public void PerformClassification(OperationContext context, ExperimentTruthTable truthTable, ClassificationDatasetSeparationEnum distributionRule, ILogBuilder log)
{
log.log("Performing classification");
if (truthTable == null)
{
truthTable = new ExperimentTruthTable();
notes.log(":: DEPLOYING IN-FOLD TRUTH TABLE ::");
truthTable.Deploy(context.featureSpace, context.spaceModel.labels.Select(x => x.name).ToList(), log);
}
DistributeTrainingAndTestSets(distributionRule, truthTable, context.featureSpace, log, context.testSet, context.trainingSet);
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, log);
log.log("Training [" + classifier.name + "] completed.");
notes.log("Testing [" + classifier.name + "] with [" + context.testSet.Count + "] feature vectors.");
context.testResults = new List <FeatureVectorWithLabelID>();
var ts = context.testSet.Select(x => x.vector);
foreach (FeatureVector fv in ts)
{
Int32 result = classifier.DoSelect(fv, log);
FeatureVectorWithLabelID fvl = new FeatureVectorWithLabelID(fv, result);
context.testResults.Add(fvl);
}
log.log("Testing [" + classifier.name + "] completed.");
}
}
public void PerformClassification(OperationContext context, ExperimentTruthTable truthTable, ClassificationDatasetSeparationEnum distributionRule, ILogBuilder log)
{
log.log("Performing classification");
if (truthTable == null)
{
truthTable = new ExperimentTruthTable();
notes.log(":: DEPLOYING IN-FOLD TRUTH TABLE ::");
log.log(":: DEPLOYING IN-FOLD TRUTH TABLE ::");
truthTable.Deploy(context.featureSpace, context.spaceModel.labels.Select(x => x.name).ToList(), log);
}
DistributeTrainingAndTestSets(distributionRule, truthTable, context.featureSpace, log, context.testSet, context.trainingSet);
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, log);
log.log("Training [" + classifier.name + "] completed.");
notes.log("Testing [" + classifier.name + "] with [" + context.testSet.Count + "] feature vectors.");
context.testResults = new List <FeatureVectorWithLabelID>();
var ts = context.testSet.Select(x => x.vector);
List <Int32> distinctResults = new List <int>();
foreach (FeatureVector fv in ts)
{
Int32 result = classifier.DoSelect(fv, log);
if (!distinctResults.Contains(result))
{
distinctResults.Add(result);
}
FeatureVectorWithLabelID fvl = new FeatureVectorWithLabelID(fv, result);
context.testResults.Add(fvl);
}
if (distinctResults.Count < truthTable.labels_without_unknown.Count)
{
List <String> no_match_labels = truthTable.labels_without_unknown.ToList();
foreach (Int32 d in distinctResults)
{
no_match_labels.Remove(truthTable.labels_without_unknown[d]);
}
log.log("WARNING --- [" + classifier.name + "] ONLY [" + distinctResults.Count + "] of [" + truthTable.labels_without_unknown.Count + "] were assigned by the classifier");
foreach (String l in no_match_labels)
{
log.log("Class [" + l + "] received no assigment");
}
foreach (var v in context.testSet)
{
var dist = v.GetDistinctValuesAtVector();
if (dist.Count < 2)
{
log.log("Test vector [" + v.name + "] has [" + dist.Count + "] distinct values at [" + v.dimensions.Length + "] dimensions!");
}
}
foreach (var v in context.trainingSet)
{
var dist = v.GetDistinctValuesAtVector();
if (dist.Count < 2)
{
log.log("Training vector [" + v.name + "] has [" + dist.Count + "] distinct values at [" + v.dimensions.Length + "] dimensions!");
}
}
}
log.log("Testing [" + classifier.name + "] completed.");
}
}
//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;
//}
}