifierEvent.NEW_BATCH) {
m_throughput = new StreamThroughput(statusMessagePrefix());
m_throughput.setSamplePeriod(m_statusFrequency);
// m_eval = new Evaluation(ce.getCurrentInstance().dataset());
m_eval = new Evaluation(ce.getStructure());
m_eval.useNoPriors();
m_dataLegend = new Vector();
m_reset = true;
m_dataPoint = new double[0];
Instances inst = ce.getStructure();
System.err.println("NEW BATCH");
m_instanceCount = 0;
if (m_windowSize > 0) {
m_window = new LinkedList<Instance>();
m_windowEval = new Evaluation(ce.getStructure());
m_windowEval.useNoPriors();
m_windowedPreds = new LinkedList<double[]>();
if (m_logger != null) {
m_logger.logMessage(
statusMessagePrefix()
+ "[IncrementalClassifierEvaluator] Chart output using windowed "
+ "evaluation over "
+ m_windowSize
+ " instances");
}
}
/*
* if (m_logger != null) { m_logger.statusMessage(statusMessagePrefix()
* + "IncrementalClassifierEvaluator: started processing...");
* m_logger.logMessage(statusMessagePrefix() +
* " [IncrementalClassifierEvaluator]" + statusMessagePrefix() +
* " started processing..."); }
*/
} else {
Instance inst = ce.getCurrentInstance();
if (inst != null) {
m_throughput.updateStart();
m_instanceCount++;
// if (inst.attribute(inst.classIndex()).isNominal()) {
double[] dist = ce.getClassifier().distributionForInstance(inst);
double pred = 0;
if (!inst.isMissing(inst.classIndex())) {
if (m_outputInfoRetrievalStats) {
// store predictions so AUC etc can be output.
m_eval.evaluateModelOnceAndRecordPrediction(dist, inst);
} else {
m_eval.evaluateModelOnce(dist, inst);
}
if (m_windowSize > 0) {
m_windowEval.evaluateModelOnce(dist, inst);
m_window.addFirst(inst);
m_windowedPreds.addFirst(dist);
if (m_instanceCount > m_windowSize) {
// "forget" the oldest prediction
Instance oldest = m_window.removeLast();
double[] oldDist = m_windowedPreds.removeLast();
oldest.setWeight(-oldest.weight());
m_windowEval.evaluateModelOnce(oldDist, oldest);
oldest.setWeight(-oldest.weight());
}
}
} else {
pred = ce.getClassifier().classifyInstance(inst);
}
if (inst.classIndex() >= 0) {
// need to check that the class is not missing
if (inst.attribute(inst.classIndex()).isNominal()) {
if (!inst.isMissing(inst.classIndex())) {
if (m_dataPoint.length < 2) {
m_dataPoint = new double[3];
m_dataLegend.addElement("Accuracy");
m_dataLegend.addElement("RMSE (prob)");
m_dataLegend.addElement("Kappa");
}
// int classV = (int) inst.value(inst.classIndex());
if (m_windowSize > 0) {
m_dataPoint[1] = m_windowEval.rootMeanSquaredError();
m_dataPoint[2] = m_windowEval.kappa();
} else {
m_dataPoint[1] = m_eval.rootMeanSquaredError();
m_dataPoint[2] = m_eval.kappa();
}
// int maxO = Utils.maxIndex(dist);
// if (maxO == classV) {
// dist[classV] = -1;
// maxO = Utils.maxIndex(dist);
// }
// m_dataPoint[1] -= dist[maxO];
} else {
if (m_dataPoint.length < 1) {
m_dataPoint = new double[1];
m_dataLegend.addElement("Confidence");
}
}
double primaryMeasure = 0;
if (!inst.isMissing(inst.classIndex())) {
if (m_windowSize > 0) {
primaryMeasure = 1.0 - m_windowEval.errorRate();
} else {
primaryMeasure = 1.0 - m_eval.errorRate();
}
} else {
// record confidence as the primary measure
// (another possibility would be entropy of
// the distribution, or perhaps average
// confidence)
primaryMeasure = dist[Utils.maxIndex(dist)];
}
// double [] dataPoint = new double[1];
m_dataPoint[0] = primaryMeasure;
// double min = 0; double max = 100;
/*
* ChartEvent e = new
* ChartEvent(IncrementalClassifierEvaluator.this, m_dataLegend,
* min, max, dataPoint);
*/
m_ce.setLegendText(m_dataLegend);
m_ce.setMin(0);
m_ce.setMax(1);
m_ce.setDataPoint(m_dataPoint);
m_ce.setReset(m_reset);
m_reset = false;
} else {
// numeric class
if (m_dataPoint.length < 1) {
m_dataPoint = new double[1];
if (inst.isMissing(inst.classIndex())) {
m_dataLegend.addElement("Prediction");
} else {
m_dataLegend.addElement("RMSE");
}
}
if (!inst.isMissing(inst.classIndex())) {
