/// <summary>
/// Prepares this instance - clears temporary data
/// </summary>
public override void prepare()
{
pageTitleCount = new instanceCountCollection <string>();
pageTitleCount.compareModeDefault = instanceCountCollectionFormulae.keyCount;
// -- nothing to prepare
}
/// <summary>
/// Gets the counter.
/// </summary>
/// <param name="ofCurrentState">if set to <c>true</c> [of current state].</param>
/// <returns></returns>
public instanceCountCollection <String> GetCounter(Boolean ofCurrentState = true)
{
instanceCountCollection <String> counter = new instanceCountCollection <string>();
//lemmaSemanticCloud cloud = null;
List <String> doneAnalysis = new List <string>();
foreach (lemmaSemanticCloud x in this.Get1stKeys())
{
//if (cloud == null) cloud = x;
foreach (lemmaSemanticCloud y in this.Get2ndKeys(x))
{
//if (!doneAnalysis.Any(d => d.Contains(x.className) && d.Contains(y.className)))
//{
// if (x != y)
// {
var nd = this[x, y];
foreach (var n in nd)
{
counter.AddInstance(n.name);
}
// doneAnalysis.Add(x.className + " " + y.className);
// }
//}
/*
* if (ofCurrentState)
* {
*
* var nd = x.nodes;
* foreach (var n in nd)
* {
* counter.AddInstance(n.name);
* }
*
* }
* else
* {
*
*
*
* }*/
}
}
instanceCountCollection <String> output = new instanceCountCollection <string>();
foreach (String n in counter.Keys)
{
output.AddInstance(n, Convert.ToInt32(Math.Sqrt(counter[n])));
}
output.reCalculate();
return(output);
}
/// <summary>
/// Gets the text token stats.
/// </summary>
/// <returns></returns>
public instanceCountCollection <string> GetTextTokenStats()
{
instanceCountCollection <string> output = new instanceCountCollection <string>();
output.AddInstanceRange((IEnumerable <string>)textContent.getTokens(true, true, true));
return(output);
}
/// <summary>
/// Calculates the entropy.
/// </summary>
/// <returns></returns>
public double CalculateEntropy()
{
instanceCountCollection <string> textStats = GetTextTokenStats();
textStats.reCalculate();
return(textStats.entropyFreq);
}
public void process()
{
stats = new instanceCountCollection <tokenQueryResultEnum>();
foreach (tokenQuerySourceEnum key in Keys)
{
foreach (tokenQueryResponse res in this[key])
{
stats.AddInstance(res.response, 1);
if (res.description.isNullOrEmpty())
{
description.Add(res.description);
}
//if (flags.Contains(res)
}
}
}
public override void Learn(IEnumerable <TextDocumentSet> documentSets)
{
foreach (TextDocumentSet docSet in documentSets)
{
stats.Add(docSet.name, new Dictionary <String, instanceCountCollection <String> >());
foreach (TextDocumentLayerCollection document in docSet)
{
String content = document.ToString();
List <String> tkns = content.getTokens(true, true, true, false, 4);
instanceCountCollection <string> ft = new instanceCountCollection <string>();
ft.AddInstanceRange(tkns);
ft.reCalculate(instanceCountCollection <string> .preCalculateTasks.all);
stats[docSet.name].Add(document.name, ft);
}
}
}
/// <summary>
/// Gets all proper tokens sorted by frequency.
