public static void evaluate(Recommender recommender, DataModel model, int samples, RunningAverage tracker, string tag) { printHeader(); IEnumerator <long> enumerator = recommender.getDataModel().getUserIDs(); while (enumerator.MoveNext()) { long current = enumerator.Current; List <RecommendedItem> items = recommender.recommend(current, model.getNumItems()); PreferenceArray prefs = model.getPreferencesFromUser(current); prefs.sortByValueReversed(); FastIDSet modelSet = new FastIDSet(); long num2 = setBits(modelSet, items, samples); FastIDSet set2 = new FastIDSet(); num2 = Math.Max(num2, setBits(set2, prefs, samples)); int max = Math.Min(mask(modelSet, set2, num2), samples); if (max >= 2) { long[] itemsL = getCommonItems(modelSet, items, max); long[] itemsR = getCommonItems(modelSet, prefs, max); double datum = scoreCommonSubset(tag, current, samples, max, itemsL, itemsR); tracker.addDatum(datum); } } }
public void addItemPref(long userID, long itemIDA, float prefValue) { PreferenceArray userPreferences = dataModel.getPreferencesFromUser(userID); try { buildAverageDiffsLock.EnterWriteLock(); FastByIDMap <RunningAverage> aMap = averageDiffs.get(itemIDA); if (aMap == null) { aMap = new FastByIDMap <RunningAverage>(); averageDiffs.put(itemIDA, aMap); } int length = userPreferences.length(); for (int i = 0; i < length; i++) { long itemIDB = userPreferences.getItemID(i); float bValue = userPreferences.getValue(i); if (itemIDA < itemIDB) { RunningAverage average = aMap.get(itemIDB); if (average == null) { average = buildRunningAverage(); aMap.put(itemIDB, average); } average.addDatum(bValue - prefValue); } else { FastByIDMap <RunningAverage> bMap = averageDiffs.get(itemIDB); if (bMap == null) { bMap = new FastByIDMap <RunningAverage>(); averageDiffs.put(itemIDB, bMap); } RunningAverage average = bMap.get(itemIDA); if (average == null) { average = buildRunningAverage(); bMap.put(itemIDA, average); } average.addDatum(prefValue - bValue); } } } finally { buildAverageDiffsLock.ExitWriteLock(); } }
private long processOneUser(long averageCount, long userID) { 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(); averageItemPref.put(itemIDA, itemAverage); } itemAverage.addDatum(prefAValue); } return(averageCount); }
public static void evaluate(Recommender recommender1, Recommender recommender2, int samples, RunningAverage tracker, string tag) { printHeader(); IEnumerator <long> enumerator = recommender1.getDataModel().getUserIDs(); while (enumerator.MoveNext()) { long current = enumerator.Current; List <RecommendedItem> items = recommender1.recommend(current, samples); List <RecommendedItem> list2 = recommender2.recommend(current, samples); FastIDSet modelSet = new FastIDSet(); long num2 = setBits(modelSet, items, samples); FastIDSet set2 = new FastIDSet(); num2 = Math.Max(num2, setBits(set2, list2, samples)); int max = Math.Min(mask(modelSet, set2, num2), samples); if (max >= 2) { long[] itemsL = getCommonItems(modelSet, items, max); long[] itemsR = getCommonItems(modelSet, list2, max); double datum = scoreCommonSubset(tag, current, samples, max, itemsL, itemsR); tracker.addDatum(datum); } } }