Package org.apache.mahout.cf.taste.model

Examples of org.apache.mahout.cf.taste.model.PreferenceArray

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    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()));
      }
    }
    possibleItemsIDs.removeAll(itemIDs);
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    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;
        }
        if (prefValue > maxPref) {
          maxPref = prefValue;
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  }
 
  private float doEstimatePreference(long userID, long itemID) throws TasteException {
    double count = 0.0;
    double totalPreference = 0.0;
    PreferenceArray prefs = getDataModel().getPreferencesFromUser(userID);
    RunningAverage[] averages = diffStorage.getDiffs(userID, itemID, prefs);
    int size = prefs.length();
    for (int i = 0; i < size; i++) {
      RunningAverage averageDiff = averages[i];
      if (averageDiff != null) {
        double averageDiffValue = averageDiff.getAverage();
        if (weighted) {
          double weight = averageDiff.getCount();
          if (stdDevWeighted) {
            double stdev = ((RunningAverageAndStdDev) averageDiff).getStandardDeviation();
            if (!Double.isNaN(stdev)) {
              weight /= 1.0 + stdev;
            }
            // If stdev is NaN, then it is because count is 1. Because we're weighting by count,
            // the weight is already relatively low. We effectively assume stdev is 0.0 here and
            // that is reasonable enough. Otherwise, dividing by NaN would yield a weight of NaN
            // and disqualify this pref entirely
            // (Thanks Daemmon)
          }
          totalPreference += weight * (prefs.getValue(i) + averageDiffValue);
          count += weight;
        } else {
          totalPreference += prefs.getValue(i) + averageDiffValue;
          count += 1.0;
        }
      }
    }
    if (count <= 0.0) {
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    return data;
  }

  public static FastByIDMap<FastIDSet> toDataMap(FastByIDMap<PreferenceArray> data) {
    for (Map.Entry<Long,Object> entry : ((FastByIDMap<Object>) (FastByIDMap<?>) data).entrySet()) {
      PreferenceArray prefArray = (PreferenceArray) entry.getValue();
      int size = prefArray.length();
      FastIDSet itemIDs = new FastIDSet(size);
      for (int i = 0; i < size; i++) {
        itemIDs.add(prefArray.getItemID(i));
      }
      entry.setValue(itemIDs);
    }
    return (FastByIDMap<FastIDSet>) (FastByIDMap<?>) data;
  }
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  public PreferenceArray getPreferencesFromUser(long userID) throws NoSuchUserException {
    FastIDSet itemIDs = preferenceFromUsers.get(userID);
    if (itemIDs == null) {
      throw new NoSuchUserException();
    }
    PreferenceArray prefArray = new BooleanUserPreferenceArray(itemIDs.size());
    int i = 0;
    LongPrimitiveIterator it = itemIDs.iterator();
    while (it.hasNext()) {
      prefArray.setUserID(i, userID);
      prefArray.setItemID(i, it.next());
      i++;
    }
    return prefArray;
  }
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  public PreferenceArray getPreferencesForItem(long itemID) throws NoSuchItemException {
    FastIDSet userIDs = preferenceForItems.get(itemID);
    if (userIDs == null) {
      throw new NoSuchItemException();
    }
    PreferenceArray prefArray = new BooleanItemPreferenceArray(userIDs.size());
    int i = 0;
    LongPrimitiveIterator it = userIDs.iterator();
    while (it.hasNext()) {
      prefArray.setUserID(i, it.next());
      prefArray.setItemID(i, itemID);
      i++;
    }
    return prefArray;
  }
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    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
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  private static void processOtherUser(long id,
                                       FastIDSet relevantItemIDs,
                                       FastByIDMap<PreferenceArray> trainingUsers,
                                       long userID2,
                                       DataModel dataModel) throws TasteException {
    PreferenceArray prefs2Array = dataModel.getPreferencesFromUser(userID2);
    if (id == userID2) {
      List<Preference> prefs2 = new ArrayList<Preference>(prefs2Array.length());
      for (Preference pref : prefs2Array) {
        prefs2.add(pref);
      }
      for (Iterator<Preference> iterator = prefs2.iterator(); iterator.hasNext();) {
        Preference pref = iterator.next();
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      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);
        }
      }
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  }
 
  private long processOneUser(long averageCount, long userID) throws TasteException {
    log.debug("Processing prefs for user {}", userID);
    // Save off prefs for the life of this loop iteration
    PreferenceArray userPreferences = dataModel.getPreferencesFromUser(userID);
    int length = userPreferences.length();
    for (int i = 0; i < length - 1; i++) {
      float prefAValue = userPreferences.getValue(i);
      long itemIDA = userPreferences.getItemID(i);
      FastByIDMap<RunningAverage> aMap = averageDiffs.get(itemIDA);
      if (aMap == null) {
        aMap = new FastByIDMap<RunningAverage>();
        averageDiffs.put(itemIDA, aMap);
      }
      for (int j = i + 1; j < length; j++) {
        // This is a performance-critical block
        long itemIDB = userPreferences.getItemID(j);
        RunningAverage average = aMap.get(itemIDB);
        if ((average == null) && (averageCount < maxEntries)) {
          average = buildRunningAverage();
          aMap.put(itemIDB, average);
          averageCount++;
        }
        if (average != null) {
          average.addDatum(userPreferences.getValue(j) - prefAValue);
        }
      }
      RunningAverage itemAverage = averageItemPref.get(itemIDA);
      if (itemAverage == null) {
        itemAverage = buildRunningAverage();
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