protected override string CreateReportLine(
ModelScorer modelScorer,
PhyloTree phyloTree,
RowData rowAndTargetData,
UniversalWorkList workList,
int rowIndex, int workListCount, int workIndex)
{
//!!!there is very similar code in ModelTesterDiscrete.cs
Dictionary <string, string> row = rowAndTargetData.Row;
string predictorVariable = row[PhyloTree.PredictorVariableColumnName];
string targetVariable = row[PhyloTree.TargetVariableColumnName]; // e.g. A@182 (amino acid "A" at position 182)
int nullIndex = int.Parse(row[PhyloTree.NullIndexColumnName]);
//Dictionary<string, bool> caseIdToNonNullPredictorValue = workList.NullIndexToPredictorToCaseIdToNonMissingValue[nullIndex][predictorVariable];
Dictionary <string, SufficientStatistics> caseIdToNonNullPredictorValue = rowAndTargetData.PredictorData; //workList.GetCaseIdToNonMissingValueForNullIndexAndPredictorVariable(nullIndex, predictorVariable);
Dictionary <string, SufficientStatistics> caseIdToNonMissingTargetValue = rowAndTargetData.TargetData;
Converter <Leaf, SufficientStatistics> targetDistributionMap = CreateSufficientStatisticsMap(caseIdToNonMissingTargetValue);
Converter <Leaf, SufficientStatistics> predictorDistributionClassFunction = CreateSufficientStatisticsMap(caseIdToNonNullPredictorValue);
int[] predictorCounts = phyloTree.CountsOfLeaves(predictorDistributionClassFunction);
int predictorFalseNameCount = predictorCounts[(int)DistributionDiscreteBinary.DistributionClass.False];
int predictorTrueNameCount = predictorCounts[(int)DistributionDiscreteBinary.DistributionClass.True];
int targetNonMissingCount = phyloTree.CountOfNonMissingLeaves(caseIdToNonMissingTargetValue);
int globalNonMissingCount = phyloTree.GlobalNonMissingCount(predictorDistributionClassFunction, targetDistributionMap);
StringBuilder stringBuilder = new StringBuilder(
SpecialFunctions.CreateTabString(
this, rowIndex, workListCount, workIndex, nullIndex, predictorVariable,
predictorFalseNameCount,
predictorTrueNameCount,
predictorTrueNameCount + predictorFalseNameCount,
targetVariable,
targetNonMissingCount,
globalNonMissingCount,
""));
bool ignoreRow = false;
foreach (int count in predictorCounts)
{
if (count == 0)
{
ignoreRow = true;
}
}
if (ignoreRow)
{
CompleteRowWithNaN(stringBuilder);
}
else
{
List <double> logLikelihoodList = new List <double>();
MessageInitializer messageInitializer =
modelScorer.CreateMessageInitializer(predictorDistributionClassFunction, targetDistributionMap, NullModelDistribution);
NullModelDistribution.InitialParamVals = null;
foreach (bool useParameter in new bool[] { false, true })
{
Score score = modelScorer.ScoreModel(messageInitializer, useParameter);
stringBuilder.Append(SpecialFunctions.CreateTabString(score, ""));
Debug.Write(SpecialFunctions.CreateTabString(score, ""));
logLikelihoodList.Add(score.Loglikelihood);
AltModelDistribution.InitialParamVals = score.OptimizationParameters;
}
double diff = logLikelihoodList[1] - logLikelihoodList[0];
double pValue = SpecialFunctions.LogLikelihoodRatioTest(Math.Max(diff, 0), ChiSquareDegreesOfFreedom);
stringBuilder.Append(SpecialFunctions.CreateTabString(diff, pValue));
Debug.WriteLine(SpecialFunctions.CreateTabString(diff, pValue));
}
return(stringBuilder.ToString());
}
//protected override NullDataCollection CreateNullDataGenerator(ModelScorer modelScorer, PhyloTree phyloTree, RangeCollection nullIndexRangeCollection, Dictionary<string, Dictionary<string, BooleanStatistics>> predictorVariableToCaseIdToRealNonMissingValue)
//{
// if (DateTime.Now.Date == new DateTime(2006, 6, 28).Date) // for testing, force it to use the parametric bootstrap
// {
// return NullDataCollection.GetInstance(
// new NullDataGeneratorAlongTree(modelScorer, phyloTree, (ModelTesterDiscrete)this),
// nullIndexRangeCollection,
// predictorVariableToCaseIdToRealNonMissingValue);
// }
// return base.CreateNullDataGenerator(modelScorer, phyloTree, nullIndexRangeCollection, predictorVariableToCaseIdToRealNonMissingValue);
//}
//public override Converter<Leaf, SufficientStatistics> CreateTargetSufficientStatisticsMap(Dictionary<string, ISufficientStatistics> caseIdToNonMissingValue)
//{
// return ISufficientStatistics.DictionaryToLeafMap(caseIdToNonMissingValue);
//}
//public override Converter<Leaf, SufficientStatistics> CreatePredictorSufficientStatisticsMap(Dictionary<string, BooleanStatistics> caseIdToNonMissingValue)
//{
// return CreateTargetSufficientStatisticsMap(caseIdToNonMissingValue);
//}
protected override string CreateReportLine(
ModelScorer modelScorer,
PhyloTree phyloTree,
RowData rowAndTargetData,
UniversalWorkList workList,
int rowIndex, int workListCount, int workIndex)
{
//!!!there is very similar code in ModelTesterGaussian.cs
// we're iterating over each predictor (e.g. hla), each target (e.g. position in the sequence,
// and each possible substring at that position).
