/**
* Compare two datasets to see if they differ.
*
* @param data1 one set of instances
* @param data2 the other set of instances
* @throws Exception if the datasets differ
*/
protected void compareDatasets(Instances data1, Instances data2) throws Exception {
if (m_CheckHeader) {
if (!data2.equalHeaders(data1)) {
throw new Exception("header has been modified\n" + data2.equalHeadersMsg(data1));
}
}
if (!(data2.numInstances() == data1.numInstances())) {
throw new Exception("number of instances has changed");
}
for (int i = 0; i < data2.numInstances(); i++) {
Instance orig = data1.instance(i);
Instance copy = data2.instance(i);
for (int j = 0; j < orig.numAttributes(); j++) {
if (orig.isMissing(j)) {
if (!copy.isMissing(j)) {
throw new Exception("instances have changed");
}
} else {
if (m_CompareValuesAsString) {
if (!orig.toString(j).equals(copy.toString(j))) {
throw new Exception("instances have changed");
}
} else {
if (Math.abs(orig.value(j) - copy.value(j)) > m_MaxDiffValues) {
throw new Exception("instances have changed");
}
}
}
if (Math.abs(orig.weight() - copy.weight()) > m_MaxDiffWeights) {
throw new Exception("instance weights have changed");
}
}
}
}
/** 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]);
}
}
}
/**
* Input an instance for filtering. Ordinarily the instance is processed and made available for
* output immediately. Some filters require all instances be read before producing output.
*
* @param instance the input instance
* @return true if the filtered instance may now be collected with output().
* @exception IllegalStateException if no input format has been defined.
* @exception Exception if there was a problem during the filtering.
*/
public boolean input(Instance instance) throws Exception {
if (getInputFormat() == null) {
throw new IllegalStateException("No input instance format defined");
}
if (m_NewBatch) {
resetQueue();
m_NewBatch = false;
}
double[] vals = new double[instance.numAttributes() + 1];
for (int i = 0; i < instance.numAttributes(); i++) {
if (instance.isMissing(i)) {
vals[i] = Instance.missingValue();
} else {
vals[i] = instance.value(i);
}
}
evaluateExpression(vals);
Instance inst = null;
if (instance instanceof SparseInstance) {
inst = new SparseInstance(instance.weight(), vals);
} else {
inst = new Instance(instance.weight(), vals);
}
copyStringValues(inst, false, instance.dataset(), getOutputFormat());
inst.setDataset(getOutputFormat());
push(inst);
return true;
}
/**
* Convert a single instance over. The converted instance is added to the end of the output queue.
*
* @param instance the instance to convert
*/
protected void convertInstance(Instance instance) {
int index = 0;
double[] vals = new double[outputFormatPeek().numAttributes()];
// Copy and convert the values
for (int i = 0; i < getInputFormat().numAttributes(); i++) {
if (m_DiscretizeCols.isInRange(i) && getInputFormat().attribute(i).isNumeric()) {
int j;
double currentVal = instance.value(i);
if (m_CutPoints[i] == null) {
if (instance.isMissing(i)) {
vals[index] = Utils.missingValue();
} else {
vals[index] = 0;
}
index++;
} else {
if (!m_MakeBinary) {
if (instance.isMissing(i)) {
vals[index] = Utils.missingValue();
} else {
for (j = 0; j < m_CutPoints[i].length; j++) {
if (currentVal <= m_CutPoints[i][j]) {
break;
}
}
vals[index] = j;
}
index++;
} else {
for (j = 0; j < m_CutPoints[i].length; j++) {
if (instance.isMissing(i)) {
vals[index] = Utils.missingValue();
} else if (currentVal <= m_CutPoints[i][j]) {
vals[index] = 0;
} else {
vals[index] = 1;
}
index++;
}
}
}
} else {
vals[index] = instance.value(i);
index++;
}
}
Instance inst = null;
if (instance instanceof SparseInstance) {
inst = new SparseInstance(instance.weight(), vals);
} else {
inst = new DenseInstance(instance.weight(), vals);
}
inst.setDataset(getOutputFormat());
copyValues(inst, false, instance.dataset(), getOutputFormat());
inst.setDataset(getOutputFormat());
push(inst);
}
/**
* Input an instance for filtering. Ordinarily the instance is processed and made available for
* output immediately. Some filters require all instances be read before producing output.
