private double entropy(Map<String, String> specifiedAttributes) {
double totalExamples = records.count();
double positiveExamples = records.countPositive(specifiedAttributes);
double negativeExamples = records.countNegative(specifiedAttributes);
return -nlog2(positiveExamples / totalExamples) - nlog2(negativeExamples / totalExamples);
}
private double entropy(
String attribute, String decision, Map<String, String> specifiedAttributes) {
double totalExamples = records.count(attribute, decision, specifiedAttributes);
double positiveExamples = records.countPositive(attribute, decision, specifiedAttributes);
double negativeExamples = records.countNegative(attribute, decision, specifiedAttributes);
// logger.info("positiveExamples is --> {}.", positiveExamples);
// logger.info("negativeExamples is --> {}.", negativeExamples);
// logger.info("totalExamples is --> {}.", totalExamples);
if (positiveExamples == 0 || negativeExamples == 0 || totalExamples == 0) return 0;
return -nlog2(positiveExamples / totalExamples) - nlog2(negativeExamples / totalExamples);
}
private double informationGain(String attribute, Map<String, String> specifiedAttributes) {
double sum = entropy(specifiedAttributes);
double examplesCount = records.count(specifiedAttributes);
if (examplesCount == 0) return sum;
Map<String, Set<String>> decisions = records.extractDecisions();
for (String decision : decisions.get(attribute)) {
double entropyPart = entropy(attribute, decision, specifiedAttributes);
// logger.info("entropyPart is --> {}.", entropyPart);
double decisionCount = records.countDecisions(attribute, decision);
sum += -(decisionCount / examplesCount) * entropyPart;
}
return sum;
}
/**
* Returns the next attribute to be chosen.
*
* <p>chosenAttributes represents the decision path from the root attribute to the node under
* consideration. usedAttributes is the set of all attributes that have been incorporated into the
* tree prior to this call to nextAttribute(), even if the attributes were not used in the path to
* the node under consideration.
*
* <p>Results are undefined if records.count() == 0.
*/
public Attribute nextAttribute(Map<String, String> chosenAttributes, Set<String> usedAttributes) {
double currentGain = 0.0, bestGain = 0.0;
String bestAttribute = "";
/*
* If there are no positive records for the already chosen attributes,
* then return a false classifier leaf. If no negative records,
* then return a true classifier leaf.
*/
if (records.countPositive(chosenAttributes) == 0) return new Attribute(false);
else if (records.countNegative(chosenAttributes) == 0) return new Attribute(true);
logger.info(
"Choosing attribute out of {} remaining attributes.",
remainingAttributes(usedAttributes).size());
logger.info("Already chosen attributes/decisions are {}.", chosenAttributes);
for (String attribute : remainingAttributes(usedAttributes)) {
// for each remaining attribute, determine the information gain of using it
// to choose among the records selected by the chosenAttributes
// if none give any information gain, return a leaf attribute,
// otherwise return the found attribute as a non-leaf attribute
currentGain = informationGain(attribute, chosenAttributes);
logger.info("Evaluating attribute {}, information gain is {}", attribute, currentGain);
if (currentGain > bestGain) {
bestAttribute = attribute;
bestGain = currentGain;
}
}
// If no attribute gives information gain, generate leaf attribute.
// Leaf is true if there are any true classifiers.
// If there is at least one negative example, then the information gain
// would be greater than 0.
if (bestGain == 0.0) {
boolean classifier = records.countPositive(chosenAttributes) > 0;
logger.warn("Creating new leaf attribute with classifier {}.", classifier);
return new Attribute(classifier);
} else {
logger.info("Creating new non-leaf attribute {}.", bestAttribute);
return new Attribute(bestAttribute);
}
}
private Set<String> remainingAttributes(Set<String> usedAttributes) {
Set<String> result = records.extractAttributes();
result.removeAll(usedAttributes);
return result;
}
