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));
}
