public virtual double evaluate(RecommenderBuilder recommenderBuilder, DataModelBuilder dataModelBuilder, DataModel dataModel, double trainingPercentage, double evaluationPercentage)
{
log.info("Beginning evaluation using {} of {}", new object[] { trainingPercentage, dataModel });
int num = dataModel.getNumUsers();
FastByIDMap <PreferenceArray> trainingPrefs = new FastByIDMap <PreferenceArray>(1 + ((int)(evaluationPercentage * num)));
FastByIDMap <PreferenceArray> testPrefs = new FastByIDMap <PreferenceArray>(1 + ((int)(evaluationPercentage * num)));
IEnumerator <long> enumerator = dataModel.getUserIDs();
while (enumerator.MoveNext())
{
long current = enumerator.Current;
if (this.random.nextDouble() < evaluationPercentage)
{
this.splitOneUsersPrefs(trainingPercentage, trainingPrefs, testPrefs, current, dataModel);
}
}
DataModel model = (dataModelBuilder == null) ? new GenericDataModel(trainingPrefs) : dataModelBuilder.buildDataModel(trainingPrefs);
Recommender recommender = recommenderBuilder.buildRecommender(model);
double num3 = this.getEvaluation(testPrefs, recommender);
log.info("Evaluation result: {}", new object[] { num3 });
return(num3);
}
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)));
}
