@Override
public Object preProcess(DocumentSet training, Object transientData) {
inverseIndex = new HashMap<>();
double[] expectedWeightPerLabelHint = new double[2];
System.out.println("Preparing clustering");
// class counts
training.forEach(
(docId, doc) -> {
int cls = doc.getClasses().contains(className) ? 1 : 0;
expectedWeightPerLabelHint[cls] += 1;
});
Function<FeatureNode, Double> estimateInfoGain =
(f) -> {
InfoGainCalc calc =
new InfoGainCalc(2, false, f.featureName, InfoGainCalc.EstimationEnum.INFO_GAIN);
f.values.forEachEntry(
(id, v) -> {
calc.addSample(
training.document(id).getClasses().contains(className) ? 1 : 0,
v / InfoGainCalc.PRECISION_INV);
return true;
});
calc.setExpectedWeightPerLabelHint(expectedWeightPerLabelHint);
return calc.estimateRelevance();
};
Function<FeatureNode, Double> evaluator =
(f) -> {
return f.eval;
};
BiFunction<FeatureNode, FeatureNode, FeatureNode> combiner =
(f1, f2) -> {
FeatureNode combined = new FeatureNode(null);
f1.values.forEachEntry(
(id, v) -> {
combined.values.put(id, v);
return true;
});
f2.values.forEachEntry(
(id, v) -> {
long o = combined.values.get(id);
if (isMaxCombiner) {
combined.values.put(id, v > o ? v : o);
} else { // if isSumCombiner
combined.values.put(id, o + v);
}
return true;
});
combined.eval = estimateInfoGain.apply(combined);
return combined;
};
// calculate feature-doc map
Map<String, TLongLongHashMap> fmap = new HashMap<>();
training.forEach(
(docId, doc) -> {
doc.getFeatureSet(sourceFeatureSet)
.forEach(
(f) -> {
TLongLongHashMap fmapt = fmap.get(f.getName());
if (fmapt == null) {
fmapt = new TLongLongHashMap();
fmap.put(f.getName(), fmapt);
}
fmapt.put(
docId, (long) (((Double) f.doubleValue()) * InfoGainCalc.PRECISION_INV));
});
});
ArrayList<FeatureNode> arrTemp = new ArrayList<>();
// convert to featureNodes
fmap.forEach(
(fname, arr) -> {
FeatureNode node = new FeatureNode(fname, arr);
node.eval = estimateInfoGain.apply(node);
arrTemp.add(node);
});
// optimization: get only best info gain features
Collections.sort(arrTemp, (n1, n2) -> -Double.compare(n1.eval, n2.eval));
for (int i = 0; i < arrTemp.size() && i < nOfBestToUse; i++) {
inverseIndex.put(arrTemp.get(i).featureName, arrTemp.get(i));
}
System.out.println("Doing clustering");
AgglomerativeSampling<FeatureNode> clustering =
new AgglomerativeSampling<>(evaluator, combiner, inverseIndex.values());
clustering.setMaxSamples(nOfBestToUse);
clustering.doClustering(nClusters);
// collect clusters
clusters = new HashMap<>();
clustering.forEachCluster(
(c) -> {
Set<String> features = new HashSet<>();
c.forEachLeaf((l) -> features.add(l.getPoint().featureName));
if (features.size() >= 1) {
clusters.put(clusters.size(), features);
}
});
// release memory
inverseIndex = null;
return null;
}
@Override
public void reset(DocumentSet docs, String className) {
this.className = className;
docs.forEach((id, doc) -> doc.removeFeatureSet(FEATURE_SET));
}
