public override EvaluationResults EvaluateModelOnData(Converter <Leaf, SufficientStatistics> v1, Converter <Leaf, SufficientStatistics> v2)
{
EvaluationResults internalResults = _internalEvaluator.EvaluateModelOnData(v2, v1);
internalResults.ModelEvaluator = this;
return(internalResults);
}
private string CreateReportLine(
ModelEvaluator modelEvaluator,
RowData rowAndTargetData,
UniversalWorkList workList,
int rowIndex, int workListCount, int workIndex)
{
Dictionary <string, string> row = rowAndTargetData.Row;
string predictorVariable = row[Tabulate.PredictorVariableColumnName];
string targetVariable = row[Tabulate.TargetVariableColumnName];
int nullIndex = int.Parse(row[Tabulate.NullIndexColumnName]);
Dictionary <string, SufficientStatistics> caseIdToNonMissingPredictorValue = rowAndTargetData.PredictorData;
Dictionary <string, SufficientStatistics> caseIdToNonMissingTargetValue = rowAndTargetData.TargetData;
Converter <Leaf, SufficientStatistics> predictorDistributionClassFunction = CreateSufficientStatisticsMap(caseIdToNonMissingPredictorValue);
Converter <Leaf, SufficientStatistics> targetDistributionClassFunction = CreateSufficientStatisticsMap(caseIdToNonMissingTargetValue);
EvaluationResults results = modelEvaluator.EvaluateModelOnData(predictorDistributionClassFunction, targetDistributionClassFunction);
string reportLine = SpecialFunctions.CreateTabString(
results.ModelEvaluator.Name, rowIndex, workListCount, workIndex, nullIndex, predictorVariable, targetVariable, results.ToString());
return(reportLine);
}
public override EvaluationResults EvaluateModelOnData(Converter <Leaf, SufficientStatistics> v1, Converter <Leaf, SufficientStatistics> v2)
{
List <Leaf> nonMissingLeaves = new List <Leaf>(100);
int seed = 0;
foreach (Leaf leaf in ModelScorer.PhyloTree.LeafCollection)
{
SufficientStatistics class1 = v1(leaf);
SufficientStatistics class2 = v2(leaf);
if (!class1.IsMissing() && !class2.IsMissing())
{
nonMissingLeaves.Add(leaf);
seed ^= (leaf.CaseName + class1.ToString() + class2.ToString()).GetHashCode();
}
}
Random rand = new Random(seed);
nonMissingLeaves = SpecialFunctions.Shuffle(nonMissingLeaves, ref rand);
int groupSize = nonMissingLeaves.Count / _crossValidateCount;
EvaluationResultsCrossValidate combinedResults = null;
double testAltLLSum = 0; // for debugging
double testNullLLSum = 0; // for debugging
for (int i = 0; i < _crossValidateCount; i++)
{
int testStart = i * groupSize;
int trainStart = testStart + groupSize;
Set <Leaf> trainSet = new Set <Leaf>(SpecialFunctions.SubList(nonMissingLeaves, trainStart, nonMissingLeaves.Count - trainStart));
trainSet.AddNewRange(SpecialFunctions.SubList(nonMissingLeaves, 0, testStart));
Converter <Leaf, SufficientStatistics> v1Train = CreateFilteredMap(v1, trainSet);
Converter <Leaf, SufficientStatistics> v2Train = CreateFilteredMap(v2, trainSet);
EvaluationResults trainingResults = InternalEvaluator.EvaluateModelOnData(v1Train, v2Train);
EvaluationResults testAndTrainResult = InternalEvaluator.EvaluateModelOnDataGivenParams(v1, v2, trainingResults);
EvaluationResultsTestGivenTrain testGivenTrainResult = EvaluationResultsTestGivenTrain.GetInstance(this, trainingResults, testAndTrainResult);
if (combinedResults == null)
{
combinedResults = EvaluationResultsCrossValidate.GetInstance(this, testGivenTrainResult);
}
else
{
combinedResults = combinedResults.AddNewResults(testGivenTrainResult);
}
if (double.IsInfinity(combinedResults.AltLL)) // no point in continuing...infinity will kill everything.
{
break;
}
#if DEBUG
double eps = 1E-10;
EvaluationResults testTrainingResults = InternalEvaluator.EvaluateModelOnDataGivenParams(v1Train, v2Train, trainingResults);
Debug.Assert(ComplexNumber.ApproxEqual(testTrainingResults.AltLL, trainingResults.AltLL, eps) &&
ComplexNumber.ApproxEqual(testTrainingResults.NullLL, trainingResults.NullLL, eps));
//Debug.Assert(testTrainingResults.Equals(trainingResults));
double newNullLL = testAndTrainResult.NullLL - trainingResults.NullLL;
double newAltLL = testAndTrainResult.AltLL - trainingResults.AltLL;
Debug.Assert(ComplexNumber.ApproxEqual(newNullLL, testGivenTrainResult.NullLL, eps));
Debug.Assert(ComplexNumber.ApproxEqual(newAltLL, testGivenTrainResult.AltLL, eps));
testNullLLSum += newNullLL;
testAltLLSum += newAltLL;
Debug.Assert(ComplexNumber.ApproxEqual(testNullLLSum, combinedResults.NullLL, eps), "Combined result has wrong NullLL");
Debug.Assert(ComplexNumber.ApproxEqual(testAltLLSum, combinedResults.AltLL, eps), "Combined result has wrong AltLL");
#endif
}
return(combinedResults);
}