Package org.apache.mahout.cf.taste.impl.common

Examples of org.apache.mahout.cf.taste.impl.common.LongPrimitiveIterator

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  private void buildAverageDiffs() throws TasteException {
    try {
      buildAveragesLock.writeLock().lock();
      DataModel dataModel = getDataModel();
      LongPrimitiveIterator it = dataModel.getUserIDs();
      while (it.hasNext()) {
        PreferenceArray prefs = dataModel.getPreferencesFromUser(it.nextLong());
        int size = prefs.length();
        for (int i = 0; i < size; i++) {
          long itemID = prefs.getItemID(i);
          RunningAverage average = itemAverages.get(itemID);
          if (average == null) {
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  public FastIDSet getCandidateItems(long userID, DataModel dataModel) throws TasteException {
    int maxPrefsPerItemConsidered = (int) Math.max(defaultMaxPrefsPerItemConsidered,
        userItemCountMultiplier * Math.log(Math.max(dataModel.getNumUsers(), dataModel.getNumItems())));
    FastIDSet possibleItemsIDs = new FastIDSet();
    FastIDSet itemIDs = dataModel.getItemIDsFromUser(userID);
    LongPrimitiveIterator itemIDIterator = itemIDs.iterator();
    while (itemIDIterator.hasNext()) {
      long itemID = itemIDIterator.next();
      PreferenceArray prefs = dataModel.getPreferencesForItem(itemID);
      int prefsConsidered = Math.min(prefs.length(), maxPrefsPerItemConsidered);
      Iterator<Preference> sampledPrefs = new FixedSizeSamplingIterator(prefsConsidered, prefs.iterator());
      while (sampledPrefs.hasNext()) {
        possibleItemsIDs.addAll(dataModel.getItemIDsFromUser(sampledPrefs.next().getUserID()));
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    DataModel dataModel = getDataModel();
    UserSimilarity userSimilarityImpl = getUserSimilarity();
   
    TopItems.Estimator<Long> estimator = new Estimator(userSimilarityImpl, userID, minSimilarity);
   
    LongPrimitiveIterator userIDs = SamplingLongPrimitiveIterator.maybeWrapIterator(dataModel.getUserIDs(),
      getSamplingRate());
   
    return TopItems.getTopUsers(n, userIDs, null, estimator);
  }
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  public RandomRecommender(DataModel dataModel) throws TasteException {
    super(dataModel);
    float maxPref = Float.NEGATIVE_INFINITY;
    float minPref = Float.POSITIVE_INFINITY;
    LongPrimitiveIterator userIterator = dataModel.getUserIDs();
    while (userIterator.hasNext()) {
      long userID = userIterator.next();
      PreferenceArray prefs = dataModel.getPreferencesFromUser(userID);
      for (int i = 0; i < prefs.length(); i++) {
        float prefValue = prefs.getValue(i);
        if (prefValue < minPref) {
          minPref = prefValue;
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  public List<RecommendedItem> recommend(long userID, int howMany, IDRescorer rescorer) throws TasteException {
    DataModel dataModel = getDataModel();
    int numItems = dataModel.getNumItems();
    List<RecommendedItem> result = new ArrayList<RecommendedItem>(howMany);
    while (result.size() < howMany) {
      LongPrimitiveIterator it = dataModel.getItemIDs();
      it.skip(random.nextInt(numItems));
      long itemID = it.next();
      if (dataModel.getPreferenceValue(userID, itemID) == null) {
        result.add(new GenericRecommendedItem(itemID, randomPref()));
      }
    }
    return result;
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  @Override
  public long[] getUserNeighborhood(long userID) throws TasteException {
   
