public static void Evaluate(IRecommender recommender, IDataModel model, int samples, IRunningAverage tracker, String tag) { printHeader(); var users = recommender.GetDataModel().GetUserIDs(); while (users.MoveNext()) { long userID = users.Current; var recs1 = recommender.Recommend(userID, model.GetNumItems()); IPreferenceArray prefs2 = model.GetPreferencesFromUser(userID); prefs2.SortByValueReversed(); FastIDSet commonSet = new FastIDSet(); long maxItemID = setBits(commonSet, recs1, samples); FastIDSet otherSet = new FastIDSet(); maxItemID = Math.Max(maxItemID, setBits(otherSet, prefs2, samples)); int max = mask(commonSet, otherSet, maxItemID); max = Math.Min(max, samples); if (max < 2) { continue; } long[] items1 = getCommonItems(commonSet, recs1, max); long[] items2 = getCommonItems(commonSet, prefs2, max); double variance = scoreCommonSubset(tag, userID, samples, max, items1, items2); tracker.AddDatum(variance); } }
public static void Evaluate(IRecommender recommender, IDataModel model, int samples, IRunningAverage tracker, String tag) { printHeader(); var users = recommender.GetDataModel().GetUserIDs(); while (users.MoveNext()) { long userID = users.Current; var recs1 = recommender.Recommend(userID, model.GetNumItems()); IPreferenceArray prefs2 = model.GetPreferencesFromUser(userID); prefs2.SortByValueReversed(); FastIDSet commonSet = new FastIDSet(); long maxItemID = setBits(commonSet, recs1, samples); FastIDSet otherSet = new FastIDSet(); maxItemID = Math.Max(maxItemID, setBits(otherSet, prefs2, samples)); int max = mask(commonSet, otherSet, maxItemID); max = Math.Min(max, samples); if (max < 2) { continue; } long[] items1 = getCommonItems(commonSet, recs1, max); long[] items2 = getCommonItems(commonSet, prefs2, max); double variance = scoreCommonSubset(tag, userID, samples, max, items1, items2); tracker.AddDatum(variance); } }
public CachingRecommender(IRecommender recommender) { //Preconditions.checkArgument(recommender != null, "recommender is null"); this.recommender = recommender; maxHowMany = new int[] { 1 }; // Use "num users" as an upper limit on cache size. Rough guess. int numUsers = recommender.GetDataModel().GetNumUsers(); recommendationsRetriever = new RecommendationRetriever(this); recommendationCache = new Cache <long, Recommendations>(recommendationsRetriever, numUsers); estimatedPrefCache = new Cache <Tuple <long, long>, float>(new EstimatedPrefRetriever(this), numUsers); refreshHelper = new RefreshHelper(() => { clear(); }); refreshHelper.AddDependency(recommender); }
public static LoadStatistics runLoad(IRecommender recommender, int howMany) { IDataModel dataModel = recommender.GetDataModel(); int numUsers = dataModel.GetNumUsers(); double sampleRate = 1000.0 / numUsers; var userSampler = SamplinglongPrimitiveIterator.MaybeWrapIterator(dataModel.GetUserIDs(), sampleRate); if (userSampler.MoveNext()) recommender.Recommend(userSampler.Current, howMany); // Warm up var callables = new List<Action>(); while (userSampler.MoveNext()) { callables.Add(new LoadCallable(recommender, userSampler.Current).call); } AtomicInteger noEstimateCounter = new AtomicInteger(); IRunningAverageAndStdDev timing = new FullRunningAverageAndStdDev(); AbstractDifferenceRecommenderEvaluator.execute(callables, noEstimateCounter, timing); return new LoadStatistics(timing); }
public static LoadStatistics runLoad(IRecommender recommender, int howMany) { IDataModel dataModel = recommender.GetDataModel(); int numUsers = dataModel.GetNumUsers(); double sampleRate = 1000.0 / numUsers; var userSampler = SamplinglongPrimitiveIterator.MaybeWrapIterator(dataModel.GetUserIDs(), sampleRate); if (userSampler.MoveNext()) { recommender.Recommend(userSampler.Current, howMany); // Warm up } var callables = new List <Action>(); while (userSampler.MoveNext()) { callables.Add(new LoadCallable(recommender, userSampler.Current).call); } AtomicInteger noEstimateCounter = new AtomicInteger(); IRunningAverageAndStdDev timing = new FullRunningAverageAndStdDev(); AbstractDifferenceRecommenderEvaluator.execute(callables, noEstimateCounter, timing); return(new LoadStatistics(timing)); }