public IPlaneContext ExecutePlaneMethod(IPlaneContext inputContext, ExperimentModelExecutionContext generalContext, ILogBuilder logger)
{
//if (generalContext == null)
//{
// generalContext = new PlanesMethodContext();
//}
IEntityPlaneContext entityInputContext = inputContext as IEntityPlaneContext;
ICorpusPlaneContext entityContext = EntityMethod.ExecutePlaneMethod(inputContext, generalContext, logger) as ICorpusPlaneContext;
IVectorPlaneContext corpusContext = CorpusMethod.ExecutePlaneMethod(entityContext, generalContext, logger) as IVectorPlaneContext;
IFeaturePlaneContext vectorContext = VectorMethod.ExecutePlaneMethod(corpusContext, generalContext, logger) as IFeaturePlaneContext;
IFeaturePlaneContext featureContext = FeatureMethod.ExecutePlaneMethod(vectorContext, generalContext, logger) as IFeaturePlaneContext;
// --- the results reporting
var evaluationMetrics = generalContext.truthTable.EvaluateTestResultsToMetricSet(featureContext.testResults, generalContext.runName + "-" + notes.folder.name, logger);
DataTableTypeExtended <classificationEval> inclassEvalTable = new DataTableTypeExtended <classificationEval>("inclass_evaluation", "Test results, per class");
evaluationMetrics.GetAllEntries().ForEach(x => inclassEvalTable.AddRow(x));
inclassEvalTable.AddRow(evaluationMetrics.GetSummary("Sum"));
notes.SaveDataTable(inclassEvalTable, notes.folder_classification);
classificationReport averagedReport = new classificationReport(evaluationMetrics, generalContext.averagingMethod);
averagedReport.Classifier = FeatureMethod.classifier.name;
averagedReport.saveObjectToXML(notes.folder_classification.pathFor(averagedReport.Name + ".xml", imbSCI.Data.enums.getWritableFileMode.overwrite, "Serialized classification evaluation results summary"));
generalContext.testSummaries.Add(averagedReport);
averagedReport.ReportToLog(notes);
featureContext.provider.Dispose();
EntityMethod.CacheProvider.Dispose();
return(generalContext);
}
/// <summary>
/// Executes the plane method, invoking contained functions according to the settings
/// </summary>
/// <param name="inputContext">The input context - related to this plane.</param>
/// <param name="generalContext">General execution context, attached to the <see cref="T:imbNLP.Toolkit.Planes.PlanesMethodDesign" /></param>
/// <param name="logger">The logger.</param>
/// <returns>
/// Retur
/// </returns>
public IPlaneContext ExecutePlaneMethod(IPlaneContext inputContext, ExperimentModelExecutionContext generalContext, ILogBuilder logger)
{
notes.logStartPhase("[4] Feature Plane - execution", "");
IFeaturePlaneContext context = inputContext as IFeaturePlaneContext;
foreach (FeatureVector vec in context.featureSpace.documents)
{
var associated = context.featureSpace.labelToDocumentAssociations.GetAllLinked(vec);
if (associated.Any())
{
Int32 lbi = generalContext.truthTable.labels_without_unknown.IndexOf(associated.First().name);
FeatureVectorWithLabelID fvl = new FeatureVectorWithLabelID(vec, lbi);
context.trainingSet.Add(fvl);
}
else
{
context.testSet.Add(vec);
}
}
if (!context.testSet.Any())
{
notes.log("TEST SET IS EMPTY ---- APPLYING 1:1 EXPERIMENT SHEME: training and test set are the same");
context.trainingSet.ForEach(x => context.testSet.Add(x.vector));
}
if ((!context.trainingSet.Any()))
{
notes.log("TRAINING SET EMPTY ---- APPLYING 1:1 EXPERIMENT SHEME: training and test set are the same");
}
else
{
notes.log("Training [" + classifier.name + "] with [" + context.trainingSet.Count + "] feature vectors.");
classifier.DoTraining(context.trainingSet, logger);
notes.log("Testing [" + classifier.name + "] with [" + context.testSet.Count + "] feature vectors.");
context.testResults = new List <FeatureVectorWithLabelID>();
foreach (FeatureVector fv in context.testSet)
{
Int32 result = classifier.DoSelect(fv, logger);
FeatureVectorWithLabelID fvl = new FeatureVectorWithLabelID(fv, result);
context.testResults.Add(fvl);
}
/*
* Dictionary<string, List<FeatureVectorWithLabelID>> byCategory = generalContext.truthTable.GroupByTrueCategory(context.testResults);
* objectTable<classificationReport> tbl = new objectTable<classificationReport>(nameof(classificationReport.Name), "inclass_" + generalContext.runName);
* classificationReport macroAverage = new classificationReport("AVG-" + generalContext.runName);
* foreach (KeyValuePair<string, List<FeatureVectorWithLabelID>> pair in byCategory)
* {
* var cReport = generalContext.EvaluateTestResults(pair.Value, pair.Key + "-" + generalContext.runName, logger);
*
* cReport.Classifier = classifier.name;
* cReport.Comment = "Tr/Ts [" + context.trainingSet.Count + "]:[" + context.testSet.Count + "]";
* String path = notes.folder_classification.pathFor(pair.Key + "_result.xml", imbSCI.Data.enums.getWritableFileMode.overwrite, "Serialized evaluation result within category [" + pair.Key + "]", true);
*
* macroAverage.AddValues(cReport);
*
* tbl.Add(cReport);
* }
* // macroAverage.DivideValues(byCategory.Keys.Count);
*
* tbl.Add(macroAverage);
*
* notes.SaveDataTable(tbl.GetDataTable(), notes.folder_classification);
*/
}
notes.logEndPhase();
return(context);
}