public string RunPrefMRF(double regularization, double learnRate, int maxEpoch, List <double> quantizer,
int topN = 10)
{
// Load OMFDistribution from file
Dictionary <Tuple <int, int>, List <double> > OMFDistributionByUserItem;
if (File.Exists(GetDataFileName("PrefOMF_")))
{
OMFDistributionByUserItem = Utils.IO <Dictionary <Tuple <int, int>, List <double> > > .LoadObject(GetDataFileName("PrefOMF_"));
}
else
{
return("Abort, Run OMF first.");
}
if (!ReadyForOrdinal)
{
GetReadyForOrdinal();
}
StringBuilder log = new StringBuilder();
log.AppendLine(Utils.PrintHeading("PrefMRF: PrefNMF based ORF"));
// Prediction
Utils.StartTimer();
DataMatrix R_predicted_expectations;
DataMatrix R_predicted_mostlikely;
// Convert PR_train into user-wise preferences
DataMatrix R_train_positions = new DataMatrix(PR_train.GetPositionMatrix());
R_train_positions.Quantization(quantizer[0], quantizer[quantizer.Count - 1] - quantizer[0], quantizer);
ORF orf = new ORF();
orf.PredictRatings(R_train_positions, R_unknown, StrongSimilarityIndicatorsByItemPref,
OMFDistributionByUserItem, regularization, learnRate, maxEpoch,
quantizer.Count, out R_predicted_expectations, out R_predicted_mostlikely);
log.AppendLine(Utils.StopTimer());
// Evaluation
var topNItemsByUser_expectations = ItemRecommendationCore.GetTopNItemsByUser(R_predicted_expectations, topN);
for (int n = 1; n <= topN; n++)
{
log.AppendLine(Utils.PrintValue("NCDG@" + n, NCDG.Evaluate(RelevantItemsByUser,
topNItemsByUser_expectations, n).ToString("0.0000")));
}
for (int n = 1; n <= topN; n++)
{
log.AppendLine(Utils.PrintValue("MAP@" + n, MAP.Evaluate(RelevantItemsByUser, topNItemsByUser_expectations, n).ToString("0.0000")));
}
return(log.ToString());
}
public void GetPositionMatrix()
{
/*
* 5 3 0 1
* 4 0 0 1
* 1 1 0 5
* 1 0 0 4
* 0 1 5 4
*/
DataMatrix R = GetSampleRatingMatrix();
PrefRelations PR = PrefRelations.CreateDiscrete(R);
// act
SparseMatrix positionMatrix = PR.GetPositionMatrix();
// assert
// How many ratings we have then how many positions we have
Debug.Assert(positionMatrix.NonZerosCount == R.AsSparseMatrix.NonZerosCount);
// Check if each rating has a corresponding position
// we have check the count so don't need to check the oppsite
foreach (Tuple <int, int, double> element in R.AsSparseMatrix.EnumerateIndexed(Zeros.AllowSkip))
{
int indexOfUser = element.Item1;
int indexOfItem = element.Item2;
double rating = element.Item3;
Debug.Assert(positionMatrix[indexOfUser, indexOfItem] != SparseMatrix.Zero);
}
}
public string GetReadyForOrdinal(bool saveLoadedData = true)
{
if (!ReadyForNumerical)
{
GetReadyForNumerical();
}
if (ReadyForOrdinal)
{
return("Is ready.");
}
StringBuilder log = new StringBuilder();
Utils.StartTimer();
log.AppendLine(Utils.PrintHeading("Prepare preferecen relation data"));
Console.WriteLine("Converting R_train into PR_train");
log.AppendLine("Converting R_train into PR_train");
PR_train = PrefRelations.CreateDiscrete(R_train);
//Console.WriteLine("Converting R_test into PR_test");
//log.AppendLine("Converting R_test into PR_test");
//PR_test = PrefRelations.CreateDiscrete(R_test);
log.AppendLine(Utils.StopTimer());
#region Prepare similarity data
if (File.Exists(GetDataFileName("USP")) &&
File.Exists(GetDataFileName("ISP")) &&
File.Exists(GetDataFileName("SSIIP")))
{
Utils.StartTimer();
Utils.PrintHeading("Load user, item, indicators variables (Pref based)");
UserSimilaritiesOfPref = Utils.IO <SimilarityData> .LoadObject(GetDataFileName("USP"));
ItemSimilaritiesOfPref = Utils.IO <SimilarityData> .LoadObject(GetDataFileName("ISP"));
StrongSimilarityIndicatorsByItemPref = Utils.IO <HashSet <Tuple <int, int> > > .LoadObject(GetDataFileName("SSIIP"));
Utils.StopTimer();
}
else
{
Utils.StartTimer();
Utils.PrintHeading("Compute user-user similarities (Pref based)");
Metric.GetCosineOfPrefRelations(PR_train, MaxCountOfNeighbors,
StrongSimilarityThreshold, out UserSimilaritiesOfPref);
Utils.StopTimer();
// For the moment, we use user-wise preferences to compute
// item-item similarities, it is not the same as user-user pref similarities
Utils.StartTimer();
Utils.PrintHeading("Compute item-item similarities (Pref based)");
DataMatrix PR_userwise_preferences = new DataMatrix(PR_train.GetPositionMatrix());
Metric.GetPearsonOfColumns(PR_userwise_preferences, MaxCountOfNeighbors, StrongSimilarityThreshold,
out ItemSimilaritiesOfPref, out StrongSimilarityIndicatorsByItemPref);
Utils.StopTimer();
if (saveLoadedData)
{
Utils.IO <SimilarityData> .SaveObject(UserSimilaritiesOfPref, GetDataFileName("USP"));
Utils.IO <SimilarityData> .SaveObject(ItemSimilaritiesOfPref, GetDataFileName("ISP"));
Utils.IO <HashSet <Tuple <int, int> > >
.SaveObject(StrongSimilarityIndicatorsByItemPref, GetDataFileName("SSIIP"));
}
Utils.StopTimer();
}
#endregion
ReadyForOrdinal = true;
return(log.ToString());
}