/***********************************************************************
This file is part of KEEL-software, the Data Mining tool for regression,
classification, clustering, pattern mining and so on.
Copyright (C) 2004-2010
F. Herrera (herrera@decsai.ugr.es)
L. S�nchez (luciano@uniovi.es)
J. Alcal�-Fdez (jalcala@decsai.ugr.es)
S. Garc�a (sglopez@ujaen.es)
A. Fern�ndez (alberto.fernandez@ujaen.es)
J. Luengo (julianlm@decsai.ugr.es)
This program is free software: you can redistribute it and/or modify
it under the terms of the GNU General Public License as published by
the Free Software Foundation, either version 3 of the License, or
(at your option) any later version.
This program is distributed in the hope that it will be useful,
but WITHOUT ANY WARRANTY; without even the implied warranty of
MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
GNU General Public License for more details.
You should have received a copy of the GNU General Public License
along with this program. If not, see http://www.gnu.org/licenses/
**********************************************************************/
package keel.Algorithms.Decision_Trees.DT_oblicuo;
import java.io.IOException;
import org.core.*;
/**
* <p>It contains the implementation of the algorithm</p>
*
*
* <p>Company: KEEL </p>
*
* @author Alberto Fern�ndez
* @version 1.0
*/
public class DT_oblicuo {
myDataset train, val, test;
String outputTr, outputTst, ficheroTree, claseMayoritaria;
int nClasses, nGenerations;
String fichTrain;
Tree arbol;
private boolean somethingWrong = false; //to check if everything is correct.
/**
* Default constructor
*/
public DT_oblicuo() {
}
/**
* It reads the data from the input files (training, validation and test) and parse all the parameters
* from the parameters array.
* @param parameters parseParameters It contains the input files, output files and parameters
*/
public DT_oblicuo(parseParameters parameters) {
train = new myDataset();
val = new myDataset();
test = new myDataset();
fichTrain = parameters.getTrainingInputFile();
try {
System.out.println("\nReading the training set: " +
parameters.getTrainingInputFile());
train.readClassificationSet(parameters.getTrainingInputFile(), true);
System.out.println("\nReading the validation set: " +
parameters.getValidationInputFile());
val.readClassificationSet(parameters.getValidationInputFile(), false);
System.out.println("\nReading the test set: " +
parameters.getTestInputFile());
test.readClassificationSet(parameters.getTestInputFile(), false);
}
catch (IOException e) {
System.err.println(
"There was a problem while reading the input data-sets: " +
e);
somethingWrong = true;
}
//We may check if there are some numerical attributes, because our algorithm may not handle them:
//somethingWrong = somethingWrong || train.hasNominalAttributes();
somethingWrong = somethingWrong || train.hasMissingAttributes();
outputTr = parameters.getTrainingOutputFile();
outputTst = parameters.getTestOutputFile();
ficheroTree = parameters.getOutputFile(0);
//Now we parse the parameters
long semilla = Long.parseLong(parameters.getParameter(0));
nGenerations = Integer.parseInt(parameters.getParameter(1));
/*
nCross = Integer.parseInt(parameters.getParameter(3));
nMut = Integer.parseInt(parameters.getParameter(4));
nClone = Integer.parseInt(parameters.getParameter(5));
nImmigration = Integer.parseInt(parameters.getParameter(6));
*/
Randomize.setSeed(semilla);
}
/**
* It launches the algorithm
*/
public void execute() {
if (somethingWrong) { //We do not execute the program
System.err.println("An error was found: the data-set has missing values.");
System.err.println("Please remove the examples with MV or apply a preprocessing step");
System.err.println("Aborting the program");
//We should not use the statement: System.exit(-1);
}
else {
//We do here the algorithm's operations
//nClasses = train.getnClasses();
int[] ejemplos = new int[train.size()];
for (int i = 0; i < ejemplos.length; i++) {
ejemplos[i] = i;
}
arbol = new Tree(null, train, train.size(), ejemplos, nGenerations);
//Finally we should fill the training and test output files
double accTr = doOutput(this.val, this.outputTr);
double accTst = doOutput(this.test, this.outputTst);
escribeResultados(accTr,accTst);
}
}
/**
* It generates the output file from a given dataset and stores it in a file
* @param dataset myDataset input dataset
* @param filename String the name of the file
* @return the Accuracy of the classifier
*/
private double doOutput(myDataset dataset, String filename) {
String output = new String("");
output = dataset.copyHeader(); //we insert the header in the output file
int aciertos = 0;
//We write the output for each example
for (int i = 0; i < dataset.getnData(); i++) {
//for classification:
String claseReal = dataset.getOutputAsString(i);
String prediccion = this.classificationOutput(dataset.getExample(i));
output += claseReal + " " + prediccion + "\n";
if (claseReal.equalsIgnoreCase(prediccion)) {
aciertos++;
}
}
Fichero.escribeFichero(filename, output);
return (1.0 * aciertos / dataset.size());
}
/**
* It returns the algorithm classification output given an input example
* @param example double[] The input example
* @return String the output generated by the algorithm
*/
private String classificationOutput(double[] example) {
//return train.claseMasFrecuente();
return arbol.clasificar(example);
}
private void escribeResultados(double accTr, double accTst){
//System.out.println(arbol.printString());
System.out.println("Accuracy in training: " + accTr);
System.out.println("Accuracy in test: " + accTst);
System.out.println("Algorithm Finished");
Fichero.escribeFichero(ficheroTree,arbol.printString());
Fichero.AnadirtoFichero(ficheroTree,"\n\nAccuracy in training: " + accTr);
Fichero.AnadirtoFichero(ficheroTree,"\nAccuracy in test: " + accTst);
}
}