@Test
public void testLSH() {
RandomGenerator random = RandomManager.getRandom();
Mean avgPercentTopRecsConsidered = new Mean();
Mean avgNDCG = new Mean();
Mean avgPercentAllItemsConsidered = new Mean();
for (int iteration = 0; iteration < ITERATIONS; iteration++) {
LongObjectMap<float[]> Y = new LongObjectMap<float[]>();
for (int i = 0; i < NUM_ITEMS; i++) {
Y.put(i, RandomUtils.randomUnitVector(NUM_FEATURES, random));
}
float[] userVec = RandomUtils.randomUnitVector(NUM_FEATURES, random);
double[] results = doTestRandomVecs(Y, userVec);
double percentTopRecsConsidered = results[0];
double ndcg = results[1];
double percentAllItemsConsidered = results[2];
log.info(
"Considered {}% of all candidates, {} nDCG, got {}% recommendations correct",
100 * percentAllItemsConsidered, ndcg, 100 * percentTopRecsConsidered);
avgPercentTopRecsConsidered.increment(percentTopRecsConsidered);
avgNDCG.increment(ndcg);
avgPercentAllItemsConsidered.increment(percentAllItemsConsidered);
}
log.info("{}", avgPercentTopRecsConsidered.getResult());
log.info("{}", avgNDCG.getResult());
log.info("{}", avgPercentAllItemsConsidered.getResult());
assertTrue(avgPercentTopRecsConsidered.getResult() > 0.8);
assertTrue(avgNDCG.getResult() > 0.8);
assertTrue(avgPercentAllItemsConsidered.getResult() < 0.09);
}
@Test
public void testLSHEffect() {
RandomGenerator random = RandomManager.getRandom();
PoissonDistribution itemPerUserDist =
new PoissonDistribution(
random,
20,
PoissonDistribution.DEFAULT_EPSILON,
PoissonDistribution.DEFAULT_MAX_ITERATIONS);
int features = 20;
ALSServingModel mainModel = new ALSServingModel(features, true, 1.0, null);
ALSServingModel lshModel = new ALSServingModel(features, true, 0.5, null);
int userItemCount = 20000;
for (int user = 0; user < userItemCount; user++) {
String userID = "U" + user;
float[] vec = VectorMath.randomVectorF(features, random);
mainModel.setUserVector(userID, vec);
lshModel.setUserVector(userID, vec);
int itemsPerUser = itemPerUserDist.sample();
Collection<String> knownIDs = new ArrayList<>(itemsPerUser);
for (int i = 0; i < itemsPerUser; i++) {
knownIDs.add("I" + random.nextInt(userItemCount));
}
mainModel.addKnownItems(userID, knownIDs);
lshModel.addKnownItems(userID, knownIDs);
}
for (int item = 0; item < userItemCount; item++) {
String itemID = "I" + item;
float[] vec = VectorMath.randomVectorF(features, random);
mainModel.setItemVector(itemID, vec);
lshModel.setItemVector(itemID, vec);
}
int numRecs = 10;
Mean meanMatchLength = new Mean();
for (int user = 0; user < userItemCount; user++) {
String userID = "U" + user;
List<Pair<String, Double>> mainRecs =
mainModel.topN(new DotsFunction(mainModel.getUserVector(userID)), null, numRecs, null);
List<Pair<String, Double>> lshRecs =
lshModel.topN(new DotsFunction(lshModel.getUserVector(userID)), null, numRecs, null);
int i = 0;
while (i < lshRecs.size() && i < mainRecs.size() && lshRecs.get(i).equals(mainRecs.get(i))) {
i++;
}
meanMatchLength.increment(i);
}
log.info("Mean matching prefix: {}", meanMatchLength.getResult());
assertTrue(meanMatchLength.getResult() >= 4.0);
meanMatchLength.clear();
for (int item = 0; item < userItemCount; item++) {
String itemID = "I" + item;
List<Pair<String, Double>> mainRecs =
mainModel.topN(
new CosineAverageFunction(mainModel.getItemVector(itemID)), null, numRecs, null);
List<Pair<String, Double>> lshRecs =
lshModel.topN(
new CosineAverageFunction(lshModel.getItemVector(itemID)), null, numRecs, null);
int i = 0;
while (i < lshRecs.size() && i < mainRecs.size() && lshRecs.get(i).equals(mainRecs.get(i))) {
i++;
}
meanMatchLength.increment(i);
}
log.info("Mean matching prefix: {}", meanMatchLength.getResult());
assertTrue(meanMatchLength.getResult() >= 5.0);
}