private void LoadUpPredictor(string modelName, int eLength, int ncLength, Converter <Hla, Hla> hlaForNormalization)
{
//Load up the predictor
string featurerizerName;
switch (modelName.ToLower())
{
//!!!would be better not to have multiple of these switch statements around - looks like a job for a Class
case "lanliedb03062007":
featurerizerName = "[email protected]";
SampleNEC = NEC.GetInstance("", new string(' ', eLength), "");
HlaFactory = HlaFactory.GetFactory("MixedWithB15AndA68");
SourceDataFileName = "lanlIedb03062007.pos.source.txt";
NameList = new string[] { "LANL", "IEDB" };
break;
default:
SpecialFunctions.CheckCondition(false, "Don't know what featurerizer to use for the model");
featurerizerName = null;
SourceDataFileName = null;
NameList = null;
break;
}
Converter <object, Set <IHashableFeature> > featurizer = FeatureLib.CreateFeaturizer(featurerizerName);
//GeneratorType generatorType = GeneratorType.ComboAndZero6SuperType;
//FeatureSerializer featureSerializer = PositiveNegativeExperimentCollection.GetFeatureSerializer();
//KmerDefinition = kmerDefinition;
//HlaResolution hlaResolution = HlaResolution.ABMixed;
string resourceName = string.Format("maxentModel{0}{1}{2}{3}.xml", modelName.Split('.')[0], SampleNEC.N.Length, SampleNEC.E.Length, SampleNEC.C.Length);
EpitopeLearningDataList = new List <EpitopeLearningDataDupHlaOK>();
using (StreamReader streamReader = Predictor.OpenResource(resourceName))
{
Logistic = (Logistic)FeatureLib.FeatureSerializer.FromXmlStreamReader(streamReader);
//Logistic.FeatureGenerator = EpitopeFeatureGenerator.GetInstance(KmerDefinition, generatorType, featureSerializer).GenerateFeatureSet;
Logistic.FeatureGenerator = FeatureLib.CreateFeaturizer(featurerizerName);
foreach (string name in NameList)
{
EpitopeLearningData epitopeLearningDataX = EpitopeLearningData.GetDbWhole(HlaFactory, SampleNEC.E.Length, name, SourceDataFileName);
Debug.Assert(epitopeLearningDataX.Count > 0, "Expect given data to have some data");
//!!!combine with previous step
EpitopeLearningDataDupHlaOK epitopeLearningData = new EpitopeLearningDataDupHlaOK(epitopeLearningDataX.Name);
foreach (KeyValuePair <Pair <string, Hla>, bool> merAndHlaAndLabel in epitopeLearningDataX)
{
Hla hlaIn = merAndHlaAndLabel.Key.Second;
Hla hlaOut = hlaForNormalization(hlaIn);
Dictionary <Hla, Dictionary <Hla, bool> > hla2ToHlaToLabel = SpecialFunctions.GetValueOrDefault(epitopeLearningData, merAndHlaAndLabel.Key.First);
Dictionary <Hla, bool> hlaToLabel = SpecialFunctions.GetValueOrDefault(hla2ToHlaToLabel, hlaOut);
hlaToLabel.Add(hlaIn, merAndHlaAndLabel.Value);
}
EpitopeLearningDataList.Add(epitopeLearningData);
}
}
HlaForNormalization = hlaForNormalization;
}
/* From [Microsoft Research]:
*
* - I’ve changed two things wrt prior corrections. First, I’m computing relative frequencies
* across length per HLA rather than per supertype (there was too much variation within
* supertype). Second, the formula that I gave you last was not quite right in that it did not
* take into account the denominator of the prior odds term. Given p_kh, the uncorrected
* probability of being an epitope according to the classifier for peptide of length k and
* HLA h, the correction is as follows:
*
* log odds := ln (p_kh/(1-p_kh))
* log odds := log odds + ln( [relFreq_kh/0.25 * (1/100)] / [1 – relFreq_kh/0.25 * (1/100)] )
* pk_corrected = exp(log odds) / (1 + exp(log odds))
*
* (Technical notes: In training, we are assuming a prior of 1/100 for each hla and k.
* In the data, the prior over hla is not uniform (e.g., there is lots of A02), but we think
* this is sampling bias. That is, we think the prior on being an epitope is roughly'
* uniform for each hla. But, the data is fairly unbiased wrt prior on epitope of length
* k reacting, given HLA. That is, biologists were looking at particular HLAs, but they
* then found the optimal length for the epitope, giving an unbiased view of which lengths
* react with which HLAs. Thus, for every HLA, we should correct the prior as a function
* of length. We used to correct by supertype, but I’m seeing too much variation within
* a given supertype. To help with smoothing, I’m using a Dirichlet(1,1,1,1) prior.
* Dividing each relFreq by 0.25 in the above formula guarantees that the overall prior is
* still 1/100.)
*
*
* From: [Microsoft Research]
* Sent: Thursday, July 27, 2006 4:25 PM
*
*
* As we discussed, I would like to write out the weight of evidence for the epitope rather
* than its posterior probability. This is logOdds minus the prior (which is implicitly 1/100
* in our training data).
*
* The formula for weight of evidence is (assuming 4 values of K, and 99 negatives per positive)
*
* priorLogOddsOfThisLengthAndHla = LogOdds((relFreq/.25) * .01);
* originalLogOdds = LogOdds(originalP);
* correctedLogOdds = originalLogOdds + priorLogOddsOfThisLengthAndHla;
* weightofEvidence = correctedLogOdds – LogOdds(0.01);
*
*/
private void CreateKToHlaToPriorLogOdds()
{
KToHlaToPriorLogOdds = new Dictionary <int, Dictionary <Hla, double> >();
_hlaSet = new Set <Hla>();
HlaFactory hlaFactory = HlaFactory.GetFactory("MixedWithB15AndA68");
_supertypeMap = new Dictionary <string, Set <Hla> >();
Dictionary <Hla, Dictionary <int, int> > hlaToLengthToLengthToSmoothedCount = CreateHlaToLengthToLengthToSmoothedCount();
foreach (Hla hla in hlaToLengthToLengthToSmoothedCount.Keys)
{
_hlaSet.AddNewOrOld(hla);
Dictionary <int, int> lengthToSmoothedCount = hlaToLengthToLengthToSmoothedCount[hla];
int smoothedTotal = ComputeSmoothedTotal(lengthToSmoothedCount);
for (int k = (int)MerLength.firstLength; k <= (int)MerLength.lastLength; ++k)
{
AddToHlaToPriorLogOdds(hla, lengthToSmoothedCount, smoothedTotal, k);
}
AddToSupertypeMap(hla);
}
AssertThatEveryKHasEveryHla();
}