/**
* Write a user or item index to the file.
*
* @param map The index to write.
*/
private void writeIndex(Long2ObjectMap<IntList> map) throws IOException {
LongSortedSet keys = LongUtils.packedSet(map.keySet());
BinaryIndexTableWriter tableWriter =
BinaryIndexTableWriter.create(format, channel, keys.size());
SortComparator indexComparator = new SortComparator();
LongIterator iter = keys.iterator();
while (iter.hasNext()) {
final long key = iter.nextLong();
int[] indexes = map.get(key).toIntArray();
if (needsSorting) {
IntArrays.quickSort(indexes, indexComparator);
}
if (translationMap != null) {
for (int i = 0; i < indexes.length; i++) {
indexes[i] = translationMap[indexes[i]];
}
}
logger.debug("writing {} indices for id {}", key, indexes.length);
tableWriter.writeEntry(key, indexes);
}
}
@Override
public void predict(long uid, @Nonnull MutableSparseVector predictions) {
logger.debug("predicting {} items for {}", predictions.keyDomain().size(), uid);
OrdRecModel params = new OrdRecModel(quantizer);
SparseVector ratings = makeUserVector(uid, userEventDao);
LongSet keySet = LongUtils.setUnion(ratings.keySet(), predictions.keyDomain());
MutableSparseVector scores = MutableSparseVector.create(keySet);
itemScorer.score(uid, scores);
params.train(ratings, scores);
logger.debug("trained parameters for {}: {}", uid, params);
Vector probabilities = Vector.createLength(params.getLevelCount());
Long2ObjectMap<IVector> distChannel = null;
if (reportDistribution) {
distChannel = predictions.addChannel(RATING_PROBABILITY_CHANNEL);
}
for (VectorEntry e : predictions.fast(VectorEntry.State.EITHER)) {
long iid = e.getKey();
double score = scores.get(iid);
params.getProbDistribution(score, probabilities);
int mlIdx = probabilities.maxElementIndex();
predictions.set(e, quantizer.getIndexValue(mlIdx));
if (distChannel != null) {
distChannel.put(e.getKey(), probabilities.immutable());
}
}
}
LongSortedSet itemSet(long item) {
return LongUtils.packedSet(item);
}
