/// <p>
/// Creates a possibly weighted {@link AbstractSimilarity}.
/// </p>
public AbstractSimilarity(IDataModel dataModel, Weighting weighting, bool centerData) : base(dataModel) {
this.weighted = weighting == Weighting.WEIGHTED;
this.centerData = centerData;
this.cachedNumItems = dataModel.GetNumItems();
this.cachedNumUsers = dataModel.GetNumUsers();
this.refreshHelper = new RefreshHelper( () => {
cachedNumItems = dataModel.GetNumItems();
cachedNumUsers = dataModel.GetNumUsers();
}
);
}
/// <p>
/// Creates a possibly weighted {@link AbstractSimilarity}.
/// </p>
public AbstractSimilarity(IDataModel dataModel, Weighting weighting, bool centerData) : base(dataModel)
{
this.weighted = weighting == Weighting.WEIGHTED;
this.centerData = centerData;
this.cachedNumItems = dataModel.GetNumItems();
this.cachedNumUsers = dataModel.GetNumUsers();
this.refreshHelper = new RefreshHelper(() => {
cachedNumItems = dataModel.GetNumItems();
cachedNumUsers = dataModel.GetNumUsers();
}
);
}
public override IList <IRecommendedItem> Recommend(long userID, int howMany, IDRescorer rescorer)
{
IDataModel dataModel = GetDataModel();
int numItems = dataModel.GetNumItems();
List <IRecommendedItem> result = new List <IRecommendedItem>(howMany);
while (result.Count < howMany)
{
var it = dataModel.GetItemIDs();
it.MoveNext();
var skipNum = random.nextInt(numItems);
for (int i = 0; i < skipNum; i++)
{
if (!it.MoveNext())
{
break;
} // skip() ??
}
long itemID = it.Current;
if (dataModel.GetPreferenceValue(userID, itemID) == null)
{
result.Add(new GenericRecommendedItem(itemID, randomPref()));
}
}
return(result);
}
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);
}
}
protected void initialize()
{
RandomWrapper random = RandomUtils.getRandom();
userVectors = new double[dataModel.GetNumUsers()][];
itemVectors = new double[dataModel.GetNumItems()][];
double globalAverage = getAveragePreference();
for (int userIndex = 0; userIndex < userVectors.Length; userIndex++)
{
userVectors[userIndex] = new double[rank];
userVectors[userIndex][0] = globalAverage;
userVectors[userIndex][USER_BIAS_INDEX] = 0; // will store user bias
userVectors[userIndex][ITEM_BIAS_INDEX] = 1; // corresponding item feature contains item bias
for (int feature = FEATURE_OFFSET; feature < rank; feature++)
{
userVectors[userIndex][feature] = random.nextGaussian() * NOISE;
}
}
for (int itemIndex = 0; itemIndex < itemVectors.Length; itemIndex++)
{
itemVectors[itemIndex] = new double[rank];
itemVectors[itemIndex][0] = 1; // corresponding user feature contains global average
itemVectors[itemIndex][USER_BIAS_INDEX] = 1; // corresponding user feature contains user bias
itemVectors[itemIndex][ITEM_BIAS_INDEX] = 0; // will store item bias
for (int feature = FEATURE_OFFSET; feature < rank; feature++)
{
itemVectors[itemIndex][feature] = random.nextGaussian() * NOISE;
}
}
}
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 Features(ALSWRFactorizer factorizer)
{
dataModel = factorizer.dataModel;
numFeatures = factorizer.numFeatures;
var random = RandomUtils.getRandom();
M = new double[dataModel.GetNumItems()][]; //numFeatures
var itemIDsIterator = dataModel.GetItemIDs();
while (itemIDsIterator.MoveNext())
{
long itemID = itemIDsIterator.Current;
int itemIDIndex = factorizer.itemIndex(itemID);
M[itemIDIndex] = new double[numFeatures];
M[itemIDIndex][0] = averateRating(itemID);
for (int feature = 1; feature < numFeatures; feature++)
{
M[itemIDIndex][feature] = random.nextDouble() * 0.1;
}
}
U = new double[dataModel.GetNumUsers()][]; //numFeatures
for (int i = 0; i < U.Length; i++)
