/**
* Select only instances with weights that contribute to the specified quantile of the weight
* distribution
*
* @param data the input instances
* @param quantile the specified quantile eg 0.9 to select 90% of the weight mass
* @return the selected instances
*/
protected Instances selectWeightQuantile(Instances data, double quantile) {
int numInstances = data.numInstances();
Instances trainData = new Instances(data, numInstances);
double[] weights = new double[numInstances];
double sumOfWeights = 0;
for (int i = 0; i < numInstances; i++) {
weights[i] = data.instance(i).weight();
sumOfWeights += weights[i];
}
double weightMassToSelect = sumOfWeights * quantile;
int[] sortedIndices = Utils.sort(weights);
// Select the instances
sumOfWeights = 0;
for (int i = numInstances - 1; i >= 0; i--) {
Instance instance = (Instance) data.instance(sortedIndices[i]).copy();
trainData.add(instance);
sumOfWeights += weights[sortedIndices[i]];
if ((sumOfWeights > weightMassToSelect)
&& (i > 0)
&& (weights[sortedIndices[i]] != weights[sortedIndices[i - 1]])) {
break;
}
}
if (m_Debug) {
System.err.println("Selected " + trainData.numInstances() + " out of " + numInstances);
}
return trainData;
}
/** Computes average class values for each attribute and value */
private void computeAverageClassValues() {
double totalCounts, sum;
Instance instance;
double[] counts;
double[][] avgClassValues = new double[getInputFormat().numAttributes()][0];
m_Indices = new int[getInputFormat().numAttributes()][0];
for (int j = 0; j < getInputFormat().numAttributes(); j++) {
Attribute att = getInputFormat().attribute(j);
if (att.isNominal()) {
avgClassValues[j] = new double[att.numValues()];
counts = new double[att.numValues()];
for (int i = 0; i < getInputFormat().numInstances(); i++) {
instance = getInputFormat().instance(i);
if (!instance.classIsMissing() && (!instance.isMissing(j))) {
counts[(int) instance.value(j)] += instance.weight();
avgClassValues[j][(int) instance.value(j)] += instance.weight() * instance.classValue();
}
}
sum = Utils.sum(avgClassValues[j]);
totalCounts = Utils.sum(counts);
if (Utils.gr(totalCounts, 0)) {
for (int k = 0; k < att.numValues(); k++) {
if (Utils.gr(counts[k], 0)) {
avgClassValues[j][k] /= counts[k];
} else {
avgClassValues[j][k] = sum / totalCounts;
}
}
}
m_Indices[j] = Utils.sort(avgClassValues[j]);
}
}
}
private void findCutOff(double[] pos, double[] neg) {
int[] pOrder = Utils.sort(pos), nOrder = Utils.sort(neg);
/*
System.err.println("\n\n???Positive: ");
for(int t=0; t<pOrder.length; t++)
System.err.print(t+":"+Utils.doubleToString(pos[pOrder[t]],0,2)+" ");
System.err.println("\n\n???Negative: ");
for(int t=0; t<nOrder.length; t++)
System.err.print(t+":"+Utils.doubleToString(neg[nOrder[t]],0,2)+" ");
*/
int pNum = pos.length, nNum = neg.length, count, p = 0, n = 0;
double fstAccu = 0.0, sndAccu = (double) pNum, split;
double maxAccu = 0, minDistTo0 = Double.MAX_VALUE;
// Skip continuous negatives
for (; (n < nNum) && (pos[pOrder[0]] >= neg[nOrder[n]]); n++, fstAccu++) ;
if (n >= nNum) { // totally seperate
m_Cutoff = (neg[nOrder[nNum - 1]] + pos[pOrder[0]]) / 2.0;
// m_Cutoff = neg[nOrder[nNum-1]];
return;
}
count = n;
while ((p < pNum) && (n < nNum)) {
