/*
* Uses the training set (current batch) to update current model stored in
* modelInfo.
*/
private boolean trainAdditionalModel(
ExampleSet trainingSet, Vector<BayBoostBaseModelInfo> modelInfo) throws OperatorException {
Model model = this.trainBaseModel(trainingSet);
trainingSet = model.apply(trainingSet);
// get the weighted performance value of the example set with
// respect to the new model
WeightedPerformanceMeasures wp = new WeightedPerformanceMeasures(trainingSet);
// debugMessage(wp);
if (this.isModelUseful(wp.getContingencyMatrix()) == false) {
// If the model is not considered to be useful then discard it.
log("Discard model because of low advantage on training data.");
return false;
} else { // Add the new model and its weights to the collection of
// models:
modelInfo.add(new BayBoostBaseModelInfo(model, wp.getContingencyMatrix()));
return true;
}
}
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);
}
/**
* This helper method takes as input the traing set and the set of models trained so far. It
* re-estimates the model weights one by one, which means that it changes the contents of the
* modelInfo container. Works with crisp base classifiers, only!
*
* @param exampleSet the training set to be used to tune the weights
* @param modelInfo the <code>Vector</code> of <code>Model</code>s, each with its biasMatrix
* @return <code>true</code> iff the <code>ExampleSet</code> contains at least one example that is
* not yet explained deterministically (otherwise: nothing left to learn)
*/
private boolean adjustBaseModelWeights(
ExampleSet exampleSet, Vector<BayBoostBaseModelInfo> modelInfo) throws OperatorException {
for (int j = 0; j < modelInfo.size(); j++) {
BayBoostBaseModelInfo consideredModelInfo = modelInfo.get(j);
Model consideredModel = consideredModelInfo.getModel();
ContingencyMatrix cm = consideredModelInfo.getContingencyMatrix();
// double[][] oldBiasMatrix = (double[][]) consideredModelInfo[1];
BayBoostStream.createOrReplacePredictedLabelFor(exampleSet, consideredModel);
exampleSet = consideredModel.apply(exampleSet);
if (exampleSet.getAttributes().getPredictedLabel().isNominal() == false) {
// Only the case of nominal base classifiers is supported!
throw new UserError(
this,
101,
new Object[] {exampleSet.getAttributes().getLabel(), "BayBoostStream base learners"});
}
WeightedPerformanceMeasures wp = new WeightedPerformanceMeasures(exampleSet);
ContingencyMatrix cmNew = wp.getContingencyMatrix();
// double[][] newBiasMatrix = wp.createLiftRatioMatrix();
if (isModelUseful(cm) == false) {
modelInfo.remove(j);
j--;
log("Discard base model because of low advantage.");
} else {
consideredModelInfo = new BayBoostBaseModelInfo(consideredModel, cmNew);
modelInfo.set(j, consideredModelInfo);
boolean stillUncoveredExamples =
(WeightedPerformanceMeasures.reweightExamples(exampleSet, cmNew, false) > 0);
if (stillUncoveredExamples == false) return false;
}
}
return true;
}
