/**
* Build a rating matrix from the rating data. Each user's ratings are first normalized by
* subtracting a baseline score (usually a mean).
*
* @param userMapping The index mapping of user IDs to column numbers.
* @param itemMapping The index mapping of item IDs to row numbers.
* @return A matrix storing the <i>normalized</i> user ratings.
*/
private RealMatrix createRatingMatrix(IdIndexMapping userMapping, IdIndexMapping itemMapping) {
final int nusers = userMapping.size();
final int nitems = itemMapping.size();
// Create a matrix with users on rows and items on columns
logger.info("creating {} by {} rating matrix", nusers, nitems);
RealMatrix matrix = MatrixUtils.createRealMatrix(nusers, nitems);
// populate it with data
Cursor<UserHistory<Event>> users = userEventDAO.streamEventsByUser();
try {
for (UserHistory<Event> user : users) {
// Get the row number for this user
int u = userMapping.getIndex(user.getUserId());
MutableSparseVector ratings = Ratings.userRatingVector(user.filter(Rating.class));
MutableSparseVector baselines = MutableSparseVector.create(ratings.keySet());
baselineScorer.score(user.getUserId(), baselines);
// TODO Populate this user's row with their ratings, minus the baseline scores
for (VectorEntry entry : ratings.fast(State.SET)) {
long itemid = entry.getKey();
int i = itemMapping.getIndex(itemid);
double rating = entry.getValue();
double baseline = baselines.get(itemid);
matrix.setEntry(u, i, rating - baseline);
}
}
} finally {
users.close();
}
return matrix;
}
@Override
public <E extends Event> UserHistory<E> getEventsForUser(long user, Class<E> type) {
UserHistory<Event> events = getEventsForUser(user);
if (events == null) {
return null;
} else {
return events.filter(type);
}
}
private DataSource downsample(DataSource data, LongSet testUsers) throws IOException {
String fileName = getFileName(data);
File output = new File(fileName);
UpToDateChecker checker = new UpToDateChecker();
checker.addInput(data.lastModified());
checker.addOutput(output);
if (!checker.isUpToDate()) {
RandomOrder<Rating> order = new RandomOrder<Rating>();
Random rng = new Random();
// write datasource
CSVWriter csv = null;
try {
csv = CSVWriter.open(output, null);
Cursor<UserHistory<Rating>> histories =
data.getUserEventDAO().streamEventsByUser(Rating.class);
for (UserHistory<Rating> ratings : histories) {
List<Rating> rats = new ArrayList<Rating>(ratings);
order.apply(rats, rng);
for (int i = 0; i < rats.size(); i++) {
if (!testUsers.contains(ratings.getUserId()) || i < retain) {
Rating rating = rats.get(i);
Preference pref = rating.getPreference();
csv.writeRow(
Lists.newArrayList(
rating.getUserId(),
rating.getItemId(),
rating.getValue(),
rating.getTimestamp()));
}
}
}
} finally {
if (csv != null) {
csv.close();
}
}
}
CSVDataSourceBuilder builder = new CSVDataSourceBuilder(data.getName());
builder.setDomain(data.getPreferenceDomain());
builder.setFile(output);
return builder.build();
}
