public void SetFVEKnowledge(IWebFVExtractorKnowledge _knowledge)
{
if (_knowledge is semanticFVExtractorKnowledge)
{
semanticFVExtractorKnowledge knowledge = (semanticFVExtractorKnowledge)_knowledge;
WLTableOfIndustryClass = knowledge.WLTableOfIndustryClass;
WLChunkTableOfIndustryClass = knowledge.WLChunkTableOfIndustryClass;
semanticCloud = knowledge.semanticCloud;
}
FVEKnowledge = _knowledge;
}
/// <summary>
/// Prepares for parallel execution.
/// </summary>
/// <param name="tools">The tools.</param>
/// <param name="_context">The context.</param>
public webProjectKnowledgeSet PrepareForParallelExecution(classifierTools tools, experimentExecutionContext _context)
{
if (caseKnowledgeSet == null)
{
caseKnowledgeSet = new webProjectKnowledgeSet();
}
if (items.Any())
{
experimentContext.notes.log("Mining Context was ready already.");
return(caseKnowledgeSet);
}
DateTime startTime = DateTime.Now;
experimentContext = _context;
List <webCaseKnowledge> cases = new List <webCaseKnowledge>();
folderNode classReportFolder = experimentContext.folder.Add("General", "General and diagnostic reports", "The folder contains general (outside k-folds) reports on analysied industries (categories), web sites and other diagnostic data");
// <---------------------------------------------------------------------------------------------------------------- [ performing pipeline ]
experimentContext.notes.log("Executing the Mining Context decomposition with the pipeline model");
foreach (IDocumentSetClass classSet in experimentContext.classes.GetClasses())
{
var pipelineContext = GetContextForPipeline(tools, classSet);
sitesByCategory.Add(classSet, new List <pipelineTaskMCSiteSubject>());
if (!pipelineContext.exitByType.ContainsKey(typeof(pipelineTaskMCSiteSubject)))
{
throw new aceGeneralException("Pipeline context output contains no web site subjects! Check the pipeline Site Task constructor.", null, pipelineContext, "Pipeline broken");
}
var sitesForContext = pipelineContext.exitByType[typeof(pipelineTaskMCSiteSubject)]; // <----- preparing
foreach (var site in sitesForContext)
{
tokenBySite.Add(site as pipelineTaskMCSiteSubject, new ConcurrentBag <pipelineTaskSubjectContentToken>());
sitesByCategory[classSet].Add(site as pipelineTaskMCSiteSubject);
webCaseKnowledge webCase = new webCaseKnowledge(site as pipelineTaskMCSiteSubject, classSet);
caseKnowledgeSet.Add(webCase);
cases.Add(webCase);
}
semanticFVExtractorKnowledge kn = new semanticFVExtractorKnowledge();
kn.name = classSet.name + "_general";
kn.relatedItemPureName = classSet.name;
kn.type = WebFVExtractorKnowledgeType.aboutCompleteCategory;
kn.Deploy(classReportFolder, experimentContext.logger);
knowledgeByClass.TryAdd(classSet, kn);
}
experimentContext.notes.log("Sorting tokens for all sites [in parallel]");
Parallel.ForEach(tokenBySite.Keys, site =>
{
var leafs = site.getAllLeafs();
foreach (var leaf in leafs)
{
pipelineTaskSubjectContentToken token = leaf as pipelineTaskSubjectContentToken;
if (token != null)
{
tokenBySite[site].Add(token);
}
}
});
foreach (var c in cases)
{
c.tokens = tokenBySite[c.MCSiteSubject];
}
experimentContext.notes.log("Building diagnostic TF-IDF master tables for all classes [in parallel]");
Boolean useIntegratedApproach = false;
if (useIntegratedApproach)
{
var valCase = experimentContext.validationCollections[experimentContext.masterExtractor.name].GetDiagnosticCase(experimentContext.classes);
Parallel.ForEach(sitesByCategory, pair =>
{
