public void apply(Example example) {
if (applicable(example)) {
double weight = 1.0d;
if (example.getAttributes().getWeight() != null) {
weight = example.getWeight();
}
coveredWeight += weight;
if (example.getLabel()
== example.getAttributes().getLabel().getMapping().getPositiveIndex()) {
positiveWeight += weight;
}
}
}
protected void prepareWeights(ExampleSet exampleSet) {
Attribute weightAttr = exampleSet.getAttributes().getWeight();
if (weightAttr == null) {
this.oldWeights = null;
com.rapidminer.example.Tools.createWeightAttribute(exampleSet);
} else { // Back up old weights
this.oldWeights = new double[exampleSet.size()];
Iterator<Example> reader = exampleSet.iterator();
for (int i = 0; (reader.hasNext() && i < oldWeights.length); i++) {
Example example = reader.next();
if (example != null) {
this.oldWeights[i] = example.getWeight();
example.setWeight(1);
}
}
}
}
private BayBoostModel retrainLastWeight(
BayBoostModel ensemble, ExampleSet exampleSet, Vector holdOutSet) throws OperatorException {
this.prepareExtendedBatch(exampleSet); // method fits by chance
int modelNum = ensemble.getNumberOfModels();
Vector<BayBoostBaseModelInfo> modelInfo = new Vector<BayBoostBaseModelInfo>();
double[] priors = ensemble.getPriors();
for (int i = 0; i < modelNum - 1; i++) {
Model model = ensemble.getModel(i);
ContingencyMatrix cm = ensemble.getContingencyMatrix(i);
modelInfo.add(new BayBoostBaseModelInfo(model, cm));
exampleSet = model.apply(exampleSet);
WeightedPerformanceMeasures.reweightExamples(exampleSet, cm, false);
}
Model latestModel = ensemble.getModel(modelNum - 1);
exampleSet = latestModel.apply(exampleSet);
// quite ugly:
double[] weights = new double[holdOutSet.size()];
Iterator it = holdOutSet.iterator();
int index = 0;
while (it.hasNext()) {
Example example = (Example) it.next();
weights[index++] = example.getWeight();
}
Iterator<Example> reader = exampleSet.iterator();
while (reader.hasNext()) {
reader.next().setWeight(0);
}
it = holdOutSet.iterator();
index = 0;
while (it.hasNext()) {
Example example = (Example) it.next();
example.setWeight(weights[index++]);
}
WeightedPerformanceMeasures wp = new WeightedPerformanceMeasures(exampleSet);
modelInfo.add(new BayBoostBaseModelInfo(latestModel, wp.getContingencyMatrix()));
return new BayBoostModel(exampleSet, modelInfo, priors);
}
public void reinitOnlineCounting(ExampleSet exampleSet) {
// counting one time all class weights
labelAttribute = exampleSet.getAttributes().getLabel();
weightAttribute = exampleSet.getAttributes().getWeight();
totalLabelWeights = new double[labelAttribute.getMapping().size()];
totalWeight = 0d;
if (exampleSet.getAttributes().getWeight() != null) {
for (Example example : exampleSet) {
double weight = example.getWeight();
totalLabelWeights[(int) example.getValue(labelAttribute)] += weight;
}
} else {
for (Example example : exampleSet) {
totalLabelWeights[(int) example.getValue(labelAttribute)] += 1d;
}
}
for (int i = 0; i < totalLabelWeights.length; i++) {
totalWeight += totalLabelWeights[i];
}
// resetting online counter for subtraction
labelWeights = new double[labelAttribute.getMapping().size()];
weight = 0;
}
