private BestSoFar <double, double> OneDOptimizationInternal(Converter <double, double> oneDRealFunction,
double logOddsLow, double logOddsHigh, double logOddsPrecision)
{
int gridLineCount = 10;
double logOddsRangeIncrement = (logOddsHigh - logOddsLow) / (double)gridLineCount;
BestSoFar <double, double> bestParameterSoFar = BestSoFar <double, double> .GetInstance(SpecialFunctions.DoubleGreaterThan);
Debug.WriteLine(SpecialFunctions.CreateTabString("parameter", "parameterLogOdds", "score"));
for (int gridLine = 0; gridLine <= gridLineCount; ++gridLine)
{
double parameterLogOdds = logOddsLow + gridLine * logOddsRangeIncrement;
double parameter = SpecialFunctions.Probability(parameterLogOdds);
double score = oneDRealFunction(parameter);
Debug.WriteLine(SpecialFunctions.CreateTabString(parameter, parameterLogOdds, score));
bestParameterSoFar.Compare(score, parameterLogOdds);
}
Debug.WriteLine("");
if (logOddsHigh - logOddsLow < logOddsPrecision)
{
return(bestParameterSoFar);
}
return(OneDOptimizationInternal(oneDRealFunction, bestParameterSoFar.Champ - logOddsRangeIncrement, bestParameterSoFar.Champ + logOddsRangeIncrement, logOddsPrecision));
}
//private BestSoFar<double, double> OneDOptimizationInternal(oneDRealFunctionDelegate oneDRealFunction,
// double start, double increment, int gridStart, int gridLines)
//{
// BestSoFar<double, double> bestParameterSoFar = BestSoFar<double, double>.GetInstance(delegate(double champScore, double challengerScore) { return challengerScore > champScore; });
// for(int gridLine = gridStart; gridLine < gridLines; ++gridLine)
// {
// double parameter = start + gridLine * increment;
// double score = oneDRealFunction(parameter);
// bestParameterSoFar.Compare(score, parameter);
// }
// if (Math.Abs(bestParameterSoFar.Champ - increment) < increment * .1)
// {
// return OneDOptimizationInternal(oneDRealFunction, 0.0, increment * .1, 1, 9);
// }
// else if (Math.Abs((1.0 - bestParameterSoFar.Champ) - increment) < increment * .1)
// {
// return OneDOptimizationInternal(oneDRealFunction, bestParameterSoFar.Champ, increment * .1, 1, 9);
// }
// else
// {
// return bestParameterSoFar;
// }
//}
static public BestSoFar <double, double> OneDOptimization(Converter <double, double> oneDRealFunction,
double low, double high, double precision, int gridLineCount)
{
double rangeIncrement = (high - low) / (double)gridLineCount;
BestSoFar <double, double> bestParameterSoFar = BestSoFar <double, double> .GetInstance(SpecialFunctions.DoubleGreaterThan);
//Debug.WriteLine(SpecialFunctions.CreateTabString("parameter", "score"));
//Debug.WriteLine(SpecialFunctions.CreateTabString(low, high, rangeIncrement));
for (int gridLine = 0; gridLine <= gridLineCount; ++gridLine)
{
double parameter = low + gridLine * rangeIncrement;
//double parameter = SpecialFunctions.Probability(parameterLogOdds);
double score = oneDRealFunction(parameter);
//Debug.WriteLine(SpecialFunctions.CreateTabString(parameter, score));
bestParameterSoFar.Compare(score, parameter);
}
//Debug.WriteLine("");
if (high - low < precision)
{
return(bestParameterSoFar);
}
return(OneDOptimization(oneDRealFunction, bestParameterSoFar.Champ - rangeIncrement, bestParameterSoFar.Champ + rangeIncrement,
precision, gridLineCount));
