//public void Deploy(FeatureVectorDictionary dataset, ILogBuilder logger)
//{
// Deploy(dataset.GetVectorsWithLabelID(d))
//}
public void Deploy(FeatureSpace space, List <String> labels, ILogBuilder logger)
{
//List<String> labels = space.labelToDocumentAssociations.GetAllDistinctNames(true);
List <FeatureVectorWithLabelID> dataset = new List <FeatureVectorWithLabelID>();
foreach (FeatureVector vec in space.documents)
{
var associated = space.labelToDocumentAssociations.GetAllLinked(vec);
Int32 lbi = -1;
FeatureVectorWithLabelID fvl = null;
if (associated.Any())
{
lbi = labels.IndexOf(associated.First().name);
}
else
{
lbi = labels.IndexOf(SpaceLabel.UNKNOWN);
}
fvl = new FeatureVectorWithLabelID(vec, lbi);
dataset.Add(fvl);
}
Deploy(dataset, logger, labels);
}
//public ExperimentTruthTable ConstructTruthTable(FeatureSpace space, ILogBuilder log)
//{
// ExperimentTruthTable output = new ExperimentTruthTable();
// output.Deploy(space, )
// List<String> labels = space.labelToDocumentAssociations.GetAllDistinctNames(true);
// List<FeatureVectorWithLabelID> dataset = new List<FeatureVectorWithLabelID>();
// foreach (FeatureVector vec in space.documents)
// {
// var associated = space.labelToDocumentAssociations.GetAllLinked(vec);
// Int32 lbi = -1;
// FeatureVectorWithLabelID fvl = null;
// if (associated.Any())
// {
// lbi = labels.IndexOf(associated.First().name);
// }
// else
// {
// lbi = labels.IndexOf(SpaceLabel.UNKNOWN);
// }
// fvl = new FeatureVectorWithLabelID(vec, lbi);
// dataset.Add(fvl);
// }
// output.Deploy(dataset, log);
// return output;
//}
public void DistributeTrainingAndTestSets(ClassificationDatasetSeparationEnum distributionRule, ExperimentTruthTable truthTable, FeatureSpace featureSpace, ILogBuilder log, List <FeatureVectorWithLabelID> testSet, List <FeatureVectorWithLabelID> trainingSet)
{
log.log("Spliting data [" + distributionRule.ToString() + "]");
List <FeatureVectorWithLabelID> __testSet = new List <FeatureVectorWithLabelID>();
List <FeatureVectorWithLabelID> __trainingSet = new List <FeatureVectorWithLabelID>();
foreach (FeatureVector vec in featureSpace.documents)
{
var associated = featureSpace.labelToDocumentAssociations.GetAllLinked(vec);
Int32 lbi = -1;
FeatureVectorWithLabelID fvl = null;
if (associated.Any())
{
lbi = truthTable.labels_without_unknown.IndexOf(associated.First().name);
}
fvl = new FeatureVectorWithLabelID(vec, lbi);
if (lbi == -1)
{
__testSet.Add(fvl);
}
else
{
__trainingSet.Add(fvl);
}
}
if (!__testSet.Any())
{
notes.log("TEST SET IS EMPTY ---- APPLYING 1:1 EXPERIMENT SHEME: training and test set are the same");
__trainingSet.ForEach(x => __testSet.Add(x));
}
else
{
if (distributionRule.HasFlag(ClassificationDatasetSeparationEnum.TestLabeled))
{
testSet.AddRange(__trainingSet, true);
}
if (distributionRule.HasFlag(ClassificationDatasetSeparationEnum.TestUnlabeled))
{
testSet.AddRange(__testSet, true);
}
if (distributionRule.HasFlag(ClassificationDatasetSeparationEnum.TrainingLabeled))
{
trainingSet.AddRange(__trainingSet, true);
}
if (distributionRule.HasFlag(ClassificationDatasetSeparationEnum.TrainingUnlabeled))
{
trainingSet.AddRange(__testSet, true);
}
}
log.log("Training [" + trainingSet.Count + "] - Testing [" + testSet.Count + "]");
//switch (distributionRule)
//{
// case ClassificationDatasetSeparationEnum.TrainingAll_TestAll:
//
// break;
// case ClassificationDatasetSeparationEnum.TrainingAll_TestUnlabeled:
// break;
// case ClassificationDatasetSeparationEnum.TrainingLabeled_TestAll:
// trainingSet.ForEach(x => testSet.Add(x.vector));
// break;
// case ClassificationDatasetSeparationEnum.TrainingLabeled_TestUnlabeled:
// // just fine
// break;
//}
}
public static DataTable MakeTable(this FeatureSpace space, FeatureVectorConstructor constructor, String name, String description)
{
DataTable table = new DataTable();
table.SetTitle(name);
table.SetDescription(description);
table.SetAdditionalInfoEntry("Documents", space.documents.Count, "Total count of document vectors");
table.SetAdditionalInfoEntry("Dimensions", constructor.dimensionFunctionSet.Count, "Number of dimensions");
DataColumn column_rank = table.Add("Nr", "Order of appereance", "N", typeof(Int32), imbSCI.Core.enums.dataPointImportance.normal).SetWidth(10);
DataColumn column_token = table.Add("Name", "Name of the document vector", "Name", typeof(String), imbSCI.Core.enums.dataPointImportance.normal).SetWidth(50);
List <DataColumn> dimensions = new List <DataColumn>();
foreach (FeatureSpaceDimensionBase dim in constructor.dimensionFunctionSet)
{
DataColumn dim_col = null;
String prefix = dimensions.Count.ToString("D3");
if (dim is FeatureSpaceDimensionSimilarity dimSim)
{
dim_col = table.Add(prefix + "-" + dimSim.classVector.name, "Dimension computed as [" + dimSim.similarityFunction.GetType().Name + "] between document vector and [" + dimSim.classVector.name + "]", "D" + prefix, typeof(Double), imbSCI.Core.enums.dataPointImportance.important, "F5");
dimensions.Add(dim_col);
}
}
DataColumn column_label = table.Add("Label", "Affiliation to a category", "Label", typeof(String), imbSCI.Core.enums.dataPointImportance.normal).SetWidth(50);
Int32 r = 1;
foreach (var docVec in space.documents)
{
var dr = table.NewRow();
dr[column_rank] = r;
dr[column_token] = docVec.name;
Int32 di = 0;
foreach (DataColumn dc in dimensions)
{
if (di < docVec.dimensions.Length)
{
Double val = docVec.dimensions[di];
dr[dc] = val;
}
di++;
}
table.Rows.Add(dr);
String lbl_str = "";
var lbl = space.labelToDocumentAssociations.GetAllLinked(docVec).FirstOrDefault();
if (lbl != null)
{
lbl_str = lbl.name;
}
dr[column_label] = lbl_str;
r++;
}
return(table);
}
