private void generateModel() {
String salida = new String("");
double max_auc = 0;
ArrayList<String> solutions = this.getAllSolutions();
models = new ArrayList<Farchd>();
int nEjemplos = train.getnData();
if (this.instances == this.MAJ) {
nEjemplos = train.getMajority();
}
boolean[] variables = new boolean[train.getnInputs()];
boolean[] ejemplos = new boolean[nEjemplos];
this.weightsAUC = new double[solutions.size() / 2]; // Hay 2 soluciones FS e IS
for (int i = 0, j = 0; i < solutions.size(); i += 2, j++) {
int vars, ejs;
vars = ejs = 0;
variables = decode(solutions.get(i));
ejemplos = decode(solutions.get(i + 1));
for (int l = 0; l < variables.length; l++) {
// variables[j] = solution[j];
if (variables[l]) vars++;
}
for (int l = 0; l < ejemplos.length; l++) {
if (ejemplos[l]) ejs++;
}
try {
Farchd model = new Farchd(train, val, test, variables, ejemplos);
/** ******** */
// double fit = model.getAUCTr();
double auc_tr = model.execute(true);
double auc_tst = model.getAUCTst();
if (auc_tr > max_auc) {
max_auc = auc_tr;
indexBest = j;
}
this.weightsAUC[j] = auc_tr;
salida +=
"Solution[" + j + "]:\t" + vars + "\t" + ejs + "\t" + auc_tr + "\t" + auc_tst + "\n";
/** ******** */
models.add(model);
} catch (Exception e) {
System.err.println("Liada maxima al generar modelo ");
e.printStackTrace(System.err);
System.exit(-1);
}
}
System.out.print(salida);
Files.writeFile(header + "_AUC.txt", salida);
}
/**
* Process a dataset file for a clustering problem.
*
* @param nfexamples Name of the dataset file
* @param train The dataset file is for training or for test
* @throws java.io.IOException if there is any semantical, lexical or sintactical error in the
* input file.
*/
public void processClusterDataset(String nfexamples, boolean train) throws IOException {
try {
// Load in memory a dataset that contains a classification problem
IS.readSet(nfexamples, train);
nData = IS.getNumInstances();
nInputs = Attributes.getInputNumAttributes();
nVariables = nInputs + Attributes.getOutputNumAttributes();
if (Attributes.getOutputNumAttributes() != 0) {
System.out.println("This algorithm can not process datasets with outputs");
System.out.println("All outputs will be removed");
}
// Initialize and fill our own tables
X = new double[nData][nInputs];
missing = new boolean[nData][nInputs];
// Maximum and minimum of inputs
iMaximum = new double[nInputs];
iMinimum = new double[nInputs];
// Maximum and minimum for output data
oMaximum = 0;
oMinimum = 0;
// All values are casted into double/integer
nClasses = 0;
for (int i = 0; i < X.length; i++) {
Instance inst = IS.getInstance(i);
for (int j = 0; j < nInputs; j++) {
X[i][j] = IS.getInputNumericValue(i, j);
missing[i][j] = inst.getInputMissingValues(j);
if (X[i][j] > iMaximum[j] || i == 0) {
iMaximum[j] = X[i][j];
}
if (X[i][j] < iMinimum[j] || i == 0) {
iMinimum[j] = X[i][j];
}
}
}
} catch (Exception e) {
System.out.println("DBG: Exception in readSet");
e.printStackTrace();
}
}
/** 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);
}
}
/**
* This private method extract the dataset and the method's parameters from the KEEL environment,
* calculates the centroids using the KMeans class and print out the results with the validation
* dataset.
*
* @param tty unused boolean parameter, kept for compatibility
* @param pc ProcessConfig object to obtain the train and test datasets and the method's
* parameters.
