public override float?getPreferenceValue(long userID, long itemID)
{
FastIDSet set = this.preferenceFromUsers.get(userID);
if (set == null)
{
throw new NoSuchUserException(userID);
}
if (set.contains(itemID))
{
return(1f);
}
return(null);
}
private static long[] getCommonItems(FastIDSet commonSet, IEnumerable <RecommendedItem> recs, int max)
{
long[] numArray = new long[max];
int num = 0;
foreach (RecommendedItem item in recs)
{
long key = item.getItemID();
if (commonSet.contains(key))
{
numArray[num++] = key;
}
if (num == max)
{
return(numArray);
}
}
return(numArray);
}
private static long[] getCommonItems(FastIDSet commonSet, PreferenceArray prefs1, int max)
{
long[] numArray = new long[max];
int num = 0;
for (int i = 0; i < prefs1.length(); i++)
{
long key = prefs1.getItemID(i);
if (commonSet.contains(key))
{
numArray[num++] = key;
}
if (num == max)
{
return(numArray);
}
}
return(numArray);
}
private static int mask(FastIDSet commonSet, FastIDSet otherSet, long maxItemID)
{
int num = 0;
for (int i = 0; i <= maxItemID; i++)
{
if (commonSet.contains((long)i))
{
if (otherSet.contains((long)i))
{
num++;
}
else
{
commonSet.remove((long)i);
}
}
}
return(num);
}
public void processOtherUser(long userID, FastIDSet relevantItemIDs, FastByIDMap <PreferenceArray> trainingUsers, long otherUserID, DataModel dataModel)
{
PreferenceArray array = dataModel.getPreferencesFromUser(otherUserID);
if (userID == otherUserID)
{
List <Preference> prefs = new List <Preference>(array.length());
foreach (Preference preference in array)
{
if (!relevantItemIDs.contains(preference.getItemID()))
{
prefs.Add(preference);
}
}
if (prefs.Count > 0)
{
trainingUsers.put(otherUserID, new GenericUserPreferenceArray(prefs));
}
}
else
{
trainingUsers.put(otherUserID, array);
}
}
public IRStatistics evaluate(RecommenderBuilder recommenderBuilder, DataModelBuilder dataModelBuilder, DataModel dataModel, IDRescorer rescorer, int at, double relevanceThreshold, double evaluationPercentage)
{
int num = dataModel.getNumItems();
RunningAverage average = new FullRunningAverage();
RunningAverage average2 = new FullRunningAverage();
RunningAverage average3 = new FullRunningAverage();
RunningAverage average4 = new FullRunningAverage();
int num2 = 0;
int num3 = 0;
IEnumerator <long> enumerator = dataModel.getUserIDs();
while (enumerator.MoveNext())
{
long current = enumerator.Current;
if (this.random.nextDouble() < evaluationPercentage)
{
Stopwatch stopwatch = new Stopwatch();
stopwatch.Start();
PreferenceArray prefs = dataModel.getPreferencesFromUser(current);
double num5 = double.IsNaN(relevanceThreshold) ? computeThreshold(prefs) : relevanceThreshold;
FastIDSet relevantItemIDs = this.dataSplitter.getRelevantItemsIDs(current, at, num5, dataModel);
int num6 = relevantItemIDs.size();
if (num6 > 0)
{
FastByIDMap <PreferenceArray> trainingUsers = new FastByIDMap <PreferenceArray>(dataModel.getNumUsers());
IEnumerator <long> enumerator2 = dataModel.getUserIDs();
while (enumerator2.MoveNext())
{
this.dataSplitter.processOtherUser(current, relevantItemIDs, trainingUsers, enumerator2.Current, dataModel);
}
DataModel model = (dataModelBuilder == null) ? new GenericDataModel(trainingUsers) : dataModelBuilder.buildDataModel(trainingUsers);
try
{
model.getPreferencesFromUser(current);
}
catch (NoSuchUserException)
{
continue;
}
int num7 = num6 + model.getItemIDsFromUser(current).size();
if (num7 >= (2 * at))
{
Recommender recommender = recommenderBuilder.buildRecommender(model);
int num8 = 0;
List <RecommendedItem> list = recommender.recommend(current, at, rescorer);
foreach (RecommendedItem item in list)
{
if (relevantItemIDs.contains(item.getItemID()))
{
num8++;
}
}
int count = list.Count;
if (count > 0)
{
average.addDatum(((double)num8) / ((double)count));
}
average2.addDatum(((double)num8) / ((double)num6));
if (num6 < num7)
{
average3.addDatum(((double)(count - num8)) / ((double)(num - num6)));
}
double num10 = 0.0;
double num11 = 0.0;
for (int i = 0; i < count; i++)
{
RecommendedItem item2 = list[i];
double num13 = 1.0 / log2(i + 2.0);
if (relevantItemIDs.contains(item2.getItemID()))
{
num10 += num13;
}
if (i < num6)
{
num11 += num13;
}
}
if (num11 > 0.0)
{
average4.addDatum(num10 / num11);
}
num2++;
if (count > 0)
{
num3++;
}
stopwatch.Stop();
log.info("Evaluated with user {} in {}ms", new object[] { current, stopwatch.ElapsedMilliseconds });
log.info("Precision/recall/fall-out/nDCG/reach: {} / {} / {} / {} / {}", new object[] { average.getAverage(), average2.getAverage(), average3.getAverage(), average4.getAverage(), ((double)num3) / ((double)num2) });
}
}
}
}
return(new IRStatisticsImpl(average.getAverage(), average2.getAverage(), average3.getAverage(), average4.getAverage(), ((double)num3) / ((double)num2)));
}
