Impala Documentation

Table::SimilarityTableSet* Impala::Application::LearnConceptFromAnnotations	(	CmdOptions &	options,
		Matrix::DistributedAccess *	da,
		String	concept,
		const ModelLocator &	modelLoc,
		Table::AnnotationTable *	annotations
	)

Definition at line 26 of file mainActiveLearner.cpp.

References Impala::Util::PropertySet::Add(), Impala::Core::Table::Copy(), Impala::Core::Matrix::DistributedAccess::GetColumnQuids(), Impala::CmdOptions::GetInt(), Impala::Util::PropertySet::GetName(), Impala::Core::Matrix::DistributedAccess::GetRowQuids(), Impala::Util::PropertySet::GetValue(), ILOG_INFO, ILOG_VAR, Impala::Core::Training::Svm::PredictForActiveLearn(), Impala::Core::Table::Select(), Impala::Util::PropertySet::Size(), and Impala::Core::Training::Svm::Train().

Referenced by RunDistributedLearningEngine().

00030 {
00031     ILOG_VAR(Application.LearnConceptFromAnnotations);
00032 
00033 
00034     ILOG_INFO("concept " << concept);
00035 
00036     //load annotation
00037     Table::QuidTable* allQuids = da->GetColumnQuids();
00038     Table::CriterionElement1InSet<Table::AnnotationTable> c(allQuids);
00039     Table::AnnotationTable* annotation = Select(annotations, c);
00040 
00041     Table::QuidTable* positive = annotation->GetPositive();
00042     Table::QuidTable* negative = annotation->GetNegative();
00043     Table::QuidTable* learnQuids = positive;
00044     learnQuids->Append(negative);
00045     delete negative;
00046 
00047     if ((positive->Size() == 0) || (negative->Size() == 0))
00048     {
00049         delete learnQuids;
00050         delete annotation;
00051         return 0;
00052     }
00053 
00054     Util::PropertySet params;
00055     params.Add("probability", 1);
00056     params.Add("cache", options.GetInt("cache"));
00057 
00058     params.Add("gamma", -1);
00059     params.Add("autoweight", 1);
00060     params.Add("kernel", "dist-precomputed");
00061     params.Add("precompute-kernel", "chi2");
00062     params.Add("evaluator", "AP");
00063     params.Add("C", 1.0);
00064     params.Add("w1", 30.0); // estimate these
00065     params.Add("w2", 1.2); // estimate these
00066     for(int i = 0; i < params.Size(); i++)
00067         ILOG_INFO(params.GetName(i) + " " + params.GetValue(i));
00068 
00069     //compute model:
00070     Training::Svm svm;
00071     Training::TrainDataSrcKernelDistributed* dataSrc =
00072         new TrainDataSrcKernelDistributed(da, annotation);
00073     svm.Train(&params, dataSrc);
00074     ILOG_INFO("saving model");
00075     SvmRepository().Add(modelLoc, &svm);
00076 
00077     // compute resulting ranking:
00078     std::vector<String> names;
00079     names.push_back(concept);
00080     Table::QuidTable* rowQuids = da->GetRowQuids();
00081     Table::SimilarityTableSet *simSet =
00082         new Table::SimilarityTableSet(names, rowQuids->Size());
00083     Table::Copy(simSet->GetQuidTable(), rowQuids);
00084 
00085     ILOG_INFO("applying model");
00086     svm.PredictForActiveLearn(da, learnQuids, simSet->GetSimTable(0));
00087     
00088     ILOG_INFO("ranking");
00089     simSet->ComputeRank(0, true);
00090     
00091     delete annotation;
00092     delete learnQuids;
00093     delete dataSrc;
00094     return simSet;
00095 }

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