*/
private void clustering_kmeans(boolean tty, ProcessConfig pc) {
try {
String linea;
ProcessDataset pd = new ProcessDataset();
linea = (String) pc.parInputData.get(ProcessConfig.IndexTrain);
if (pc.parNewFormat) pd.processClusterDataset(linea, true);
else pd.procesa_clustering_old(linea);
int ndatos = pd.getNdata(); // Number of examples
int nvariables = pd.getNvariables(); // Number of variables
int nentradas = pd.getNinputs(); // Number of inputs
pd.showDatasetStatistics();
System.out.println("Number of examples=" + ndatos);
System.out.println("Number of inputs=" + nentradas);
double[][] X = pd.getX(); // Input data
double[] emaximo = pd.getImaximum(); // Maximum and Minimum for input data
double[] eminimo = pd.getIminimum();
int[] neparticion = new int[nentradas];
int s;
s = pc.parNClusters;
KMeans KM = new KMeans(X, s, rand);
double fallos = 0;
try {
for (int i = 0; i < X.length; i++) {
int clase = KM.nearestCentroid(X[i]);
// System.out.println("pattern="+i+" cluster="+clase);
}
} catch (Exception e) {
System.out.println(e.toString());
}
// Clusters in the test set
ProcessDataset pdt = new ProcessDataset();
int nprueba, npentradas, npvariables;
linea = (String) pc.parInputData.get(ProcessConfig.IndexTestKMeans);
if (pc.parNewFormat) pdt.processClusterDataset(linea, false);
else pdt.procesa_clustering_old(linea);
nprueba = pdt.getNdata();
npvariables = pdt.getNvariables();
npentradas = pdt.getNinputs();
pdt.showDatasetStatistics();
if (npentradas != nentradas) throw new IOException("Error in test file");
double[][] Xp = pdt.getX();
int[] Co = new int[Xp.length];
// Test set is classified
try {
for (int i = 0; i < Xp.length; i++) {
Co[i] = KM.nearestCentroid(Xp[i]);
// System.out.println("pattern test="+i+" cluster="+Co[i]);
}
} catch (Exception e) {
System.out.println(e.toString());
}
// Output format for clustering algorithms
pc.results(Xp, Co);
KM.print();
} catch (FileNotFoundException e) {
System.err.println(e + " Training data not found");
} catch (IOException e) {
System.err.println(e + " Read error");
}
}
/**
* Process a dataset file for a classification problem.
*
* @param nfejemplos Name of the dataset file
* @param train The dataset file is for training or for test
* @throws java.io.IOException if there is any semantical, lexical or sintactical error in the
* input file.
*/
public void processClassifierDataset(String nfejemplos, boolean train) throws IOException {
try {
// Load in memory a dataset that contains a classification problem
IS.readSet(nfejemplos, train);
nData = IS.getNumInstances();
nInputs = Attributes.getInputNumAttributes();
nVariables = nInputs + Attributes.getOutputNumAttributes();
// Check that there is only one output variable and
// it is nominal
if (Attributes.getOutputNumAttributes() > 1) {
System.out.println("This algorithm can not process MIMO datasets");
System.out.println("All outputs but the first one will be removed");
}
boolean noOutputs = false;
if (Attributes.getOutputNumAttributes() < 1) {
System.out.println("This algorithm can not process datasets without outputs");
System.out.println("Zero-valued output generated");
noOutputs = true;
}
// Initialize and fill our own tables
X = new double[nData][nInputs];
missing = new boolean[nData][nInputs];
C = new int[nData];
// Maximum and minimum of inputs
iMaximum = new double[nInputs];
iMinimum = new double[nInputs];
// Maximum and minimum for output data
oMaximum = 0;
oMinimum = 0;
// All values are casted into double/integer
nClasses = 0;
for (int i = 0; i < X.length; i++) {
Instance inst = IS.getInstance(i);
for (int j = 0; j < nInputs; j++) {
X[i][j] = IS.getInputNumericValue(i, j);