{
U[i] = new double[numFeatures];
}
}
protected override FastIDSet doGetCandidateItems(long[] preferredItemIDs, IDataModel dataModel) {
FastIDSet possibleItemIDs = new FastIDSet(dataModel.GetNumItems());
var allItemIDs = dataModel.GetItemIDs();
while (allItemIDs.MoveNext()) {
possibleItemIDs.Add(allItemIDs.Current);
}
possibleItemIDs.RemoveAll(preferredItemIDs);
return possibleItemIDs;
}
protected override FastIDSet doGetCandidateItems(long[] preferredItemIDs, IDataModel dataModel)
{
FastIDSet possibleItemIDs = new FastIDSet(dataModel.GetNumItems());
var allItemIDs = dataModel.GetItemIDs();
while (allItemIDs.MoveNext())
{
possibleItemIDs.Add(allItemIDs.Current);
}
possibleItemIDs.RemoveAll(preferredItemIDs);
return(possibleItemIDs);
}
public void initializeM()
{
ALSWRFactorizer.Features features = new ALSWRFactorizer.Features(factorizer);
double[][] M = features.getM();
Assert.AreEqual(3.333333333, M[0][0], EPSILON);
Assert.AreEqual(5, M[1][0], EPSILON);
Assert.AreEqual(2.5, M[2][0], EPSILON);
Assert.AreEqual(4.333333333, M[3][0], EPSILON);
for (int itemIndex = 0; itemIndex < dataModel.GetNumItems(); itemIndex++)
{
for (int feature = 1; feature < 3; feature++)
{
Assert.True(M[itemIndex][feature] >= 0);
Assert.True(M[itemIndex][feature] <= 0.1);
}
}
}
protected virtual void prepareTraining()
{
RandomWrapper random = RandomUtils.getRandom();
userVectors = new double[dataModel.GetNumUsers()][]; //numFeatures
itemVectors = new double[dataModel.GetNumItems()][];
double globalAverage = getAveragePreference();
for (int userIndex = 0; userIndex < userVectors.Length; userIndex++)
{
userVectors[userIndex] = new double[numFeatures];
userVectors[userIndex][0] = globalAverage;
userVectors[userIndex][USER_BIAS_INDEX] = 0; // will store user bias
userVectors[userIndex][ITEM_BIAS_INDEX] = 1; // corresponding item feature contains item bias
for (int feature = FEATURE_OFFSET; feature < numFeatures; feature++)
{
userVectors[userIndex][feature] = random.nextGaussian() * randomNoise;
}
}
for (int itemIndex = 0; itemIndex < itemVectors.Length; itemIndex++)
{
itemVectors[itemIndex] = new double[numFeatures];
itemVectors[itemIndex][0] = 1; // corresponding user feature contains global average
itemVectors[itemIndex][USER_BIAS_INDEX] = 1; // corresponding user feature contains user bias
itemVectors[itemIndex][ITEM_BIAS_INDEX] = 0; // will store item bias
for (int feature = FEATURE_OFFSET; feature < numFeatures; feature++)
{
itemVectors[itemIndex][feature] = random.nextGaussian() * randomNoise;
}
}
cachePreferences();
shufflePreferences();
}
public double UserSimilarity(long userID1, long userID2)
{
IDataModel dataModel = getDataModel();
FastIDSet prefs1 = dataModel.GetItemIDsFromUser(userID1);
FastIDSet prefs2 = dataModel.GetItemIDsFromUser(userID2);
long prefs1Size = prefs1.Count();
long prefs2Size = prefs2.Count();
long intersectionSize =
prefs1Size < prefs2Size?prefs2.IntersectionSize(prefs1) : prefs1.IntersectionSize(prefs2);
if (intersectionSize == 0)
{
return(Double.NaN);
}
long numItems = dataModel.GetNumItems();
double logLikelihood =
LogLikelihood.logLikelihoodRatio(intersectionSize,
prefs2Size - intersectionSize,
prefs1Size - intersectionSize,
numItems - prefs1Size - prefs2Size + intersectionSize);
return(1.0 - 1.0 / (1.0 + logLikelihood));
}
private void buildMappings()
{
userIDMapping = createIDMapping(dataModel.GetNumUsers(), dataModel.GetUserIDs());
itemIDMapping = createIDMapping(dataModel.GetNumItems(), dataModel.GetItemIDs());
}
/// Creates this on top of the given {@link ItemSimilarity}.
