public void DeployAndRun(ILogBuilder log, SpaceModel _space, folderNode folder)
{
filter.Deploy(log, folder);
weightedFeatures = new WeightDictionary(name + "_weg" + filter.limit.ToString(), "weighted features, before filter");
selectedFeatures = new WeightDictionary(name + "_sel" + filter.limit.ToString(), "selected weighted featyres");
var selected = filter.SelectFeatures(_space, log, folder, weightedFeatures);
foreach (var pair in selected)
{
selectedFeatures.AddEntry(pair.Key, pair.Value);
}
weightedFeatures.Save(folder, log, WeightDictionary.GetDictionaryFilename(weightedFeatures.name, folder));
selectedFeatures.Save(folder, log, WeightDictionary.GetDictionaryFilename(selectedFeatures.name, folder));
}
public override ExperimentDataSetFoldContextPair <OperationContext> Execute(ILogBuilder logger, OperationContext executionContextMain = null, ExperimentModelExecutionContext executionContextExtra = null)
{
ExperimentDataSetFoldContextPair <OperationContext> output = new ExperimentDataSetFoldContextPair <OperationContext>(fold, executionContextMain);
Open();
Boolean skip = false;
// String fn = setup.OutputFilename;
String p_m = WeightDictionary.GetDictionaryFilename(setup.OutputFilename, fold_notes.folder); //FeatureWeightModel.GetModelDefinitionFilename(setup.OutputFilename, fold_notes.folder);
//String p_d = FeatureWeightModel.GetModelDataFilename(setup.OutputFilename, fold_notes.folder);
if (setup.skipIfExisting)
{
if (File.Exists(p_m))
{
logger.log("WeightTable [" + p_m + "] found, skipping the operation");
skip = true;
}
}
if (!skip)
{
notes.log("Rendering primary view");
// ------------------- PRIMARY CONTEXT
output.context.DeployDataSet(fold, logger);
primaryEntityOperation.TextRendering(output.context, notes);
//primaryEntityOperation.TextPreblendFilter(output.context, notes);
//primaryEntityOperation.TextBlending(output.context, notes);
corpusOperation.SpaceModelPopulation(output.context, notes);
corpusOperation.SpaceModelCategories(output.context, notes);
corpusOperation.FeatureSelection(output.context, notes, requirements.MayUseSelectedFeatures);
OperationContext primaryContext = output.context;
// ------------------- SECONDARY CONTEXT
output.context = new OperationContext();
notes.log("Rendering secondary view");
output.context.DeployDataSet(fold, logger);
secondaryEntityOperation.TextRendering(output.context, notes);
// secondaryEntityOperation.TextPreblendFilter(output.context, notes);
// secondaryEntityOperation.TextBlending(output.context, notes);
corpusOperation.SpaceModelPopulation(output.context, notes);
corpusOperation.SpaceModelCategories(output.context, notes);
corpusOperation.FeatureSelection(output.context, notes, requirements.MayUseSelectedFeatures);
OperationContext secondaryContext = output.context;
ProjectionDictionary projectionPairs = DocumentRankingTools.ConstructPairDictionary(primaryContext.spaceModel.documents, secondaryContext.spaceModel.documents);
DocumentSelectResult drmContext = output.context.PrepareContext(rankingOperation, fold_notes.folder, logger);
drmContext = rankingOperation.ExecuteEvaluation(drmContext, logger);
drmContext.description = "Document score assigned to the primary text render" + name;
drmContext.saveObjectToXML(fold_notes.folder.pathFor("DS_" + name + "_projection_score.xml", imbSCI.Data.enums.getWritableFileMode.overwrite, "Projection within [" + name + "] operation"));
TokenFrequencyAndScoreDictionary tokenFrequencyAndScoreDictionary = ProjectionTools.ProjectPrimaryTermsToScores(projectionPairs, drmContext, logger);
WeightDictionary wt = tokenFrequencyAndScoreDictionary.ConstructWeightDictionary();
wt.name = setup.OutputFilename;
wt.description = "Projected PrimaryView to ScoreTable - WeightTable, constructed from [" + projectionPairs.Count + "] render pairs. Document ranking: " + drmContext.description;
wt.Save(fold_notes.folder, logger, setup.OutputFilename);
// wt.saveObjectToXML(p_m);
}
Close();
return(output);
}