*
* @param instance the input instance
* @return true if the filtered instance may now be collected with output().
* @throws IllegalStateException if no input structure has been defined.
*/
@Override
public boolean input(Instance instance) {
if (getInputFormat() == null) {
throw new IllegalStateException("No input instance format defined");
}
if (m_NewBatch) {
resetQueue();
m_NewBatch = false;
}
if (getOutputFormat().numAttributes() == 0) {
return false;
}
if (m_selectedAttributes.length == 0) {
push(instance);
} else {
double vals[] = new double[getOutputFormat().numAttributes()];
for (int i = 0; i < instance.numAttributes(); i++) {
double currentV = instance.value(i);
if (!m_selectedCols.isInRange(i)) {
vals[i] = currentV;
} else {
if (currentV == Utils.missingValue()) {
vals[i] = currentV;
} else {
String currentS = instance.attribute(i).value((int) currentV);
String replace =
m_ignoreCase ? m_renameMap.get(currentS.toLowerCase()) : m_renameMap.get(currentS);
if (replace == null) {
vals[i] = currentV;
} else {
vals[i] = getOutputFormat().attribute(i).indexOfValue(replace);
}
}
}
}
Instance inst = null;
if (instance instanceof SparseInstance) {
inst = new SparseInstance(instance.weight(), vals);
} else {
inst = new DenseInstance(instance.weight(), vals);
}
inst.setDataset(getOutputFormat());
copyValues(inst, false, instance.dataset(), getOutputFormat());
inst.setDataset(getOutputFormat());
push(inst);
}
return true;
}
/**
* Constructs an instance suitable for passing to the model for scoring
*
* @param incoming the incoming instance
* @return an instance with values mapped to be consistent with what the model is expecting
*/
protected Instance mapIncomingFieldsToModelFields(Instance incoming) {
Instances modelHeader = m_model.getHeader();
double[] vals = new double[modelHeader.numAttributes()];
for (int i = 0; i < modelHeader.numAttributes(); i++) {
if (m_attributeMap[i] < 0) {
// missing or type mismatch
vals[i] = Utils.missingValue();
continue;
}
Attribute modelAtt = modelHeader.attribute(i);
Attribute incomingAtt = incoming.dataset().attribute(m_attributeMap[i]);
if (incoming.isMissing(incomingAtt.index())) {
vals[i] = Utils.missingValue();
continue;
}
if (modelAtt.isNumeric()) {
vals[i] = incoming.value(m_attributeMap[i]);
} else if (modelAtt.isNominal()) {
String incomingVal = incoming.stringValue(m_attributeMap[i]);
int modelIndex = modelAtt.indexOfValue(incomingVal);
if (modelIndex < 0) {
vals[i] = Utils.missingValue();
} else {
vals[i] = modelIndex;
}
} else if (modelAtt.isString()) {
vals[i] = 0;
modelAtt.setStringValue(incoming.stringValue(m_attributeMap[i]));
}
}
if (modelHeader.classIndex() >= 0) {
// set class to missing value
vals[modelHeader.classIndex()] = Utils.missingValue();
}
Instance newInst = null;
if (incoming instanceof SparseInstance) {
newInst = new SparseInstance(incoming.weight(), vals);
} else {
newInst = new DenseInstance(incoming.weight(), vals);
}
newInst.setDataset(modelHeader);
return newInst;
}
/**
* Convert a single instance over. The converted instance is added to the end of the output queue.
