/**
* Exports the simple user IDs and associated item IDs in the data model.
*
* @return a {@link FastByIDMap} mapping user IDs to {@link FastIDSet}s representing that user's
* associated items
*/
public static FastByIDMap<FastIDSet> toDataMap(DataModel dataModel) throws TasteException {
FastByIDMap<FastIDSet> data = new FastByIDMap<>(dataModel.getNumUsers());
LongPrimitiveIterator it = dataModel.getUserIDs();
while (it.hasNext()) {
long userID = it.nextLong();
data.put(userID, dataModel.getItemIDsFromUser(userID));
}
return data;
}
public static void main(String[] args) throws IOException, TasteException {
String file = "datafile/item.csv";
DataModel model = new FileDataModel(new File(file));
UserSimilarity user = new EuclideanDistanceSimilarity(model);
NearestNUserNeighborhood neighbor = new NearestNUserNeighborhood(NEIGHBORHOOD_NUM, user, model);
Recommender r = new GenericUserBasedRecommender(model, neighbor, user);
LongPrimitiveIterator iter = model.getUserIDs();
while (iter.hasNext()) {
long uid = iter.nextLong();
List<RecommendedItem> list = r.recommend(uid, RECOMMENDER_NUM);
System.out.printf("uid:%s", uid);
for (RecommendedItem ritem : list) {
System.out.printf("(%s,%f)", ritem.getItemID(), ritem.getValue());
}
System.out.println();
}
}
private void buildAverageDiffs() throws TasteException {
try {
buildAveragesLock.writeLock().lock();
DataModel dataModel = getDataModel();
LongPrimitiveIterator it = dataModel.getUserIDs();
while (it.hasNext()) {
long userID = it.nextLong();
PreferenceArray prefs = dataModel.getPreferencesFromUser(userID);
int size = prefs.length();
for (int i = 0; i < size; i++) {
long itemID = prefs.getItemID(i);
float value = prefs.getValue(i);
addDatumAndCreateIfNeeded(itemID, value, itemAverages);
addDatumAndCreateIfNeeded(userID, value, userAverages);
overallAveragePrefValue.addDatum(value);
}
}
} finally {
buildAveragesLock.writeLock().unlock();
}
}
public static void main(String[] args)
throws FileNotFoundException, TasteException, IOException, OptionException {
DataModel model;
model = new FileDataModel(new File("datasets/ratingsForMahout.dat"));
File movieMapFile = new File("datasets/moviesForMahout.dat");
HashMap<Long, String> movieMap = new HashMap<Long, String>();
Scanner scan = new Scanner(movieMapFile);
while (scan.hasNextLine()) {
String[] line = scan.nextLine().split("\\|");
movieMap.put(Long.parseLong(line[0]), line[1]);
}
scan.close();
UserSimilarity userSimilarity = new PearsonCorrelationSimilarity(model);
UserNeighborhood neighborhood = new NearestNUserNeighborhood(3, userSimilarity, model);
Recommender recommender = new GenericUserBasedRecommender(model, neighborhood, userSimilarity);
Recommender cachingRecommender = new CachingRecommender(recommender);
for (LongPrimitiveIterator it = model.getUserIDs(); it.hasNext(); ) {
long userId = it.nextLong();
List<RecommendedItem> recommendations = cachingRecommender.recommend(userId, 10);
if (recommendations.size() == 0) {
System.out.println("User " + userId + ": no recommendations");
}
for (RecommendedItem recommendedItem : recommendations) {
System.out.println(
"User "
+ userId
+ ": "
+ movieMap.get(recommendedItem.getItemID())
+ "; value="
+ recommendedItem.getValue());
}
}
}
@Override
public double evaluate(
RecommenderBuilder recommenderBuilder,
DataModelBuilder dataModelBuilder,
DataModel dataModel,
double trainingPercentage,
double evaluationPercentage)
throws TasteException {
Preconditions.checkNotNull(recommenderBuilder);
Preconditions.checkNotNull(dataModel);
Preconditions.checkArgument(
trainingPercentage >= 0.0 && trainingPercentage <= 1.0,
"Invalid trainingPercentage: "
+ trainingPercentage
+ ". Must be: 0.0 <= trainingPercentage <= 1.0");
Preconditions.checkArgument(
evaluationPercentage >= 0.0 && evaluationPercentage <= 1.0,
"Invalid evaluationPercentage: "
+ evaluationPercentage
+ ". Must be: 0.0 <= evaluationPercentage <= 1.0");
log.info("Beginning evaluation using {} of {}", trainingPercentage, dataModel);
int numUsers = dataModel.getNumUsers();
FastByIDMap<PreferenceArray> trainingPrefs =
new FastByIDMap<PreferenceArray>(1 + (int) (evaluationPercentage * numUsers));
FastByIDMap<PreferenceArray> testPrefs =
new FastByIDMap<PreferenceArray>(1 + (int) (evaluationPercentage * numUsers));
totalOfTrainingRatingsFromSource = 0;
totalOfTrainingRatingsFromTargetWithContext = 0;
totalOfTrainingRatingsFromTargetWithoutContext = 0;
totalOfTestRatings = 0;
LongPrimitiveIterator it = dataModel.getUserIDs();
while (it.hasNext()) {
long userID = it.nextLong();
if (random.nextDouble() < evaluationPercentage) {
splitOneUsersPrefs(trainingPercentage, trainingPrefs, testPrefs, userID, dataModel);
}
}
// System.out.println("Training (Source, TargetWithoutContext, TargetWithContext):
// "+totalOfTrainingRatingsFromSource+"/"+totalOfTrainingRatingsFromTargetWithoutContext+"/"+totalOfTrainingRatingsFromTargetWithContext);
// int totalTraining =
// (totalOfTrainingRatingsFromSource+totalOfTrainingRatingsFromTargetWithContext+totalOfTrainingRatingsFromTargetWithoutContext);
// System.out.println("Training/Test: "+totalTraining+"/"+totalOfTestRatings);
DataModel newDataModel =
dataModel instanceof ContextualDataModel
? new ContextualDataModel(trainingPrefs)
: new GenericDataModel(trainingPrefs);
DataModel trainingModel =
dataModelBuilder == null ? newDataModel : dataModelBuilder.buildDataModel(trainingPrefs);
Recommender recommender = recommenderBuilder.buildRecommender(trainingModel);
double result = getEvaluation(testPrefs, recommender);
log.info("Evaluation result: {}", result);
return result;
}