/// </summary>
/// <param name="input_contentTokens">The input content tokens.</param>
/// <param name="tokenLengthMin">The token length minimum.</param>
/// <param name="input_ignoredTokens">The input ignored tokens.</param>
/// <returns></returns>
public List <string> GetAllProperTokensSortedByFrequency(IEnumerable <string> input_contentTokens, int tokenLengthMin, List <string> input_ignoredTokens)
{
instanceCountCollection <string> tokenFrequency = new instanceCountCollection <string>();
if (input_ignoredTokens == null)
{
input_ignoredTokens = new List <string>();
}
// <----- preprocessing token input
foreach (string token in input_contentTokens)
{
bool useOk = true;
if (useOk && (token.isNullOrEmptyString()))
{
useOk = false;
}
if (useOk && (token == Environment.NewLine))
{
useOk = false;
}
if (useOk && (token.Length < tokenLengthMin))
{
useOk = false;
}
if (useOk && (input_ignoredTokens.Contains(token)))
{
useOk = false;
}
if (useOk && (globalIgnoreList.Contains(token)))
{
useOk = false;
}
if (useOk)
{
tokenFrequency.AddInstance(token);
}
}
List <string> tokenToTest = tokenFrequency.getSorted();
return(tokenToTest);
}
public Int32 GetDominantClass()
{
instanceCountCollection <Int32> counter = new instanceCountCollection <int>();
foreach (FeatureVector fv in this)
{
Int32 dd = fv.GetDominantDimension();
if (dd > -1)
{
counter.AddInstance(dd);
}
}
if (counter.Count == 0)
{
return(-1);
}
return(counter.getSorted(1).First());
}
/// <summary>
/// Prepares the factor by processing the context
/// </summary>
/// <param name="context">The context.</param>
/// <param name="log">The log.</param>
public override void Prepare(DocumentSelectResult context, ILogBuilder log)
{
statsByAssignedID.Clear();
foreach (DocumentSelectResultEntry docEntry in context.items)
{
instanceCountCollection <string> ft = new instanceCountCollection <string>();
if (docEntry.type.HasFlag(DocumentSelectEntryType.spaceDocument))
{
SpaceDocumentModel document = docEntry.spaceDocument;
foreach (var term in document.terms.GetTokens())
{
ft.AddInstance(term, document.terms.GetTokenFrequency(term));
}
}
else if (docEntry.type.HasFlag(DocumentSelectEntryType.textDocument))
{
String content = docEntry.textDocument.content; // document.ToString();
List <String> tkns = content.getTokens(true, true, true, false, 4);
foreach (String tkn in tkns)
{
String stem = tkn;
if (useStems)
{
stem = context.stemmingContext.Stem(tkn);
}
ft.AddInstance(stem);
}
}
statsByAssignedID.Add(docEntry.AssignedID, ft);
assignedIDs.Add(docEntry.AssignedID);
}
}
/// <summary>
/// Exports the text report
/// </summary>
/// <param name="folder">The folder.</param>
/// <param name="reduced">if set to <c>true</c> [reduced].</param>
/// <param name="prefix">The prefix.</param>
public void ExportTextReports(folderNode folder, Boolean reduced, String prefix = "")
{
foreach (lemmaSemanticCloud x in this.Get1stKeys())
{
instanceCountCollection <string> c = GetCounter(reduced);
var srt = c.getSorted();
String fn = prefix + x.className;
if (reduced)
{
fn = fn + "_reduced_";
}
else
{
fn = fn + "_initial_";
}
fn = fn + "_overlap.txt";
fn = folder.pathFor(fn, imbSCI.Data.enums.getWritableFileMode.overwrite, "Cloud Frequency report for all terms in the Cloud Matrix");
List <String> lines = new List <string>();
foreach (string ci in srt)
{
if (reduced)
{
if (c[ci] > 1)
{
lines.Add(String.Format("{1} : {0}", ci, c[ci] - 1));
}
}
else
{
lines.Add(String.Format("{1} : {0}", ci, c[ci]));
}
}
File.WriteAllLines(fn, lines);
}
}
/// <summary>
/// Prepares this instance - clears temporary data
/// </summary>
public override void prepare()
{
titleWords = new instanceCountCollection <string>();
// -- nothing to prepare
}
/// <summary>
/// Processes the complex.
/// </summary>
/// <param name="chunkTable">The chunk table.</param>
/// <param name="termTable">The term table.</param>
/// <param name="output">The output.</param>
/// <param name="logger">The logger.</param>
/// <param name="subjects">The subjects.</param>