double update;
if (!inst.isMissing(inst.classIndex())) {
if (m_windowSize > 0) {
update = m_windowEval.rootMeanSquaredError();
} else {
update = m_eval.rootMeanSquaredError();
}
} else {
update = pred;
}
m_dataPoint[0] = update;
if (update > m_max) {
m_max = update;
}
if (update < m_min) {
m_min = update;
}
}
m_ce.setLegendText(m_dataLegend);
m_ce.setMin((inst.isMissing(inst.classIndex()) ? m_min : 0));
m_ce.setMax(m_max);
m_ce.setDataPoint(m_dataPoint);
m_ce.setReset(m_reset);
m_reset = false;
}
notifyChartListeners(m_ce);
}
m_throughput.updateEnd(m_logger);
}
if (ce.getStatus() == IncrementalClassifierEvent.BATCH_FINISHED || inst == null) {
if (m_logger != null) {
m_logger.logMessage(
"[IncrementalClassifierEvaluator]"
+ statusMessagePrefix()
+ " Finished processing.");
}
m_throughput.finished(m_logger);
// save memory if using windowed evaluation for charting
m_windowEval = null;
m_window = null;
m_windowedPreds = null;
if (m_textListeners.size() > 0) {
String textTitle = ce.getClassifier().getClass().getName();
textTitle = textTitle.substring(textTitle.lastIndexOf('.') + 1, textTitle.length());
String results =
"=== Performance information ===\n\n"
+ "Scheme: "
+ textTitle
+ "\n"
+ "Relation: "
+ m_eval.getHeader().relationName()
+ "\n\n"
+ m_eval.toSummaryString();
if (m_eval.getHeader().classIndex() >= 0
&& m_eval.getHeader().classAttribute().isNominal()
&& (m_outputInfoRetrievalStats)) {
results += "\n" + m_eval.toClassDetailsString();
}
if (m_eval.getHeader().classIndex() >= 0
&& m_eval.getHeader().classAttribute().isNominal()) {
results += "\n" + m_eval.toMatrixString();
}
textTitle = "Results: " + textTitle;
TextEvent te = new TextEvent(this, results, textTitle);
notifyTextListeners(te);
}
}
}
} catch (Exception ex) {
if (m_logger != null) {
m_logger.logMessage(
"[IncrementalClassifierEvaluator]"
+ statusMessagePrefix()
+ " Error processing prediction "
+ ex.getMessage());
m_logger.statusMessage(
statusMessagePrefix() + "ERROR: problem processing prediction (see log for details)");
}
ex.printStackTrace();
stop();
}
}
/**
* Recursively generates a tree. A derivative of the buildTree function from the
* "weka.classifiers.trees.RandomTree" class, with the following changes made:
*
* <ul>
* <li>m_ClassProbs are now remembered only in leaves, not in every node of the tree
* <li>m_Distribution has been removed
* <li>members of dists, splits, props and vals arrays which are not used are dereferenced prior
* to recursion to reduce memory requirements
* <li>a check for "branch with no training instances" is now (FastRF 0.98) made before
* recursion; with the current implementation of splitData(), empty branches can appear only
* with nominal attributes with more than two categories
* <li>each new 'tree' (i.e. node or leaf) is passed a reference to its 'mother forest',
* necessary to look up parameters such as maxDepth and K
* <li>pre-split entropy is not recalculated unnecessarily
* <li>uses DataCache instead of weka.core.Instances, the reference to the DataCache is stored
* as a field in FastRandomTree class and not passed recursively down new buildTree() calls
* <li>similarly, a reference to the random number generator is stored in a field of the
* DataCache
* <li>m_ClassProbs are now normalized by dividing with number of instances in leaf, instead of
* forcing the sum of class probabilities to 1.0; this has a large effect when
* class/instance weights are set by user
* <li>a little imprecision is allowed in checking whether there was a decrease in entropy after
* splitting
* <li>0.99: the temporary arrays splits, props, vals now are not wide as the full number of
* attributes in the dataset (of which only "k" columns of randomly chosen attributes get
* filled). Now, it's just a single array which gets replaced as the k features are
* evaluated sequentially, but it gets replaced only if a next feature is better than a
* previous one.
