private Dataset GetLocalFlatDataset(Node current, Dataset dataset)
{
ClassHierarchy hierarchy = ((Data.HierarchicalAttribute)dataset.Metadata.Target).Hierarchy;
Node[] siblings = hierarchy.GetSiblings(current);
List <string> negativeClassValues = new List <string>();
foreach (Node sibling in siblings)
{
negativeClassValues.Add(sibling.Name);
}
Dataset local = dataset.GetBinaryFlatLabelSubDataset(current.Name, negativeClassValues.ToArray());
return(local);
}
public static EnsemblePerNodeTopDownClassifier CreateEnsembleDSPerNodeTopDownClassifier(List <Data.Dataset> trainingSets, IClassificationAlgorithm algorithm)
{
ClassHierarchy hierarchy = ((Data.HierarchicalAttribute)trainingSets[0].Metadata.Target).Hierarchy;
EnsemblePerNodeTopDownClassifier topDownClassifier = new EnsemblePerNodeTopDownClassifier(hierarchy);
Data.Attribute[] metaAttributes = new Data.Attribute[2];
string[] values = new string[trainingSets.Count];
for (int i = 0; i < metaAttributes.Length; i++)
{
values[i] = trainingSets[i].Metadata.DatasetName;
}
metaAttributes[0] = new Data.Attribute("Dataset", 0, values);
metaAttributes[1] = new Data.Attribute("Prediction", 1, new string[] { "yes", "no" });
Data.Attribute target = new Data.Attribute("class", metaAttributes.Length, new string[] { "yes", "no" });
Metadata meta = new Metadata("metaModel", metaAttributes, target);
Node current = hierarchy.Root;
Queue <Node> queue = new Queue <Node>();
foreach (Node node in hierarchy.GetNodes(current.Children))
{
queue.Enqueue(node);
}
while (queue.Count != 0)
{
current = queue.Dequeue();
Node[] siblings = hierarchy.GetSiblings(current);
List <string> negativeClassValues = new List <string>();
foreach (Node sibling in siblings)
{
negativeClassValues.Add(sibling.Name);
}
Dataset dsLocal = new Dataset(meta.Clone());
int i = 0;
foreach (Dataset trainingSet in trainingSets)
{
Dataset local = trainingSet.GetBinaryFlatLabelSubDataset(current.Name, negativeClassValues.ToArray());
algorithm.TrainingSet = local;
IClassifier classifier = algorithm.CreateClassifier();
topDownClassifier.AddClassifier(current.Name, local.Metadata.DatasetName, classifier);
//Example metaExample=new Example(dsLocal,i,
}
//algorithm.TrainingSet = local;
//IClassifier classifier = algorithm.CreateClassifier();
//topDownClassifier.PutClassifier(current.Name, trainingSet.Metadata.DatasetName, classifier);
//if (current.Children != null)
// foreach (string child in current.Children)
// queue.Enqueue(hierarchy[child]);
}
return(topDownClassifier);
}
