public override void SetUp()
{
base.SetUp();
FastByIDMap <IPreferenceArray> userData = new FastByIDMap <IPreferenceArray>();
userData.Put(1L, new GenericUserPreferenceArray(new List <IPreference>()
{
new GenericPreference(1L, 1L, 5.0f),
new GenericPreference(1L, 2L, 5.0f),
new GenericPreference(1L, 3L, 2.0f)
}));
userData.Put(2L, new GenericUserPreferenceArray(new List <IPreference>()
{
new GenericPreference(2L, 1L, 2.0f),
new GenericPreference(2L, 3L, 3.0f),
new GenericPreference(2L, 4L, 5.0f)
}));
userData.Put(3L, new GenericUserPreferenceArray(new List <IPreference>()
{
new GenericPreference(3L, 2L, 5.0f),
new GenericPreference(3L, 4L, 3.0f)
}));
userData.Put(4L, new GenericUserPreferenceArray(new List <IPreference>()
{
new GenericPreference(4L, 1L, 3.0f),
new GenericPreference(4L, 4L, 5.0f)
}));
dataModel = new GenericDataModel(userData);
factorizer = new ALSWRFactorizer(dataModel, 3, 0.065, 10);
}
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 void ratingVector()
{
IPreferenceArray prefs = dataModel.GetPreferencesFromUser(1);
double[] ratingVector = ALSWRFactorizer.ratingVector(prefs);
Assert.AreEqual(prefs.Length(), ratingVector.Length);
Assert.AreEqual(prefs.Get(0).GetValue(), ratingVector[0], EPSILON);
Assert.AreEqual(prefs.Get(1).GetValue(), ratingVector[1], EPSILON);
Assert.AreEqual(prefs.Get(2).GetValue(), ratingVector[2], EPSILON);
}
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 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 override void SetUp()
{
base.SetUp();
FastByIDMap<IPreferenceArray> userData = new FastByIDMap<IPreferenceArray>();
userData.Put(1L, new GenericUserPreferenceArray( new List<IPreference>() {new GenericPreference(1L, 1L, 5.0f),
new GenericPreference(1L, 2L, 5.0f),
new GenericPreference(1L, 3L, 2.0f) } ));
userData.Put(2L, new GenericUserPreferenceArray( new List<IPreference>() {new GenericPreference(2L, 1L, 2.0f),
new GenericPreference(2L, 3L, 3.0f),
new GenericPreference(2L, 4L, 5.0f) } ));
userData.Put(3L, new GenericUserPreferenceArray( new List<IPreference>() {new GenericPreference(3L, 2L, 5.0f),
new GenericPreference(3L, 4L, 3.0f) } ));
userData.Put(4L, new GenericUserPreferenceArray(new List<IPreference>() {new GenericPreference(4L, 1L, 3.0f),
new GenericPreference(4L, 4L, 5.0f)}));
dataModel = new GenericDataModel(userData);
factorizer = new ALSWRFactorizer(dataModel, 3, 0.065, 10);
}
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];
}