// Compare the next in the two lists
if (pos[pOrder[p]] >= neg[nOrder[n]]) { // Neg has less log-odds
fstAccu += 1.0;
split = neg[nOrder[n]];
n++;
} else {
sndAccu -= 1.0;
split = pos[pOrder[p]];
p++;
}
count++;
if ((fstAccu + sndAccu > maxAccu)
|| ((fstAccu + sndAccu == maxAccu) && (Math.abs(split) < minDistTo0))) {
maxAccu = fstAccu + sndAccu;
m_Cutoff = split;
minDistTo0 = Math.abs(split);
}
}
}
/**
* computes the thresholds for outliers and extreme values
*
* @param instances the data to work on
*/
protected void computeThresholds(Instances instances) {
int i;
double[] values;
int[] sortedIndices;
int half;
int quarter;
double q1;
double q2;
double q3;
m_UpperExtremeValue = new double[m_AttributeIndices.length];
m_UpperOutlier = new double[m_AttributeIndices.length];
m_LowerOutlier = new double[m_AttributeIndices.length];
m_LowerExtremeValue = new double[m_AttributeIndices.length];
m_Median = new double[m_AttributeIndices.length];
m_IQR = new double[m_AttributeIndices.length];
for (i = 0; i < m_AttributeIndices.length; i++) {
// non-numeric attribute?
if (m_AttributeIndices[i] == NON_NUMERIC) continue;
// sort attribute data
values = instances.attributeToDoubleArray(m_AttributeIndices[i]);
sortedIndices = Utils.sort(values);
// determine indices
half = sortedIndices.length / 2;
quarter = half / 2;
if (sortedIndices.length % 2 == 1) {
q2 = values[sortedIndices[half]];
} else {
q2 = (values[sortedIndices[half]] + values[sortedIndices[half + 1]]) / 2;
}
if (half % 2 == 1) {
q1 = values[sortedIndices[quarter]];
q3 = values[sortedIndices[sortedIndices.length - quarter - 1]];
} else {
q1 = (values[sortedIndices[quarter]] + values[sortedIndices[quarter + 1]]) / 2;
q3 =
(values[sortedIndices[sortedIndices.length - quarter - 1]]
+ values[sortedIndices[sortedIndices.length - quarter]])
/ 2;
}
// determine thresholds and other values
m_Median[i] = q2;
m_IQR[i] = q3 - q1;
m_UpperExtremeValue[i] = q3 + getExtremeValuesFactor() * m_IQR[i];
m_UpperOutlier[i] = q3 + getOutlierFactor() * m_IQR[i];
m_LowerOutlier[i] = q1 - getOutlierFactor() * m_IQR[i];
m_LowerExtremeValue[i] = q1 - getExtremeValuesFactor() * m_IQR[i];
}
}
/**
* Gets the index of the instance with the closest threshold value to the desired target
*
* @param tcurve a set of instances that have been generated by this class
* @param threshold the target threshold
* @return the index of the instance that has threshold closest to the target, or -1 if this could
* not be found (i.e. no data, or bad threshold target)
*/
public static int getThresholdInstance(Instances tcurve, double threshold) {
if (!RELATION_NAME.equals(tcurve.relationName())
|| (tcurve.numInstances() == 0)
|| (threshold < 0)
|| (threshold > 1.0)) {
return -1;
}
if (tcurve.numInstances() == 1) {
return 0;
}
double[] tvals = tcurve.attributeToDoubleArray(tcurve.numAttributes() - 1);
int[] sorted = Utils.sort(tvals);
return binarySearch(sorted, tvals, threshold);
}
/**
* Calculates the n point precision result, which is the precision averaged over n evenly spaced
* (w.r.t recall) samples of the curve.
*
* @param tcurve a previously extracted threshold curve Instances.
* @param n the number of points to average over.
* @return the n-point precision.