*
* @param instance the instance to convert
*/
private void convertInstance(Instance instance) {
Instance inst = null;
if (instance instanceof SparseInstance) {
double[] newVals = new double[instance.numAttributes()];
int[] newIndices = new int[instance.numAttributes()];
double[] vals = instance.toDoubleArray();
int ind = 0;
for (int j = 0; j < instance.numAttributes(); j++) {
double value;
if (instance.attribute(j).isNumeric()
&& (!Instance.isMissingValue(vals[j]))
&& (getInputFormat().classIndex() != j)) {
value = vals[j] - m_Means[j];
if (value != 0.0) {
newVals[ind] = value;
newIndices[ind] = j;
ind++;
}
} else {
value = vals[j];
if (value != 0.0) {
newVals[ind] = value;
newIndices[ind] = j;
ind++;
}
}
}
double[] tempVals = new double[ind];
int[] tempInd = new int[ind];
System.arraycopy(newVals, 0, tempVals, 0, ind);
System.arraycopy(newIndices, 0, tempInd, 0, ind);
inst = new SparseInstance(instance.weight(), tempVals, tempInd, instance.numAttributes());
} else {
double[] vals = instance.toDoubleArray();
for (int j = 0; j < getInputFormat().numAttributes(); j++) {
if (instance.attribute(j).isNumeric()
&& (!Instance.isMissingValue(vals[j]))
&& (getInputFormat().classIndex() != j)) {
vals[j] = (vals[j] - m_Means[j]);
}
}
inst = new Instance(instance.weight(), vals);
}
inst.setDataset(instance.dataset());
push(inst);
}
/**
* Convert a single instance over if the class is nominal. The converted instance is added to the
* end of the output queue.
*
* @param instance the instance to convert
*/
private void convertInstanceNominal(Instance instance) {
if (!m_needToTransform) {
push(instance);
return;
}
double[] vals = new double[outputFormatPeek().numAttributes()];
int attSoFar = 0;
for (int j = 0; j < getInputFormat().numAttributes(); j++) {
Attribute att = getInputFormat().attribute(j);
if ((!att.isNominal()) || (j == getInputFormat().classIndex())) {
vals[attSoFar] = instance.value(j);
attSoFar++;
} else {
if ((att.numValues() <= 2) && (!m_TransformAll)) {
vals[attSoFar] = instance.value(j);
attSoFar++;
} else {
if (instance.isMissing(j)) {
for (int k = 0; k < att.numValues(); k++) {
vals[attSoFar + k] = instance.value(j);
}
} else {
for (int k = 0; k < att.numValues(); k++) {
if (k == (int) instance.value(j)) {
vals[attSoFar + k] = 1;
} else {
vals[attSoFar + k] = 0;
}
}
}
attSoFar += att.numValues();
}
}
}
Instance inst = null;
if (instance instanceof SparseInstance) {
inst = new SparseInstance(instance.weight(), vals);
} else {
inst = new DenseInstance(instance.weight(), vals);
}
inst.setDataset(getOutputFormat());
copyValues(inst, false, instance.dataset(), getOutputFormat());
inst.setDataset(getOutputFormat());
push(inst);
}
@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;
}
}
/**
* Convert an input instance
*
* @param current the input instance to convert
* @return a transformed instance
* @throws Exception if a problem occurs
*/
protected Instance convertInstance(Instance current) throws Exception {
double[] vals = new double[getOutputFormat().numAttributes()];
int index = 0;
for (int j = 0; j < current.numAttributes(); j++) {
if (j != current.classIndex()) {
if (m_unchanged != null && m_unchanged.attribute(current.attribute(j).name()) != null) {
vals[index++] = current.value(j);
} else {
Estimator[] estForAtt = m_estimatorLookup.get(current.attribute(j).name());
for (int k = 0; k < current.classAttribute().numValues(); k++) {
if (current.isMissing(j)) {
vals[index++] = Utils.missingValue();
} else {
double e = estForAtt[k].getProbability(current.value(j));
vals[index++] = e;
}
}
}
}
}
vals[vals.length - 1] = current.classValue();
DenseInstance instNew = new DenseInstance(current.weight(), vals);
return instNew;
}
/**
* Splits the given set of instances into subsets.