knowledgeByClass.TryAdd(pair.Key, experimentContext.masterExtractor.DoFVExtractionForClassViaCases(valCase.trainingCases[pair.Key.classID], pair.Key, valCase, experimentContext.tools, experimentContext.logger));
});
}
else
{
Parallel.ForEach(sitesByCategory, pair =>
{
IDocumentSetClass category = pair.Key;
List <pipelineTaskMCSiteSubject> sites = pair.Value;
var lt = BuildLemmaTableForClass(tools, category, sites);
lt.Save();
// lt.SaveAs(classReportFolder.pathFor(lt.info.Name), imbSCI.Data.enums.getWritableFileMode.overwrite);
});
}
experimentContext.notes.log("Saving lexic resource cache subset - for later reuse in case of repeated experiment run");
tools.SaveCache();
if (!useIntegratedApproach)
{
experimentContext.notes.log("Performing chunk construction for all web sites in all categories [in serial]");
foreach (IDocumentSetClass classSet in experimentContext.classes.GetClasses())
{
BuildChunksForClass(tools, classSet);
}
foreach (IDocumentSetClass classSet in experimentContext.classes.GetClasses())
{
experimentContext.masterExtractor.chunkTableConstructor.process(chunksByCategory[classSet], cnt_level.mcPage, knowledgeByClass[classSet].WLChunkTableOfIndustryClass, null, experimentContext.logger, false);
}
}
if (tools.operation.doCreateDiagnosticMatrixAtStart)
{
experimentContext.notes.log("Performing diagnostic analysis on all categories...[doCreateDiagnosticMatrixAtStart=true]");
folderNode matrixReport = classReportFolder.Add("clouds", "More reports on semantic cloud", "Directory contains exported DirectedGraphs, varous matrix derivates, combined cloud and other diagnostic things");
List <lemmaSemanticCloud> clouds = new List <lemmaSemanticCloud>();
List <lemmaSemanticCloud> filteredClouds = new List <lemmaSemanticCloud>();
var converter = lemmaSemanticCloud.GetDGMLConverter();
foreach (IDocumentSetClass classSet in experimentContext.classes.GetClasses())
{
// experimentContext.masterExtractor.chunkTableConstructor.process(chunksByCategory[classSet], cnt_level.mcPage, knowledgeByClass[classSet].WLChunkTableOfIndustryClass, null, experimentContext.logger, false);
var cloud = experimentContext.masterExtractor.CloudConstructor.process(knowledgeByClass[classSet].WLChunkTableOfIndustryClass, knowledgeByClass[classSet].WLTableOfIndustryClass, knowledgeByClass[classSet].semanticCloud, experimentContext.logger, tokenBySite.Keys.ToList(), tools.GetLemmaResource());
knowledgeByClass[classSet].semanticCloud.className = classSet.name;
clouds.Add(cloud);
if (experimentContext.tools.operation.doUseSimpleGraphs)
{
cloud.GetSimpleGraph(true).Save(matrixReport.pathFor("cloud_initial_" + classSet.name, imbSCI.Data.enums.getWritableFileMode.none, "Initial version of full-sample set, diagnostic Semantic Cloud for category [" + classSet.name + "]"));
}
else
{
converter.ConvertToDMGL(cloud).Save(matrixReport.pathFor("cloud_initial_" + classSet.name, imbSCI.Data.enums.getWritableFileMode.none, "Initial version of full-sample set, diagnostic Semantic Cloud for category [" + classSet.name + "]"));
}
knowledgeByClass[classSet].semanticCloudFiltered = knowledgeByClass[classSet].semanticCloud.CloneIntoType <lemmaSemanticCloud>(true);
knowledgeByClass[classSet].semanticCloudFiltered.className = classSet.name;
filteredClouds.Add(knowledgeByClass[classSet].semanticCloudFiltered);
}
cloudMatrix matrix = new cloudMatrix("CloudMatrix", "Diagnostic cloud matrix created from the complete sample set of [" + clouds.Count() + "] classes");
matrix.build(filteredClouds, experimentContext.logger);
lemmaSemanticCloud mergedCloudInitial = matrix.GetUnifiedCloud();