* <li>0.99: the SortedIndices are now not cut up into smaller arrays on every split, but rather
* re-sorted within the same array in the splitDataNew(), and passed down to buildTree() as
* the original large matrix, but with start and end points explicitly specified
* </ul>
*
* @param sortedIndices the indices of the instances of the whole bootstrap replicate
* @param startAt First index of the instance to consider in this split; inclusive.
* @param endAt Last index of the instance to consider; inclusive.
* @param classProbs the class distribution
* @param debug whether debugging is on
* @param attIndicesWindow the attribute window to choose attributes from
* @param depth the current depth
*/
protected void buildTree(
int[][] sortedIndices,
int startAt,
int endAt,
double[] classProbs,
boolean debug,
int[] attIndicesWindow,
int depth) {
m_Debug = debug;
int sortedIndicesLength = endAt - startAt + 1;
// Check if node doesn't contain enough instances or is pure
// or maximum depth reached, make leaf.
if ((sortedIndicesLength < Math.max(2, getMinNum())) // small
|| Utils.eq(classProbs[Utils.maxIndex(classProbs)], Utils.sum(classProbs)) // pure
|| ((getMaxDepth() > 0) && (depth >= getMaxDepth())) // deep
) {
m_Attribute = -1; // indicates leaf (no useful attribute to split on)
// normalize by dividing with the number of instances (as of ver. 0.97)
// unless leaf is empty - this can happen with splits on nominal
// attributes with more than two categories
if (sortedIndicesLength != 0)
for (int c = 0; c < classProbs.length; c++) {
classProbs[c] /= sortedIndicesLength;
}
m_ClassProbs = classProbs;
this.data = null;
return;
} // (leaf making)
// new 0.99: all the following are for the best attribute only! they're updated while
// sequentially through the attributes
double val = Double.NaN; // value of splitting criterion
double[][] dist =
new double[2]
[data.numClasses]; // class distributions (contingency table), indexed first by branch,
// then by class
double[] prop = new double[2]; // the branch sizes (as fraction)
double split = Double.NaN; // split point
// Investigate K random attributes
int attIndex = 0;
int windowSize = attIndicesWindow.length;
int k = getKValue();
boolean sensibleSplitFound = false;
double prior = Double.NaN;
double bestNegPosterior = -Double.MAX_VALUE;
int bestAttIdx = -1;
while ((windowSize > 0) && (k-- > 0 || !sensibleSplitFound)) {
int chosenIndex = data.reusableRandomGenerator.nextInt(windowSize);
attIndex = attIndicesWindow[chosenIndex];
// shift chosen attIndex out of window
attIndicesWindow[chosenIndex] = attIndicesWindow[windowSize - 1];
attIndicesWindow[windowSize - 1] = attIndex;
windowSize--;
// new: 0.99
double candidateSplit =
distributionSequentialAtt(
prop, dist, bestNegPosterior, attIndex, sortedIndices[attIndex], startAt, endAt);
if (Double.isNaN(candidateSplit)) {
continue; // we did not improve over a previous attribute! "dist" is unchanged from before
}
// by this point we know we have an improvement, so we keep the new split point
split = candidateSplit;
bestAttIdx = attIndex;
if (Double.isNaN(
prior)) { // needs to be computed only once per branch - is same for all attributes (even
// regardless of missing values)
prior = SplitCriteria.entropyOverColumns(dist);
}
double negPosterior =
-SplitCriteria.entropyConditionedOnRows(dist); // this is an updated dist
if (negPosterior > bestNegPosterior) {
bestNegPosterior = negPosterior;
} else {
throw new IllegalArgumentException("Very strange!");
}
val = prior - (-negPosterior); // we want the greatest reduction in entropy
if (val > 1e-2) { // we allow some leeway here to compensate
sensibleSplitFound = true; // for imprecision in entropy computation
}
} // feature by feature in window
if (sensibleSplitFound) {
m_Attribute = bestAttIdx; // find best attribute
m_SplitPoint = split;
m_Prop = prop;
prop = null; // can be GC'ed
// int[][][] subsetIndices =