    DataModel dataModel = getDataModel();
    FastIDSet neighborhood = new FastIDSet();
    LongPrimitiveIterator usersIterable = SamplingLongPrimitiveIterator.maybeWrapIterator(dataModel
        .getUserIDs(), getSamplingRate());
    UserSimilarity userSimilarityImpl = getUserSimilarity();
   
    while (usersIterable.hasNext()) {
      long otherUserID = usersIterable.next();
      if (userID != otherUserID) {
        double theSimilarity = userSimilarityImpl.userSimilarity(userID, otherUserID);
        if (!Double.isNaN(theSimilarity) && (theSimilarity >= threshold)) {
          neighborhood.add(otherUserID);
        }
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  @Override
  public double getSimilarity(FastIDSet cluster1, FastIDSet cluster2) throws TasteException {
    if (cluster1.isEmpty() || cluster2.isEmpty()) {
      return Double.NaN;
    }
    LongPrimitiveIterator someUsers = SamplingLongPrimitiveIterator.maybeWrapIterator(cluster1.iterator(),
      samplingRate);
    double greatestSimilarity = Double.NEGATIVE_INFINITY;
    while (someUsers.hasNext()) {
      long userID1 = someUsers.next();
      LongPrimitiveIterator it2 = cluster2.iterator();
      while (it2.hasNext()) {
        double theSimilarity = similarity.userSimilarity(userID1, it2.next());
        if (theSimilarity > greatestSimilarity) {
          greatestSimilarity = theSimilarity;
        }
      }
    }
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    FastIDSet itemIDSet = new FastIDSet();
    for (Map.Entry<Long, FastIDSet> entry : preferenceFromUsers.entrySet()) {
      long userID = entry.getKey();
      FastIDSet itemIDs = entry.getValue();
      itemIDSet.addAll(itemIDs);
      LongPrimitiveIterator it = itemIDs.iterator();
      while (it.hasNext()) {
        long itemID = it.next();
        FastIDSet userIDs = preferenceForItems.get(itemID);
        if (userIDs == null) {
          userIDs = new FastIDSet(2);
          preferenceForItems.put(itemID, userIDs);
        }
        userIDs.add(userID);
      }
    }

    this.itemIDs = itemIDSet.toArray();
    itemIDSet = null; // Might help GC -- this is big
    Arrays.sort(itemIDs);

    this.userIDs = new long[userData.size()];
    int i = 0;
    LongPrimitiveIterator it = userData.keySetIterator();
    while (it.hasNext()) {
      userIDs[i++] = it.next();
    }
    Arrays.sort(userIDs);

    this.timestamps = timestamps;
  }
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   * @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<FastIDSet>(dataModel.getNumUsers());
    LongPrimitiveIterator it = dataModel.getUserIDs();
    while (it.hasNext()) {
      long userID = it.nextLong();
      data.put(userID, dataModel.getItemIDsFromUser(userID));
    }
    return data;
  }
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    int numItems = dataModel.getNumItems();
    RunningAverage precision = new FullRunningAverage();
    RunningAverage recall = new FullRunningAverage();
    RunningAverage fallOut = new FullRunningAverage();
    LongPrimitiveIterator it = dataModel.getUserIDs();
    while (it.hasNext()) {
      long userID = it.nextLong();
      if (random.nextDouble() < evaluationPercentage) {
        long start = System.currentTimeMillis();
        FastIDSet relevantItemIDs = new FastIDSet(at);
        PreferenceArray prefs = dataModel.getPreferencesFromUser(userID);
        int size = prefs.length();
        if (size < 2 * at) {
          // Really not enough prefs to meaningfully evaluate this user
          continue;
        }

        // List some most-preferred items that would count as (most) "relevant" results
        double theRelevanceThreshold =
            Double.isNaN(relevanceThreshold) ? computeThreshold(prefs) : relevanceThreshold;
        prefs.sortByValueReversed();
        for (int i = 0; (i < size) && (relevantItemIDs.size() < at); i++) {
          if (prefs.getValue(i) >= theRelevanceThreshold) {
            relevantItemIDs.add(prefs.getItemID(i));
          }
        }
        int numRelevantItems = relevantItemIDs.size();
        if (numRelevantItems > 0) {
          FastByIDMap<PreferenceArray> trainingUsers = new FastByIDMap<PreferenceArray>(dataModel
              .getNumUsers());
          LongPrimitiveIterator it2 = dataModel.getUserIDs();
          while (it2.hasNext()) {
            processOtherUser(userID, relevantItemIDs, trainingUsers, it2
                .nextLong(), dataModel);
          }

          DataModel trainingModel = dataModelBuilder == null ? new GenericDataModel(trainingUsers)
              : dataModelBuilder.buildDataModel(trainingUsers);
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