/// <summary>
/// Transforms to fv dictionary.
/// </summary>
/// <param name="context">The context.</param>
/// <param name="TermWeightModel">The term weight model.</param>
/// <param name="function">The function.</param>
/// <returns></returns>
public static FeatureVectorSetDictionary TransformToFVDictionaryAsCategorySimilarity(this DocumentSelectResult context, FeatureWeightModel TermWeightModel, IVectorSimilarityFunction function, ILogBuilder log)
{
log.log("... Category Similarity ...");
List <string> selectedTerms = context.selectedFeatures.GetKeys(); //.entries.Select(x => x.name)?.ToList();
Dictionary <String, WeightDictionary> categoryDictionarties = new Dictionary <string, WeightDictionary>();
foreach (SpaceLabel label in context.spaceModel.labels)
{
Relationship <SpaceLabel, SpaceCategoryModel> categoryModel = context.spaceModel.LabelToCategoryLinks.GetAllRelationships(label).FirstOrDefault();
var c = TermWeightModel.GetWeights(selectedTerms, categoryModel.NodeB, context.spaceModel, label);
categoryDictionarties.Add(label.name, c);
}
FeatureVectorSetDictionary dict = new FeatureVectorSetDictionary();
String domainNameLast = "";
Double total = context.Count;
Int32 i = 0;
Int32 p = (context.Count / 20);
foreach (var entry in context.items)
{
i++;
WeightDictionary documentWeights = TermWeightModel.GetWeights(selectedTerms, entry.spaceDocument, context.spaceModel);
FeatureVector fv = new FeatureVector(entry.AssignedID);
fv.dimensions = new double[context.spaceModel.labels.Count];
Int32 c = 0;
Parallel.ForEach(context.spaceModel.labels, (label) =>
{
var docToClassSimilarity = function.ComputeSimilarity(categoryDictionarties[label.name], documentWeights);
fv.dimensions[context.spaceModel.labels.IndexOf(label)] = docToClassSimilarity;
});
Int32 r = i % p;
if (r == 0)
{
log.Append(" [" + i.GetRatio(context.Count).ToString("P2") + "] ");
}
dict.GetOrAdd(entry.DomainID).Add(fv, -1);
}
foreach (KeyValuePair <string, FeatureVectorWithLabelIDSet> pair in dict)
{
pair.Value.CloseDeploy();
}
log.log("... Preparation done...");
return(dict);
}
/*
* public static WeightDictionary GetChildrenWT(WeightDictionary output, SpaceDocumentModel model, FeatureWeightModel weightModel, SpaceModel space, List<string> FV)
* {
* if (output == null)
* {
* output = new WeightDictionary(model.name, "");
* }
* if (model.Children.Any())
* {
* foreach (var child in model.Children)
* {
* GetChildrenWT(output, child, weightModel, space, FV);
*
* }
*
* }
* else
* {
* var wd = weightModel.GetWeights(FV, model, space);
* output.Merge(wd.index.Values, model.weight);
* }
* return output;
*
* }*/
public static T BlendToVector <T>(this SpaceDocumentModel model, FeatureWeightModel weightModel, SpaceModel space, List <string> FV) where T : VectorDocument, new()
{
T output = new T();
output.name = model.name;
var leafs = model.GetLeafs();
foreach (var leaf in leafs)
{
var wd = weightModel.GetWeights(FV, model, space);
output.terms.Merge(wd);
}
//output.terms =
//output.Merge(wd.index.Values, model.weight);
//WeightDictionary wd = new WeightDictionary(model.name, "");
// GetChildrenWT(wd, model, weightModel, space, FV);
return(output);
}
///// <summary>
///// Transforms to fv dictionary.
