/***********************************************************************
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.Rule_Learning.Rules6;
/**
* <p>Title: Algorithm</p>
*
* <p>Description: It contains the implementation of the algorithm</p>
*
*
* <p>Company: KEEL </p>
*
* @author Alberto Fernandez
* @version 1.0
*/
import java.io.IOException;
import org.core.*;
import java.util.*;
public class Algorithm {
myDataset train, val, test;
String outputTr, outputTst, outputReglas;
int BeamWidth;
int minPos;
int minNeg;
//int nClasses;
//We may declare here the algorithm's parameters
private boolean somethingWrong = false; //to check if everything is correct.
/**
* Default constructor
*/
public Algorithm() {
}
/**
* 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 Algorithm(parseParameters parameters) {
train = new myDataset();
val = new myDataset();
test = new myDataset();
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.hasNumericalAttributes();
somethingWrong = somethingWrong || train.hasMissingAttributes();
outputTr = parameters.getTrainingOutputFile();
outputTst = parameters.getTestOutputFile();
outputReglas = parameters.getReglasOutputFile();
//Now we parse the parameters, for example:
BeamWidth = Integer.parseInt(parameters.getParameter(0));
minPos = Integer.parseInt(parameters.getParameter(1));
minNeg = Integer.parseInt(parameters.getParameter(2));
}
/**
* It launches the algorithm
*/
public void execute() {
if (somethingWrong) { //We do not execute the program
System.err.println("An error was found, either the data-set have numerical values or missing values.");
System.err.println("Aborting the program");
//We should not use the statement: System.exit(-1);
} else {
LinkedList<Regla> RuleSet = new LinkedList <Regla> ();
TreeMap<Integer, Boolean> marcados= new TreeMap<Integer,Boolean>();
//controla que no quede en bucle infinito para alguna reglas
//que no son capaces de mejorar en ningun momento a la regla sin condiciones
int iteraciones = 0;
//se repite hasta que todos los ejemplos del dataset esten marcados
while (train.getnData()!= marcados.size() && iteraciones<5){
//para cada fila del dataset
for(int i=0; i<train.getnData();i++){
//si no esta en el vector de marcados se analiza la fila
if(!marcados.containsKey(i)){
//se llama al procedimiento induce_one_rul, que devuelve una regla
Regla regla = InduceOneRule.induce_One_Rule(i, train, BeamWidth, minPos, minNeg);
//regla.mostrarRegla();
//si la regla no es vacia, se marca el ejemplo añadiendolo a marcados
if (!regla.getAntecedente().isEmpty()){
marcados.put(i,true);
RuleSet.add(regla);
}
}
}
iteraciones++;
}
//eliminamos reglas repetidas del RuleSet
LinkedList <Regla> reglas_aux = new LinkedList <Regla> ();
boolean repetida = false;
for(int tam=0; tam<RuleSet.size();tam++){
//si la regla es igual a otra regla contenida en el conjunto
for(int i=0; i<reglas_aux.size();i++){
if(RuleSet.get(tam).equals(reglas_aux.get(i)))
repetida = true;//encuentra una repetida
}
//si no se repite la almacena en la lista auxiliar
if(!repetida) reglas_aux.add(RuleSet.get(tam));
repetida = false; //se reestrablece el valor de repetida
}
RuleSet = reglas_aux;
BaseReglas conjunto_reglas = new BaseReglas(RuleSet);
conjunto_reglas.mostrarReglas();
//finalmente guardamos la base de reglas en fichero
conjunto_reglas.ficheroReglas(outputReglas);
//###################Comprobamos con el fochero de validacion#############
LinkedList<String> resultado_val = conjunto_reglas.compruebaReglas(val);
//###################Comprobamos con el fochero de test#############
LinkedList<String> resultado_test = conjunto_reglas.compruebaReglas(test);
//Finally we should fill the training and test output files
doOutput(this.val, this.outputTr, resultado_val);
doOutput(this.test, this.outputTst, resultado_test);
System.out.println("Algorithm Finished");
}
}
/**
* 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
*/
private void doOutput(myDataset dataset, String filename, LinkedList<String> resultado) {
String output = new String("");
output = dataset.copyHeader(); //we insert the header in the output file
Double noacertados=0.0;
Double noclasificados=0.0;
//We write the output for each example
for (int i = 0; i < dataset.getnData(); i++) {
//for classification:
output += dataset.getOutputAsString(i) + " " +
resultado.get(i) + "\n";
if (resultado.get(i).compareTo("No clasificado") == 0){
noclasificados++;
}else if(dataset.getOutputAsString(i).compareTo(resultado.get(i)) != 0){
noacertados++;
}
}
Fichero.escribeFichero(filename, output);
}
}