void RandomSplitJobManager::GenerateFinalResultsReport ()
{
KKStr reportFileName = osGetRootName (ManagerName ()) + "_Results.html;";
ofstream f (reportFileName.Str ());
f << "Run Time Parameters" << endl
<< "Run Time" << "\t" << osGetLocalDateTime () << endl
<< "configFileName" << "\t" << configFileName << endl
<< "DataFileName" << "\t" << dataFileName << endl
<< "Format" << "\t" << format->DriverName () << endl
<< "DataIndexFileName" << "\t" << dataIndexFileName << endl
<< "NumFolds" << "\t" << numFolds << endl
<< "NumSplits" << "\t" << numSplits << endl
<< "splitFraction" << "\t" << splitFraction << endl
<< endl;
KKJobList::const_iterator idx;
ConfusionMatrix2 avgResults (*(this->MLClasses ()));
KKB::uint x = 0;
for (idx = Jobs ()->begin (); idx != Jobs ()->end (); idx++)
{
RandomSplitJobPtr j = dynamic_cast<RandomSplitJobPtr> (*idx);
if (j->RandomSplitsResults () != NULL)
{
f << endl
<< "Random Split[" << j->SplitNum () << "]" << endl;
j->RandomSplitsResults ()->PrintConfusionMatrixTabDelimited (f);
f << endl << endl;
j->PrintClassCounts (f);
f << endl << endl;
avgResults.AddIn (*(j->RandomSplitsResults ()), log);
x++;
}
}
f << endl << "Mean Average of all random Splits." << endl;
avgResults.FactorCounts (1.0 / (double)x);
avgResults.PrintConfusionMatrixTabDelimited (f);
f << endl
<< endl
<< endl
<< endl
<< "Class Counts" << endl
<< endl;
kkuint32 numClasses = (kkuint32)mlClasses->size ();
VectorFloat classAccs;
VectorDouble knownCounts;
VectorDouble predCounts;
VectorDouble adjCounts;
VectorDouble adjCountsStdError;
VectorDouble predDelta;
VectorDouble adjDelta;
KKStr l1, l2, l3;
mlClasses->ExtractThreeTitleLines (l1, l2, l3);
VectorKKStr knownCountLines;
VectorKKStr predCountLines;
VectorKKStr adjCountLines;
VectorKKStr deltaPredCountLines;
VectorKKStr deltaAdjCountLines;
VectorKKStr accLines;
ConfusionMatrix2 totalCM (*MLClasses ());
int totalCmCount = 0;
// Known Counts
for (idx = Jobs ()->begin (); idx != Jobs ()->end (); idx++)
{
RandomSplitJobPtr j = dynamic_cast<RandomSplitJobPtr> (*idx);
if (j->RandomSplitsResults () != NULL)
{
KKStr splitNumStr = StrFormatInt (j->SplitNum (), "ZZZ0");
j->GetClassCounts (classAccs, knownCounts, predCounts, adjCounts, adjCountsStdError, predDelta, adjDelta);
totalCM.AddIn (*(j->RandomSplitsResults ()), log);
totalCmCount++;
KKStr accLine = "Acc By Class\t" + splitNumStr;
KKStr knownLine = "Known\t" + splitNumStr;
KKStr predLine = "Predicted\t" + splitNumStr;
KKStr adjLine = "Adjusted\t" + splitNumStr;
KKStr deltaPredLine = "Delta Pred\t" + splitNumStr;
KKStr deltaAdjLine = "Delta Adj\t" + splitNumStr;
double totalAcc = 0.0;
double totalDeltaPred = 0.0;
double totalDeltaAdj = 0.0;
for (x = 0; x < numClasses; x++)
{
accLine << "\t" << StrFormatDouble (classAccs [x], "zz0.00") << "%";
knownLine << "\t" << StrFormatDouble (knownCounts [x], "-Z,ZZZ,ZZ0.0");
predLine << "\t" << StrFormatDouble (predCounts [x], "-Z,ZZZ,ZZ0.0");
adjLine << "\t" << StrFormatDouble (adjCounts [x], "-Z,ZZZ,ZZ0.0");
deltaPredLine << "\t" << StrFormatDouble (predDelta [x], "-Z,ZZZ,ZZ0.0");
deltaAdjLine << "\t" << StrFormatDouble (adjDelta [x], "-Z,ZZZ,ZZ0.0");
totalAcc += classAccs [x];
totalDeltaPred += fabs (predDelta[x]);
totalDeltaAdj += fabs (adjDelta[x]);
}
accLine << "\t" << StrFormatDouble ((totalAcc / (double)classAccs.size ()), "ZZ0.00") << "%";
deltaPredLine << "\t" << StrFormatDouble ((totalDeltaPred / (double)predDelta.size ()), "ZZ0.00");
deltaAdjLine << "\t" << StrFormatDouble ((totalDeltaAdj / (double)adjDelta.size ()), "ZZ0.00");
accLines.push_back (accLine);
knownCountLines.push_back (knownLine);
predCountLines.push_back (predLine);
adjCountLines.push_back (adjLine);
deltaPredCountLines.push_back (deltaPredLine);
deltaAdjCountLines.push_back (deltaAdjLine);
}
}
double factor = 0.0;
if (totalCmCount > 0)
factor = 1.0 / (double)totalCmCount;
totalCM.FactorCounts (factor);
f << endl << endl
<< "Average Confusion Matrix" << endl
<< endl;
totalCM.PrintConfusionMatrixTabDelimited (f);
f << "" << "\t" << "" << "\t" << l1 << endl
<< "" << "\t" << "Split" << "\t" << l2 << endl
<< "Description" << "\t" << "Num" << "\t" << l3 << endl;
f << endl << endl;
for (x = 0; x < knownCountLines.size (); x++)
f << knownCountLines[x] << endl;
f << endl << endl;
for (x = 0; x < predCountLines.size (); x++)
f << predCountLines[x] << endl;
f << endl << endl;
for (x = 0; x < adjCountLines.size (); x++)
f << adjCountLines[x] << endl;
f << endl << endl;
for (x = 0; x < deltaPredCountLines.size (); x++)
f << deltaPredCountLines[x] << endl;
f << endl << endl;
for (x = 0; x < deltaAdjCountLines.size (); x++)
f << deltaAdjCountLines[x] << endl;
f << endl << endl;
for (x = 0; x < knownCountLines.size (); x++)
f << accLines[x] << endl;
VectorFloat avgAccuracies = totalCM.AccuracyByClass ();
f << "Avg-Accuracies";
for (x = 0; x < avgAccuracies.size (); x++)
f << "\t" << StrFormatDouble (avgAccuracies[x], "zz0.00") << "%";
f << "\t" << StrFormatDouble (totalCM.Accuracy (), "zz0.00") << "%";
f << endl;
f << endl << endl;
f.close ();
} /* GenerateFinalResultsReport */
