public string RunPrefNMFbasedOMF(int maxEpoch, double learnRate, double regularizationOfUser,
double regularizationOfItem, int factorCount, List<double> quantizer, int topN)
{
if (!ReadyForOrdinal) { GetReadyForOrdinal(); }
StringBuilder log = new StringBuilder();
log.AppendLine(Utils.PrintHeading("PrefNMF based OMF"));
// =============PrefNMF prediction on Train+Unknown============
// 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());
//PrefRelations PR_unknown = PrefRelations.CreateDiscrete(R_all);
// R_all is far too slow, change the data structure
//Dictionary<int, List<Tuple<int, int>>> PR_unknown = new Dictionary<int, List<Tuple<int, int>>>();
//Dictionary<int, List<int>> PR_unknown_cache = new Dictionary<int, List<int>>();
Dictionary<int, List<int>> ItemsByUser_train = R_train.GetItemsByUser();
Dictionary<int, List<int>> ItemsByUser_unknown = R_unknown.GetItemsByUser();
Dictionary<int, List<int>> PR_unknown = new Dictionary<int, List<int>>(ItemsByUser_train);
List<int> keys = new List<int>(ItemsByUser_train.Keys);
foreach(var key in keys)
{
PR_unknown[key].AddRange(ItemsByUser_unknown[key]);
}
/*
foreach (var row in R_unknown.Matrix.EnumerateRowsIndexed())
{
int indexOfUser = row.Item1;
PR_unknown_cache[indexOfUser] = new List<int>();
Vector<double> itemsOfUser = row.Item2;
foreach (var item in itemsOfUser.EnumerateIndexed(Zeros.AllowSkip))
{
PR_unknown_cache[indexOfUser].Add(item.Item1);
}
}
foreach (var row in R_train.Matrix.EnumerateRowsIndexed())
{
int indexOfUser = row.Item1;
Vector<double> itemsOfUser = row.Item2;
foreach (var item in itemsOfUser.EnumerateIndexed(Zeros.AllowSkip))
{
PR_unknown_cache[indexOfUser].Add(item.Item1);
}
}
*/
Utils.StartTimer();
SparseMatrix PR_predicted = PrefNMF.PredictPrefRelations(PR_train, PR_unknown,
maxEpoch, learnRate, regularizationOfUser, regularizationOfItem, factorCount, quantizer);
// Both predicted and train need to be quantized
// otherwise OMF won't accept
//PR_predicted.quantization(0, 1.0,
// new List<double> { Config.Preferences.LessPreferred,
// Config.Preferences.EquallyPreferred, Config.Preferences.Preferred });
DataMatrix R_predictedByPrefNMF = new DataMatrix(PR_predicted);// new DataMatrix(PR_predicted.GetPositionMatrix());
// PR_train itself is already in quantized form!
//PR_train.quantization(0, 1.0, new List<double> { Config.Preferences.LessPreferred, Config.Preferences.EquallyPreferred, Config.Preferences.Preferred });
DataMatrix R_train_positions = new DataMatrix(PR_train.GetPositionMatrix());
R_train_positions.Quantization(quantizer[0], quantizer[quantizer.Count - 1] - quantizer[0], quantizer);
log.AppendLine(Utils.StopTimer());
// =============OMF prediction on Train+Unknown============
log.AppendLine(Utils.PrintHeading("Ordinal Matrix Factorization with PrefNMF as scorer"));
Utils.StartTimer();
Dictionary<Tuple<int, int>, List<double>> OMFDistributionByUserItem;
DataMatrix R_predicted;
log.AppendLine(OMF.PredictRatings(R_train_positions.Matrix, R_unknown.Matrix, R_predictedByPrefNMF.Matrix,
quantizer, out R_predicted, out OMFDistributionByUserItem));
log.AppendLine(Utils.StopTimer());
// TopN Evaluation
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("PrefOMF_")))
{
Utils.IO<Dictionary<Tuple<int, int>, List<double>>>.SaveObject(OMFDistributionByUserItem, GetDataFileName("PrefOMF_"));
}
return log.ToString();
}
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();
}