// Then we ask the question, Does the presence of predictor (e.g. hla)
// influence the probability that target (e.g. mer in position n1pos) will show up?
// nullIndex specifies whether this is the true data or randomized data.
Dictionary <string, string> row = rowAndTargetData.Row;
string predictorVariable = row[PhyloTree.PredictorVariableColumnName]; // e.g. hla
string targetVariable = row[PhyloTree.TargetVariableColumnName]; // e.g. A@182 (amino acid "A" at position 182)
int nullIndex = int.Parse(row[PhyloTree.NullIndexColumnName]);
//Dictionary<string, bool> caseIdToNonMissingPredictorValue = workList.NullIndexToPredictorToCaseIdToNonMissingValue[nullIndex][predictorVariable];
Dictionary <string, SufficientStatistics> caseIdToNonMissingPredictorValue = rowAndTargetData.PredictorData; //workList.GetCaseIdToNonMissingValueForNullIndexAndPredictorVariable(nullIndex, predictorVariable);
Dictionary <string, SufficientStatistics> caseIdToNonMissingTargetValue = rowAndTargetData.TargetData;
IEnumerator <SufficientStatistics> enumerator = caseIdToNonMissingPredictorValue.Values.GetEnumerator();
enumerator.MoveNext();
SufficientStatistics representative = enumerator.Current;
bool predictorIsBoolean = representative is BooleanStatistics;
Converter <Leaf, SufficientStatistics> targetDistributionClassFunction = CreateSufficientStatisticsMap(caseIdToNonMissingTargetValue);
Converter <Leaf, SufficientStatistics> predictorDistributionClassFunction = CreateSufficientStatisticsMap(caseIdToNonMissingPredictorValue);
int[] predictorCounts = predictorIsBoolean ?
phyloTree.CountsOfLeaves(predictorDistributionClassFunction, NullModelDistribution) : new int[2];
int[] targetCounts = phyloTree.CountsOfLeaves(targetDistributionClassFunction, NullModelDistribution);
int predictorFalseNameCount = predictorCounts[(int)DistributionDiscreteBinary.DistributionClass.False];
int predictorTrueNameCount = predictorCounts[(int)DistributionDiscreteBinary.DistributionClass.True];
int targetFalseNameCount = targetCounts[(int)DistributionDiscreteBinary.DistributionClass.False];
int targetTrueNameCount = targetCounts[(int)DistributionDiscreteBinary.DistributionClass.True];
int[] fisherCounts = predictorIsBoolean ?
phyloTree.FisherCounts(predictorDistributionClassFunction, targetDistributionClassFunction) : new int[4];
int globalNonMissingCount = predictorIsBoolean ?
fisherCounts[0] + fisherCounts[1] + fisherCounts[2] + fisherCounts[3] :
phyloTree.GlobalNonMissingCount(predictorDistributionClassFunction, targetDistributionClassFunction);
StringBuilder stringBuilder = new StringBuilder(
SpecialFunctions.CreateTabString(this, rowIndex, workListCount, workIndex, nullIndex, predictorVariable,
predictorFalseNameCount,
predictorTrueNameCount,
predictorTrueNameCount + predictorFalseNameCount,
targetVariable,
targetFalseNameCount,
targetTrueNameCount,
targetTrueNameCount + targetFalseNameCount,
fisherCounts[0], fisherCounts[1], fisherCounts[2], fisherCounts[3],
globalNonMissingCount,
""));
bool ignoreRow = false;
foreach (int[] counts in new int[][] { predictorIsBoolean?predictorCounts : new int[] { 1, 1 }, targetCounts })
{
foreach (int count in counts)
{
if (count == 0)
{
ignoreRow = true;
}
}
}
if (ignoreRow)
{
CompleteRowWithNaN(stringBuilder);
}
else
{
double targetMarginal = (double)targetTrueNameCount / (double)(targetTrueNameCount + targetFalseNameCount);
double predictorMarginal = (double)predictorTrueNameCount / (double)(predictorTrueNameCount + predictorFalseNameCount);
double diff = ComputeLLR(modelScorer, phyloTree, stringBuilder, targetMarginal, predictorMarginal, predictorDistributionClassFunction, targetDistributionClassFunction);
double pValue = SpecialFunctions.LogLikelihoodRatioTest(Math.Max(diff, 0), ChiSquareDegreesOfFreedom);
stringBuilder.Append(SpecialFunctions.CreateTabString(diff, pValue));
}
return(stringBuilder.ToString());
}