///// </summary>
///// <param name="context">The context.</param>
///// <param name="TermWeightModel">The term weight model.</param>
///// <param name="function">The function.</param>
///// <returns></returns>
//public static FeatureVectorSetDictionary TransformToFVDictionaryAsPageInCategorySimilarity(this DocumentSelectResult context, FeatureWeightModel TermWeightModel, IVectorSimilarityFunction function, ILogBuilder log)
//{
// log.log("... Page Similarity ...");
// List<string> selectedTerms = context.selectedFeatures.GetKeys();
// var ByDomain = context.GetByDomain(log);
// Dictionary<string, List<string>> assignIDByLabel = context.featureSpace.labelToDocumentAssociations.GetAllRelationShipByName(true);
// var ByCategory = context.GetByAssignIDCategory(assignIDByLabel,log);
// Dictionary<String, List<DocumentSelectResultEntry>> EntryByLabel = new Dictionary<string, List<DocumentSelectResultEntry>>();
// Dictionary<String, WeightDictionary> documentDictionarties = new Dictionary<string, WeightDictionary>();
// foreach (var entry in context.items)
// {
// WeightDictionary documentWeights = TermWeightModel.GetWeights(selectedTerms, entry.spaceDocument, context.spaceModel);
// documentDictionarties.Add(entry.AssignedID, documentWeights);
// }
// FeatureVectorSetDictionary dict = new FeatureVectorSetDictionary();
// Double total = context.Count;
// Int32 i = 0;
// Int32 p = (context.Count / 10);
// //List<List<Double>> matrix = new List<List<double>>();
// //foreach (var entry in context.items)
// //{
// // matrix.Add(new List<double>());
// //}
// //for (int x = 0; x < context.items.Count; x++)
// //{
// // for (int y = 0; y < context.items.Count; x++)
// // {
// // }
// //}
// ConcurrentDictionary<String, Double> computedPairs = new ConcurrentDictionary<string, double>();
// foreach (var domainPair in ByCategory)
// {
// List<DocumentSelectResultEntry> relatives = ByCategory[domainPair.Key].ToList();
// foreach (var entry in relatives)
// {
// i++;
// FeatureVector fv = new FeatureVector(entry.AssignedID);
// // List<Double> d = new List<>();
// fv.dimensions = new double[relatives.Count - 1];
// // List<String> keys = documentDictionarties.Keys.ToList();
// Int32 hostInd = relatives.IndexOf(entry);
// Int32 c = 0;
// //foreach (var pair in documentDictionarties)
// //{
// Parallel.ForEach(relatives, (pair) =>
// {
// Int32 ind = relatives.IndexOf(pair); // keys.IndexOf(pair.AssignedID);
// if (ind >= hostInd)
// {
// ind = ind - 1;
// }
// if (pair.AssignedID != entry.AssignedID)
// {
// Double docToClassSimilarity = 0;
// if (computedPairs.ContainsKey(entry.AssignedID + pair.AssignedID))
// {
// docToClassSimilarity = computedPairs[entry.AssignedID + pair.AssignedID];
// }
// else if (computedPairs.ContainsKey(pair.AssignedID + entry.AssignedID))
// {
// docToClassSimilarity = computedPairs[pair.AssignedID + entry.AssignedID];
// }
// else
// {
// var vecA = documentDictionarties[pair.AssignedID];
// var vecB = documentDictionarties[entry.AssignedID];
// docToClassSimilarity = function.ComputeSimilarity(vecA, vecB);
// if (docToClassSimilarity > 0)
// {
// }
// if (!computedPairs.ContainsKey(entry.AssignedID + pair.AssignedID))
// {
// computedPairs.GetOrAdd(entry.AssignedID + pair.AssignedID, docToClassSimilarity);
// //computedPairs.AddOrUpdate(entry.AssignedID + pair.Key, docToClassSimilarity);
// }
// else if (!computedPairs.ContainsKey(pair.AssignedID + entry.AssignedID))
// {
// computedPairs.GetOrAdd(pair.AssignedID + entry.AssignedID, docToClassSimilarity);
// }
// }
// fv.dimensions[ind] = docToClassSimilarity;
// }
// });
// Int32 r = i % p;
// if (r == 0)
// {
// log.Append(" [" + i.GetRatio(context.Count).ToString("P2") + "] ");
// }
// dict.GetOrAdd(entry.DomainID).Add(fv, -1);
// }
// }
// //foreach (KeyValuePair<string, FeatureVectorWithLabelIDSet> pair in dict)
// //{
// // pair.Value.CloseDeploy();
// //}
// log.log("... Preparation finished ...");
// return dict;
//}
/// <summary>
/// Transforms to fv dictionary.