mergedCloudInitial.Save(matrixReport.pathFor("unified_initial_cloud.xml", imbSCI.Data.enums.getWritableFileMode.overwrite, "Serialized object - Initial version of Semantic Cloud built as union of full-sample set Semantic Clouds of all categories"));
var reductions = matrix.TransformClouds(experimentContext.masterExtractor.settings.semanticCloudFilter, experimentContext.logger);
var p = matrixReport.pathFor("reductions_nodes.txt", imbSCI.Data.enums.getWritableFileMode.overwrite, "Report on Cloud Matrix transformation process");
File.WriteAllLines(p, reductions);
matrix.BuildTable(experimentContext.masterExtractor.settings.semanticCloudFilter, cloudMatrixDataTableType.initialState | cloudMatrixDataTableType.maxCloudFrequency | cloudMatrixDataTableType.absoluteValues).GetReportAndSave(matrixReport, appManager.AppInfo, "matrix_max_cf_initial", true, experimentContext.tools.operation.doReportsInParalell);
matrix.BuildTable(experimentContext.masterExtractor.settings.semanticCloudFilter, cloudMatrixDataTableType.initialState | cloudMatrixDataTableType.overlapSize | cloudMatrixDataTableType.absoluteValues).GetReportAndSave(matrixReport, appManager.AppInfo, "matrix_overlap_size_initial", true, experimentContext.tools.operation.doReportsInParalell);
matrix.BuildTable(experimentContext.masterExtractor.settings.semanticCloudFilter, cloudMatrixDataTableType.initialState | cloudMatrixDataTableType.overlapValue | cloudMatrixDataTableType.absoluteValues).GetReportAndSave(matrixReport, appManager.AppInfo, "matrix_overlap_value_initial", true, experimentContext.tools.operation.doReportsInParalell);
matrix.ExportTextReports(matrixReport, true, "matrix_cf");
matrix.ExportTextReports(matrixReport, false, "matrix_cf");
lemmaSemanticCloud mergedCloudAfterReduction = matrix.GetUnifiedCloud();
mergedCloudAfterReduction.Save(matrixReport.pathFor("unified_reduced_cloud.xml", imbSCI.Data.enums.getWritableFileMode.overwrite, "Serialized object -Version of all-categories diagnostic Semantic Cloud, after Cloud Matrix filter was applied"));
if (experimentContext.tools.operation.doUseSimpleGraphs)
{
mergedCloudInitial.GetSimpleGraph(true).Save(matrixReport.pathFor("unified_initial_cloud", imbSCI.Data.enums.getWritableFileMode.overwrite, "DirectedGraphML file - unified Semantic Cloud, before Cloud Matrix filter was applied - Open this in VisualStudo)"));
}
else
{
converter = lemmaSemanticCloud.GetDGMLConverter();
converter.ConvertToDMGL(mergedCloudInitial).Save(matrixReport.pathFor("unified_initial_cloud", imbSCI.Data.enums.getWritableFileMode.overwrite, "DirectedGraphML file - unified Semantic Cloud, before Cloud Matrix filter was applied - Open this in VisualStudo)"));
}
// <-------- analysis -----------------------------------------------------------------------------------
DataTableTypeExtended <freeGraphReport> cloudReports = new DataTableTypeExtended <freeGraphReport>();
foreach (var cl in filteredClouds)
{
freeGraphReport fgReport = new freeGraphReport(cl);
fgReport.Save(matrixReport);
cloudReports.AddRow(fgReport);
}
freeGraphReport unifiedReport = new freeGraphReport(mergedCloudAfterReduction);
unifiedReport.Save(matrixReport);
cloudReports.AddRow(unifiedReport);
cloudReports.GetReportAndSave(matrixReport, appManager.AppInfo, "analysis_SemanticClouds");
// <-------- analysis -----------------------------------------------------------------------------------
foreach (IDocumentSetClass classSet in experimentContext.classes.GetClasses())
{