/// The cache is sized according to properties of the given {@link DataModel}.
public CachingItemSimilarity(IItemSimilarity similarity, IDataModel dataModel) : this(similarity, dataModel.GetNumItems())
{
;
}
public virtual int GetNumItems()
{
return(_delegate.GetNumItems());
}
public IRStatistics Evaluate(IRecommenderBuilder recommenderBuilder,
IDataModelBuilder dataModelBuilder,
IDataModel dataModel,
IDRescorer rescorer,
int at,
double relevanceThreshold,
double evaluationPercentage)
{
//Preconditions.checkArgument(recommenderBuilder != null, "recommenderBuilder is null");
//Preconditions.checkArgument(dataModel != null, "dataModel is null");
//Preconditions.checkArgument(at >= 1, "at must be at least 1");
//Preconditions.checkArgument(evaluationPercentage > 0.0 && evaluationPercentage <= 1.0,
// "Invalid evaluationPercentage: " + evaluationPercentage + ". Must be: 0.0 < evaluationPercentage <= 1.0");
int numItems = dataModel.GetNumItems();
IRunningAverage precision = new FullRunningAverage();
IRunningAverage recall = new FullRunningAverage();
IRunningAverage fallOut = new FullRunningAverage();
IRunningAverage nDCG = new FullRunningAverage();
int numUsersRecommendedFor = 0;
int numUsersWithRecommendations = 0;
var it = dataModel.GetUserIDs();
while (it.MoveNext())
{
long userID = it.Current;
if (random.nextDouble() >= evaluationPercentage)
{
// Skipped
continue;
}
var stopWatch = new System.Diagnostics.Stopwatch();
stopWatch.Start();
IPreferenceArray prefs = dataModel.GetPreferencesFromUser(userID);
// List some most-preferred items that would count as (most) "relevant" results
double theRelevanceThreshold = Double.IsNaN(relevanceThreshold) ? computeThreshold(prefs) : relevanceThreshold;
FastIDSet relevantItemIDs = dataSplitter.GetRelevantItemsIDs(userID, at, theRelevanceThreshold, dataModel);
int numRelevantItems = relevantItemIDs.Count();
if (numRelevantItems <= 0)
{
continue;
}
FastByIDMap <IPreferenceArray> trainingUsers = new FastByIDMap <IPreferenceArray>(dataModel.GetNumUsers());
var it2 = dataModel.GetUserIDs();
while (it2.MoveNext())
{
dataSplitter.ProcessOtherUser(userID, relevantItemIDs, trainingUsers, it2.Current, dataModel);
}
IDataModel trainingModel = dataModelBuilder == null ? new GenericDataModel(trainingUsers)
: dataModelBuilder.BuildDataModel(trainingUsers);
try {
trainingModel.GetPreferencesFromUser(userID);
} catch (NoSuchUserException nsee) {
continue; // Oops we excluded all prefs for the user -- just move on
}
int size = numRelevantItems + trainingModel.GetItemIDsFromUser(userID).Count();
if (size < 2 * at)
{
// Really not enough prefs to meaningfully evaluate this user
continue;
}
IRecommender recommender = recommenderBuilder.BuildRecommender(trainingModel);
int intersectionSize = 0;
var recommendedItems = recommender.Recommend(userID, at, rescorer);
foreach (IRecommendedItem recommendedItem in recommendedItems)
{
if (relevantItemIDs.Contains(recommendedItem.GetItemID()))
{
intersectionSize++;
}
}
int numRecommendedItems = recommendedItems.Count;
// Precision
if (numRecommendedItems > 0)
{
precision.AddDatum((double)intersectionSize / (double)numRecommendedItems);
}
// Recall
recall.AddDatum((double)intersectionSize / (double)numRelevantItems);
// Fall-out
if (numRelevantItems < size)
{
fallOut.AddDatum((double)(numRecommendedItems - intersectionSize)