*
* @exception Exception if something goes wrong
*/
public final Instances[] split(Instances data) throws Exception {
Instances[] instances = new Instances[m_numSubsets];
double[] weights;
double newWeight;
Instance instance;
int subset, i, j;
for (j = 0; j < m_numSubsets; j++)
instances[j] = new Instances((Instances) data, data.numInstances());
for (i = 0; i < data.numInstances(); i++) {
instance = ((Instances) data).instance(i);
weights = weights(instance);
subset = whichSubset(instance);
if (subset > -1) instances[subset].add(instance);
else
for (j = 0; j < m_numSubsets; j++)
if (Utils.gr(weights[j], 0)) {
newWeight = weights[j] * instance.weight();
instances[j].add(instance);
instances[j].lastInstance().setWeight(newWeight);
}
}
for (j = 0; j < m_numSubsets; j++) instances[j].compactify();
return instances;
}
/**
* Find all the instances in the dataset covered/not covered by the rule in given index, and the
* correponding simple statistics and predicted class distributions are stored in the given double
* array, which can be obtained by getSimpleStats() and getDistributions().<br>
*
* @param index the given index, assuming correct
* @param insts the dataset to be covered by the rule
* @param stats the given double array to hold stats, side-effected
* @param dist the given array to hold class distributions, side-effected if null, the
* distribution is not necessary
* @return the instances covered and not covered by the rule
*/
private Instances[] computeSimpleStats(
int index, Instances insts, double[] stats, double[] dist) {
Rule rule = (Rule) m_Ruleset.elementAt(index);
Instances[] data = new Instances[2];
data[0] = new Instances(insts, insts.numInstances());
data[1] = new Instances(insts, insts.numInstances());
for (int i = 0; i < insts.numInstances(); i++) {
Instance datum = insts.instance(i);
double weight = datum.weight();
if (rule.covers(datum)) {
data[0].add(datum); // Covered by this rule
stats[0] += weight; // Coverage
if ((int) datum.classValue() == (int) rule.getConsequent())
stats[2] += weight; // True positives
else stats[4] += weight; // False positives
if (dist != null) dist[(int) datum.classValue()] += weight;
} else {
data[1].add(datum); // Not covered by this rule
stats[1] += weight;
if ((int) datum.classValue() != (int) rule.getConsequent())
stats[3] += weight; // True negatives
else stats[5] += weight; // False negatives
}
}
return data;
}
/**
* Boosting method. Boosts any classifier that can handle weighted instances.
*
* @param data the training data to be used for generating the boosted classifier.
* @throws Exception if the classifier could not be built successfully
*/
protected void buildClassifierWithWeights(Instances data) throws Exception {
Instances trainData, training, trainingWeightsNotNormalized;
int numInstances = data.numInstances();
Random randomInstance = new Random(m_Seed);
double minLoss = Double.MAX_VALUE;
// Create a copy of the data so that when the weights are diddled
// with it doesn't mess up the weights for anyone else
trainingWeightsNotNormalized = new Instances(data, 0, numInstances);
// Do boostrap iterations
for (m_NumIterationsPerformed = -1;
m_NumIterationsPerformed < m_Classifiers.length;
m_NumIterationsPerformed++) {
if (m_Debug) {
System.err.println("Training classifier " + (m_NumIterationsPerformed + 1));
}
training = new Instances(trainingWeightsNotNormalized);
normalizeWeights(training, m_SumOfWeights);
// Select instances to train the classifier on
if (m_WeightThreshold < 100) {
trainData = selectWeightQuantile(training, (double) m_WeightThreshold / 100);
} else {
trainData = new Instances(training, 0, numInstances);
}
// Build classifier
if (m_NumIterationsPerformed == -1) {
m_ZeroR = new weka.classifiers.rules.ZeroR();
m_ZeroR.buildClassifier(data);
} else {
if (m_Classifiers[m_NumIterationsPerformed] instanceof Randomizable)
((Randomizable) m_Classifiers[m_NumIterationsPerformed])
.setSeed(randomInstance.nextInt());
m_Classifiers[m_NumIterationsPerformed].buildClassifier(trainData);
}
// Update instance weights
setWeights(trainingWeightsNotNormalized, m_NumIterationsPerformed);
// Has progress been made?
double loss = 0;
for (Instance inst : trainingWeightsNotNormalized) {
loss += Math.log(inst.weight());
}
if (m_Debug) {
System.err.println("Current loss on log scale: " + loss);
}
if ((m_NumIterationsPerformed > -1) && (loss > minLoss)) {
if (m_Debug) {
System.err.println("Loss has increased: bailing out.");
}
break;
}
minLoss = loss;
}
}
/**
* Adds the prediction intervals as additional attributes at the end. Since classifiers can
* returns varying number of intervals per instance, the dataset is filled with missing values for
* non-existing intervals.