/// </summary>
/// <param name="context">The context.</param>
/// <param name="TermWeightModel">The term weight model.</param>
/// <param name="function">The function.</param>
/// <returns></returns>
public static FeatureVectorSetDictionary TransformToFVDictionaryAsSiteSimilarity(this DocumentSelectResult context, FeatureWeightModel TermWeightModel, IVectorSimilarityFunction function, ILogBuilder log)
{
log.log("... Site Similarity ...");
List <string> selectedTerms = context.selectedFeatures.GetKeys(); //.entries.Select(x => x.name)?.ToList();
Dictionary <String, WeightDictionary> categoryDictionarties = new Dictionary <string, WeightDictionary>();
Dictionary <String, WeightDictionary> documentDictionarties = new Dictionary <string, WeightDictionary>();
var byDomain = context.GetByDomain(log);
FeatureVectorSetDictionary dict = new FeatureVectorSetDictionary();
Double total = context.Count;
Int32 i = 0;
Int32 p = (context.Count / 10);
foreach (var pair in byDomain)
{
i++;
SpaceDocumentModel siteModel = new SpaceDocumentModel();
foreach (var ent in pair.Value)
{
WeightDictionary documentWeights = TermWeightModel.GetWeights(selectedTerms, ent.spaceDocument, context.spaceModel);
documentDictionarties.Add(ent.AssignedID, documentWeights);
siteModel.Children.Add(ent.spaceDocument);
//siteModel.terms.MergeDictionary(ent.spaceDocument.terms);
}
siteModel.Flatten(false);
categoryDictionarties.Add(pair.Key, TermWeightModel.GetWeights(selectedTerms, siteModel, context.spaceModel));
foreach (var ent in pair.Value)
{
FeatureVector fv = new FeatureVector(ent.AssignedID);
fv.dimensions = new double[context.spaceModel.labels.Count];
// documentDictionarties[ent.AssignedID].entries
var docToClassSimilarity = function.ComputeSimilarity(categoryDictionarties[pair.Key], documentDictionarties[ent.AssignedID]);
fv.dimensions[0] = docToClassSimilarity;
dict.GetOrAdd(pair.Key).Add(fv, -1);
}
Int32 r = i % p;
if (r == 0)
{
log.Append(" [" + i.GetRatio(context.Count).ToString("P2") + "] ");
}
}
foreach (KeyValuePair <string, FeatureVectorWithLabelIDSet> pair in dict)
{
pair.Value.CloseDeploy();
}
log.log("... Preparation finished ...");
return(dict);
}
/// <summary>
/// Transforms to fv dictionary.