}
private void ReportPerHla(OptimizationParameterList qmrrParams, Dictionary <string, BestSoFar <double, TrueCollection> > peptideToBestHlaAssignmentSoFar,
string directory, string name)
{
string fileName = string.Format(@"{0}\NoisyOr.PeptideHlaProbability.{1}.new.txt", directory, name);
using (StreamWriter output = File.CreateText(fileName))
{
output.WriteLine(SpecialFunctions.CreateTabString("Peptide", "HLA", "LogOdds", "Probability"));
foreach (QmrrPartialModel qmrrPartialModel in QmrrPartialModelCollection)
{
QmrrModelMissingAssignment aQmrrModelMissingAssignment = QmrrModelMissingAssignment.GetInstance(ModelLikelihoodFactories, qmrrPartialModel, qmrrParams);
BestSoFar <double, TrueCollection> bestHlaAssignment = peptideToBestHlaAssignmentSoFar[qmrrPartialModel.Peptide];
Set <Hla> trueCollectionFullAsSet = new Set <Hla>(bestHlaAssignment.Champ);
double loglikelihoodFull = bestHlaAssignment.ChampsScore;
foreach (Hla hla in trueCollectionFullAsSet)
{
bool known = qmrrPartialModel.KnownHlaSet.Contains(hla);
if (!known)
{
Set <Hla> allLessOne = trueCollectionFullAsSet.SubtractElement(hla);
TrueCollection trueCollectionWithout = TrueCollection.GetInstance(allLessOne);
double loglikelihoodWithout = aQmrrModelMissingAssignment.LogLikelihoodOfCompleteModelConditionedOnKnownHlas(trueCollectionWithout);
double logOdds = loglikelihoodFull - loglikelihoodWithout;
double probabilityFull = Math.Exp(loglikelihoodFull);
double probabilityWithout = Math.Exp(loglikelihoodWithout);
double probability = probabilityFull / (probabilityFull + probabilityWithout);
Debug.Assert(logOdds >= 0); // real assert
output.WriteLine(SpecialFunctions.CreateTabString(qmrrPartialModel.Peptide, hla, logOdds, probability));
}
}
}
}
}
internal void CreateNoSwitchablesHlaAssignment()
{
BestHlaAssignmentSoFar = BestSoFar <double, TrueCollection> .GetInstance(SpecialFunctions.DoubleGreaterThan);
TrueCollection trueCollection = TrueCollection.GetInstance(QmrrModelMissingAssignment.KnownHlaSet);
double scoreOnNoSwitchables = QmrrModelMissingAssignment.LogLikelihoodOfCompleteModelConditionedOnKnownHlas(trueCollection);
BestHlaAssignmentSoFar.Compare(scoreOnNoSwitchables, trueCollection);
}
internal override Dictionary <Hla, PValueDetails> CreateHlaToPValueDetails(int nullIndex, string peptide, Dictionary <string, Set <Hla> > pidToHlaSetAll, Set <Hla> setOfHlasToConsiderAdding, TextWriter writer)
{
Dictionary <Hla, PValueDetails> hlaToPValueDetails = new Dictionary <Hla, PValueDetails>();
if (setOfHlasToConsiderAdding.Count == 0)
{
return(hlaToPValueDetails);
}
Dictionary <string, double> pidToReactValue = ReactTableUnfiltered[peptide];
Set <Hla> knownHlaSet = KnownTable(peptide);
SpecialFunctions.CheckCondition(setOfHlasToConsiderAdding.Intersection(knownHlaSet).Count == 0);
Set <Hla> bestHlaSetSoFar = Set <Hla> .GetInstance();
while (setOfHlasToConsiderAdding.Count > 0)
{
BestSoFar <PValueDetails, Hla> bestHlaToAddSoFar = BestSoFar <PValueDetails, Hla> .GetInstance(delegate(PValueDetails pValueDetails1, PValueDetails pValueDetails2) { return(pValueDetails1.Diff.CompareTo(pValueDetails2.Diff)); });
foreach (Hla hla in setOfHlasToConsiderAdding) //!!!only look at hla's of patients with reactivity to this peptide (how effects nulls?)
