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 string RunNMFbasedORF(double regularization, double learnRate,
int maxEpoch, List <double> quantizer, int topN = 0)
{
// Load OMFDistribution from file
Dictionary <Tuple <int, int>, List <double> > OMFDistributionByUserItem;
if (File.Exists(GetDataFileName("RatingOMF_")))
{
OMFDistributionByUserItem = Utils.IO <Dictionary <Tuple <int, int>, List <double> > > .LoadObject(GetDataFileName("RatingOMF_"));
}
else
{
return("Abort, Run OMF first.");
}
if (!ReadyForNumerical)
{
GetReadyForNumerical();
}
StringBuilder log = new StringBuilder();
log.AppendLine(Utils.PrintHeading("NMF based ORF"));
// Prediction
Utils.StartTimer();
DataMatrix R_predicted_expectations;
DataMatrix R_predicted_mostlikely;
ORF orf = new ORF();
orf.PredictRatings(R_train, R_unknown, StrongSimilarityIndicatorsByItemRating,
OMFDistributionByUserItem, regularization, learnRate, maxEpoch,
quantizer.Count, out R_predicted_expectations, out R_predicted_mostlikely);
log.AppendLine(Utils.StopTimer());
// Numerical Evaluation
log.AppendLine(Utils.PrintValue("RMSE", RMSE.Evaluate(R_test, R_predicted_expectations).ToString("0.0000")));
log.AppendLine(Utils.PrintValue("MAE", RMSE.Evaluate(R_test, R_predicted_mostlikely).ToString("0.0000")));
// Top-N Evaluation
if (topN != 0)
{
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());
}