/// <param name="resolver">The resolver.</param>
/// <returns></returns>
protected lemmaSemanticCloud processAlternative(webLemmaTermTable chunkTable, webLemmaTermTable termTable, lemmaSemanticCloud output, ILogBuilder logger, List <pipelineTaskMCSiteSubject> subjects, ITextResourceResolver resolver)
{
if (output == null)
{
output = new lemmaSemanticCloud();
}
lemmaSemanticConstruct c = new lemmaSemanticConstruct(subjects);
List <webLemmaTerm> allChunks = chunkTable.GetList();
// <--------------------------------- DETECTING THE MOST IMPORTANT TERMS
IEnumerable <webLemmaTerm> vipChunks = null;
if (subjects.Count > 1)
{
vipChunks = allChunks.Where(x => x.documentSetFrequency > settings.documentSetFreqLowLimit);
}
else
{
vipChunks = allChunks;
}
instanceCountCollection <String> lemmaCounter = new instanceCountCollection <string>();
List <List <String> > primaryLemmaList = new List <List <String> >();
foreach (webLemmaTerm chunk in vipChunks)
{
var lemmas = chunk.nominalForm.SplitSmart(" ", "", true, true);
lemmas = lemmas.Where(x => x.Length > 2).ToList();
lemmaCounter.AddInstanceRange(lemmas);
}
c.RelevantTerms = lemmaCounter.getSorted();
lemmaCounter.reCalculate();
foreach (String term in c.RelevantTerms)
{
if (lemmaCounter[term] == lemmaCounter.maxFreq)
{
c.PrimaryTerms.Add(term);
}
else if (lemmaCounter[term] > lemmaCounter.minFreq)
{
c.SecondaryTerms.Add(term);
}
else
{
c.ReserveTerms.Add(term);
}
}
c.CollectRelevantTerms(settings.doReserveTermsForClass);
c.LogConstruct(logger);
// <---------------------------------
var docSetFreq = allChunks.Where(x => c.RelevantTerms.Any(y => x.nominalForm.SplitSmart(" ", "", true, true).Contains(y)));
foreach (webLemmaTerm chunk in docSetFreq)
{
var lemmas = chunk.nominalForm.SplitSmart(" ", "", true, true);
lemmas = lemmas.Where(x => x.Length > 2).ToList();
if (lemmas.Count > 1)
{
lemmas.Sort((x, y) => String.CompareOrdinal(x, y));
c.lemmasList.Add(lemmas);
c.weightDict.Add(lemmas, chunk);
c.nodeNames.AddRange(lemmas, true);
}
}
return(BuildCloud(c, chunkTable, termTable, output, logger, resolver));
}
/// <summary>
/// Gets the value for cell targeted
/// </summary>
/// <param name="x">The x.</param>
/// <param name="y">The y.</param>
/// <param name="type">The type.</param>
/// <param name="counter">The counter.</param>
/// <returns></returns>
public Double GetCellNumber(lemmaSemanticCloud x, lemmaSemanticCloud y, cloudMatrixDataTableType type, instanceCountCollection <String> counter)
{
Double output = 0;
List <freeGraphNodeBase> selected = this[x, y];
Double min = MaxCloudFrequency;
Double max = MinCloudFrequency;
if (type.HasFlag(cloudMatrixDataTableType.overlapValue))
{
if (type.HasFlag(cloudMatrixDataTableType.initialState))
{
output = selected.Sum(s => s.weight);
}
else
{
output = x.GetOverlap(y).Sum(s => s.weight);
}
}
if (output == 0)
{
if (type.HasFlag(cloudMatrixDataTableType.normalizedValues))
{
if (type.HasFlag(cloudMatrixDataTableType.overlapSize))
{
if (type.HasFlag(cloudMatrixDataTableType.initialState))
{
output = selected.Count.GetRatio(MaxOverlap);
}
else
{
if (x == y)
{
output = 0;
}
else
{
output = x.GetOverlap(y).Count.GetRatio(selected.Count);
}
}
}
else if (type.HasFlag(cloudMatrixDataTableType.maxCloudFrequency) || type.HasFlag(cloudMatrixDataTableType.minCloudFrequency))
{
for (int i = 0; i < selected.Count; i++)
{
freeGraphNodeBase ne = selected[i];
min = Math.Min(min, (Double)counter[ne.name]);
max = Math.Max(max, (Double)counter[ne.name]);
}
if (type.HasFlag(cloudMatrixDataTableType.maxCloudFrequency))
{
output = max.GetRatio(MaxCloudFrequency);
}
else
{
output = min.GetRatio(MinCloudFrequency);
}
}
}
else
{
if (type.HasFlag(cloudMatrixDataTableType.overlapSize))
{
if (type.HasFlag(cloudMatrixDataTableType.initialState))
{
output = selected.Count;
}
else
{
if (x == y)
{
output = 0;
}
else
{
output = x.GetOverlap(y).Count;
}
}
}
else if (type.HasFlag(cloudMatrixDataTableType.maxCloudFrequency) || type.HasFlag(cloudMatrixDataTableType.minCloudFrequency))