// new int[dist.length][data.numAttributes][];
// splitData( subsetIndices, m_Attribute,
// m_SplitPoint, sortedIndices );
// int numInstancesBeforeSplit = sortedIndices[0].length;
int belowTheSplitStartsAt =
splitDataNew(m_Attribute, m_SplitPoint, sortedIndices, startAt, endAt);
m_Successors = new FastRandomTree[dist.length]; // dist.length now always == 2
for (int i = 0; i < dist.length; i++) {
m_Successors[i] = new FastRandomTree();
m_Successors[i].m_MotherForest = this.m_MotherForest;
m_Successors[i].data = this.data;
// new in 0.99 - used in distributionSequentialAtt()
m_Successors[i].tempDists = this.tempDists;
m_Successors[i].tempDistsOther = this.tempDistsOther;
m_Successors[i].tempProps = this.tempProps;
// check if we're about to make an empty branch - this can happen with
// nominal attributes with more than two categories (as of ver. 0.98)
if (belowTheSplitStartsAt - startAt == 0) {
// in this case, modify the chosenAttDists[i] so that it contains
// the current, before-split class probabilities, properly normalized
// by the number of instances (as we won't be able to normalize
// after the split)
for (int j = 0; j < dist[i].length; j++) dist[i][j] = classProbs[j] / sortedIndicesLength;
}
if (i == 0) { // before split
m_Successors[i].buildTree(
sortedIndices,
startAt,
belowTheSplitStartsAt - 1,
dist[i],
m_Debug,
attIndicesWindow,
depth + 1);
} else { // after split
m_Successors[i].buildTree(
sortedIndices,
belowTheSplitStartsAt,
endAt,
dist[i],
m_Debug,
attIndicesWindow,
depth + 1);
}
dist[i] = null;
}
sortedIndices = null;
} else { // ------ make leaf --------
m_Attribute = -1;
// normalize by dividing with the number of instances (as of ver. 0.97)
// unless leaf is empty - this can happen with splits on nominal attributes
if (sortedIndicesLength != 0)
for (int c = 0; c < classProbs.length; c++) {
classProbs[c] /= sortedIndicesLength;
}
m_ClassProbs = classProbs;
}
this.data = null; // dereference all pointers so data can be GC'd after tree is built
}
public boolean correctlyClassifies(Instance inst) {
return Utils.maxIndex(getVotesForInstance(inst)) == (int) inst.classValue();
}
/**
* 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;
}
/**
* Returns a description of the classifier.
*
* @return a description of the classifier as a string.
*/
public String toString() {
if (m_entries == null) {
return "Decision Table: No model built yet.";
} else {
StringBuffer text = new StringBuffer();
text.append(
"Decision Table:"
+ "\n\nNumber of training instances: "
+ m_numInstances
+ "\nNumber of Rules : "
+ m_entries.size()
+ "\n");
if (m_useIBk) {
text.append("Non matches covered by IB1.\n");
} else {
text.append("Non matches covered by Majority class.\n");
}
text.append(m_search.toString());
/*text.append("Best first search for feature set,\nterminated after "+
m_maxStale+" non improving subsets.\n"); */
text.append("Evaluation (for feature selection): CV ");
if (m_CVFolds > 1) {
text.append("(" + m_CVFolds + " fold) ");
} else {
text.append("(leave one out) ");
}
text.append("\nFeature set: " + printFeatures());
if (m_displayRules) {
// find out the max column width
int maxColWidth = 0;
for (int i = 0; i < m_dtInstances.numAttributes(); i++) {
if (m_dtInstances.attribute(i).name().length() > maxColWidth) {
maxColWidth = m_dtInstances.attribute(i).name().length();
}
if (m_classIsNominal || (i != m_dtInstances.classIndex())) {
Enumeration e = m_dtInstances.attribute(i).enumerateValues();
while (e.hasMoreElements()) {
String ss = (String) e.nextElement();
if (ss.length() > maxColWidth) {
maxColWidth = ss.length();
}
}
}
}
text.append("\n\nRules:\n");
StringBuffer tm = new StringBuffer();
for (int i = 0; i < m_dtInstances.numAttributes(); i++) {
if (m_dtInstances.classIndex() != i) {
int d = maxColWidth - m_dtInstances.attribute(i).name().length();
tm.append(m_dtInstances.attribute(i).name());
for (int j = 0; j < d + 1; j++) {
tm.append(" ");
}
}
}