/// </summary>
/// <param name="context">The context.</param>
/// <param name="TermWeightModel">The term weight model.</param>
/// <param name="function">The function.</param>
/// <returns></returns>
public static FeatureVectorSetDictionary TransformToFVDictionaryAsPageSimilarity(this DocumentSelectResult context, FeatureWeightModel TermWeightModel, IVectorSimilarityFunction function, ScoreComputationModeEnum groupmode, ILogBuilder log)
{
List <string> selectedTerms = context.selectedFeatures.GetKeys();
Dictionary <String, WeightDictionary> documentDictionarties = new Dictionary <string, WeightDictionary>();
foreach (var entry in context.items)
{
WeightDictionary documentWeights = TermWeightModel.GetWeights(selectedTerms, entry.spaceDocument, context.spaceModel);
documentDictionarties.Add(entry.AssignedID, documentWeights);
}
FeatureVectorSetDictionary dict = new FeatureVectorSetDictionary();
Double total = context.Count;
Int32 i = 0;
Int32 p = (context.Count / 10);
Dictionary <string, List <DocumentSelectResultEntry> > relative_groups = null;
if (groupmode == ScoreComputationModeEnum.category)
{
Dictionary <string, List <string> > assignIDByLabel = context.spaceModel.LabelToDocumentLinks.GetAllRelationShipByName(true);
relative_groups = context.GetByAssignIDCategory(assignIDByLabel, log);
if (assignIDByLabel.ContainsKey(SpaceLabel.UNKNOWN))
{
assignIDByLabel.Remove(SpaceLabel.UNKNOWN);
}
log.log("... Page Similarity ... Groups by category");
}
else if (groupmode == ScoreComputationModeEnum.site)
{
relative_groups = context.GetByDomain(log);
log.log("... Page Similarity ... Groups by site");
}
else if (groupmode == ScoreComputationModeEnum.dataset)
{
relative_groups = new Dictionary <string, List <DocumentSelectResultEntry> >();
relative_groups.Add("dataset", context.items);
log.log("... Page Similarity ... dataset");
}
ConcurrentDictionary <String, Double> computedPairs = new ConcurrentDictionary <string, double>();
foreach (var domainPair in relative_groups)
{
List <DocumentSelectResultEntry> relatives = domainPair.Value; //relative_groups[domainPair.Key].ToList();
foreach (var entry in relatives)
{
i++;
FeatureVector fv = new FeatureVector(entry.AssignedID);
// List<Double> d = new List<>();
fv.dimensions = new double[relatives.Count - 1];
// List<String> keys = documentDictionarties.Keys.ToList();
Int32 hostInd = relatives.IndexOf(entry);
Int32 c = 0;
//foreach (var pair in documentDictionarties)
//{
Parallel.ForEach(relatives, (pair) =>
{
Int32 ind = relatives.IndexOf(pair); // keys.IndexOf(pair.AssignedID);
if (ind >= hostInd)
{
ind = ind - 1;
}
if (pair.AssignedID != entry.AssignedID)
{
Double docToClassSimilarity = 0;
if (computedPairs.ContainsKey(entry.AssignedID + pair.AssignedID))
{
docToClassSimilarity = computedPairs[entry.AssignedID + pair.AssignedID];
}
else if (computedPairs.ContainsKey(pair.AssignedID + entry.AssignedID))
{
docToClassSimilarity = computedPairs[pair.AssignedID + entry.AssignedID];
}
else
{
var vecA = documentDictionarties[pair.AssignedID];
var vecB = documentDictionarties[entry.AssignedID];
docToClassSimilarity = function.ComputeSimilarity(vecA, vecB);
if (docToClassSimilarity > 0)
{
}
if (!computedPairs.ContainsKey(entry.AssignedID + pair.AssignedID))
{
computedPairs.GetOrAdd(entry.AssignedID + pair.AssignedID, docToClassSimilarity);
//computedPairs.AddOrUpdate(entry.AssignedID + pair.Key, docToClassSimilarity);
}
else if (!computedPairs.ContainsKey(pair.AssignedID + entry.AssignedID))
{
computedPairs.GetOrAdd(pair.AssignedID + entry.AssignedID, docToClassSimilarity);
}
}
fv.dimensions[ind] = docToClassSimilarity;
}
});
Int32 r = i % p;
if (r == 0)
{
log.Append(" [" + i.GetRatio(context.Count).ToString("P2") + "] ");
}
dict.GetOrAdd(domainPair.Key).Add(fv, -1);
}
}
log.log("... Preparation finished ...");
return(dict);
}