{
for (int i = 0; i < selected.Count; i++)
{
freeGraphNodeBase ne = selected[i];
min = Math.Min(min, (Double)counter[ne.name]);
max = Math.Max(max, (Double)counter[ne.name]);
}
if (type.HasFlag(cloudMatrixDataTableType.maxCloudFrequency))
{
output = max;
}
else
{
output = min;
}
}
}
}
return(output);
}
public industryLemmaRankTable process(webLemmaTermTable chunkTable, webLemmaTermTable termTable, industryLemmaRankTable output)
{
List <webLemmaTerm> allChunks = chunkTable.GetList();
var docSetFreq = allChunks.Where(x => x.documentSetFrequency > 1);
instanceCountCollection <String> termCounter = new instanceCountCollection <string>();
aceDictionarySet <String, String> dict = new aceDictionarySet <string, string>();
foreach (webLemmaTerm chunk in docSetFreq)
{
var lemmas = chunk.nominalForm.SplitSmart(textMapBase.SEPARATOR, "", true, true);
lemmas = lemmas.Where(x => x.Length > 2).ToList();
termCounter.AddInstanceRange(lemmas);
foreach (String lm in lemmas)
{
foreach (String lmi in lemmas)
{
if (lmi != lm)
{
dict[lm].AddUnique(lmi);
}
}
}
}
List <String> primaries = new List <string>();
foreach (var pair in termCounter)
{
if (termCounter[pair] > 1)
{
primaries.Add(pair);
industryLemmaTerm lemma = output.GetOrCreate(pair);
lemma.termType = industryLemmaTermType.primary;
lemma.weight = settings.PrimaryTermFactor * termTable[lemma.name].weight;
lemma.nominalForm = pair;
output.AddOrUpdate(lemma);
if (dict.ContainsKey(lemma.nominalForm))
{
foreach (String secLemmas in dict[lemma.nominalForm])
{
industryLemmaTerm lemmaSec = output.GetOrCreate(secLemmas);
if (lemmaSec.termType == industryLemmaTermType.none)
{
lemmaSec.termType = industryLemmaTermType.secondary;
lemmaSec.weight = settings.SecondaryTermFactor * termTable[lemmaSec.name].weight;
lemmaSec.nominalForm = secLemmas;
output.AddOrUpdate(lemmaSec);
}
}
}
}
}
//var reserveChunks = allChunks.Where(x => x.nominalForm.ContainsAny(primaries));
//aceDictionarySet<String, String> dictReserve = new aceDictionarySet<string, string>();
//foreach (webLemmaTerm chunk in reserveChunks)
//{
// var lemmas = chunk.nominalForm.SplitSmart(textMapBase.SEPARATOR, "", true, true);
// lemmas = lemmas.Where(x => x.Length > 2).ToList();
// String prim = lemmas.FirstOrDefault(x => primaries.Contains(x));
// if (!prim.isNullOrEmpty())
// {
// foreach (String lm in lemmas)
// {
// if (prim != lm)
// {
// dictReserve[prim].AddUnique(lm);
// }
// }
// }
//}
//foreach (String prim in primaries)
//{
// if (dictReserve.ContainsKey(prim))
// {
// foreach (String res in dictReserve[prim])
// {
// industryLemmaTerm resLemma = output.GetOrCreate(res);
// if (resLemma.termType == industryLemmaTermType.none)
// {
// resLemma.nominalForm = res;
// resLemma.weight = settings.ReserveTermFactor *termTable[resLemma.name].weight;
// resLemma.termType = industryLemmaTermType.reserve;
// }
// output.AddOrUpdate(resLemma);
// }
// }
//}
return(output);
}
public static lemmaSemanticConstruct NextIteration(lemmaSemanticConstruct lastIteration, ITextResourceResolver resolver, List <webLemmaTerm> allChunks, cloudConstructorSettings settings, List <pipelineTaskMCSiteSubject> subjects, ILogBuilder logger)
{
var cl = lastIteration;
var c = new lemmaSemanticConstruct(subjects);
c.createdInIteration = lastIteration.createdInIteration + 1;
c.PTCountMin = Math.Min(lastIteration.PTCountMin, lastIteration.PrimaryTerms.Count);
c.PTCountMax = Math.Max(lastIteration.PTCountMax, lastIteration.PrimaryTerms.Count);
if (!c.isCaseCloud)
{
c.onTopChunks.AddRange(allChunks.Where(x => x.documentSetFrequency > (settings.documentSetFreqLowLimit + lastIteration.createdInIteration)));
}
else
{
if (!settings.doFactorToCaseClouds)
{
c.OptimizationDone = true;
}
c.onTopChunks = allChunks;
}
if (!c.isCaseCloud)
{
instanceCountCollection <String> lemmaCounter = new instanceCountCollection <string>();
List <List <String> > primaryLemmaList = new List <List <String> >();
foreach (webLemmaTerm chunk in c.onTopChunks)
{