{
PValueDetails pValueDetails = CreateAPValueDetail(nullIndex, peptide, pidToHlaSetAll, knownHlaSet, bestHlaSetSoFar, hla);
bestHlaToAddSoFar.Compare(pValueDetails, hla);
}
//Debug.WriteLine("");
PValueDetails bestPValueDetails = bestHlaToAddSoFar.ChampsScore; //!!!weird that PValue details is the score and the object
if (bestPValueDetails.PValue() > PValueCutOff)
{
break;
}
Hla hlaToAdd = bestHlaToAddSoFar.Champ;
setOfHlasToConsiderAdding.Remove(hlaToAdd);
bestHlaSetSoFar = bestHlaSetSoFar.Union(hlaToAdd);
hlaToPValueDetails.Add(hlaToAdd, bestPValueDetails);
writer.WriteLine(bestPValueDetails);
//Debug.WriteLine(bestPValueDetails);
writer.Flush();
}
return(hlaToPValueDetails);
}
private void ReportPerHlaAssignment(Dictionary <string, BestSoFar <double, TrueCollection> > peptideToBestHlaAssignmentSoFar,
string directory, string name)
{
string fileName = string.Format(@"{0}\NoisyOr.HlasPerPeptide.{1}.new.txt", directory, name);
using (StreamWriter output = File.CreateText(fileName))
{
output.WriteLine(SpecialFunctions.CreateTabString("Peptide", "HLAAssignment", "LogLikelihood"));
foreach (QmrrPartialModel qmrrPartialModel in QmrrPartialModelCollection)
{
BestSoFar <double, TrueCollection> bestHlaAssignment = peptideToBestHlaAssignmentSoFar[qmrrPartialModel.Peptide];
TrueCollection trueCollectionFull = bestHlaAssignment.Champ;
double loglikelihoodFull = bestHlaAssignment.ChampsScore;
output.WriteLine(SpecialFunctions.CreateTabString(qmrrPartialModel.Peptide, trueCollectionFull, loglikelihoodFull));
}
}
}
static public double Optimize(FunctionToOptimizeDelegate functionToOptimize,
ref List <double> point, List <double> low, List <double> high,
OptimizationParameterList qmrrParamsStart, int numberOfIterationsOverParameters, double precision, int gridLineCount)
{
double eps = 1e-9;
//Debug.WriteLine(qmrrParamsStart.ToStringHeader());
double oldScore = double.NaN;
//Debug.WriteLine(SpecialFunctions.CreateTabString("iterationOverParameters", "iParameter", "Probability", "LogOdds", "Score"));
for (int iterationOverParameters = 0; iterationOverParameters < numberOfIterationsOverParameters; ++iterationOverParameters)
{
double newScore = double.NaN;
int doSearchParameterCount = 0;
for (int iParameter = 0; iParameter < point.Count; ++iParameter)
{
if (qmrrParamsStart.DoSearch(iParameter))
{
++doSearchParameterCount;
List <double> pointClone = new List <double>(point);
BestSoFar <double, double> bestParamSoFar =
OneDOptimization(
delegate(double parameter) { pointClone[iParameter] = parameter; return(functionToOptimize(pointClone)); },
low[iParameter], high[iParameter], precision, gridLineCount);
point[iParameter] = bestParamSoFar.Champ;
//Debug.WriteLine(SpecialFunctions.CreateTabString(iterationOverParameters, iParameter, SpecialFunctions.Probability(bestParamSoFar.Champ), bestParamSoFar.Champ, bestParamSoFar.ChampsScore));
newScore = bestParamSoFar.ChampsScore;
//Debug.WriteLine("END ITER:" + SpecialFunctions.CreateTabString2(point) + SpecialFunctions.CreateTabString("", newScore));
}
}
if ((!double.IsNaN(oldScore) && Math.Abs(oldScore - newScore) < eps) ||
doSearchParameterCount < 2) //If only 0 or 1 searchable params, then one pass is enough
{
oldScore = newScore;
break;
}
oldScore = newScore;
}
return(oldScore);
}
protected OptimizationParameterList FindBestParams(string peptide,
Set <Hla> candidateHlaSet,
Set <Hla> hlaWithLinkZero,
Dictionary <string, Set <Hla> > patientList,
//Dictionary<string, Dictionary<string,double>> reactTable,
out double score)
{
//SpecialFunctions.CheckCondition(false, "Regression test this to be sure that switch to new optimization method didn't change anything important - cmk 5/1/2006");
Set <Hla> knownHlaSet = KnownTable(peptide);
TrueCollection trueCollection = TrueCollection.GetInstance(candidateHlaSet, knownHlaSet);
OptimizationParameterList qmrrParamsThisPeptide = CreateQmrrParamsStartForTheseCandidateHlas(trueCollection.CreateHlaAssignmentAsSet(), hlaWithLinkZero);
//OptimizationParameterList qmrrParamsThisPeptide = CreateQmrrParamsX(causePrior, leakProbability, trueCollection.CreateHlaAssignmentAsSet());
ModelLikelihoodFactories modelLikelihoodFactories = ModelLikelihoodFactories.GetInstanceLinkPerHla(qmrrParamsThisPeptide, trueCollection.CreateHlaAssignmentAsSet());
QmrrPartialModelCollection singletonQmrrPartialModelCollection =
QmrrPartialModelCollection.GetInstance(
peptide,
modelLikelihoodFactories,
qmrrParamsThisPeptide,
patientList,
ReactTableUnfiltered,
_knownTable,
HlaFactory.Name //!!!would it be better to pass the actual factory???
