public void evaluateBestIndividual(IntegerVectorIndividual ind, EvolutionState state)
throws Exception {
int[] genome = ((IntegerVectorIndividual) ind).genome;
// ajustar com parametros "globais"
// ClusWrapper.initialization(Dataset.getPath()+Dataset.getFileName() +
// "-train.arff",Dataset.getPath()+Dataset.getFileName() + "-train.arff", Main.targets,false);
int currentFold = Dataset.getCurrentFold();
String trainSet =
Dataset.getPath() + Dataset.getFileName() + "_fold" + (currentFold) + ".train";
String trainValidSet =
Dataset.getPath() + Dataset.getFileName() + "_fold" + (currentFold) + ".trainvalid";
String valSet = Dataset.getPath() + Dataset.getFileName() + "_fold" + (currentFold) + ".valid";
String testSet = Dataset.getPath() + Dataset.getFileName() + "_fold" + (currentFold) + ".test";
// String trainSet = Dataset.getPath()+Dataset.getFileName() +
// "_train_fold_"+(currentFold+1)+".arff";
// String testSet = Dataset.getPath()+Dataset.getFileName() +
// "_test_fold_"+(currentFold+1)+".arff";
// ClusWrapper.initialization(Dataset.getPath()+Dataset.getFileName() + "-train.arff",
// Dataset.getPath()+Dataset.getFileName() + "-test.arff", Main.targets,Main.randomForest);
myMeasures measures = new myMeasures();
if (Main.mlTask > 0) {
double auroc[] = new double[3];
double auprc[] = new double[3];
/*if (Main.mlTask == 2) { // multi-threads)
ClusWrapperNonStatic object = new ClusWrapperNonStatic();
object.initialization(trainSet,valSet, Main.targets,Main.randomForest,true);
measures = object.evaluateIndividualClassification(genome,true);
}
else { // ==1, single thread
// true = classification or false = regression
*/
ClusWrapper.clus = null;
ClusWrapper.initialization(trainSet, valSet, Main.targets, Main.randomForest, true);
measures = ClusWrapper.evaluateIndividualClassification(genome, true);
// }
auroc[1] = measures.getAUROC()[1];
auprc[1] = measures.getAUPRC()[1];
/*if (Main.mlTask == 2) { // multi-threads)
ClusWrapperNonStatic object = new ClusWrapperNonStatic();
object.initialization(trainValidSet,testSet, Main.targets,Main.randomForest,true);
measures = object.evaluateIndividualClassification(genome,true);
}
else { // ==1, single thread
*/
// true = classification or false = regression
ClusWrapper.clus =
null; // we really need to do this! otherwise, cluswrapper does not re-read the
// corresponding partition.
ClusWrapper.initialization(trainValidSet, testSet, Main.targets, Main.randomForest, true);
measures = ClusWrapper.evaluateIndividualClassification(genome, true);
// }
auroc[0] = measures.getAUROC()[0];
auprc[0] = measures.getAUPRC()[0];
auroc[2] = measures.getAUROC()[1];
auprc[2] = measures.getAUPRC()[1];
;
/* System.err.println("trainValidSet: "+ trainValidSet);
System.err.println("testSet: "+ testSet);
System.err.println("auroc[0]: "+ auroc[0]);
System.err.println("auroc[2]: "+ auroc[2]);
*/
for (int i = 0; i < 3; i++) {
Main.measuresSingle[i][0][Dataset.getCurrentFold()][(Integer) state.job[0]] = auroc[i];
Main.measuresSingle[i][1][Dataset.getCurrentFold()][(Integer) state.job[0]] = auprc[i];
}
// Printing results
String test = new String();
for (int i = 0; i < 2; i++)
test =
test
+ Main.measuresSingle[2][i][Dataset.getCurrentFold()][(Integer) state.job[0]]
+ ",";
Main.pwTest.println(test);
String full = new String();
for (int i = 0; i < 2; i++) {
for (int j = 0; j < 3; j++) {
full =
full
+ Main.measuresSingle[j][i][Dataset.getCurrentFold()][(Integer) state.job[0]]
+ ",";
}
}
Main.pwAll.println(full);
} else {
double mae[] = new double[3];