var lemmas = chunk.nominalForm.SplitSmart(" ", "", true, true);
lemmaCounter.AddInstanceRange(lemmas);
}
lemmaCounter.reCalculate();
foreach (String st in lemmaCounter)
{
if (lemmaCounter.maxFreq == 1 || lemmaCounter[st] > 1)
{
var lu = resolver.GetLexicUnit(st, logger);
if (lu == null)
{
c.TrashBin.AddUnique(st);
}
else
{
var tg = lu.GetTagFromGramTags <pos_type>(pos_type.none);
if (tg.Contains(pos_type.N))
{
c.PrimaryTerms.AddUnique(st);
}
else if (tg.Contains(pos_type.A))
{
c.SecondaryTerms.AddUnique(st);
}
else
{
c.TrashBin.AddUnique(st);
}
}
}
}
; // <---------------------------- Primary terms extracted
if (c.PrimaryTerms.Count == 0)
{
if (c.SecondaryTerms.Any())
{
logger.log(":: Moving Adjective terms [" + c.SecondaryTerms.Count + "] to Primary Terms category, as no Nouns were qualified to the cateogry");
c.PrimaryTerms.AddRange(c.SecondaryTerms);
c.SecondaryTerms.Clear();
}
}
}
instanceCountCollection <String> secondCounter = new instanceCountCollection <string>();
foreach (webLemmaTerm chunk in allChunks)
{
var lemmas = chunk.nominalForm.SplitSmart(" ", "", true, true);
secondCounter.AddInstanceRange(lemmas);
}
foreach (webLemmaTerm chunk in allChunks)
{
var lemmas = chunk.nominalForm.SplitSmart(" ", "", true, true);
if (lemmas.ContainsAny(c.PrimaryTerms))
{
if (c.onTopChunks.Contains(chunk))
{
c.primaryChunks.Add(chunk);
}
else
{
c.secondaryChunks.Add(chunk);
}
foreach (String lm in lemmas)
{
if (c.NotProcessed(lm))
{
var lu = resolver.GetLexicUnit(lm, logger);
if (lu == null)
{
c.TrashBin.AddUnique(lm);
}
else
{
var tg = lu.GetTagFromGramTags <pos_type>(pos_type.none);
if (tg.ContainsAny(new pos_type[] { pos_type.N, pos_type.A }))
{
c.SecondaryTerms.AddUnique(lm);
}
else
{
c.TrashBin.AddUnique(lm);
}
}
}
}
}
else
{
foreach (String lm in lemmas)
{
if (secondCounter[lm] > settings.termInChunkLowerLimit)
{
if (c.NotProcessed(lm))
{
var lu = resolver.GetLexicUnit(lm, logger);
if (lu == null)
{
c.TrashBin.AddUnique(lm);
}
else
{
var tg = lu.GetTagFromGramTags <pos_type>(pos_type.none);
if (tg.ContainsAny(new pos_type[] { pos_type.N, pos_type.A }))
{
c.ReserveTerms.AddUnique(lm);
}
else
{
c.TrashBin.AddUnique(lm);
}
}
}
}
else
{
c.TrashBin.AddUnique(lm);
}
}
}
}
if (c.OptimizationDone)
{
return(c);
}
c.PTCountMin = Math.Min(lastIteration.PTCountMin, c.PrimaryTerms.Count);
c.PTCountMax = Math.Max(lastIteration.PTCountMax, c.PrimaryTerms.Count);
if (c.PrimaryTerms.Count <= settings.primaryTermLowTargetCount)
{
if (lastIteration.PrimaryTerms.Count < c.PrimaryTerms.Count)
{
logger.log("[" +
c.createdInIteration.ToString("D3") + "] PrimaryTerms count [" + c.PrimaryTerms.Count + "] after [" + c.createdInIteration + "] iterations optimized ---- Max[" + c.PTCountMax + "] Min[" + c.PTCountMin + "] T:" + Thread.CurrentThread.Name);
}
else
{
logger.log("[" + c.createdInIteration.ToString("D3") + "] PrimaryTerms count changed from [" + lastIteration.PrimaryTerms.Count + "] to [" + c.PrimaryTerms.Count + "] --- Max[" + c.PTCountMax + "] Min[" + c.PTCountMin + "] T:" + Thread.CurrentThread.Name);
logger.log("[" +
c.createdInIteration.ToString("D3") + "] previous PrimaryTerms count [" + lastIteration.PrimaryTerms.Count + "] accepted, after [" + c.createdInIteration + "] ---- Max[" + c.PTCountMax + "] Min[" + c.PTCountMin + "] T:" + Thread.CurrentThread.Name);
c = lastIteration;
}
c.OptimizationDone = true;
}
else
{
logger.log("[" + c.createdInIteration.ToString("D3") + "] PrimaryTerms count changed from [" + lastIteration.PrimaryTerms.Count + "] to [" + c.PrimaryTerms.Count + "] --- Max[" + c.PTCountMax + "] Min[" + c.PTCountMin + "] T:" + Thread.CurrentThread.Name);
}
return(c);
}
public bool discoverGram(termExploreItem item, ILogBuilder loger, bool debug = true)
{
//List<termExploreItem> inst = new List<termExploreItem>();
//exploreModel.instances.ForEach(x => inst.Add(x));
//inst.Add(exploreModel);
// instanceCountCollection<pos_type> pct = new instanceCountCollection<pos_type>();
bool failed = false;
//// <--------------- Trying to resolve alone
