private AttributeExpansionSuggestion[] getBestSplitSuggestions(
MultiLabelSplitCriterion criterion) {
List<AttributeExpansionSuggestion> bestSuggestions =
new LinkedList<AttributeExpansionSuggestion>();
for (int i = 0; i < this.inputsToLearn.length; i++) {
if (attributesMask[
inputsToLearn[i]]) { // Should always be true (check trainOnInstance(). Remove?
AttributeStatisticsObserver obs = this.attributeObservers.get(inputsToLearn[i]);
if (obs != null) {
AttributeExpansionSuggestion bestSuggestion =
obs.getBestEvaluatedSplitSuggestion(criterion, literalStatistics, inputsToLearn[i]);
if (bestSuggestion == null) {
// ALL attributes must have a best suggestion. Adding dummy suggestion with minimal
// merit.
bestSuggestion =
new AttributeExpansionSuggestion(
new NumericRulePredicate(inputsToLearn[i], 0, true), null, -Double.MAX_VALUE);
}
bestSuggestions.add(bestSuggestion);
}
}
}
return bestSuggestions.toArray(new AttributeExpansionSuggestion[bestSuggestions.size()]);
}
@Override
public void trainOnInstance(MultiLabelInstance instance) {
int numInputs = 0;
if (attributesMask == null) numInputs = initializeAttibutesMask(instance);
// learn for all output attributes if not specified at construction time
int numOutputs = instance.numberOutputTargets();
if (!hasStarted) {
if (this.learner.isRandomizable()) this.learner.setRandomSeed(this.randomGenerator.nextInt());
if (outputsToLearn == null) {
outputsToLearn = new int[numOutputs];
for (int i = 0; i < numOutputs; i++) {
outputsToLearn[i] = i;
}
}
if (inputsToLearn == null) {
inputsToLearn = new int[numInputs];
int ct = 0;
for (int i = 0; i < instance.numInputAttributes(); i++) { // TODO JD: check with mask?
if (attributesMask[i]) {
inputsToLearn[ct] = i;
ct++;
}
}
}
literalStatistics = new DoubleVector[outputsToLearn.length];
varianceShift = new double[outputsToLearn.length];
for (int i = 0; i < outputsToLearn.length; i++) {
literalStatistics[i] = new DoubleVector(new double[5]);
varianceShift[i] = instance.valueOutputAttribute(outputsToLearn[i]);
}
instanceHeader = (InstancesHeader) instance.dataset();
hasStarted = true;
}
double weight = instance.weight();
DoubleVector[] exampleStatistics = new DoubleVector[outputsToLearn.length];
for (int i = 0; i < outputsToLearn.length; i++) {
double target = instance.valueOutputAttribute(outputsToLearn[i]);
double sum = weight * target;
double squaredSum = weight * target * target;
double sumShifted = weight * target - varianceShift[i];
double squaredSumShifted = weight * (target - varianceShift[i]) * (target - varianceShift[i]);
exampleStatistics[i] =
new DoubleVector(new double[] {weight, sum, squaredSum, sumShifted, squaredSumShifted});
literalStatistics[i].addValues(exampleStatistics[i].getArrayRef());
}
if (this.attributeObservers == null)
this.attributeObservers = new AutoExpandVector<AttributeStatisticsObserver>();
for (int i = 0; i < inputsToLearn.length; i++) {
if (attributesMask[inputsToLearn[i]]) { // this is checked above. Remove?
AttributeStatisticsObserver obs = this.attributeObservers.get(inputsToLearn[i]);
if (obs == null) {
if (instance.attribute(inputsToLearn[i]).isNumeric()) {
obs = ((NumericStatisticsObserver) numericStatisticsObserver.copy());
} else if (instance
.attribute(inputsToLearn[i])
.isNominal()) { // just to make sure its nominal (in the future there may be ordinal?
obs = ((NominalStatisticsObserver) nominalStatisticsObserver.copy());
}
this.attributeObservers.set(inputsToLearn[i], obs);
}
obs.observeAttribute(instance.valueInputAttribute(inputsToLearn[i]), exampleStatistics);
}
}
// Transform instance for learning
Instance transformedInstance = instanceTransformer.sourceInstanceToTarget(instance);
Prediction prediction = null;
Prediction targetPrediction = learner.getPredictionForInstance(transformedInstance);
if (targetPrediction != null)
prediction = instanceTransformer.targetPredictionToSource(targetPrediction);
if (prediction != null) errorMeasurer.addPrediction(prediction, instance);
learner.trainOnInstance(transformedInstance);
weightSeen += instance.weight();
}