/// <summary>
/// Prepares the specified context.
/// </summary>
/// <param name="context">The context.</param>
/// <param name="log">The log.</param>
/// <exception cref="ArgumentException">context</exception>
public override void Prepare(DocumentSelectResult context, ILogBuilder log)
{
/*
* String p_m = WeightDictionary.GetDictionaryFilename(, context.folder);
*
* if (File.Exists(p_m))
* {
* //objectSerialization.loadObjectFromXML<WeightDictionary>(p_m, log);
*
* }
*/
weightDictionary = WeightDictionary.LoadFile(WeightDictionary.GetDictionaryFilename(dictionaryFile, context.folder), log);
if (context.spaceModel == null)
{
String msg = "Error: TermWeight factor requires SpaceModel declared in the context for operation";
throw new ArgumentException(msg, nameof(context));
}
if (context.query.isNullOrEmpty())
{
context.query.QueryTerms = context.query.QueryTerms.Trim();
List <String> tkns = context.query.QueryTerms.getTokens(true, true, true, false, 4);
foreach (String tkn in tkns)
{
queryTerms.Add(context.stemmingContext.Stem(tkn));
}
}
}
/// <summary>
/// Prepares the specified context.
/// </summary>
/// <param name="context">The context.</param>
/// <param name="log">The log.</param>
/// <exception cref="ArgumentException">context</exception>
public override void Prepare(DocumentSelectResult context, ILogBuilder log)
{
String p_m = FeatureWeightModel.GetModelDefinitionFilename(modelDefinitionFile, context.folder);
String p_d = FeatureWeightModel.GetModelDataFilename(modelDefinitionFile, context.folder);
TermWeightModel = FeatureWeightModel.LoadModel(p_m, log);
//if (File.Exists(p_m))
//{
// //TermWeightModel = objectSerialization.loadObjectFromXML<FeatureWeightModel>(p_m, log);
//}
TermWeightModel.Deploy(log);
if (context.spaceModel == null)
{
String msg = "Error: TermWeight factor requires SpaceModel declared in the context for operation";
throw new ArgumentException(msg, nameof(context));
}
if (File.Exists(p_d) && useStoredData)
{
WeightingModelDataSet data = objectSerialization.loadObjectFromXML <WeightingModelDataSet>(p_d, log);
TermWeightModel.LoadModelDataSet(data, log);
if (useSelectedFeatures)
{
SelectedTerms = WeightDictionary.LoadFile(WeightDictionary.GetDictionaryFilename(modelDefinitionFile + "_sf", context.folder), log);
}
}
else
{
TermWeightModel.PrepareTheModel(context.spaceModel, log);
}
if (context.query.isNullOrEmpty())
{
context.query.QueryTerms = context.query.QueryTerms.Trim();
List <String> tkns = context.query.QueryTerms.getTokens(true, true, true, false, 4);
foreach (String tkn in tkns)
{
queryTerms.Add(context.stemmingContext.Stem(tkn));
}
}
}
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));
}
/// <summary>
/// Prepares the specified context.