//foreach (termExploreItem item in inst)
//{
if (loger != null)
{
loger.AppendLine("Item:" + item.inputForm);
}
instanceCountCollection <object> res = termDiscoveryResolver.resolveQuery(item.inputForm);
res.reCalculate();
if (res.Count > 0)
{
List <object> sorted = res.getSorted();
if (item.gramSet.getPosType() != pos_type.none)
{
sorted.RemoveAll(x => x is pos_type);
}
gramFlags gf = new gramFlags();
if (sorted.Any(x => x is pos_type))
{
gf.Set((pos_type)sorted.First(x => x is pos_type));
}
//pct.AddInstance(gf.type, 1);
var tl = posConverter.posTypeVsPattern[gf.type];
sorted.RemoveAll(x => !tl.Contains(x.GetType()));
if (loger != null)
{
loger.AppendLine("Votes:");
for (int i = 0; i < Math.Max(sorted.Count(), 20); i++)
{
loger.Append(sorted[i].ToString() + "; ");
}
}
if (sorted.Any(x => x is pos_gender))
{
gf.Set((pos_gender)sorted.First(x => x is pos_gender));
}
if (sorted.Any(x => x is pos_gramaticalCase))
{
gf.Set((pos_gramaticalCase)sorted.First(x => x is pos_gramaticalCase));
}
if (sorted.Any(x => x is pos_verbform))
{
gf.Set((pos_verbform)sorted.First(x => x is pos_verbform));
}
if (sorted.Any(x => x is pos_number))
{
gf.Set((pos_number)sorted.First(x => x is pos_number));
}
if (sorted.Any(x => x is pos_degree))
{
gf.Set((pos_degree)sorted.First(x => x is pos_degree));
}
if (sorted.Any(x => x is pos_person))
{
gf.Set((pos_person)sorted.First(x => x is pos_person));
}
if (loger != null)
{
loger.AppendLine("Final gram:" + gf.ToString());
}
item.gramSet.Add(gf);
}
else
{
if (item.inputForm.Length < 4)
{
return(false);
}
//item.flags = termExploreItemEnumFlag.none;
failed = true;
}
return(failed);
}
/// <summary>
/// Builds the table.
/// </summary>
/// <param name="settings">The settings.</param>
/// <param name="type">The type.</param>
/// <returns></returns>
public DataTable BuildTable(cloudMatrixSettings settings, cloudMatrixDataTableType type)
{
DataTable table = new DataTable();
table.SetTitle("CloudMatrix_" + name);
table.SetDescription(description.or("Semantic cloud matrix report"));
List <lemmaSemanticCloud> clouds = this.Get1stKeys().ToList();
Int32 ci = 0;
foreach (lemmaSemanticCloud cl in clouds)
{
table.SetAdditionalInfoEntry("Cloud " + ci, cl.className);
if (cl.className.isNullOrEmpty())
{
cl.className = "C" + ci.ToString("D2");
}
if (cl.name.isNullOrEmpty())
{
cl.name = cl.className;
}
ci++;
}
instanceCountCollection <String> counter = GetCounter(type.HasFlag(cloudMatrixDataTableType.initialState));
String format = "F5";
if (type.HasFlag(cloudMatrixDataTableType.normalizedValues))
{
format = "F5";
}
else
{
format = "";
}
table.Add("Class", "Name of DocumentSetClass attached to the semantic clouds", "", typeof(String), imbSCI.Core.enums.dataPointImportance.normal);
for (int i = 0; i < clouds.Count; i++)
{
table.Add(clouds[i].className, clouds[i].description, "C_" + i.ToString(), typeof(Double), imbSCI.Core.enums.dataPointImportance.normal, format, clouds[i].className);
}
table.Add("LemmasInitial", "Number of lemmas in the cloud, before reduction", "", typeof(Int32), imbSCI.Core.enums.dataPointImportance.important, "", "Lemmas - initial");
table.Add("LinkRateInitial", "Link per node ratio, initial state", "", typeof(Double), imbSCI.Core.enums.dataPointImportance.normal, "F3", "Link rate initial");
table.Add("LemmasAfter", "Number of lemmas in the cloud, after reduction", "", typeof(Int32), imbSCI.Core.enums.dataPointImportance.important, "", "Lemmas - after");
table.Add("LinkRateAfter", "Link per node ratio, after reduction", "", typeof(Int32), imbSCI.Core.enums.dataPointImportance.normal, "F3", "Link rate after");
for (int y = 0; y < clouds.Count; y++)
{
DataRow dr = table.NewRow();
dr["Class"] = clouds[y].className;
for (int x = 0; x < clouds.Count; x++)
{
if (y == x)
{
dr[clouds[x].className] = 0;
}
else
{
dr[clouds[x].className] = GetCellNumber(clouds[x], clouds[y], type, counter);
}
}
dr["LemmasInitial"] = numberOfLemmas[clouds[y]];
dr["LemmasAfter"] = clouds[y].CountNodes();