missing[i][j] = inst.getInputMissingValues(j);
if (X[i][j] > iMaximum[j] || i == 0) {
iMaximum[j] = X[i][j];
}
if (X[i][j] < iMinimum[j] || i == 0) {
iMinimum[j] = X[i][j];
}
}
if (noOutputs) {
C[i] = 0;
} else {
C[i] = (int) IS.getOutputNumericValue(i, 0);
}
if (C[i] > nClasses) {
nClasses = C[i];
}
}
nClasses++;
System.out.println("Number of classes=" + nClasses);
} catch (Exception e) {
System.out.println("DBG: Exception in readSet");
e.printStackTrace();
}
}
/**
* Main Function
*
* @param args the Command line arguments. Only one is processed: the name of the file containing
* the
* <p>parameters
*/
public static void main(String[] args) throws IOException {
double[][] X;
double[][] Y;
int nInpt, nOutpl, ndata, i, j;
Rbfn net;
try {
// Help required
if (args.length > 0) {
if (args[0].equals("--help")
|| args[0].equals("-help")
|| args[0].equals("-h")
|| args[0].equals("-?")) {
doHelp();
return;
}
}
System.out.println("- Executing doRbfnDec " + args.length);
// Reading parameters
String paramFile = (args.length > 0) ? args[0] : "parameters.txt";
setParameters(paramFile);
System.out.println(" - Parameters file: " + paramFile);
// Random generator setup
if (reallySeed) {
Randomize.setSeed((long) seed);
}
// Reading Training dataset
ProcDataset Dtrn = new ProcDataset(trnFile, true);
// Training
System.out.println("Modeling Dataset");
Dtrn.processModelDataset();
nInpt = Dtrn.getninputs();
nOutpl = 1; // PD.getnvariables()-nInpt;
ndata = Dtrn.getndata();
Y = new double[ndata][1];
X = Dtrn.getX();
double[] auxY;
auxY = Dtrn.getY();
for (i = 0; i < ndata; i++) Y[i][0] = auxY[i];
// Building and training the net
net = new Rbfn(nNeuronsIni, X, ndata, nInpt, nOutpl);
net.decremental(X, Y, ndata, percent, alfa);
double[] obtained = new double[ndata];
net.testModeling(X, ndata, obtained);
Dtrn.generateResultsModeling(outTrnFile, auxY, obtained);
// TEST
ProcDataset Dtst = new ProcDataset(tstFile, false);
Dtst.processModelDataset();
nInpt = Dtst.getninputs();
nOutpl = 1; // PD.getnvariables()-nInpt;
ndata = Dtst.getndata();
X = Dtst.getX();
auxY = Dtst.getY();
Y = new double[ndata][1];
for (i = 0; i < ndata; i++) Y[i][0] = auxY[i];
obtained = new double[ndata];
net.testModeling(X, ndata, obtained);
Dtst.generateResultsModeling(outTstFile, auxY, obtained);
RBFUtils.createOutputFile(trnFile, outRbfFile);
net.printRbfn(outRbfFile);
if (Dtrn.datasetType() == 2) System.out.println("This is not a clustering algorithm");
System.out.println(
"- End of doRbfnDec. See results in output files named according to "
+ paramFile
+ " parameters file.");
} catch (Exception e) {
throw new InternalError(e.toString());
}
}
/** Process the training and test files provided in the parameters file to the constructor. */
public void process() {
// declarations
double[] outputs;
double[] outputs2;
Instance neighbor;
double dist, mean;
int actual;
Randomize rnd = new Randomize();
Instance ex;
gCenter kmeans = null;
int iterations = 0;
double E;
double prevE;
int totalMissing = 0;
boolean allMissing = true;
rnd.setSeed(semilla);
// PROCESS
try {
// Load in memory a dataset that contains a classification problem
IS.readSet(input_train_name, true);
int in = 0;
int out = 0;
ndatos = IS.getNumInstances();
nvariables = Attributes.getNumAttributes();
nentradas = Attributes.getInputNumAttributes();
nsalidas = Attributes.getOutputNumAttributes();
X = new String[ndatos][nvariables]; // matrix with transformed data
kmeans = new gCenter(K, ndatos, nvariables);
timesSeen = new FreqList[nvariables];
mostCommon = new String[nvariables];
// first, we choose k 'means' randomly from all
// instances
totalMissing = 0;
for (int i = 0; i < ndatos; i++) {
