/**
* Collect the correct segments for this example. These are defined as all segments with
* non-NEGATIVE labels, and all unit-length negative labels not inside a positives label.
*/
private Segmentation correctSegments(
CandidateSegmentGroup g, ExampleSchema schema, int maxSegmentSize) {
Segmentation result = new Segmentation(schema);
int pos, len;
for (pos = 0; pos < g.getSequenceLength(); ) {
boolean addedASegmentStartingAtPos = false;
for (len = 1; !addedASegmentStartingAtPos && len <= maxSegmentSize; len++) {
Instance inst = g.getSubsequenceInstance(pos, pos + len);
ClassLabel label = g.getSubsequenceLabel(pos, pos + len);
if (inst != null && !label.isNegative()) {
result.add(
new Segmentation.Segment(
pos, pos + len, schema.getClassIndex(label.bestClassName())));
addedASegmentStartingAtPos = true;
pos += len;
}
}
if (!addedASegmentStartingAtPos) {
// Instance inst = g.getSubsequenceInstance(pos,pos+1);
// ClassLabel label = g.getSubsequenceLabel(pos,pos+1);
result.add(
new Segmentation.Segment(
pos, pos + 1, schema.getClassIndex(ExampleSchema.NEG_CLASS_NAME)));
pos += 1;
}
}
return result;
}
@Override
public Segmenter batchTrain(SegmentDataset dataset) {
ExampleSchema schema = dataset.getSchema();
innerLearner =
SequenceUtils.duplicatePrototypeLearner(innerLearnerPrototype, schema.getNumberOfClasses());
ProgressCounter pc =
new ProgressCounter(
"training segments " + innerLearnerPrototype.toString(),
"sequence",
numberOfEpochs * dataset.getNumberOfSegmentGroups());
for (int epoch = 0; epoch < numberOfEpochs; epoch++) {
// dataset.shuffle();
// statistics for curious researchers
int sequenceErrors = 0;
int transitionErrors = 0;
int transitions = 0;
for (Iterator<CandidateSegmentGroup> i = dataset.candidateSegmentGroupIterator();
i.hasNext(); ) {
Classifier c = new SequenceUtils.MultiClassClassifier(schema, innerLearner);
if (DEBUG) log.debug("classifier is: " + c);
CandidateSegmentGroup g = i.next();
Segmentation viterbi =
new SegmentCollinsPerceptronLearner.ViterbiSearcher(c, schema, maxSegmentSize)
.bestSegments(g);
if (DEBUG) log.debug("viterbi " + maxSegmentSize + "\n" + viterbi);
Segmentation correct = correctSegments(g, schema, maxSegmentSize);
if (DEBUG) log.debug("correct segments:\n" + correct);
boolean errorOnThisSequence = false;
// accumulate weights for transitions associated with each class k
Hyperplane[] accumPos = new Hyperplane[schema.getNumberOfClasses()];
Hyperplane[] accumNeg = new Hyperplane[schema.getNumberOfClasses()];
for (int k = 0; k < schema.getNumberOfClasses(); k++) {
accumPos[k] = new Hyperplane();
accumNeg[k] = new Hyperplane();
}
int fp = compareSegmentsAndIncrement(schema, viterbi, correct, accumNeg, +1, g);
if (fp > 0) errorOnThisSequence = true;
int fn = compareSegmentsAndIncrement(schema, correct, viterbi, accumPos, +1, g);
if (fn > 0) errorOnThisSequence = true;
if (errorOnThisSequence) sequenceErrors++;
transitionErrors += fp + fn;
if (errorOnThisSequence) {
sequenceErrors++;
String subPopId = g.getSubpopulationId();
Object source = "no source";
for (int k = 0; k < schema.getNumberOfClasses(); k++) {
// System.out.println("adding class="+k+" example: "+accumPos[k]);
innerLearner[k].addExample(
new Example(
new HyperplaneInstance(accumPos[k], subPopId, source),
ClassLabel.positiveLabel(+1.0)));
innerLearner[k].addExample(
new Example(
new HyperplaneInstance(accumNeg[k], subPopId, source),
ClassLabel.negativeLabel(-1.0)));
}
}
transitions += correct.size();
pc.progress();
} // sequence i
System.out.println(
"Epoch "
+ epoch
+ ": sequenceErr="
+ sequenceErrors
+ " transitionErrors="
+ transitionErrors
+ "/"
+ transitions);
if (transitionErrors == 0) break;
} // epoch
pc.finished();
for (int k = 0; k < schema.getNumberOfClasses(); k++) {
innerLearner[k].completeTraining();
}
Classifier c = new SequenceUtils.MultiClassClassifier(schema, innerLearner);
return new SegmentCollinsPerceptronLearner.ViterbiSegmenter(c, schema, maxSegmentSize);
}