/// </summary>
/// <param name="context">The context.</param>
/// <param name="log">The log.</param>
/// <exception cref="ArgumentException">context</exception>
public override void Prepare(DocumentSelectResult context, ILogBuilder log)
{
//context.folder.GetOrFindFiles("*", dictionaryFile + "*.xml");
scoreDictionary = FeatureVectorDictionaryWithDimensions.LoadFile(context.folder, dictionaryFile, log); // WeightDictionary.LoadFile(WeightDictionary.GetDictionaryFilename(dictionaryFile, context.folder), log);
if (scoreDictionary == null)
{
String msg = "Error: Failed to find score dictionary [" + dictionaryFile + "] in " + context.folder.path;
throw new ArgumentException(msg, nameof(context));
}
if (useMachineLearning)
{
#region --------------- PREPARING TERM WEIGHT MODEL
String p_m = FeatureWeightModel.GetModelDefinitionFilename(modelDefinitionFile, context.folder);
String p_d = FeatureWeightModel.GetModelDataFilename(modelDefinitionFile, context.folder);
if (TermWeightModel == null)
{
TermWeightModel = FeatureWeightModel.LoadModel(p_m, log);
}
TermWeightModel.Deploy(log);
if (context.spaceModel == null)
{
String msg = "Error: TermWeight factor requires SpaceModel declared in the context for operation";
throw new ArgumentException(msg, nameof(context));
}
if (File.Exists(p_d) && useStoredData)
{
WeightingModelDataSet data = objectSerialization.loadObjectFromXML <WeightingModelDataSet>(p_d, log);
TermWeightModel.LoadModelDataSet(data, log);
if (useSelectedFeatures)
{
SelectedTerms = WeightDictionary.LoadFile(WeightDictionary.GetDictionaryFilename(modelDefinitionFile + "_sf", context.folder), log);
}
}
else
{
TermWeightModel.PrepareTheModel(context.spaceModel, log);
}
if (SelectedTerms.Count == 0)
{
SelectedTerms = context.selectedFeatures;
}
List <String> sel_tkns = new List <String>();
sel_tkns.AddRange(SelectedTerms.index.Values.Select(x => x.name));
if (!sel_tkns.Any())
{
sel_tkns.AddRange(context.spaceModel.terms_known_label.GetTokens());
}
#endregion
fvConstructor.Deploy(featureMethod.constructor, sel_tkns);
classifier = featureMethod.classifierSettings.GetClassifier();
sc_id = scoreDictionary.GetVectorsWithLabelID(null, criterion).ToNameVsLabelID();
List <FeatureVectorWithLabelID> trainingSet = new List <FeatureVectorWithLabelID>();
foreach (var item in context.items)
{
if (sc_id.ContainsKey(item.AssignedID))
{
WeightDictionary dc_vec = TermWeightModel.GetWeights(sel_tkns, item.spaceDocument, context.spaceModel);
var n_vec = fvConstructor.ConstructFeatureVector(dc_vec, item.AssignedID);
FeatureVectorWithLabelID id_vec = new FeatureVectorWithLabelID(n_vec, sc_id[item.AssignedID]);
trainingSet.Add(id_vec);
}
}
log.log("Training [" + classifier.name + "] with [" + sc_id.Count + "] feature vectors.");
classifier.DoTraining(trainingSet, log);
}
}
public override ExperimentDataSetFoldContextPair <OperationContext> Execute(ILogBuilder logger, OperationContext executionContextMain = null, ExperimentModelExecutionContext executionContextExtra = null)
{
ExperimentDataSetFoldContextPair <OperationContext> output = new ExperimentDataSetFoldContextPair <OperationContext>(fold, executionContextMain);
Open();
String p_m = FeatureWeightModel.GetModelDefinitionFilename(setup.OutputFilename, fold_notes.folder);
String p_d = FeatureWeightModel.GetModelDataFilename(setup.OutputFilename, fold_notes.folder);
String w_t = WeightDictionary.GetDictionaryFilename(setup.OutputFilename, fold_notes.folder);
Boolean skip = false;
if (setup.skipIfExisting)
{
if (File.Exists(p_m) && File.Exists(p_d) && File.Exists(w_t))
{
logger.log("WeightTable [" + p_d + "] found, skipping the operation");
skip = true;
}
}
if (!skip)
{
output.context.DeployDataSet(fold, logger);
entityOperation.TextRendering(output.context, notes);
/*
* entityOperation.TextPreblendFilter(output.context, notes);
*
* entityOperation.TextBlending(output.context, notes);
*/
corpusOperation.SpaceModelPopulation(output.context, notes);
corpusOperation.SpaceModelCategories(output.context, notes);
corpusOperation.FeatureSelection(output.context, notes, requirements.MayUseSelectedFeatures);
output.context.SelectedFeatures.Save(fold_notes.folder, notes, setup.OutputFilename + "_fs");
//corpusOperation.weightModel.
corpusOperation.weightModel.PrepareTheModel(output.context.spaceModel, logger);
var wt_s = corpusOperation.weightModel.GetElementFactors(output.context.SelectedFeatures.GetKeys(), output.context.spaceModel);
wt_s.Save(fold_notes.folder, notes, setup.OutputFilename);
corpusOperation.weightModel.Save(setup.OutputFilename, fold_notes.folder, notes);
OperationContextReport reportOperation = new OperationContextReport();
reportOperation.DeploySettingsBase(notes);
reportOperation.GenerateReports(output.context, setup.reportOptions, notes);
}
Close();
return(output);
}
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);
}