double mse[] = new double[3];
double rmse[] = new double[3];
// true = classification or false = regression
ClusWrapper.initialization(trainSet, valSet, Main.targets, Main.randomForest, false);
measures = ClusWrapper.evaluateIndividual(genome, true);
mae[1] = measures.getMAE()[1];
mse[1] = measures.getMSE()[1];
rmse[1] = measures.getRMSE()[1]; // wrmse[1] = measures.getWRMSE()[0];
ClusWrapper.initialization(trainValidSet, testSet, Main.targets, Main.randomForest, false);
measures = ClusWrapper.evaluateIndividual(genome, true);
mae[0] = measures.getMAE()[0];
mse[0] = measures.getMSE()[0];
rmse[0] = measures.getRMSE()[0]; // wrmse[0] = measures.getWRMSE()[0];
mae[2] = measures.getMAE()[1];
mse[2] = measures.getMSE()[1];
rmse[2] = measures.getRMSE()[1]; // wrmse[2] = measures.getWRMSE()[1];
for (int i = 0; i < 3; i++) {
Main.measuresSingle[i][0][Dataset.getCurrentFold()][(Integer) state.job[0]] = mae[i];
Main.measuresSingle[i][1][Dataset.getCurrentFold()][(Integer) state.job[0]] = mse[i];
Main.measuresSingle[i][2][Dataset.getCurrentFold()][(Integer) state.job[0]] = rmse[i];
}
// Printing results
String test = new String();
for (int i = 0; i < 3; i++)
test =
test
+ Main.measuresSingle[2][i][Dataset.getCurrentFold()][(Integer) state.job[0]]
+ ",";
Main.pwTest.println(test);
String full = new String();
for (int i = 0; i < 3; i++) {
for (int j = 0; j < 3; j++) {
full =
full
+ Main.measuresSingle[j][i][Dataset.getCurrentFold()][(Integer) state.job[0]]
+ ",";
}
}
Main.pwAll.println(full);
}
// Main.pEvolution.println(mae[0] +","+ mse[0] +","+ rmse[0] +","+ mae[1] +","+ mse[1] +","+
// rmse[1]);
}
/** Method for evaluating individuals * */
@Override
public void evaluate(
EvolutionState state,
Individual ind,
int subpopulation,
int threadnum,
ClusWrapperNonStatic objectClusWrapperNonStatic) {
if (ind.evaluated) return;
if (!(ind instanceof IntegerVectorIndividual))
state.output.fatal("Whoa! It's not an IntegerVectorIndividual!!!", null);
boolean isIdeal;
try {
((IntegerVectorIndividual) ind).genome =
genomeAdjustment(((IntegerVectorIndividual) ind).genome);
int[] genome = ((IntegerVectorIndividual) ind).genome;
/*
System.out.print("Genome = ["+genome[0]);
for (int i = 1; i < genome.length; i++)
System.out.print(","+genome[i]);
System.out.println("]");
*/
// int currentFold = Dataset.getCurrentFold();
// String trainSet = Dataset.getPath()+Dataset.getFileName() +
// "_train_fold_"+(currentFold+1)+".arff";
// String testSet = Dataset.getPath()+Dataset.getFileName() +
// "_test_fold_"+(currentFold+1)+".arff";
// ClusWrapper.initialization(trainSet,trainSet, Main.targets,Main.randomForest);
// System.out.println("train set = "+trainSet);
myMeasures measures = new myMeasures();
if (Main.mlTask > 0) { // classification
if (Main.mlTask == 2) { // multi-threads)
/*
int currentFold = Dataset.getCurrentFold();
String trainSet = Dataset.getPath()+Dataset.getFileName() + "_fold"+(currentFold)+".train";
String valSet = Dataset.getPath()+Dataset.getFileName() + "_fold"+(currentFold)+".valid";
if(objectClusWrapperNonStatic.initialisationRun){
System.out.println("I've been intialised already");
}
/*else{
objectClusWrapperNonStatic.initialization(trainSet,valSet, Main.targets,Main.randomForest,true);
}*/
measures = objectClusWrapperNonStatic.evaluateIndividualClassification(genome, true);
// System.out.println("AUROC = " + measures.getAUROC()[1]);
} else { // == 1 (single thread)
measures = ClusWrapper.evaluateIndividualClassification(genome, true);
