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 FastIDSet GetRelevantItemsIDs(long userID,
int at,
double relevanceThreshold,
IDataModel dataModel)
{
IPreferenceArray prefs = dataModel.GetPreferencesFromUser(userID);
FastIDSet relevantItemIDs = new FastIDSet(at);
prefs.SortByValueReversed();
for (int i = 0; i < prefs.Length() && relevantItemIDs.Count() < at; i++)
{
if (prefs.GetValue(i) >= relevanceThreshold)
{
relevantItemIDs.Add(prefs.GetItemID(i));
}
}
return(relevantItemIDs);
}