dr["LinkRateInitial"] = numberOfLinks[clouds[y]].GetRatio(numberOfLemmas[clouds[y]]);
dr["LinkRateAfter"] = clouds[y].CountLinks().GetRatio(clouds[y].CountNodes());
table.Rows.Add(dr);
}
if (type.HasFlag(cloudMatrixDataTableType.overlapValue))
{
DataRow dr = table.NewRow();
dr["Class"] = "Weight sums";
for (int y = 0; y < clouds.Count; y++)
{
Double sum = 0;
for (int x = 0; x < clouds.Count; x++)
{
sum += this[clouds[x], clouds[y]].Sum(c => c.weight); // GetCellNumber(clouds[x], clouds[y], type, counter);
}
dr[clouds[y].className] = sum;
//dr[clouds[x].name] = clouds[x].nodes.Sum(s => s.weight);
}
dr["LemmasInitial"] = 0;
dr["LemmasAfter"] = 0;
dr["LinkRateInitial"] = 0;
dr["LinkRateAfter"] = 0;
table.Rows.Add(dr);
}
var ty = type.getEnumListFromFlags <cloudMatrixDataTableType>();
foreach (cloudMatrixDataTableType t in ty)
{
table.SetAdditionalInfoEntry(t.toStringSafe(), t.toStringSafe().imbTitleCamelOperation(true));
}
if (type.HasFlag(cloudMatrixDataTableType.initialState))
{
table.AddExtra("The table shows the state of the matrix before transformation (filtration).");
}
else
{
table.AddExtra("The table shows the state of the matrix after transformation (filtration).");
}
if (type.HasFlag(cloudMatrixDataTableType.overlapSize))
{
table.AddExtra("Values in the table are showing number of lemmas that are common to the clouds (of x and y axis).");
}
else if (type.HasFlag(cloudMatrixDataTableType.maxCloudFrequency))
{
table.AddExtra("Values in the table are showing highest Cloud Frequency for a term (at x and y axis).");
}
else if (type.HasFlag(cloudMatrixDataTableType.minCloudFrequency))
{
table.AddExtra("Values in the table are showing lowest Cloud Frequency for a term (at x and y axis).");
}
else if (type.HasFlag(cloudMatrixDataTableType.overlapValue))
{
table.AddExtra("Values in the table are showing sum of local weights for overlapping terms. The last row contains sum of weights for the class cloud.");
}
if (type.HasFlag(cloudMatrixDataTableType.normalizedValues))
{
if (type.HasFlag(cloudMatrixDataTableType.overlapSize))
{
table.AddExtra("The values are normalized to 0-1, where 1 is overlap size in initial state for each x,y cell.");
}
else
{
table.AddExtra("The values are normalized to 0-1.");
}
}
else
{
table.AddExtra("The values are absolute.");
}
table.SetAdditionalInfoEntry("Max. CF", MaxCloudFrequency);
table.SetAdditionalInfoEntry("Min. CF", MinCloudFrequency);
table.SetAdditionalInfoEntry("Max. Overlap", MaxOverlap);
table.SetAdditionalInfoEntry("Min. Overlap", MinOverlap);
return(table);
}
/// <summary>
/// Transforms the clouds, related
/// </summary>
/// <param name="settings">The settings.</param>
/// <param name="logger">The logger.</param>
/// <param name="reductionReportName">Name of the reduction report.</param>
/// <returns>
/// Notes about reduced weights
/// </returns>
public cloudMatrixReductionReport TransformClouds(cloudMatrixSettings settings, ILogBuilder logger, String reductionReportName = "")
{
cloudMatrixReductionReport reductions = new cloudMatrixReductionReport();
reductions.name = reductionReportName;
instanceCountCollection <String> counter = GetCounter(false);
List <String> passNames = new List <string>();
List <String> removeNames = new List <string>();
List <String> removeByLPFNames = new List <string>();
List <String> setMiniNames = new List <string>();
// lemmaSemanticCloud cloud = this.First().Key;
MinCloudFrequency = counter.minFreq;
MaxCloudFrequency = counter.maxFreq;
Double lowPass = settings.lowPassFilter;
if (!settings.isActive)
{
logger.log("Cloud matrix disabled");
return(reductions);
}
if (settings.isFilterInAdaptiveMode)
{
lowPass = (MinCloudFrequency - 1) + lowPass;
if (lowPass > MaxCloudFrequency)
{
lowPass = MaxCloudFrequency;
}
if (lowPass < 1)
{
lowPass = 1;
}
logger.log(": Cloud matrix filter in adaptive mode - cut off frequency set: " + lowPass);
}
var sorted = counter.getSorted();
// <------------------------------------------------------------------------------------------ LOW PASS FILTER LIST