*/
protected void addPredictionIntervals() {
int maxNum;
int num;
int i;
int n;
FastVector preds;
FastVector atts;
Instances data;
Instance inst;
Instance newInst;
double[] values;
double[][] predInt;
// determine the maximum number of intervals
maxNum = 0;
preds = m_Evaluation.predictions();
for (i = 0; i < preds.size(); i++) {
num = ((NumericPrediction) preds.elementAt(i)).predictionIntervals().length;
if (num > maxNum) maxNum = num;
}
// create new header
atts = new FastVector();
for (i = 0; i < m_PlotInstances.numAttributes(); i++)
atts.addElement(m_PlotInstances.attribute(i));
for (i = 0; i < maxNum; i++) {
atts.addElement(new Attribute("predictionInterval_" + (i + 1) + "-lowerBoundary"));
atts.addElement(new Attribute("predictionInterval_" + (i + 1) + "-upperBoundary"));
atts.addElement(new Attribute("predictionInterval_" + (i + 1) + "-width"));
}
data = new Instances(m_PlotInstances.relationName(), atts, m_PlotInstances.numInstances());
data.setClassIndex(m_PlotInstances.classIndex());
// update data
for (i = 0; i < m_PlotInstances.numInstances(); i++) {
inst = m_PlotInstances.instance(i);
// copy old values
values = new double[data.numAttributes()];
System.arraycopy(inst.toDoubleArray(), 0, values, 0, inst.numAttributes());
// add interval data
predInt = ((NumericPrediction) preds.elementAt(i)).predictionIntervals();
for (n = 0; n < maxNum; n++) {
if (n < predInt.length) {
values[m_PlotInstances.numAttributes() + n * 3 + 0] = predInt[n][0];
values[m_PlotInstances.numAttributes() + n * 3 + 1] = predInt[n][1];
values[m_PlotInstances.numAttributes() + n * 3 + 2] = predInt[n][1] - predInt[n][0];
} else {
values[m_PlotInstances.numAttributes() + n * 3 + 0] = Utils.missingValue();
values[m_PlotInstances.numAttributes() + n * 3 + 1] = Utils.missingValue();
values[m_PlotInstances.numAttributes() + n * 3 + 2] = Utils.missingValue();
}
}
// create new Instance
newInst = new DenseInstance(inst.weight(), values);
data.add(newInst);
}
m_PlotInstances = data;
}
/**
* Normalize the weights for the next iteration.
*
* @param training the training instances
* @throws Exception if something goes wrong
*/
protected void normalizeWeights(Instances training, double oldSumOfWeights) throws Exception {
// Renormalize weights
double newSumOfWeights = training.sumOfWeights();
for (Instance instance : training) {
instance.setWeight(instance.weight() * oldSumOfWeights / newSumOfWeights);
}
}
/**
* Normalize the instance
*
* @param inst instance to be normalized
* @return a new Instance with normalized values
*/
private Instance normalizeInstance(Instance inst) {
double[] vals = inst.toDoubleArray();
double sum = Utils.sum(vals);
for (int i = 0; i < vals.length; i++) {
vals[i] /= sum;
}
return new DenseInstance(inst.weight(), vals);
}
protected void updateClassifier(Instance instance, boolean updateDictionary) throws Exception {
if (!instance.classIsMissing()) {
int classIndex = (int) instance.classValue();
m_probOfClass[classIndex] += instance.weight();
tokenizeInstance(instance, updateDictionary);
m_t++;
}
}
/**
* Processes the given data (may change the provided dataset) and returns the modified version.
* This method is called in batchFinished().
*
* @param instances the data to process
* @return the modified data
* @throws Exception in case the processing goes wrong
* @see #batchFinished()
*/
protected Instances process(Instances instances) throws Exception {
Instances result;
int i;
int n;
double[] values;
String value;
Instance inst;
Instance newInst;
// we need the complete input data!
if (!isFirstBatchDone()) setOutputFormat(determineOutputFormat(getInputFormat()));
result = new Instances(getOutputFormat());
for (i = 0; i < instances.numInstances(); i++) {
inst = instances.instance(i);
values = inst.toDoubleArray();
for (n = 0; n < values.length; n++) {
if (!m_Cols.isInRange(n) || !instances.attribute(n).isNumeric() || inst.isMissing(n))
continue;
// get index of value
if (instances.attribute(n).type() == Attribute.DATE) value = inst.stringValue(n);
else value = Utils.doubleToString(inst.value(n), MAX_DECIMALS);
values[n] = result.attribute(n).indexOfValue(value);
}
// generate new instance
if (inst instanceof SparseInstance) newInst = new SparseInstance(inst.weight(), values);
else newInst = new DenseInstance(inst.weight(), values);
// copy possible string, relational values
newInst.setDataset(getOutputFormat());
copyValues(newInst, false, inst.dataset(), getOutputFormat());
result.add(newInst);
}
return result;
}
/**
* Compare two datasets to see if they differ.