/ (double)(numItems - numRelevantItems));
}
// nDCG
// In computing, assume relevant IDs have relevance 1 and others 0
double cumulativeGain = 0.0;
double idealizedGain = 0.0;
for (int i = 0; i < numRecommendedItems; i++)
{
IRecommendedItem item = recommendedItems[i];
double discount = 1.0 / log2(i + 2.0); // Classical formulation says log(i+1), but i is 0-based here
if (relevantItemIDs.Contains(item.GetItemID()))
{
cumulativeGain += discount;
}
// otherwise we're multiplying discount by relevance 0 so it doesn't do anything
// Ideally results would be ordered with all relevant ones first, so this theoretical
// ideal list starts with number of relevant items equal to the total number of relevant items
if (i < numRelevantItems)
{
idealizedGain += discount;
}
}
if (idealizedGain > 0.0)
{
nDCG.AddDatum(cumulativeGain / idealizedGain);
}
// Reach
numUsersRecommendedFor++;
if (numRecommendedItems > 0)
{
numUsersWithRecommendations++;
}
stopWatch.Stop();
log.Info("Evaluated with user {} in {}ms", userID, stopWatch.ElapsedMilliseconds);
log.Info("Precision/recall/fall-out/nDCG/reach: {} / {} / {} / {} / {}",
precision.GetAverage(), recall.GetAverage(), fallOut.GetAverage(), nDCG.GetAverage(),
(double)numUsersWithRecommendations / (double)numUsersRecommendedFor);
}
return(new IRStatisticsImpl(
precision.GetAverage(),
recall.GetAverage(),
fallOut.GetAverage(),
nDCG.GetAverage(),
(double)numUsersWithRecommendations / (double)numUsersRecommendedFor));
}
public Features(ALSWRFactorizer factorizer) {
dataModel = factorizer.dataModel;
numFeatures = factorizer.numFeatures;
var random = RandomUtils.getRandom();
M = new double[dataModel.GetNumItems()][]; //numFeatures
var itemIDsIterator = dataModel.GetItemIDs();
while (itemIDsIterator.MoveNext()) {
long itemID = itemIDsIterator.Current;
int itemIDIndex = factorizer.itemIndex(itemID);
M[itemIDIndex] = new double[numFeatures];
M[itemIDIndex][0] = averateRating(itemID);
for (int feature = 1; feature < numFeatures; feature++) {
M[itemIDIndex][feature] = random.nextDouble() * 0.1;
}
}
U = new double[dataModel.GetNumUsers()][]; //numFeatures
for (int i=0; i<U.Length; i++)
U[i] = new double[numFeatures];
}
public override Factorization Factorize()
{
log.Info("starting to compute the factorization...");
Features features = new Features(this);
/// feature maps necessary for solving for implicit feedback
IDictionary <int, double[]> userY = null;
IDictionary <int, double[]> itemY = null;
if (usesImplicitFeedback)
{
userY = userFeaturesMapping(dataModel.GetUserIDs(), dataModel.GetNumUsers(), features.getU());
itemY = itemFeaturesMapping(dataModel.GetItemIDs(), dataModel.GetNumItems(), features.getM());
}
IList <Task> tasks;
for (int iteration = 0; iteration < numIterations; iteration++)
{
log.Info("iteration {0}", iteration);
/// fix M - compute U
tasks = new List <Task>();
var userIDsIterator = dataModel.GetUserIDs();
try {
ImplicitFeedbackAlternatingLeastSquaresSolver implicitFeedbackSolver = usesImplicitFeedback
? new ImplicitFeedbackAlternatingLeastSquaresSolver(numFeatures, lambda, alpha, itemY) : null;
while (userIDsIterator.MoveNext())
{
long userID = userIDsIterator.Current;
var itemIDsFromUser = dataModel.GetItemIDsFromUser(userID).GetEnumerator();
IPreferenceArray userPrefs = dataModel.GetPreferencesFromUser(userID);