List <String> doNotReduceWeight = new List <string>();
foreach (String n in sorted) // <--------- performing cut of filter
{
if (settings.doCutOffByCloudFrequency)
{
Int32 freq = counter[n];
Boolean passOk = true;
if (counter[n] > lowPass)
{
passOk = false;
}
if (passOk)
{
passNames.AddUnique(n);
}
else
{
if (settings.doAssignMicroWeightInsteadOfRemoval)
{
// passNames.AddUnique(n);
setMiniNames.AddUnique(n);
// reductions.Add("All", n, "[" + n + "] weight set to the microWeightNoiseGate limit");
// doNotReduceWeight.Add(n);
}
else
{
removeByLPFNames.AddUnique(n);
//reductions.Add("[" + n + "] was removed");
}
}
}
else
{
passNames.Add(n);
}
}
// <------------------------------------------------------------------------------------------ LOW PASS FILTER LIST
foreach (lemmaSemanticCloud y in this.Get1stKeys())
{
y.RebuildIndex();
y.description = y.description + " filtered version of cloud";
reductions.Nodes += y.CountNodes();
reductions.InitialWeight += y.nodes.Sum(x => x.weight);
}
foreach (lemmaSemanticCloud cloud in this.Get1stKeys())
{
// <--- apply LPF
foreach (String setMini in setMiniNames)
{
var node = cloud.GetNode(setMini, true);
if (node != null)
{
reductions.Add(cloud.name, node.name, node.weight, settings.microWeightNoiseGate, cloudMatrixReductionAction.LowPassFilter);
node.weight = settings.microWeightNoiseGate;
}
}
if (settings.doDivideWeightWithCloudFrequency || settings.doUseSquareFunctionOfCF)
{
Int32 rem = 0;
foreach (String n in passNames)
{
var node = cloud.GetNode(n, true);
if (node != null)
{
Double cf = counter[n];
if (settings.doDemoteAnyRepeatingSecondaryTerm)
{
if (cf > 1)
{
if (node.type == 1)
{
node.type = 0;
reductions.Add(cloud.name, node.name, node.weight, node.weight, cloudMatrixReductionAction.Demotion);
//node.weight = node.weight * 0.5;
}
}
}
if (settings.doRemoveAnyRepeatingPrimaryTerm)
{
if (cf > 1)
{
if (node.type == 2)
{
reductions.Add(cloud.name, node.name, node.weight, 0, cloudMatrixReductionAction.Demotion);
node.weight = 0;
}
}
}
else if (settings.doDemoteAnyRepeatingPrimaryTerm)
{
if (cf > 1)
{
if (node.type == 2)
{
reductions.Add(cloud.name, node.name, node.weight, node.weight, cloudMatrixReductionAction.Demotion);
//node.weight = node.weight * 0.5;
node.type = 1;
}
}
}
if (!doNotReduceWeight.Contains(n))
{
if (node.weight > 0)
{
//var cfd = cf + 1;
if (cf > 1)
{
Double nw = node.weight;
if (settings.doUseSquareFunctionOfCF)
{
node.weight = node.weight.GetRatio(cf * cf);
}
else
{
node.weight = node.weight.GetRatio(cf);
}
if (nw > node.weight)
{
reductions.Add(cloud.name, node.name, nw, node.weight, cloudMatrixReductionAction.CF_function);
// reductions.Add("Term [" + node.name + "] weight [" + nw.ToString("F5") + "] reduced to [" + node.weight + "] in " + cloud.className + " CF[" + cf + "]");
}
}
}
if (node.weight > settings.microWeightNoiseGate)
{
}
else
{
if (node.weight < settings.microWeightNoiseGate)
{
removeNames.AddUnique(n);
//y.Remove(n);
rem++;
}
}
}
}
}
}
}
foreach (lemmaSemanticCloud y in this.Get1stKeys())
{
Int32 rem = 0;
foreach (String n in removeNames)
{
var node = y.GetNode(n);
if (y.Remove(n))
{
rem++;
reductions.Add(y.name, node.name, node.weight, 0, cloudMatrixReductionAction.Microweight);
//reductions.Add("Term [" + n + "] removed from [" + y.className + "]");
}
}
foreach (String n in removeByLPFNames)
{
var node = y.GetNode(n);
if (y.Remove(n))
{
rem++;
reductions.Add(y.name, node.name, node.weight, 0, cloudMatrixReductionAction.LPFRemoval);
}
}
if (rem > 0)
{
logger.log(y.className + ": Terms removed[" + rem.ToString("D6") + "] left[" + y.CountNodes().ToString("D6") + "]");
}
}
foreach (lemmaSemanticCloud y in this.Get1stKeys())
{
y.RebuildIndex();
y.description = y.description + " filtered version of cloud";
// reductions.Nodes += y.CountNodes();
reductions.ReducedWeight += y.nodes.Sum(x => x.weight);
}
logger.log("Clouds transformation done.");
return(reductions);
}
/// <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);
}