/// <summary>
/// Initializes a new instance of the <see cref="MyRegressionTree"/> class.
/// </summary>
/// <param name="exampleData">The example data.</param>
public MyRegressionTree(ITreeExampleData exampleData)
{
this.existingFeatures = new List <string>();
this.leaves = new List <Node>();
// For every row in the example data
foreach (var exampleRow in exampleData.ExampleRows)
{
foreach (var example in exampleRow.Items)
{
// only get as many possible Features as are defined in the dimensions
if (this.existingFeatures.Count >= exampleData.Dimensions)
{
break;
}
// if feature is not in the list -> add it
if (!this.existingFeatures.Contains(example.RelatedFeature.Name))
{
this.existingFeatures.Add(example.RelatedFeature.Name);
}
}
}
// Init the rootnode as concrete node
this.rootNode = new RegressionTreeNode(this, exampleData.ExampleRows, null);
}
/// <summary>
/// Gets the predicition accuracy.
/// </summary>
/// <param name="testSet">The test set.</param>
/// <returns>
/// The calculated prediction accuracy.
/// </returns>
public override double GetPredicitionAccuracy(ITreeExampleData testSet)
{
double testExampleCount = testSet.ExampleRows.Count;
double deviationCount = 0;
foreach (var example in testSet.ExampleRows)
{
if (example.Classification.ClassifiedAs != example.Class)
{
deviationCount++;
}
}
return(1 - deviationCount / testExampleCount);
}
/// <summary>
/// Prunes the specified mytree.
/// </summary>
/// <param name="mytree">The mytree.</param>
/// <param name="testSet">The test set.</param>
/// <returns></returns>
public Tree Prune(Tree mytree, ITreeExampleData testSet)
{
// At the beginning -> Validate Set
// Then calculate some kind of prediction accuracy factor
bool changed = false;
do
{
tree.ValidateTestSet(testSet);
double startAccuracy = this.GetPredicitionAccuracy(testSet);
changed = false;
foreach (Node leaf in mytree.GetLeaves())
{
if (leaf.Parent != null)
{
Node leftTemp = leaf.Parent.LeftNode;
Node rightTemp = leaf.Parent.RightNode;
leaf.Parent.LeftNode = null;
leaf.Parent.RightNode = null;
mytree.ValidateTestSet(testSet);
double tempAccuracy = this.GetPredicitionAccuracy(testSet);
if (tempAccuracy < startAccuracy)
{
// reverse the change
leaf.Parent.LeftNode = leftTemp;
leaf.Parent.RightNode = rightTemp;
}
else
{
// leave it as it is and continue
leaf.Parent.OutgoingSplit = null;
changed = true;
}
}
}
} while (changed);
return(mytree);
}
/// <summary>
/// Splits the specified tree.
/// </summary>
/// <param name="tree">The tree.</param>
/// <param name="gardener">The gardener.</param>
/// <param name="testdata">The testdata.</param>
private void Split(DecisionTree.Implementation.Tree tree, IGardener gardener, ITreeExampleData testdata)
{
tree.Split();
if (this.PruneBox.IsChecked == true)
{
gardener.Prune(tree, testdata);
}
DecisionTreeWPFRenderer renderer = new DecisionTreeWPFRenderer(tree, this.TreeCanvas);
renderer.Visualize();
}
/// <summary> /// Gets the predicition accuracy. /// </summary> /// <param name="testSet">The test set.</param> /// <returns>The calculated prediction accuracy.</returns> public abstract double GetPredicitionAccuracy(ITreeExampleData testSet);