tasks.Add(Task.Factory.StartNew(() => {
List <double[]> featureVectors = new List <double[]>();
while (itemIDsFromUser.MoveNext())
{
long itemID = itemIDsFromUser.Current;
featureVectors.Add(features.getItemFeatureColumn(itemIndex(itemID)));
}
var userFeatures = usesImplicitFeedback
? implicitFeedbackSolver.solve(sparseUserRatingVector(userPrefs))
: AlternatingLeastSquaresSolver.solve(featureVectors, ratingVector(userPrefs), lambda, numFeatures);
features.setFeatureColumnInU(userIndex(userID), userFeatures);
}
));
}
} finally {
// queue.shutdown();
try {
Task.WaitAll(tasks.ToArray(), 1000 * dataModel.GetNumUsers());
} catch (AggregateException e) {
log.Warn("Error when computing user features", e);
throw e;
}
}
/// fix U - compute M
//queue = createQueue();
tasks = new List <Task>();
var itemIDsIterator = dataModel.GetItemIDs();
try {
ImplicitFeedbackAlternatingLeastSquaresSolver implicitFeedbackSolver = usesImplicitFeedback
? new ImplicitFeedbackAlternatingLeastSquaresSolver(numFeatures, lambda, alpha, userY) : null;
while (itemIDsIterator.MoveNext())
{
long itemID = itemIDsIterator.Current;
IPreferenceArray itemPrefs = dataModel.GetPreferencesForItem(itemID);
tasks.Add(Task.Factory.StartNew(() => {
var featureVectors = new List <double[]>();
foreach (IPreference pref in itemPrefs)
{
long userID = pref.GetUserID();
featureVectors.Add(features.getUserFeatureColumn(userIndex(userID)));
}
var itemFeatures = usesImplicitFeedback
? implicitFeedbackSolver.solve(sparseItemRatingVector(itemPrefs))
: AlternatingLeastSquaresSolver.solve(featureVectors, ratingVector(itemPrefs), lambda, numFeatures);
features.setFeatureColumnInM(itemIndex(itemID), itemFeatures);
}));
}
} finally {
try {
Task.WaitAll(tasks.ToArray(), 1000 * dataModel.GetNumItems());
//queue.awaitTermination(dataModel.getNumItems(), TimeUnit.SECONDS);
} catch (AggregateException e) {
log.Warn("Error when computing item features", e);
throw e;
}
}
}
log.Info("finished computation of the factorization...");
return(createFactorization(features.getU(), features.getM()));
}
public IRStatistics Evaluate(IRecommenderBuilder recommenderBuilder,
IDataModelBuilder dataModelBuilder,
IDataModel dataModel,
IDRescorer rescorer,
int at,
double relevanceThreshold,
double evaluationPercentage) {
//Preconditions.checkArgument(recommenderBuilder != null, "recommenderBuilder is null");
//Preconditions.checkArgument(dataModel != null, "dataModel is null");
//Preconditions.checkArgument(at >= 1, "at must be at least 1");
//Preconditions.checkArgument(evaluationPercentage > 0.0 && evaluationPercentage <= 1.0,
// "Invalid evaluationPercentage: " + evaluationPercentage + ". Must be: 0.0 < evaluationPercentage <= 1.0");
int numItems = dataModel.GetNumItems();
IRunningAverage precision = new FullRunningAverage();
IRunningAverage recall = new FullRunningAverage();
IRunningAverage fallOut = new FullRunningAverage();
IRunningAverage nDCG = new FullRunningAverage();
int numUsersRecommendedFor = 0;
int numUsersWithRecommendations = 0;
var it = dataModel.GetUserIDs();
while (it.MoveNext()) {
long userID = it.Current;
if (random.nextDouble() >= evaluationPercentage) {
// Skipped
continue;
}
var stopWatch = new System.Diagnostics.Stopwatch();
stopWatch.Start();
IPreferenceArray prefs = dataModel.GetPreferencesFromUser(userID);
