Impala Documentation

int Impala::Application::TrainModel	(	Training::Factory *	trainFactory,
		DataFactory *	dataFactory,
		bool	distKernel
	)

Definition at line 46 of file mainTrainModel.cpp.

References Impala::Core::DataFactory::CanMakeConceptModel(), Impala::Core::Training::AveragePrecision::Compute(), Impala::Core::Training::AveragePrecision::ComputeReversed(), Impala::Core::Training::Factory::GetBestFileParams(), ILOG_DEBUG, ILOG_VAR, main(), Impala::Core::DataFactory::MakeAnnotation(), Impala::Core::Training::Factory::MakeClassifier(), Impala::Core::DataFactory::MakeConceptList(), Impala::Core::Training::Factory::MakeDataSrc(), Impala::Core::Training::Classifier::Predict(), Impala::Util::Mpi::SelectWorkLoadInPlace(), Impala::Util::PropertySet::Size(), Impala::Core::Training::Classifier::Train(), Impala::Core::DataFactory::WriteConceptModel(), and Impala::Core::DataFactory::WriteScoreOnSelf().

Referenced by mainTrainModel().

00048 {
00049     ILOG_VAR(main);
00050     std::vector<String> conceptList = dataFactory->MakeConceptList();
00051     if(!distKernel)
00052         Util::Mpi::SelectWorkLoadInPlace(&conceptList);
00053     for (int i=0 ; i<conceptList.size(); ++i)
00054     {
00055         String concept = conceptList[i];
00056 
00057         if(!dataFactory->CanMakeConceptModel(concept))
00058         {
00059             ILOG_WARNING_NODE("cannot create model for " << concept <<
00060                          "; skipping concept");
00061             continue;
00062         }
00063         ILOG_INFO_NODE("concept " << concept);
00064 
00065         // read params
00066         Util::PropertySet params = 
00067             trainFactory->GetBestFileParams(dataFactory, concept);
00068         ILOG_INFO_NODE(params);
00069         if(params.Size() == 0)
00070         {
00071             ILOG_WARNING_NODE("no best file found for " << concept <<
00072                          "; skipping concept");
00073             continue;
00074         }
00075 
00076         //load annotation
00077         Table::AnnotationTable* annotation = dataFactory->MakeAnnotation(concept);
00078         if(annotation == 0)
00079         {
00080             ILOG_WARNING_NODE("no annotation found for " << concept << 
00081                          "; skipping concept");
00082             continue;
00083         }
00084         annotation->Sort();
00085 
00086         //compute model
00087         TrainDataSrc* src = trainFactory->MakeDataSrc(annotation, dataFactory);
00088         Core::Training::Classifier* svm = trainFactory->MakeClassifier("svm");
00089         svm->Train(&params, src);
00090         dataFactory->WriteConceptModel(concept, svm);
00091 
00092         // score on self
00093         Table::ScoreTable* ranking = svm->Predict(src);
00094         Training::AveragePrecision ap(annotation);
00095         double score = ap.Compute(ranking);
00096         if(score < ap.ComputeReversed(ranking))
00097         {
00098             ILOG_ERROR_NODE("reversed ranking > normal ranking => logic error in svm");
00099         }
00100         delete ranking;
00101         dataFactory->WriteScoreOnSelf(concept, score);
00102         ILOG_INFO_NODE("score on self = " << score);
00103         ILOG_DEBUG("deleting svm");
00104         delete svm;
00105         ILOG_DEBUG_NODE("deleting src");
00106         delete src;
00107         ILOG_DEBUG_NODE("deleting annotation");
00108         delete annotation;
00109         ILOG_DEBUG_NODE("done iteration");
00110     }
00111     return 0;
00112 }

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