private void init(string pid1, string pid2)
{
SetDistance();
PointFact pf1 = _Facts[pid1] as PointFact;
PointFact pf2 = _Facts[pid2] as PointFact;
string p1URI = pf1.Id;
string p2URI = pf2.Id;
float v = (pf1.Point - pf2.Point).magnitude;
MMTTerm lhs =
new OMA(
new OMS(MMTURIs.Metric),
new List <MMTTerm> {
new OMS(p1URI),
new OMS(p2URI)
}
);
MMTTerm valueTp = new OMS(MMTURIs.RealLit);
MMTTerm value = new OMF(v);
//see point label
MMTValueDeclaration mmtDecl = new MMTValueDeclaration(this.Label, lhs, valueTp, value);
AddFactResponse.sendAdd(mmtDecl, out this._URI);
}
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());
}
public unsafe void AddSection(ushort iSection, OMF flags, int offset, int cb)
{
var inst = ISymNGenWriter2Inst;
var func = (delegate * unmanaged <IntPtr, ushort, OMF, int, int, int>)(*(*(void ***)inst + 4));
int hr = func(inst, iSection, flags, offset, cb);
if (hr != 0)
{
Marshal.ThrowExceptionForHR(hr);
}
}
public override bool Equals(object obj)
{
if (!(obj is OMF))
{
return(false);
}
OMF omf = (OMF)obj;
return(kind.Equals(omf.kind) &&
f.Equals(omf.f));
}
private MMTDeclaration generateNot90DegreeAngleDeclaration(float val, string p1URI, string p2URI, string p3URI)
{
MMTTerm lhs =
new OMA(
new OMS(MMTURIs.Angle),
new List <MMTTerm> {
new OMS(p1URI),
new OMS(p2URI),
new OMS(p3URI)
}
);
MMTTerm valueTp = new OMS(MMTURIs.RealLit);
MMTTerm value = new OMF(val);
return(new MMTValueDeclaration(this.Label, lhs, valueTp, value));
}
void ISymNGenWriter.AddSection(ushort iSection, OMF flags, int offset, int cb) => AddSection(iSection, flags, offset, cb);
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());
}
