public override double GetEvaluation(IDictionary<User, ICollection<Preference>> testUserPrefs,
Recommender recommender)
{
RunningAverage average = new FullRunningAverage();
foreach (KeyValuePair<User, ICollection<Preference>> entry in testUserPrefs)
{
foreach (Preference realPref in entry.Value)
{
User testUser = entry.Key;
try
{
double estimatedPreference =
recommender.EstimatePreference(testUser.ID, realPref.Item.ID);
if (!double.IsNaN(estimatedPreference))
{
double diff = realPref.Value - estimatedPreference;
average.AddDatum(diff * diff);
}
}
catch (NoSuchElementException nsee)
{
// It's possible that an item exists in the test data but not training data in which case
// NSEE will be thrown. Just ignore it and move on.
log.Info("Element exists in test data but not training data: " + testUser.ID, nsee);
}
}
}
return Math.Sqrt(average.Average);
}
public IRStatistics Evaluate(RecommenderBuilder recommenderBuilder,
DataModel dataModel,
int at,
double relevanceThreshold,
double evaluationPercentage)
{
if (recommenderBuilder == null) {
throw new ArgumentNullException("recommenderBuilder is null");
}
if (dataModel == null) {
throw new ArgumentNullException("dataModel is null");
}
if (at < 1) {
throw new ArgumentException("at must be at least 1");
}
if (double.IsNaN(evaluationPercentage) || evaluationPercentage <= 0.0 || evaluationPercentage > 1.0)
{
throw new ArgumentException("Invalid evaluationPercentage: " + evaluationPercentage);
}
if (double.IsNaN(relevanceThreshold)) {
throw new ArgumentException("Invalid relevanceThreshold: " + evaluationPercentage);
}
RunningAverage precision = new FullRunningAverage();
RunningAverage recall = new FullRunningAverage();
foreach (User user in dataModel.GetUsers())
{
Object id = user.ID;
if (random.NextDouble() < evaluationPercentage)
{
ICollection<Item> relevantItems = new HashedSet<Item>(/* at */);
Preference[] prefs = user.GetPreferencesAsArray();
foreach (Preference pref in prefs)
{
if (pref.Value >= relevanceThreshold)
{
relevantItems.Add(pref.Item);
}
}
int numRelevantItems = relevantItems.Count;
if (numRelevantItems > 0)
{
ICollection<User> trainingUsers = new List<User>(dataModel.GetNumUsers());
foreach (User user2 in dataModel.GetUsers())
{
if (id.Equals(user2.ID))
{
ICollection<Preference> trainingPrefs = new List<Preference>();
prefs = user2.GetPreferencesAsArray();
foreach (Preference pref in prefs)
{
if (!relevantItems.Contains(pref.Item))
{
trainingPrefs.Add(pref);
}
}
if (trainingPrefs.Count > 0)
{
User trainingUser = new GenericUser<String>(id.ToString(), trainingPrefs);
trainingUsers.Add(trainingUser);
}
}
else
{
trainingUsers.Add(user2);
}
}
DataModel trainingModel = new GenericDataModel(trainingUsers);
Recommender recommender = recommenderBuilder.BuildRecommender(trainingModel);
try
{
trainingModel.GetUser(id);
}
catch (NoSuchElementException)
{
continue; // Oops we excluded all prefs for the user -- just move on
}
int intersectionSize = 0;
foreach (RecommendedItem recommendedItem in recommender.Recommend(id, at))
{
if (relevantItems.Contains(recommendedItem.Item))
{
intersectionSize++;
}
}
precision.AddDatum((double) intersectionSize / (double) at);
recall.AddDatum((double) intersectionSize / (double) numRelevantItems);
}
}
}
return new IRStatisticsImpl(precision.Average, recall.Average);
}
