public string RunNMFbasedOMF(int maxEpoch, double learnRate, double regularization, int factorCount,
List <double> quantizer, int topN = 0)
{
if (!ReadyForNumerical)
{
GetReadyForNumerical();
}
StringBuilder log = new StringBuilder();
log.AppendLine(Utils.PrintHeading("NMF based OMF"));
// NMF Prediction
// Get ratings from scorer, for both train and test
// R_all contains indexes of all ratings both train and test
DataMatrix R_all = new DataMatrix(R_unknown.UserCount, R_unknown.ItemCount);
R_all.MergeNonOverlap(R_unknown);
R_all.MergeNonOverlap(R_train.IndexesOfNonZeroElements());
Utils.StartTimer();
DataMatrix R_predictedByNMF = NMF.PredictRatings(R_train, R_all, maxEpoch,
learnRate, regularization, factorCount);
log.AppendLine(Utils.StopTimer());
// OMF Prediction
log.AppendLine(Utils.PrintHeading("Ordinal Matrix Factorization with NMF as scorer"));
Utils.StartTimer();
Dictionary <Tuple <int, int>, List <double> > OMFDistributionByUserItem;
DataMatrix R_predicted;
log.AppendLine(OMF.PredictRatings(R_train.Matrix, R_unknown.Matrix, R_predictedByNMF.Matrix,
quantizer, out R_predicted, out OMFDistributionByUserItem));
log.AppendLine(Utils.StopTimer());
// Numerical Evaluation
log.AppendLine(Utils.PrintValue("RMSE", RMSE.Evaluate(R_test, R_predicted).ToString("0.0000")));
log.AppendLine(Utils.PrintValue("MAE", MAE.Evaluate(R_test, R_predicted).ToString("0.0000")));
// TopN Evaluation
if (topN != 0)
{
var topNItemsByUser = ItemRecommendationCore.GetTopNItemsByUser(R_predicted, topN);
for (int n = 1; n <= topN; n++)
{
log.AppendLine(Utils.PrintValue("NCDG@" + n, NCDG.Evaluate(RelevantItemsByUser, topNItemsByUser, n).ToString("0.0000")));
}
for (int n = 1; n <= topN; n++)
{
log.AppendLine(Utils.PrintValue("MAP@" + n, MAP.Evaluate(RelevantItemsByUser, topNItemsByUser, n).ToString("0.0000")));
}
}
// Save OMFDistribution to file
if (!File.Exists(GetDataFileName("RatingOMF_")))
{
Utils.IO <Dictionary <Tuple <int, int>, List <double> > > .SaveObject(OMFDistributionByUserItem, GetDataFileName("RatingOMF_"));
}
return(log.ToString());
}
/// <summary>
/// Rating based Non-negative Matrix Factorization
/// </summary>
public string RunNMF(int maxEpoch, double learnRate, double regularization,
int factorCount, int topN = 0)
{
if (!ReadyForNumerical)
{
GetReadyForNumerical();
}
StringBuilder log = new StringBuilder();
log.AppendLine(Utils.PrintHeading("NMF"));
// Prediction
Utils.StartTimer();
DataMatrix R_predicted = NMF.PredictRatings(R_train, R_unknown, maxEpoch,
learnRate, regularization, factorCount);
log.AppendLine(Utils.StopTimer());
// Numerical Evaluation
log.AppendLine(Utils.PrintValue("RMSE", RMSE.Evaluate(R_test, R_predicted).ToString("0.0000")));
log.AppendLine(Utils.PrintValue("MAE", MAE.Evaluate(R_test, R_predicted).ToString("0.0000")));
// TopN Evaluation
if (topN != 0)
{
var topNItemsByUser = ItemRecommendationCore.GetTopNItemsByUser(R_predicted, topN);
for (int n = 1; n <= topN; n++)
{
log.AppendLine(Utils.PrintValue("NCDG@" + n, NCDG.Evaluate(RelevantItemsByUser, topNItemsByUser, n).ToString("0.0000")));
}
for (int n = 1; n <= topN; n++)
{
log.AppendLine(Utils.PrintValue("MAP@" + n, MAP.Evaluate(RelevantItemsByUser, topNItemsByUser, n).ToString("0.0000")));
}
}
return(log.ToString());
}