*
* @param data1 one set of instances
* @param data2 the other set of instances
* @throws Exception if the datasets differ
*/
protected void compareDatasets(Instances data1, Instances data2) throws Exception {
if (data1.numAttributes() != data2.numAttributes())
throw new Exception("number of attributes has changed");
if (!(data2.numInstances() == data1.numInstances()))
throw new Exception("number of instances has changed");
for (int i = 0; i < data2.numInstances(); i++) {
Instance orig = data1.instance(i);
Instance copy = data2.instance(i);
for (int j = 0; j < orig.numAttributes(); j++) {
if (orig.isMissing(j)) {
if (!copy.isMissing(j)) throw new Exception("instances have changed");
} else if (!orig.toString(j).equals(copy.toString(j))) {
throw new Exception("instances have changed");
}
if (orig.weight() != copy.weight()) throw new Exception("instance weights have changed");
}
}
}
/**
* Turn the list of nearest neighbors into a probability distribution.
*
* @param neighbours the list of nearest neighboring instances
* @param distances the distances of the neighbors
* @return the probability distribution
* @throws Exception if computation goes wrong or has no class attribute
*/
protected double[] makeDistribution(Instances neighbours, double[] distances) throws Exception {
double total = 0, weight;
double[] distribution = new double[m_NumClasses];
// Set up a correction to the estimator
if (m_ClassType == Attribute.NOMINAL) {
for (int i = 0; i < m_NumClasses; i++) {
distribution[i] = 1.0 / Math.max(1, m_Train.numInstances());
}
total = (double) m_NumClasses / Math.max(1, m_Train.numInstances());
}
for (int i = 0; i < neighbours.numInstances(); i++) {
// Collect class counts
Instance current = neighbours.instance(i);
distances[i] = distances[i] * distances[i];
distances[i] = Math.sqrt(distances[i] / m_NumAttributesUsed);
switch (m_DistanceWeighting) {
case WEIGHT_INVERSE:
weight = 1.0 / (distances[i] + 0.001); // to avoid div by zero
break;
case WEIGHT_SIMILARITY:
weight = 1.0 - distances[i];
break;
default: // WEIGHT_NONE:
weight = 1.0;
break;
}
weight *= current.weight();
try {
switch (m_ClassType) {
case Attribute.NOMINAL:
distribution[(int) current.classValue()] += weight;
break;
case Attribute.NUMERIC:
distribution[0] += current.classValue() * weight;
break;
}
} catch (Exception ex) {
throw new Error("Data has no class attribute!");
}
total += weight;
}
// Normalise distribution
if (total > 0) {
Utils.normalize(distribution, total);
}
return distribution;
}
/**
* Creates a new instance the same as one instance (the "destination") but with some attribute
* values copied from another instance (the "source")
*
* @param source the source instance
* @param dest the destination instance
* @return the new merged instance
*/
protected Instance mergeInstances(Instance source, Instance dest) {
Instances outputFormat = outputFormatPeek();
double[] vals = new double[outputFormat.numAttributes()];
for (int i = 0; i < vals.length; i++) {
if ((i != outputFormat.classIndex()) && (m_SelectedCols.isInRange(i))) {
if (source != null) {
vals[i] = source.value(i);
} else {
vals[i] = Instance.missingValue();
}
} else {
vals[i] = dest.value(i);
}
}
Instance inst = null;
if (dest instanceof SparseInstance) {
inst = new SparseInstance(dest.weight(), vals);
} else {
inst = new Instance(dest.weight(), vals);
}
inst.setDataset(dest.dataset());
return inst;
}
/**
* processes the given instance (may change the provided instance) and returns the modified
* version.