// System.out.println("static AUROC = " + measures.getAUROC()[1]);
}
double auroc = measures.getAUROC()[1];
double auprc = measures.getAUPRC()[1];
if (Main.fitnessType == 0) { // AUROC fitness
if (Utils.eq(auroc, 1)) isIdeal = true;
else isIdeal = false;
((SimpleFitness) ind.fitness).setFitness(state, (float) auroc, isIdeal);
} else if (Main.fitnessType == 1) { // AUPRC fitness
if (Utils.eq(auprc, 1)) isIdeal = true;
else isIdeal = false;
((SimpleFitness) ind.fitness).setFitness(state, (float) auprc, isIdeal);
} else {
if (Utils.eq(auroc, 1)) isIdeal = true;
else isIdeal = false;
((SimpleFitness) ind.fitness).setFitness(state, (float) auroc, isIdeal);
}
ind.evaluated = true;
int currentGen = (int) state.generation;
// System.out.println("current generation = "+currentGen);
// System.out.println("number of generations = "+state.numGenerations);
// disable for allowing multi-thread
if ((currentGen == 0) || (currentGen == (state.numGenerations - 1))) { // first job
// ClusWrapper.initialization(trainSet, testSet, Main.targets,Main.randomForest);
// measures = new myMeasures();
// measures = ClusWrapper.evaluateIndividual(genome,true);
if (currentGen == 0)
Main.pFirstGen.println(
measures.getAUROC()[0]
+ ","
+ measures.getAUPRC()[0]
+ ","
+ measures.getAUROC()[1]
+ ","
+ measures.getAUPRC()[1]);
else {
Main.pLastGen.println(
measures.getAUROC()[0]
+ ","
+ measures.getAUPRC()[0]
+ ","
+ measures.getAUROC()[1]
+ ","
+ measures.getAUPRC()[1]);
}
}
} else { // regression
// System.out.println("Main.targes = " + Main.targets);
measures = ClusWrapper.evaluateIndividual(genome, true);
double mae = measures.getMAE()[1];
double mse = measures.getMSE()[1];
double rmse = measures.getRMSE()[1];
// double wrmse = measures.getWRMSE()[1];
if (Main.fitnessType == 0) { // MAE fitness
if (Utils.eq(mae, 0)) isIdeal = true;
else isIdeal = false;
// f.setStandardizedFitness(state, mae);
((SimpleFitness) ind.fitness).setFitness(state, ((float) 10000 - mae), isIdeal);
} else if (Main.fitnessType == 1) { // MSE fitness
if (Utils.eq(mse, 0)) isIdeal = true;
else isIdeal = false;
((SimpleFitness) ind.fitness).setFitness(state, ((float) 10000 - mse), isIdeal);
} else if (Main.fitnessType == 2) { // RMSE fitness
if (Utils.eq(rmse, 0)) isIdeal = true;
else isIdeal = false;
((SimpleFitness) ind.fitness).setFitness(state, ((float) 10000 - rmse), isIdeal);
} else {
if (Utils.eq(mae, 0)) isIdeal = true;
else isIdeal = false;
((SimpleFitness) ind.fitness).setFitness(state, ((float) 10000 - mae), isIdeal);
}
ind.evaluated = true;
int currentGen = (int) state.generation;
// System.out.println("current generation = "+currentGen);
// System.out.println("number of generations = "+state.numGenerations);
// disable for allowing multi-thread
if ((currentGen == 0) || (currentGen == (state.numGenerations - 1))) { // first job
// ClusWrapper.initialization(trainSet, testSet, Main.targets,Main.randomForest);
// measures = new myMeasures();
// measures = ClusWrapper.evaluateIndividual(genome,true);
if (currentGen == 0)
Main.pFirstGen.println(
measures.getMAE()[0]
+ ","
+ measures.getMSE()[0]
+ ","
+ measures.getRMSE()[0]
+ ","
+ measures.getMAE()[1]
+ ","
+ measures.getMSE()[1]
+ ","
+ measures.getRMSE()[1]);
else {
Main.pLastGen.println(
measures.getMAE()[0]
+ ","
+ measures.getMSE()[0]
+ ","
+ measures.getRMSE()[0]
+ ","
+ measures.getMAE()[1]
+ ","
+ measures.getMSE()[1]
+ ","
+ measures.getRMSE()[1]);
}
}
//
}
} catch (Exception e1) {
// TODO Auto-generated catch block
e1.printStackTrace();
}
}