public void estimatePreference()
{
var dataModelMock = new DynamicMock( typeof( IDataModel) );
var factorizerMock = new DynamicMock( typeof(IFactorizer) );
var factorization = new Factorization_estimatePreference_TestMock();
factorizerMock.ExpectAndReturn("Factorize", factorization);
//EasyMock.replay(dataModel, factorizer, factorization);
SVDRecommender svdRecommender = new SVDRecommender( (IDataModel)dataModelMock.MockInstance, (IFactorizer)factorizerMock.MockInstance);
float estimate = svdRecommender.EstimatePreference(1L, 5L);
Assert.AreEqual(1, estimate, EPSILON);
factorizerMock.Verify();
Assert.AreEqual(1, factorization.getItemFeaturesCallCount );
Assert.AreEqual(1, factorization.getUserFeaturesCallCount);
//EasyMock.verify(dataModel, factorizer, factorization);
}
public void recommend()
{
var dataModelMock = new DynamicMock( typeof(IDataModel) );
var preferencesFromUserMock = new DynamicMock( typeof(IPreferenceArray) );
var candidateItemsStrategyMock = new DynamicMock( typeof(ICandidateItemsStrategy) );
var factorizerMock = new DynamicMock( typeof(IFactorizer) );
var factorization = new Factorization_recommend_TestMock();
FastIDSet candidateItems = new FastIDSet();
candidateItems.Add(5L);
candidateItems.Add(3L);
factorizerMock.ExpectAndReturn("Factorize", factorization);
dataModelMock.ExpectAndReturn("GetPreferencesFromUser", preferencesFromUserMock.MockInstance, (1L));
candidateItemsStrategyMock.ExpectAndReturn("GetCandidateItems", candidateItems,
1L, preferencesFromUserMock.MockInstance, dataModelMock.MockInstance);
//EasyMock.replay(dataModel, candidateItemsStrategy, factorizer, factorization);
SVDRecommender svdRecommender = new SVDRecommender(
(IDataModel)dataModelMock.MockInstance,
(IFactorizer)factorizerMock.MockInstance,
(ICandidateItemsStrategy)candidateItemsStrategyMock.MockInstance);
IList<IRecommendedItem> recommendedItems = svdRecommender.Recommend(1L, 5);
Assert.AreEqual(2, recommendedItems.Count);
Assert.AreEqual(3L, recommendedItems[0].GetItemID());
Assert.AreEqual(2.0f, recommendedItems[0].GetValue(), EPSILON);
Assert.AreEqual(5L, recommendedItems[1].GetItemID());
Assert.AreEqual(1.0f, recommendedItems[1].GetValue(), EPSILON);
dataModelMock.Verify();
candidateItemsStrategyMock.Verify();
factorizerMock.Verify();
Assert.AreEqual(2, factorization.getItemFeaturesCallCount);
Assert.AreEqual(2, factorization.getUserFeaturesCallCount);
//EasyMock.verify(dataModel, candidateItemsStrategy, factorizer, factorization);
}
public void toyExampleImplicit()
{
var observations = new double[4,4] {
{ 5.0, 5.0, 2.0, 0 },
{ 2.0, 0, 3.0, 5.0 },
{ 0, 5.0, 0, 3.0 },
{ 3.0, 0, 0, 5.0 } };
var preferences = new double[4, 4] {
{ 1.0, 1.0, 1.0, 0 },
{ 1.0, 0, 1.0, 1.0 },
{ 0, 1.0, 0, 1.0 },
{ 1.0, 0, 0, 1.0 } };
double alpha = 20;
ALSWRFactorizer factorizer = new ALSWRFactorizer(dataModel, 3, 0.065, 5, true, alpha);
SVDRecommender svdRecommender = new SVDRecommender(dataModel, factorizer);
IRunningAverage avg = new FullRunningAverage();
for (int sliceIdx = 0; sliceIdx < preferences.GetLength(0); sliceIdx++) {
var slice = MatrixUtil.viewRow(preferences, sliceIdx);
for (var eIndex=0; eIndex<slice.Length; eIndex++) {
var e = slice[eIndex];
long userID = sliceIdx + 1;
long itemID = eIndex + 1;
if (!Double.IsNaN(e)) {
double pref = e;
double estimate = svdRecommender.EstimatePreference(userID, itemID);
double confidence = 1 + alpha * observations[sliceIdx, eIndex];
double err = confidence * (pref - estimate) * (pref - estimate);
avg.AddDatum(err);
Console.WriteLine("Comparing preference of user [{0}] towards item [{1}], was [{2}] with confidence [{3}] "
+ "estimate is [{4}]", sliceIdx, eIndex, pref, confidence, estimate);
}
}
}
double rmse = Math.Sqrt(avg.GetAverage());
Console.WriteLine("RMSE: {0}", rmse);
Assert.True(rmse < 0.4);
}
public void toyExample()
{
SVDRecommender svdRecommender = new SVDRecommender(dataModel, factorizer);
/// a hold out test would be better, but this is just a toy example so we only check that the
/// factorization is close to the original matrix
IRunningAverage avg = new FullRunningAverage();
var userIDs = dataModel.GetUserIDs();
while (userIDs.MoveNext()) {
long userID = userIDs.Current;
foreach (IPreference pref in dataModel.GetPreferencesFromUser(userID)) {
double rating = pref.GetValue();
double estimate = svdRecommender.EstimatePreference(userID, pref.GetItemID());
double err = rating - estimate;
avg.AddDatum(err * err);
}
}
double rmse = Math.Sqrt(avg.GetAverage());
Assert.True(rmse < 0.2);
}
public void testRecommenderWithSyntheticData()
{
setUpSyntheticData();
factorizer= new ParallelSGDFactorizer(dataModel, rank, lambda, numIterations, 0.01, 1, 0, 0);
svdRecommender = new SVDRecommender(dataModel, factorizer);
/// a hold out test would be better, but this is just a toy example so we only check that the
/// factorization is close to the original matrix
IRunningAverage avg = new FullRunningAverage();
var userIDs = dataModel.GetUserIDs();
while (userIDs.MoveNext()) {
long userID = userIDs.Current;
foreach (IPreference pref in dataModel.GetPreferencesFromUser(userID)) {
double rating = pref.GetValue();
double estimate = svdRecommender.EstimatePreference(userID, pref.GetItemID());
double err = rating - estimate;
avg.AddDatum(err * err);
}
}
double rmse = Math.Sqrt(avg.GetAverage());
logger.Info("rmse: " + rmse);
Assert.True(rmse < 0.2);
}