*
* @param instance the instance to process
* @return the modified data
* @throws Exception in case the processing goes wrong
*/
protected Instance process(Instance instance) throws Exception {
Instance result;
double[] values;
int i;
values = new double[m_Indices.length];
for (i = 0; i < m_Indices.length; i++) values[i] = instance.value(m_Indices[i]);
result = new DenseInstance(instance.weight(), values);
result.setDataset(getOutputFormat());
copyValues(result, false, instance.dataset(), getOutputFormat());
result.setDataset(getOutputFormat());
return result;
}
/**
* 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);
}
}
}
/**
* processes the given instance (may change the provided instance) and returns the modified
* version.
*
* @param instance the instance to process
* @return the modified data
* @throws Exception in case the processing goes wrong
*/
protected Instance process(Instance instance) throws Exception {
Instance result;
Attribute att;
double[] values;
int i;
// adjust indices
values = new double[instance.numAttributes()];
for (i = 0; i < instance.numAttributes(); i++) {
att = instance.attribute(i);
if (!att.isNominal() || !m_AttributeIndices.isInRange(i) || instance.isMissing(i))
values[i] = instance.value(i);
else values[i] = m_NewOrder[i][(int) instance.value(i)];
}
// create new instance
result = new DenseInstance(instance.weight(), values);
return result;
}
@Override
public void updateNode(Instance inst) throws Exception {
super.updateDistribution(inst);
for (int i = 0; i < inst.numAttributes(); i++) {
Attribute a = inst.attribute(i);
if (i != inst.classIndex()) {
ConditionalSufficientStats stats = m_nodeStats.get(a.name());
if (stats == null) {
if (a.isNumeric()) {
stats = new GaussianConditionalSufficientStats();
} else {
stats = new NominalConditionalSufficientStats();
}
m_nodeStats.put(a.name(), stats);
}
stats.update(
inst.value(a), inst.classAttribute().value((int) inst.classValue()), inst.weight());
}
}
}
@Override
public void trainOnInstanceImpl(Instance inst) {
double lambda_d = 1.0;
for (int i = 0; i < this.ensemble.length; i++) {
double k =
this.pureBoostOption.isSet()
? lambda_d
: MiscUtils.poisson(lambda_d, this.classifierRandom);
if (k > 0.0) {
Instance weightedInst = (Instance) inst.copy();
weightedInst.setWeight(inst.weight() * k);
this.ensemble[i].trainOnInstance(weightedInst);
}
if (this.ensemble[i].correctlyClassifies(inst)) {
this.scms[i] += lambda_d;
lambda_d *= this.trainingWeightSeenByModel / (2 * this.scms[i]);
} else {
this.swms[i] += lambda_d;
lambda_d *= this.trainingWeightSeenByModel / (2 * this.swms[i]);
}
}
}
/**
* Sets the weights for the next iteration.
*
* @param training the training instances
* @throws Exception if something goes wrong
*/
protected void setWeights(Instances training, int iteration) throws Exception {
for (Instance instance : training) {
double reweight = 1;
double prob = 1, shrinkage = m_Shrinkage;
if (iteration == -1) {
prob = m_ZeroR.distributionForInstance(instance)[0];
shrinkage = 1.0;
} else {
prob = m_Classifiers[iteration].distributionForInstance(instance)[0];
// Make sure that probabilities are never 0 or 1 using ad-hoc smoothing
prob = (m_SumOfWeights * prob + 1) / (m_SumOfWeights + 2);
}
if (instance.classValue() == 1) {
reweight = shrinkage * 0.5 * (Math.log(prob) - Math.log(1 - prob));
} else {
reweight = shrinkage * 0.5 * (Math.log(1 - prob) - Math.log(prob));
}
instance.setWeight(instance.weight() * Math.exp(reweight));
}
}
protected void tokenizeInstance(Instance instance, boolean updateDictionary) {
if (m_inputVector == null) {
m_inputVector = new LinkedHashMap<String, Count>();
} else {
m_inputVector.clear();
}
if (m_useStopList && m_stopwords == null) {
m_stopwords = new Stopwords();
try {
if (getStopwords().exists() && !getStopwords().isDirectory()) {
m_stopwords.read(getStopwords());
}
} catch (Exception ex) {
ex.printStackTrace();
}
}