);
BestSoFar <double, TrueCollection> bestSoFar = BestSoFar <double, TrueCollection> .GetInstance(SpecialFunctions.DoubleGreaterThan);
bestSoFar.Compare(double.NegativeInfinity, trueCollection);
Dictionary <string, BestSoFar <double, TrueCollection> > peptideToBestHlaAssignmentSoFar = new Dictionary <string, BestSoFar <double, TrueCollection> >();
peptideToBestHlaAssignmentSoFar.Add(peptide, bestSoFar);
QmrrlModelMissingParametersCollection aQmrrlModelMissingParametersCollection
= QmrrlModelMissingParametersCollection.GetInstance(modelLikelihoodFactories, singletonQmrrPartialModelCollection, peptideToBestHlaAssignmentSoFar);
OptimizationParameterList qmrrParamsEnd = aQmrrlModelMissingParametersCollection.FindBestParams(qmrrParamsThisPeptide, out score);
return(qmrrParamsEnd);
}
override public double Run(Converter <OptimizationParameter, double> oneDRealFunction,
OptimizationParameter param)
{
double low = param.LowForSearch;
double high = param.HighForSearch;
Converter <double, double> oneDRealFunctionInDoubleSpace = delegate(double d)
{
param.ValueForSearch = d;
return(oneDRealFunction(param));
};
double initParamValue = param.ValueForSearch;
BestSoFar <double, double> bestFound = OneDOptimizationX(oneDRealFunctionInDoubleSpace, low, high, GridLineCount, Tolerance);
bestFound.Compare(oneDRealFunctionInDoubleSpace(initParamValue), initParamValue); // make sure we didn't get any worse
//bestInput = bestFound.Champ;
param.ValueForSearch = bestFound.Champ;
return(bestFound.ChampsScore);
}
internal static QmrrlModelMissingParameters GetInstance(ModelLikelihoodFactories modelLikelihoodFactories, QmrrPartialModel qmrrPartialModel, BestSoFar <double, TrueCollection> bestSoFar)
{
QmrrlModelMissingParameters aQmrrlModelMissingParameters = new QmrrlModelMissingParameters();
aQmrrlModelMissingParameters.QmrrPartialModel = qmrrPartialModel;
aQmrrlModelMissingParameters.TrueCollection = bestSoFar.Champ;
aQmrrlModelMissingParameters.ModelLikelihoodFactories = modelLikelihoodFactories;
aQmrrlModelMissingParameters.LogLikelihoodOfCompleteModelConditionedOnKnownHlas = modelLikelihoodFactories.MissingParametersDelegateFactory(qmrrPartialModel, bestSoFar.Champ);
return(aQmrrlModelMissingParameters);
}
private void CreateBestParamsSoFar()
{
BestParamsSoFar = BestSoFar <double, OptimizationParameterList> .GetInstance(SpecialFunctions.DoubleGreaterThan);
BestParamsSoFar.Compare(double.NegativeInfinity, QmrrParamsStart);
}
