void JobValidation::EvaluateNode ()
{
log.Level (9) << " " << endl;
log.Level (9) << "JobValidation::EvaluteNode JobId[" << jobId << "]" << endl;
status = BinaryJobStatus::Started;
bool configFileFormatGood = true;
TrainingConfiguration2Ptr config = new TrainingConfiguration2 ();
config->Load (configFileName, false, log);
if (!config->FormatGood ())
configFileFormatGood;
config->SetFeatureNums (features);
config->C_Param (cParm);
config->Gamma (gammaParm);
config->A_Param (aParm);
config->SelectionMethod (processor->SelectionMethod ());
switch (processor->ResultType ())
{
case FinalResultType::MfsFeaturesSel:
case FinalResultType::NoTuningAllFeatures:
case FinalResultType::MfsParmsTuned:
case FinalResultType::MfsParmsTunedFeaturesSel:
config->MachineType (SVM_MachineType::OneVsOne);
break;
case FinalResultType::BfsFeaturesSel:
case FinalResultType::BfsParmsTuned:
case FinalResultType::BfsFeaturesSelParmsTuned:
config->MachineType (SVM_MachineType::BinaryCombos);
break;
}
bool cancelFlag = false;
FeatureVectorListPtr trainData = processor->TrainingData ();
FeatureVectorListPtr validationData = processor->ValidationData ();
VectorDouble trainDataMeans = trainData->ExtractMeanFeatureValues ();
VectorDouble validationDataMeans = validationData->ExtractMeanFeatureValues ();
CrossValidationPtr crossValidation = new CrossValidation
(config,
trainData,
processor->MLClasses (),
processor->NumOfFolds (),
processor->AlreadyNormalized (),
processor->FileDesc (),
log,
cancelFlag
);
delete classedCorrectly;
classedCorrectlySize = validationData->QueueSize ();
classedCorrectly = new bool[classedCorrectlySize];
crossValidation->RunValidationOnly (validationData, classedCorrectly, log);
testAccuracy = crossValidation->Accuracy ();
testAccuracyNorm = crossValidation->AccuracyNorm ();
testAvgPredProb = (float)crossValidation->AvgPredProb () * 100.0f;
testFMeasure = (float)crossValidation->ConfussionMatrix ()->FMeasure (processor->PositiveClass (), log);
if (processor->GradingMethod () == GradingMethodType::Accuracy)
testGrade = testAccuracy;
else if (processor->GradingMethod () == GradingMethodType::AccuracyNorm)
testGrade = testAccuracyNorm;
else if (processor->GradingMethod () == GradingMethodType::FMeasure)
testGrade = testFMeasure;
else
testGrade = testAccuracy;
testNumSVs = crossValidation->NumOfSupportVectors ();
{
// Save results of this Split in Results file.
processor->Block ();
{
uint fn = 0;
ofstream rl ("FinalResults.log", ios_base::app);
rl << endl << endl
<< "ConfigFileName" << "\t" << configFileName << "\t" << "Format Good[" << (configFileFormatGood ? "Yes" : "No") << endl
<< "SummaryResultsFileName" << "\t" << processor->SummaryResultsFileName () << endl
<< "Configuration CmdLine" << "\t" << config->SVMparamREF (log).ToString () << endl
<< "ImagesPerClass" << "\t" << config->ImagesPerClass () << endl
<< endl;
rl << endl << endl
<< "Training Data Status" << endl
<< endl;
trainData->PrintClassStatistics (rl);
rl << endl << endl;
rl << "TrainingDataMeans";
for (fn = 0; fn < trainDataMeans.size (); fn++)
rl << "\t" << trainDataMeans[fn];
rl << endl;
rl << "ValidationDataMeans";
for (fn = 0; fn < validationDataMeans.size (); fn++)
rl << "\t" << validationDataMeans[fn];
rl << endl
<< endl;
crossValidation->ConfussionMatrix ()->PrintConfusionMatrixTabDelimited (rl);
rl << endl << endl << endl << endl;
rl.close ();
}
{
ofstream f (processor->SummaryResultsFileName ().Str (), ios_base::app);
ValidationResults r (processor->ResultType (),
config,
crossValidation,
trainData,
osGetHostName ().value_or ("*** unknown ***"),
classedCorrectlySize,
classedCorrectly,
this,
log
);
r.Write (f);
f.close ();
}
processor->EndBlock ();
}
delete crossValidation; crossValidation = NULL;
delete config; config = NULL;
status = BinaryJobStatus::Done;
} /* EvaluateNode */
