void GradeClassification::GradeExamplesAgainstGroundTruth (FeatureVectorListPtr examplesToGrade,
FeatureVectorListPtr groundTruth
)
{
log.Level (10) << "GradeClassification::GradeExamplesAgainstGroundTruth" << endl;
groundTruth->SortByRootName ();
MLClassConstPtr unknownClass = mlClasses->GetUnKnownClass ();
MLClassConstListPtr classes = NULL;
{
MLClassConstListPtr examplesToGradeClasses = examplesToGrade->ExtractMLClassConstList ();
MLClassConstListPtr groundTruthClasses = groundTruth->ExtractMLClassConstList ();
classes = MLClassConstList::MergeClassList (*examplesToGradeClasses, *groundTruthClasses);
delete examplesToGradeClasses;
delete groundTruthClasses;
}
uint16 maxHierarchialLevel = 0;
{
MLClassConstList::iterator idx;
for (idx = classes->begin (); idx != classes->end (); idx++)
{
MLClassConstPtr c = *idx;
maxHierarchialLevel = Max (maxHierarchialLevel, c->NumHierarchialLevels ());
}
}
// Create ConfusionMatrix objects for each posible level of Hierarchy. The 'resultsSummary' vector will
// end up owning the instances of 'ConfusionMatrix2' and th edestructr will be responable for deleting them.
uint curLevel = 0;
vector<ConfusionMatrix2Ptr> cmByLevel;
for (curLevel = 0; curLevel < maxHierarchialLevel; curLevel++)
{
MLClassConstListPtr classesThisLevel = classes->ExtractListOfClassesForAGivenHierarchialLevel (curLevel);
ConfusionMatrix2Ptr cm = new ConfusionMatrix2 (*classesThisLevel);
cmByLevel.push_back (cm);
}
ConfusionMatrix2 cm (*classes);
ImageFeaturesList::iterator idx;
for (idx = examplesToGrade->begin (); idx != examplesToGrade->end (); idx++)
{
ImageFeaturesPtr exampleToGrade = *idx;
MLClassConstPtr predictedClass = exampleToGrade->MLClass ();
float origSize = exampleToGrade->OrigSize ();
float probability = exampleToGrade->Probability ();
KKStr rootName = osGetRootName (exampleToGrade->ImageFileName ());
FeatureVectorPtr groundTruthExample = groundTruth->LookUpByRootName (rootName);
MLClassConstPtr groundTruthClass = unknownClass;
if (groundTruthExample)
groundTruthClass = groundTruthExample->MLClass ();
cm.Increment (groundTruthClass, predictedClass, (int)origSize, probability, log);
for (curLevel = 0; curLevel < maxHierarchialLevel; curLevel++)
{
MLClassConstPtr groundTruthClasssThisLevel = groundTruthClass->MLClassForGivenHierarchialLevel (curLevel);
MLClassConstPtr predictedClassThisLevel = predictedClass->MLClassForGivenHierarchialLevel (curLevel);
cmByLevel[curLevel]->Increment (groundTruthClasssThisLevel, predictedClassThisLevel, (int)origSize, probability, log);
}
}
//cm.PrintTrueFalsePositivesTabDelimited (*report);
{
// report Hierarchial results
for (curLevel = 0; curLevel < maxHierarchialLevel; curLevel++)
{
log.Level (10) << "GradeClassification::GradeExamplesAgainstGroundTruth Printing Level[" << curLevel << "]" << endl;
*report << endl << endl << endl
<< "Confusion Matrix Training Level[" << maxHierarchialLevel << "] Preduction Level[" << (curLevel + 1) << "]" << endl
<< endl;
cmByLevel[curLevel]->PrintConfusionMatrixTabDelimited (*report);
resultsSummary.push_back (SummaryRec (maxHierarchialLevel, curLevel + 1, cmByLevel[curLevel]));
}
*report << endl << endl << endl;
}
log.Level (10) << "GradeClassification::GradeExamplesAgainstGroundTruth Exiting" << endl;
} /* GradeExamplesAgainstGroundTruth */