*/
public static double getNPointPrecision(Instances tcurve, int n) {
if (!RELATION_NAME.equals(tcurve.relationName()) || (tcurve.numInstances() == 0)) {
return Double.NaN;
}
int recallInd = tcurve.attribute(RECALL_NAME).index();
int precisInd = tcurve.attribute(PRECISION_NAME).index();
double[] recallVals = tcurve.attributeToDoubleArray(recallInd);
int[] sorted = Utils.sort(recallVals);
double isize = 1.0 / (n - 1);
double psum = 0;
for (int i = 0; i < n; i++) {
int pos = binarySearch(sorted, recallVals, i * isize);
double recall = recallVals[sorted[pos]];
double precis = tcurve.instance(sorted[pos]).value(precisInd);
/*
System.err.println("Point " + (i + 1) + ": i=" + pos
+ " r=" + (i * isize)
+ " p'=" + precis
+ " r'=" + recall);
*/
// interpolate figures for non-endpoints
while ((pos != 0) && (pos < sorted.length - 1)) {
pos++;
double recall2 = recallVals[sorted[pos]];
if (recall2 != recall) {
double precis2 = tcurve.instance(sorted[pos]).value(precisInd);
double slope = (precis2 - precis) / (recall2 - recall);
double offset = precis - recall * slope;
precis = isize * i * slope + offset;
/*
System.err.println("Point2 " + (i + 1) + ": i=" + pos
+ " r=" + (i * isize)
+ " p'=" + precis2
+ " r'=" + recall2
+ " p''=" + precis);
*/
break;
}
}
psum += precis;
}
return psum / n;
}
/**
* Sorts the evaluated attribute list
*
* @return an array of sorted (highest eval to lowest) attribute indexes
* @throws Exception of sorting can't be done.
*/
public double[][] rankedAttributes() throws Exception {
int i, j;
if (m_attributeList == null || m_attributeMerit == null) {
throw new Exception(
"Search must be performed before a ranked " + "attribute list can be obtained");
}
int[] ranked = Utils.sort(m_attributeMerit);
// reverse the order of the ranked indexes
double[][] bestToWorst = new double[ranked.length][2];
for (i = ranked.length - 1, j = 0; i >= 0; i--) {
bestToWorst[j++][0] = ranked[i];
}
// convert the indexes to attribute indexes
for (i = 0; i < bestToWorst.length; i++) {
int temp = ((int) bestToWorst[i][0]);
bestToWorst[i][0] = m_attributeList[temp];
bestToWorst[i][1] = m_attributeMerit[temp];
}
if (m_numToSelect > bestToWorst.length) {
throw new Exception("More attributes requested than exist in the data");
}
if (m_numToSelect <= 0) {
if (m_threshold == -Double.MAX_VALUE) {
m_calculatedNumToSelect = bestToWorst.length;
} else {
determineNumToSelectFromThreshold(bestToWorst);
}
}
/* if (m_numToSelect > 0) {
determineThreshFromNumToSelect(bestToWorst);
} */
return bestToWorst;
}
// Build a polytree on the tree
protected int[][] polyTree(Instances D, Instances[] newD) throws Exception {
L = (D == null) ? newD[0].classIndex() : D.classIndex();
CD = new double[L][L];
numVisited = 0;
int root = 0;
int[][] pa = new int[L][0];
visited = new boolean[L];
flagCB = new boolean[L];
Arrays.fill(visited, false);
Arrays.fill(flagCB, false);
if (depMode) {
// Calculate the conditional MI matrix
if (newD == null) CD = conDepMatrix(D);
if (newD != null) CD = conDepMatrix(newD);
} else {
// Calculate the marginal normalized MI matrix
CD = StatUtilsPro.NormMargDep(D);
}
// Build the tree skeleton
int[][] paTree = skeleton(CD);
// Find the causal basins
int[][] paPoly = new int[L][L];
causalBasin(root, paTree, paPoly);
// If causal basin can't cover all labels, build a directed tree (paTemp)
int[][] paTemp = new int[L][0];
root = -1;
for (int j = 0; j < L; j++) {
for (int k = j; k < L; k++) {
if (paPoly[j][k] == 1) {
root = j;
Arrays.fill(visited, false);
visited[root] = true;
treeify(root, paPoly, paTemp);
break;
}
}
if (root != -1) break;
}
// Save the parents of every node in the polytree (pa)
for (int j = 0; j < L; j++) {
for (int k = j; k < L; k++) {
if (paPoly[j][k] == 3) pa[j] = A.append(pa[j], k);