for (int i = 0; i < instance.numAttributes(); i++) {
if (instance.attribute(i).isString() && !instance.isMissing(i)) {
m_tokenizer.tokenize(instance.stringValue(i));
while (m_tokenizer.hasMoreElements()) {
String word = m_tokenizer.nextElement();
if (m_lowercaseTokens) {
word = word.toLowerCase();
}
word = m_stemmer.stem(word);
if (m_useStopList) {
if (m_stopwords.is(word)) {
continue;
}
}
Count docCount = m_inputVector.get(word);
if (docCount == null) {
m_inputVector.put(word, new Count(instance.weight()));
} else {
docCount.m_count += instance.weight();
}
}
}
}
if (updateDictionary) {
int classValue = (int) instance.classValue();
LinkedHashMap<String, Count> dictForClass = m_probOfWordGivenClass.get(classValue);
// document normalization
double iNorm = 0;
double fv = 0;
if (m_normalize) {
for (Count c : m_inputVector.values()) {
// 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);
}
for (Map.Entry<String, Count> feature : m_inputVector.entrySet()) {
String word = feature.getKey();
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);
}
// check all classes
for (int i = 0; i < m_data.numClasses(); i++) {
LinkedHashMap<String, Count> dict = m_probOfWordGivenClass.get(i);
if (dict.get(word) == null) {
dict.put(word, new Count(m_leplace));
m_wordsPerClass[i] += m_leplace;
}
}
Count dictCount = dictForClass.get(word);
/*
* if (dictCount == null) { dictForClass.put(word, new Count(m_leplace +
* freq)); m_wordsPerClass[classValue] += (m_leplace + freq); } else {
*/
dictCount.m_count += freq;
m_wordsPerClass[classValue] += freq;
// }
}
pruneDictionary();
}
}
/**
* 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();
;
}
}
}
}
}
/**
* 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 {
// can classifier handle the data?
getCapabilities().testWithFail(instances);
// remove instances with missing class
instances = new Instances(instances);
instances.deleteWithMissingClass();
m_headerInfo = new Instances(instances, 0);
m_numClasses = instances.numClasses();
m_numAttributes = instances.numAttributes();
m_probOfWordGivenClass = new double[m_numClasses][];
/*
initialising the matrix of word counts
NOTE: Laplace estimator introduced in case a word that does not appear for a class in the
training set does so for the test set
*/
for (int c = 0; c < m_numClasses; c++) {
m_probOfWordGivenClass[c] = new double[m_numAttributes];
for (int att = 0; att < m_numAttributes; att++) {
m_probOfWordGivenClass[c][att] = 1;
}
}
// enumerate through the instances
Instance instance;
int classIndex;
double numOccurences;
double[] docsPerClass = new double[m_numClasses];
double[] wordsPerClass = new double[m_numClasses];
java.util.Enumeration enumInsts = instances.enumerateInstances();
while (enumInsts.hasMoreElements()) {
instance = (Instance) enumInsts.nextElement();
classIndex = (int) instance.value(instance.classIndex());
docsPerClass[classIndex] += instance.weight();
for (int a = 0; a < instance.numValues(); a++)
if (instance.index(a) != instance.classIndex()) {
if (!instance.isMissing(a)) {
numOccurences = instance.valueSparse(a) * instance.weight();
if (numOccurences < 0)
throw new Exception("Numeric attribute values must all be greater or equal to zero.");
wordsPerClass[classIndex] += numOccurences;
m_probOfWordGivenClass[classIndex][instance.index(a)] += numOccurences;
}
}
}
/*
normalising probOfWordGivenClass values
and saving each value as the log of each value
*/
for (int c = 0; c < m_numClasses; c++)
for (int v = 0; v < m_numAttributes; v++)
m_probOfWordGivenClass[c][v] =
Math.log(m_probOfWordGivenClass[c][v] / (wordsPerClass[c] + m_numAttributes - 1));
/*
calculating Pr(H)
NOTE: Laplace estimator introduced in case a class does not get mentioned in the set of
training instances
*/
final double numDocs = instances.sumOfWeights() + m_numClasses;
m_probOfClass = new double[m_numClasses];
for (int h = 0; h < m_numClasses; h++)
m_probOfClass[h] = (double) (docsPerClass[h] + 1) / numDocs;
}