// List some most-preferred items that would count as (most) "relevant" results
double theRelevanceThreshold = Double.IsNaN(relevanceThreshold) ? computeThreshold(prefs) : relevanceThreshold;
FastIDSet relevantItemIDs = dataSplitter.GetRelevantItemsIDs(userID, at, theRelevanceThreshold, dataModel);
int numRelevantItems = relevantItemIDs.Count();
if (numRelevantItems <= 0) {
continue;
}
FastByIDMap<IPreferenceArray> trainingUsers = new FastByIDMap<IPreferenceArray>(dataModel.GetNumUsers());
var it2 = dataModel.GetUserIDs();
while (it2.MoveNext()) {
dataSplitter.ProcessOtherUser(userID, relevantItemIDs, trainingUsers, it2.Current, dataModel);
}
IDataModel trainingModel = dataModelBuilder == null ? new GenericDataModel(trainingUsers)
: dataModelBuilder.BuildDataModel(trainingUsers);
try {
trainingModel.GetPreferencesFromUser(userID);
} catch (NoSuchUserException nsee) {
continue; // Oops we excluded all prefs for the user -- just move on
}
int size = numRelevantItems + trainingModel.GetItemIDsFromUser(userID).Count();
if (size < 2 * at) {
// Really not enough prefs to meaningfully evaluate this user
continue;
}
IRecommender recommender = recommenderBuilder.BuildRecommender(trainingModel);
int intersectionSize = 0;
var recommendedItems = recommender.Recommend(userID, at, rescorer);
foreach (IRecommendedItem recommendedItem in recommendedItems) {
if (relevantItemIDs.Contains(recommendedItem.GetItemID())) {
intersectionSize++;
}
}
int numRecommendedItems = recommendedItems.Count;
// Precision
if (numRecommendedItems > 0) {
precision.AddDatum((double) intersectionSize / (double) numRecommendedItems);
}
// Recall
recall.AddDatum((double) intersectionSize / (double) numRelevantItems);
// Fall-out
if (numRelevantItems < size) {
fallOut.AddDatum((double) (numRecommendedItems - intersectionSize)
/ (double) (numItems - numRelevantItems));
}
// nDCG
// In computing, assume relevant IDs have relevance 1 and others 0
double cumulativeGain = 0.0;
double idealizedGain = 0.0;
for (int i = 0; i < numRecommendedItems; i++) {
IRecommendedItem item = recommendedItems[i];
double discount = 1.0 / log2(i + 2.0); // Classical formulation says log(i+1), but i is 0-based here
if (relevantItemIDs.Contains(item.GetItemID())) {
cumulativeGain += discount;
}
// otherwise we're multiplying discount by relevance 0 so it doesn't do anything
// Ideally results would be ordered with all relevant ones first, so this theoretical
// ideal list starts with number of relevant items equal to the total number of relevant items
if (i < numRelevantItems) {
idealizedGain += discount;
}
}
if (idealizedGain > 0.0) {
nDCG.AddDatum(cumulativeGain / idealizedGain);
}
// Reach
numUsersRecommendedFor++;
if (numRecommendedItems > 0) {
numUsersWithRecommendations++;
}
stopWatch.Stop();
log.Info("Evaluated with user {} in {}ms", userID, stopWatch.ElapsedMilliseconds);
log.Info("Precision/recall/fall-out/nDCG/reach: {} / {} / {} / {} / {}",
precision.GetAverage(), recall.GetAverage(), fallOut.GetAverage(), nDCG.GetAverage(),
(double) numUsersWithRecommendations / (double) numUsersRecommendedFor);
}
return new IRStatisticsImpl(
precision.GetAverage(),
recall.GetAverage(),
fallOut.GetAverage(),
nDCG.GetAverage(),
(double) numUsersWithRecommendations / (double) numUsersRecommendedFor);
}
public override int GetNumItems()
{
return(_delegate.GetNumItems());
}