public void TestIUF()
{
List<User> users = new List<User>(5);
users.Add(GetUser("test1", 0.1));
users.Add(GetUser("test2", 0.2, 0.3));
users.Add(GetUser("test3", 0.4, 0.5, 0.6));
users.Add(GetUser("test4", 0.7, 0.8, 0.9, 1.0));
users.Add(GetUser("test5", 1.0, 1.0, 1.0, 1.0, 1.0));
GenericDataModel dummy = new GenericDataModel(users);
InverseUserFrequency iuf = new InverseUserFrequency(dummy, 10.0);
User user = dummy.GetUser("test5");
for (int i = 0; i < 5; i++)
{
Preference pref = user.GetPreferenceFor(i.ToString());
Assert.IsNotNull(pref);
Assert.AreEqual(Math.Log(5.0 / (double)(5 - i)) / Math.Log(iuf.LogBase),
iuf.GetTransformedValue(pref), EPSILON);
}
// Make sure this doesn't throw an exception
iuf.Refresh();
}
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);
}