if (paPoly[j][k] == 2) pa[k] = A.append(pa[k], j);
}
}
for (int j = 0; j < L; j++) {
if (pa[j].length < 1) {
for (int v : paTemp[j]) {
pa[j] = A.append(pa[j], v);
paPoly[j][v] = 3;
paPoly[v][j] = 2;
}
}
}
// Rank the labels in the polytree (rank)
root = 0;
int[] rank = new int[L];
Arrays.fill(rank, 0);
Arrays.fill(visited, false);
rankLabel(root, paPoly, rank);
chainOrder = Utils.sort(rank);
// Enhance the polytree
int[] temp = new int[] {};
double thCD = 0.0005;
for (int j : chainOrder) {
for (int k : temp) {
if (paPoly[j][k] != 3) {
if (j < k && CD[j][k] > thCD) pa[j] = A.append(pa[j], k);
if (j > k && CD[k][j] > thCD) pa[j] = A.append(pa[j], k);
}
}
temp = A.append(temp, j);
}
return pa;
}
/*
Sort, in increasing order, the instances and the meanDistance arrays.
*/
public void sort() {
indexes = Utils.sort(meanDistance);
};
/**
* Calculates the performance stats for the desired class and return results as a set of
* Instances.
*
* @param predictions the predictions to base the curve on
* @param classIndex index of the class of interest.
* @return datapoints as a set of instances.
*/
public Instances getCurve(FastVector predictions, int classIndex) {
if ((predictions.size() == 0)
|| (((NominalPrediction) predictions.elementAt(0)).distribution().length <= classIndex)) {
System.out.println(
"Foooobared "
+ predictions.size()
+ " "
+ ((NominalPrediction) predictions.elementAt(0)).distribution().length
+ " "
+ classIndex);
return null;
}
double totPos = 0, totNeg = 0;
double[] probs = getProbabilities(predictions, classIndex);
// Get distribution of positive/negatives
for (int i = 0; i < probs.length; i++) {
NominalPrediction pred = (NominalPrediction) predictions.elementAt(i);
if (pred.actual() == Prediction.MISSING_VALUE) {
System.err.println(getClass().getName() + " Skipping prediction with missing class value");
continue;
}
if (pred.weight() < 0) {
System.err.println(getClass().getName() + " Skipping prediction with negative weight");
continue;
}
if (pred.actual() == classIndex) {
totPos += pred.weight();
} else {
totNeg += pred.weight();
}
}
Instances insts = makeHeader();
int[] sorted = Utils.sort(probs);
TwoClassStats tc = new TwoClassStats(totPos, totNeg, 0, 0);
double threshold = 0;
double cumulativePos = 0;
double cumulativeNeg = 0;
for (int i = 0; i < sorted.length; i++) {
if ((i == 0) || (probs[sorted[i]] > threshold)) {
tc.setTruePositive(tc.getTruePositive() - cumulativePos);
tc.setFalseNegative(tc.getFalseNegative() + cumulativePos);
tc.setFalsePositive(tc.getFalsePositive() - cumulativeNeg);
tc.setTrueNegative(tc.getTrueNegative() + cumulativeNeg);
threshold = probs[sorted[i]];
insts.add(makeInstance(tc, threshold));
cumulativePos = 0;
cumulativeNeg = 0;
if (i == sorted.length - 1) {
break;
}
}
NominalPrediction pred = (NominalPrediction) predictions.elementAt(sorted[i]);
if (pred.actual() == Prediction.MISSING_VALUE) {
System.err.println(getClass().getName() + " Skipping prediction with missing class value");
continue;
}
if (pred.weight() < 0) {
System.err.println(getClass().getName() + " Skipping prediction with negative weight");
continue;
}
if (pred.actual() == classIndex) {
cumulativePos += pred.weight();
} else {
cumulativeNeg += pred.weight();
}
/*
System.out.println(tc + " " + probs[sorted[i]]
+ " " + (pred.actual() == classIndex));
*/
/*if ((i != (sorted.length - 1)) &&
((i == 0) ||
(probs[sorted[i]] != probs[sorted[i - 1]]))) {
insts.add(makeInstance(tc, probs[sorted[i]]));
}*/
}
// make sure a zero point gets into the curve
if (tc.getFalseNegative() != totPos || tc.getTrueNegative() != totNeg) {
tc = new TwoClassStats(0, 0, totNeg, totPos);
threshold = probs[sorted[sorted.length - 1]] + 10e-6;
insts.add(makeInstance(tc, threshold));
}
return insts;
}
/**
* Generates the classifier.
