/**
* Converts the tree under this node to a string
*
* @param treeLevel the depth of this node; the root of a tree should have treeLevel = 0
* @param deviation the global deviation of the class column, used for evaluating relative errors
* @return the converted string
*/
public final String treeToString(int treeLevel, double deviation) {
int i;
StringBuffer text = new StringBuffer();
if (type == true) {
text.append("\n");
for (i = 1; i <= treeLevel; i++) {
text.append(" ");
}
if (itemsets.getAttribute(splitAttr).name().charAt(0) != '[') {
text.append(
itemsets.getAttribute(splitAttr).name()
+ " <= "
+ M5.doubleToStringG(splitValue, 1, 3)
+ " ");
} else {
text.append(itemsets.getAttribute(splitAttr).name() + " false : ");
}
treeLevel++;
text.append(leftNode.treeToString(treeLevel, deviation));
treeLevel--;
for (i = 1; i <= treeLevel; i++) {
text.append(" ");
}
if (itemsets.getAttribute(splitAttr).name().charAt(0) != '[') {
text.append(
itemsets.getAttribute(splitAttr).name()
+ " > "
+ M5.doubleToStringG(splitValue, 1, 3)
+ " ");
} else {
text.append(itemsets.getAttribute(splitAttr).name() + " true : ");
}
treeLevel++;
text.append(rightNode.treeToString(treeLevel, deviation));
treeLevel--;
} else { // LEAF
text.append(" THEN LM" + lm + "\n");
/* if(deviation > 0.0)
text.append(" (" + itemsets.numItemsets() + "/" +
M5.doubleToStringG((100. * errors.rootMeanSqrErr /
deviation),1,3) + "%)\n");
else text.append(" (" + itemsets.numItemsets() + ")\n");*/
}
return text.toString();
}
/**
* Converts the predictions by the tree under this node to a string
*
* @param inst itemsets
* @param smooth =true using the smoothed models; otherwise, the unsmoothed
* @param lmNo the number of the associated linear model
* @return the converted string
* @exception Exception if something goes wrong
*/
public final String predictionsToString(MyDataset inst, int lmNo, boolean smooth)
throws Exception {
int i, lmNum;
double value;
StringBuffer text = new StringBuffer();
text.append(
" Predicting test itemsets ("
+ inst.getAttribute(inst.getClassIndex()).name()
+ ", column "
+ (inst.getClassIndex() + 1)
+ ")\n\n");
for (i = 0; i <= inst.numItemsets() - 1; i++) {
lmNum = this.leafNum(inst.itemset(i));
if (lmNo == 0 || lmNo == lmNum) {
text.append(" Predicting " + i + " (LM" + lmNum + "): ");
text.append(inst.itemset(i).toString() + "\n");
value = this.predict(inst.itemset(i), smooth);
if (inst.itemset(i).classIsMissing() == false) {
text.append(
" Actual value: "
+ M5.doubleToStringG(inst.itemset(i).getClassValue(), 9, 4)
+ " Prediction: "
+ M5.doubleToStringG(value, 9, 4)
+ " Abs. error: "
+ M5.doubleToStringG(Math.abs(inst.itemset(i).getClassValue() - value), 9, 4)
+ "\n\n");
} else {
text.append(
" Actual value: missing Prediction: "
+ M5.doubleToStringG(value, 9, 4)
+ " Abs. Error: undefined\n\n");
}
}
}
return text.toString();
}