Instance inst = IS.getInstance(i);
if (inst.existsAnyMissingValue()) totalMissing++;
}
if (totalMissing == ndatos) allMissing = true;
else allMissing = false;
for (int numMeans = 0; numMeans < K; numMeans++) {
do {
actual = (int) (ndatos * rnd.Rand());
ex = IS.getInstance(actual);
} while (ex.existsAnyMissingValue() && !allMissing);
kmeans.copyCenter(ex, numMeans);
}
// now, iterate adjusting clusters' centers and
// instances to them
prevE = 0;
iterations = 0;
do {
for (int i = 0; i < ndatos; i++) {
Instance inst = IS.getInstance(i);
kmeans.setClusterOf(inst, i);
}
// set new centers
kmeans.recalculateCenters(IS);
// compute RMSE
E = 0;
for (int i = 0; i < ndatos; i++) {
Instance inst = IS.getInstance(i);
E += kmeans.distance(inst, kmeans.getClusterOf(i));
}
iterations++;
// System.out.println(iterations+"\t"+E);
if (Math.abs(prevE - E) == 0) iterations = maxIter;
else prevE = E;
} while (E > minError && iterations < maxIter);
for (int i = 0; i < ndatos; i++) {
Instance inst = IS.getInstance(i);
in = 0;
out = 0;
for (int j = 0; j < nvariables; j++) {
Attribute a = Attributes.getAttribute(j);
direccion = a.getDirectionAttribute();
tipo = a.getType();
if (direccion == Attribute.INPUT) {
if (tipo != Attribute.NOMINAL && !inst.getInputMissingValues(in)) {
X[i][j] = new String(String.valueOf(inst.getInputRealValues(in)));
} else {
if (!inst.getInputMissingValues(in)) X[i][j] = inst.getInputNominalValues(in);
else {
actual = kmeans.getClusterOf(i);
X[i][j] = new String(kmeans.valueAt(actual, j));
}
}
in++;
} else {
if (direccion == Attribute.OUTPUT) {
if (tipo != Attribute.NOMINAL && !inst.getOutputMissingValues(out)) {
X[i][j] = new String(String.valueOf(inst.getOutputRealValues(out)));
} else {
if (!inst.getOutputMissingValues(out)) X[i][j] = inst.getOutputNominalValues(out);
else {
actual = kmeans.getClusterOf(i);
X[i][j] = new String(kmeans.valueAt(actual, j));
}
}
out++;
}
}
}
}
} catch (Exception e) {
System.out.println("Dataset exception = " + e);
e.printStackTrace();
System.exit(-1);
}
write_results(output_train_name);
/** ************************************************************************************ */
// does a test file associated exist?
if (input_train_name.compareTo(input_test_name) != 0) {
try {
// Load in memory a dataset that contains a classification problem
IStest.readSet(input_test_name, false);
int in = 0;
int out = 0;
ndatos = IStest.getNumInstances();
nvariables = Attributes.getNumAttributes();
nentradas = Attributes.getInputNumAttributes();
nsalidas = Attributes.getOutputNumAttributes();
for (int i = 0; i < ndatos; i++) {
Instance inst = IStest.getInstance(i);
in = 0;
out = 0;
for (int j = 0; j < nvariables; j++) {
Attribute a = Attributes.getAttribute(j);
direccion = a.getDirectionAttribute();
tipo = a.getType();
if (direccion == Attribute.INPUT) {
if (tipo != Attribute.NOMINAL && !inst.getInputMissingValues(in)) {
X[i][j] = new String(String.valueOf(inst.getInputRealValues(in)));
} else {
if (!inst.getInputMissingValues(in)) X[i][j] = inst.getInputNominalValues(in);
else {
actual = kmeans.getClusterOf(i);
X[i][j] = new String(kmeans.valueAt(actual, j));
}
}
in++;
} else {
if (direccion == Attribute.OUTPUT) {
if (tipo != Attribute.NOMINAL && !inst.getOutputMissingValues(out)) {
X[i][j] = new String(String.valueOf(inst.getOutputRealValues(out)));
} else {
if (!inst.getOutputMissingValues(out)) X[i][j] = inst.getOutputNominalValues(out);
else {
actual = kmeans.getClusterOf(i);
X[i][j] = new String(kmeans.valueAt(actual, j));
}
}
out++;
}
}
}
}
} catch (Exception e) {
System.out.println("Dataset exception = " + e);
e.printStackTrace();
System.exit(-1);
}
write_results(output_test_name);
}
}