*
* @param instances set of instances serving as training data
* @throws Exception if the classifier has not been generated successfully
*/
public void buildClassifier(Instances instances) throws Exception {
if (!m_weightByConfidence) {
TINY = 0.0;
}
// can classifier handle the data?
getCapabilities().testWithFail(instances);
// remove instances with missing class
instances = new Instances(instances);
instances.deleteWithMissingClass();
m_ClassIndex = instances.classIndex();
m_NumClasses = instances.numClasses();
m_globalCounts = new double[m_NumClasses];
m_maxEntrop = Math.log(m_NumClasses) / Math.log(2);
m_Instances = new Instances(instances, 0); // Copy the structure for ref
m_intervalBounds = new double[instances.numAttributes()][2 + (2 * m_NumClasses)];
for (int j = 0; j < instances.numAttributes(); j++) {
boolean alt = false;
for (int i = 0; i < m_NumClasses * 2 + 2; i++) {
if (i == 0) {
m_intervalBounds[j][i] = Double.NEGATIVE_INFINITY;
} else if (i == m_NumClasses * 2 + 1) {
m_intervalBounds[j][i] = Double.POSITIVE_INFINITY;
} else {
if (alt) {
m_intervalBounds[j][i] = Double.NEGATIVE_INFINITY;
alt = false;
} else {
m_intervalBounds[j][i] = Double.POSITIVE_INFINITY;
alt = true;
}
}
}
}
// find upper and lower bounds for numeric attributes
for (int j = 0; j < instances.numAttributes(); j++) {
if (j != m_ClassIndex && instances.attribute(j).isNumeric()) {
for (int i = 0; i < instances.numInstances(); i++) {
Instance inst = instances.instance(i);
if (!inst.isMissing(j)) {
if (inst.value(j) < m_intervalBounds[j][((int) inst.classValue() * 2 + 1)]) {
m_intervalBounds[j][((int) inst.classValue() * 2 + 1)] = inst.value(j);
}
if (inst.value(j) > m_intervalBounds[j][((int) inst.classValue() * 2 + 2)]) {
m_intervalBounds[j][((int) inst.classValue() * 2 + 2)] = inst.value(j);
}
}
}
}
}
m_counts = new double[instances.numAttributes()][][];
// sort intervals
for (int i = 0; i < instances.numAttributes(); i++) {
if (instances.attribute(i).isNumeric()) {
int[] sortedIntervals = Utils.sort(m_intervalBounds[i]);
// remove any duplicate bounds
int count = 1;
for (int j = 1; j < sortedIntervals.length; j++) {
if (m_intervalBounds[i][sortedIntervals[j]]
!= m_intervalBounds[i][sortedIntervals[j - 1]]) {
count++;
}
}
double[] reordered = new double[count];
count = 1;
reordered[0] = m_intervalBounds[i][sortedIntervals[0]];
for (int j = 1; j < sortedIntervals.length; j++) {
if (m_intervalBounds[i][sortedIntervals[j]]
!= m_intervalBounds[i][sortedIntervals[j - 1]]) {
reordered[count] = m_intervalBounds[i][sortedIntervals[j]];
count++;
}
}
m_intervalBounds[i] = reordered;
m_counts[i] = new double[count][m_NumClasses];
} else if (i != m_ClassIndex) { // nominal attribute
m_counts[i] = new double[instances.attribute(i).numValues()][m_NumClasses];
}
}
// collect class counts
