private double getAUC(myDataset data) {
double auc = 0;
int totalClasses = 0;
for (int i = 0; i < this.confusionMatrix.length; i++) {
if (data.numberInstances(i) > 0) {
totalClasses++;
double tp = 1.0 * confusionMatrix[i][i] / data.numberInstances(i);
// System.err.print("TP["+i+"]: "+tp);
for (int j = 0; j < this.confusionMatrix[i].length; j++) {
if ((j != i) && (data.numberInstances(j) > 0)) {
double fp = 1.0 * this.confusionMatrix[j][i] / data.numberInstances(j);
// System.err.print(", FP["+j+"]: "+fp);
double auc_j = (tp - fp + 1) / 2.0;
// System.err.print(", AUC["+j+"]: "+auc_j);
auc += auc_j;
}
}
}
// System.err.println("");
}
double tpr = 1.0 * confusionMatrix[0][0] / data.numberInstances(0);
double fpr = 1.0 * confusionMatrix[1][0] / data.numberInstances(1);
double auc1 = ((1 + tpr - fpr) / 2.0);
double auc2 = (auc / (totalClasses * (totalClasses - 1)));
return auc2;
}
/** It launches the algorithm */
public void execute() {
if (this.somethingWrong) { // We do not execute the program
System.err.println("An error was found, either the data-set has missing values.");
System.err.println(
"Please remove the examples with missing data or apply a MV preprocessing.");
System.err.println("Aborting the program");
// We should not use the statement: System.exit(-1);
} else {
// We do here the algorithm's operations
int nClasses = train.getnClasses();
aprioriClassDistribution = new double[nClasses];
for (int i = 0; i < nClasses; i++) {
aprioriClassDistribution[i] = 1.0 * val.numberInstances(i) / val.size();
}
if (model) { // el modelo no esta generado en fichero previamente
NSGA2 search =
new NSGA2(
train, seed, populationSize, maxTrials, crossover, mutation, instances, fitness);
try {
search.execute();
} catch (Exception e) {
e.printStackTrace(System.err);
}
}
// Finally we should fill the training and test output files
this.generateModel();
double avgTr = this.doOutput(val, this.outputTr, false);
double aucTr = getAUC(val);
double avgTst = this.doOutput(test, this.outputTst, true);
double aucTst = getAUC(test);
System.out.print("AUC Train: " + aucTr);
System.out.println("; AvgAcc Train: " + avgTr);
System.out.print("AUC Test: " + aucTst);
System.out.println("; AvgAcc Test: " + avgTst);
totalTime = System.currentTimeMillis() - startTime;
System.out.println("Algorithm Finished: " + totalTime);
}
}