@Test
public void testTwoLikes() {
SparseVector vec = sum.summarize(History.forUser(42, Like.create(42, 39), Like.create(42, 67)));
assertThat(vec.size(), equalTo(2));
assertThat(vec.get(39), equalTo(1.0));
assertThat(vec.get(67), equalTo(1.0));
}
@Override
public Double get(long key) {
if (vector.containsKey(key)) {
return vector.get(key);
} else {
return defaultReturnValue();
}
}
@Test
public void testLikeBatch() {
SparseVector vec =
sum.summarize(
History.forUser(
42, Like.create(42, 39), LikeBatch.create(42, 67, 402), Like.create(42, 39)));
assertThat(vec.size(), equalTo(2));
assertThat(vec.get(39), equalTo(2.0));
assertThat(vec.get(67), equalTo(402.0));
}
/**
* Check that we score items but do not provide scores for items the user has previously rated.
* User 5 has rated only item 8 previously.
*/
@Test
public void testItemScorerNoRating() {
long[] items = {7, 8};
ItemItemScorer scorer = session.get(ItemItemScorer.class);
assertThat(scorer, notNullValue());
SparseVector scores = scorer.score(5, LongArrayList.wrap(items));
assertThat(scores, notNullValue());
assertThat(scores.size(), equalTo(1));
assertThat(scores.get(7), not(notANumber()));
assertThat(scores.containsKey(8), equalTo(false));
}
/** Compute the DCG of a list of items with respect to a value vector. */
static double computeDCG(LongList items, SparseVector values) {
final double lg2 = log(2);
double gain = 0;
int rank = 0;
LongIterator iit = items.iterator();
while (iit.hasNext()) {
final long item = iit.nextLong();
final double v = values.get(item, 0);
rank++;
if (rank < 2) {
gain += v;
} else {
gain += v * lg2 / log(rank);
}
}
return gain;
}
/**
* Check that we score items but do not provide scores for items the user has previously rated.
* User 5 has rated only item 8 previously.
*/
@Test
public void testItemScorerChannels() {
long[] items = {7, 8};
ItemItemScorer scorer = session.get(ItemItemScorer.class);
assertThat(scorer, notNullValue());
SparseVector scores = scorer.score(5, LongArrayList.wrap(items));
assertThat(scores, notNullValue());
assertThat(scores.size(), equalTo(1));
assertThat(scores.get(7), not(notANumber()));
assertThat(
scores.getChannelVector(ItemItemScorer.NEIGHBORHOOD_SIZE_SYMBOL).get(7),
closeTo(1.0, 1.0e-5));
assertThat(scores.containsKey(8), equalTo(false));
long[] items2 = {7, 8, 9};
scorer = session.get(ItemItemScorer.class);
assertThat(scorer, notNullValue());
scores = scorer.score(2, LongArrayList.wrap(items2));
assertThat(
scores.getChannelVector(ItemItemScorer.NEIGHBORHOOD_SIZE_SYMBOL).get(9),
closeTo(3.0, 1.0e-5)); // 1, 7, 8
}
@Test
public void testMeanBaseline() {
ItemScorer pred = makeGlobalMean();
SparseVector pv = pred.score(10L, itemSet(2l));
assertEquals(RATINGS_DAT_MEAN, pv.get(2l), 0.00001);
}
@Test
public void testEmptyConstructor() {
SparseVector v = new ImmutableSparseVector();
assertThat(v.isEmpty(), equalTo(true));
assertThat(v.get(15, Double.NaN), notANumber());
}