for (int i = 0; i < instances.numInstances(); i++) {
Instance inst = instances.instance(i);
m_globalCounts[(int) instances.instance(i).classValue()] += inst.weight();
for (int j = 0; j < instances.numAttributes(); j++) {
if (!inst.isMissing(j) && j != m_ClassIndex) {
if (instances.attribute(j).isNumeric()) {
double val = inst.value(j);
int k;
for (k = m_intervalBounds[j].length - 1; k >= 0; k--) {
if (val > m_intervalBounds[j][k]) {
m_counts[j][k][(int) inst.classValue()] += inst.weight();
break;
} else if (val == m_intervalBounds[j][k]) {
m_counts[j][k][(int) inst.classValue()] += (inst.weight() / 2.0);
m_counts[j][k - 1][(int) inst.classValue()] += (inst.weight() / 2.0);
;
break;
}
}
} else {
// nominal attribute
m_counts[j][(int) inst.value(j)][(int) inst.classValue()] += inst.weight();
;
}
}
}
}
}
private double[] calculateRegionProbs(int j, int i) throws Exception {
double[] sumOfProbsForRegion = new double[m_trainingData.classAttribute().numValues()];
for (int u = 0; u < m_numOfSamplesPerRegion; u++) {
double[] sumOfProbsForLocation = new double[m_trainingData.classAttribute().numValues()];
m_weightingAttsValues[m_xAttribute] = getRandomX(j);
m_weightingAttsValues[m_yAttribute] = getRandomY(m_panelHeight - i - 1);
m_dataGenerator.setWeightingValues(m_weightingAttsValues);
double[] weights = m_dataGenerator.getWeights();
double sumOfWeights = Utils.sum(weights);
int[] indices = Utils.sort(weights);
// Prune 1% of weight mass
int[] newIndices = new int[indices.length];
double sumSoFar = 0;
double criticalMass = 0.99 * sumOfWeights;
int index = weights.length - 1;
int counter = 0;
for (int z = weights.length - 1; z >= 0; z--) {
newIndices[index--] = indices[z];
sumSoFar += weights[indices[z]];
counter++;
if (sumSoFar > criticalMass) {
break;
}
}
indices = new int[counter];
System.arraycopy(newIndices, index + 1, indices, 0, counter);
for (int z = 0; z < m_numOfSamplesPerGenerator; z++) {
m_dataGenerator.setWeightingValues(m_weightingAttsValues);
double[][] values = m_dataGenerator.generateInstances(indices);
for (int q = 0; q < values.length; q++) {
if (values[q] != null) {
System.arraycopy(values[q], 0, m_vals, 0, m_vals.length);
m_vals[m_xAttribute] = m_weightingAttsValues[m_xAttribute];
m_vals[m_yAttribute] = m_weightingAttsValues[m_yAttribute];
// classify the instance
m_dist = m_classifier.distributionForInstance(m_predInst);
for (int k = 0; k < sumOfProbsForLocation.length; k++) {
sumOfProbsForLocation[k] += (m_dist[k] * weights[q]);
}
}
}
}
for (int k = 0; k < sumOfProbsForRegion.length; k++) {
sumOfProbsForRegion[k] += (sumOfProbsForLocation[k] * sumOfWeights);
}
}
// average
Utils.normalize(sumOfProbsForRegion);
// cache
double[] tempDist = new double[sumOfProbsForRegion.length];
System.arraycopy(sumOfProbsForRegion, 0, tempDist, 0, sumOfProbsForRegion.length);
return tempDist;
}