var cloud = knowledgeByClass[classSet].semanticCloudFiltered; // .WLChunkTableOfIndustryClass, knowledgeByClass[classSet].WLTableOfIndustryClass, knowledgeByClass[classSet].semanticCloud, experimentContext.logger, tokenBySite.Keys.ToList());
if (experimentContext.tools.operation.doUseSimpleGraphs)
{
cloud.GetSimpleGraph(true).Save(matrixReport.pathFor("unified_initial_cloud", imbSCI.Data.enums.getWritableFileMode.overwrite, "DirectedGraphML file - unified Semantic Cloud, before Cloud Matrix filter was applied - Open this in VisualStudo)"));
}
else
{
converter = lemmaSemanticCloud.GetDGMLConverter();
converter.ConvertToDMGL(cloud).Save(matrixReport.pathFor("unified_initial_cloud", imbSCI.Data.enums.getWritableFileMode.overwrite, "DirectedGraphML file - unified Semantic Cloud, before Cloud Matrix filter was applied - Open this in VisualStudo)"));
}
//converter.ConvertToDMGL(cloud).Save(matrixReport.pathFor("cloud_reduced_" + classSet.name, imbSCI.Data.enums.getWritableFileMode.none, "DirectedGraphML file - Initial version of Semantic Cloud built as union of full-sample set Semantic Clouds of all categories (Open this with VS)"), imbSCI.Data.enums.getWritableFileMode.overwrite);
}
instanceCountCollection <String> tfcounter = new instanceCountCollection <string>();
foreach (IDocumentSetClass classSet in experimentContext.classes.GetClasses())
{
var wlt = knowledgeByClass[classSet].WLTableOfIndustryClass.GetDataTable();
wlt.DefaultView.Sort = "termFrequency desc";
var sorted = wlt.DefaultView.ToTable();
var tbl = wlt.GetClonedShema <DataTable>(true);
tbl.CopyRowsFrom(sorted, 0, 100);
tbl.GetReportAndSave(classReportFolder, appManager.AppInfo, classSet.name + "_WebLemma", true, experimentContext.tools.operation.doReportsInParalell);
var cht = knowledgeByClass[classSet].WLChunkTableOfIndustryClass.GetDataTable();
cht.DefaultView.Sort = "termFrequency desc";
var csorted = cht.DefaultView.ToTable();
tbl = cht.GetClonedShema <DataTable>(true);
tbl.CopyRowsFrom(csorted, 0, 100);
tbl.GetReportAndSave(classReportFolder, appManager.AppInfo, classSet.name + "_Chunks", true, experimentContext.tools.operation.doReportsInParalell);
tfcounter.AddInstanceRange(knowledgeByClass[classSet].WLTableOfIndustryClass.unresolved);
knowledgeByClass[classSet].OnBeforeSave();
}
List <String> countSorted = tfcounter.getSorted();
StringBuilder sb = new StringBuilder();
foreach (String s in countSorted)
{
sb.AppendLine(String.Format("{1} : {0}", s, tfcounter[s]));
}
String pt = classReportFolder.pathFor("unresolved_tokens.txt", imbSCI.Data.enums.getWritableFileMode.none, "Cloud Frequency list of all unresolved letter-only tokens");
File.WriteAllText(pt, sb.ToString());
}
if (tools.operation.doFullDiagnosticReport)
{
experimentContext.notes.log("Generating full diagnostic report on classes...");
DataTable rep = null;
foreach (IDocumentSetClass classSet in experimentContext.classes.GetClasses())
{
rep = this.GetClassKnowledgeReport(classSet, rep);
}
rep.SetAdditionalInfoEntry("Experiment", experimentContext.setup.name);
rep.AddExtra("Experiment: " + experimentContext.setup.name);
rep.AddExtra("Info: " + experimentContext.setup.description);
rep.SetDescription("Structural report for all classes in the experiment");
rep.GetReportAndSave(classReportFolder, appManager.AppInfo, "structural_class_report", true, experimentContext.tools.operation.doReportsInParalell);
}
classReportFolder.generateReadmeFiles(appManager.AppInfo);
experimentContext.notes.log("Mining Context preprocessing done in [" + DateTime.Now.Subtract(startTime).TotalMinutes.ToString("F2") + "] minutes");
return(caseKnowledgeSet);
}
