/**
* Recreate a tree node content with the specified data based on the tree node's existing content.
*
* @param first The start(inclusive) index of the train data used for creating the tree node
* content.
* @param last The end(exclusive) index of the train data used for creating the tree node content.
* @param node The tree node whose content need to be recreated.
* @return The recreated tree node content.
*/
private TreeNodeContent createContent(int first, int last, TreeNode node) {
// Compute the total weight and its class distribution of [first
// last) prune cases
float totalWeight = 0;
AttributeDelegate classAttributeDelegate =
this.attributeDelegates[dataSet.getClassAttributeIndex()];
float[] totalClassDistri = new float[this.dataSet.getClassCount()];
Arrays.fill(totalClassDistri, 0);
for (int i = first; i < last; i++) {
int classLabel = classAttributeDelegate.getClassBranch(this.cases[i]);
totalClassDistri[classLabel] += this.weight[cases[i]];
}
// Find the original classification of the tree node
String nodeClassification = node.getContent().getClassification();
String[] classValues = this.dataSet.getClassValues();
int maxClassIndex = indexOf(nodeClassification, classValues);
// Find the most probable classification of the prune data on
// the current tree node
for (int i = 0; i < totalClassDistri.length; i++) {
totalWeight += totalClassDistri[i];
if (totalClassDistri[i] > totalClassDistri[maxClassIndex]) maxClassIndex = i;
}
String classification = classValues[maxClassIndex];
// Estimate the leafError of the tree node with the [first last)
// prune data
float basicLeafError = totalWeight - totalClassDistri[maxClassIndex];
float extraLeafError = Estimator.getExtraError(totalWeight, basicLeafError);
float estimatedLeafError = basicLeafError + extraLeafError;
return new TreeNodeContent(totalWeight, totalClassDistri, classification, estimatedLeafError);
}
/** Initialize a tree pruner which prunes the built decision tree */
TreePruner(
DataSet dataSet, AttributeDelegate[] attributeDelegates, TreeNode root, DecisionTree dt) {
this.dataSet = dataSet;
this.attributeDelegates = attributeDelegates;
this.root = root;
this.dt = dt;
// ReInitialize the data sequence and their weight
int caseCount = dataSet.getCaseCount();
this.cases = new int[caseCount];
for (int i = 0; i < this.cases.length; i++) this.cases[i] = i;
this.weight = new float[caseCount];
Arrays.fill(this.weight, 1.0f);
// Reset the cases and weight array of all attributes delegate
// objects
for (AttributeDelegate attributeDelegate : attributeDelegates) {
attributeDelegate.setCasesWeight(this.cases, this.weight);
}
/* float errorAfterPrune = */ ebpPrune(this.root, 0, caseCount, true);
}