tm.append(m_dtInstances.attribute(m_dtInstances.classIndex()).name() + " ");
for (int i = 0; i < tm.length() + 10; i++) {
text.append("=");
}
text.append("\n");
text.append(tm);
text.append("\n");
for (int i = 0; i < tm.length() + 10; i++) {
text.append("=");
}
text.append("\n");
Enumeration e = m_entries.keys();
while (e.hasMoreElements()) {
DecisionTableHashKey tt = (DecisionTableHashKey) e.nextElement();
text.append(tt.toString(m_dtInstances, maxColWidth));
double[] ClassDist = (double[]) m_entries.get(tt);
if (m_classIsNominal) {
int m = Utils.maxIndex(ClassDist);
try {
text.append(m_dtInstances.classAttribute().value(m) + "\n");
} catch (Exception ee) {
System.out.println(ee.getMessage());
}
} else {
text.append((ClassDist[0] / ClassDist[1]) + "\n");
}
}
for (int i = 0; i < tm.length() + 10; i++) {
text.append("=");
}
text.append("\n");
text.append("\n");
}
return text.toString();
}
}
/**
* Select the best value for k by hold-one-out cross-validation. If the class attribute is
* nominal, classification error is minimised. If the class attribute is numeric, mean absolute
* error is minimised
*/
protected void crossValidate() {
try {
if (m_NNSearch instanceof weka.core.neighboursearch.CoverTree)
throw new Exception(
"CoverTree doesn't support hold-one-out "
+ "cross-validation. Use some other NN "
+ "method.");
double[] performanceStats = new double[m_kNNUpper];
double[] performanceStatsSq = new double[m_kNNUpper];
for (int i = 0; i < m_kNNUpper; i++) {
performanceStats[i] = 0;
performanceStatsSq[i] = 0;
}
m_kNN = m_kNNUpper;
Instance instance;
Instances neighbours;
double[] origDistances, convertedDistances;
for (int i = 0; i < m_Train.numInstances(); i++) {
if (m_Debug && (i % 50 == 0)) {
System.err.print("Cross validating " + i + "/" + m_Train.numInstances() + "\r");
}
instance = m_Train.instance(i);
neighbours = m_NNSearch.kNearestNeighbours(instance, m_kNN);
origDistances = m_NNSearch.getDistances();
for (int j = m_kNNUpper - 1; j >= 0; j--) {
// Update the performance stats
convertedDistances = new double[origDistances.length];
System.arraycopy(origDistances, 0, convertedDistances, 0, origDistances.length);
double[] distribution = makeDistribution(neighbours, convertedDistances);
double thisPrediction = Utils.maxIndex(distribution);
if (m_Train.classAttribute().isNumeric()) {
thisPrediction = distribution[0];
double err = thisPrediction - instance.classValue();
performanceStatsSq[j] += err * err; // Squared error
performanceStats[j] += Math.abs(err); // Absolute error
} else {
if (thisPrediction != instance.classValue()) {
performanceStats[j]++; // Classification error
}
}
if (j >= 1) {
neighbours = pruneToK(neighbours, convertedDistances, j);
}
}
}
// Display the results of the cross-validation
for (int i = 0; i < m_kNNUpper; i++) {
if (m_Debug) {
System.err.print("Hold-one-out performance of " + (i + 1) + " neighbors ");
}
if (m_Train.classAttribute().isNumeric()) {
if (m_Debug) {
if (m_MeanSquared) {
System.err.println(
"(RMSE) = " + Math.sqrt(performanceStatsSq[i] / m_Train.numInstances()));
} else {
System.err.println("(MAE) = " + performanceStats[i] / m_Train.numInstances());
}
}
} else {
if (m_Debug) {
System.err.println("(%ERR) = " + 100.0 * performanceStats[i] / m_Train.numInstances());
}
}
}
// Check through the performance stats and select the best
// k value (or the lowest k if more than one best)
double[] searchStats = performanceStats;
if (m_Train.classAttribute().isNumeric() && m_MeanSquared) {
searchStats = performanceStatsSq;
}
double bestPerformance = Double.NaN;
int bestK = 1;
for (int i = 0; i < m_kNNUpper; i++) {
if (Double.isNaN(bestPerformance) || (bestPerformance > searchStats[i])) {
bestPerformance = searchStats[i];
bestK = i + 1;
}
}
m_kNN = bestK;
if (m_Debug) {
System.err.println("Selected k = " + bestK);
}
m_kNNValid = true;
} catch (Exception ex) {
throw new Error("Couldn't optimize by cross-validation: " + ex.getMessage());
}
}
