/***********************************************************************
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.Associative_Classification.ClassifierCPAR;
import java.io.IOException;
import org.core.*;
/**
* It contains the implementation of the CPAR algorithm
*
* @author Written by Jesus Alcala (University of Granada) 09/02/2010
* @version 1.0
* @since JDK1.5
*/
public class CPAR {
myDataset train, val, test;
String outputTr, outputTst, fileDB, fileRB, fileTime, fileHora, data;
DataBase dataBase;
RuleBase ruleBase;
PRM prm;
long startTime, totalTime;
//We may declare here the algorithm's parameters
double alfa, delta, min_gain;
int K;
private boolean somethingWrong = false; //to check if everything is correct.
/**
* Default constructor
*/
public CPAR() {
}
/**
* 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 CPAR(parseParameters parameters) {
this.startTime = System.currentTimeMillis();
this.train = new myDataset();
this.val = new myDataset();
this.test = new myDataset();
try {
System.out.println("\nReading the training set: " + parameters.getTrainingInputFile());
this.train.readClassificationSet(parameters.getTrainingInputFile(), true);
System.out.println("\nReading the validation set: " + parameters.getValidationInputFile());
this.val.readClassificationSet(parameters.getValidationInputFile(), false);
System.out.println("\nReading the test set: " + parameters.getTestInputFile());
this.test.readClassificationSet(parameters.getTestInputFile(), false);
}
catch (IOException e) {
System.err.println("There was a problem while reading the input data-sets: " + e);
this.somethingWrong = true;
}
//We may check if there are some numerical attributes, because our algorithm may not handle them:
//somethingWrong = somethingWrong || train.hasNumericalAttributes();
this.somethingWrong = this.somethingWrong || this.train.hasMissingAttributes();
this.outputTr = parameters.getTrainingOutputFile();
this.outputTst = parameters.getTestOutputFile();
this.fileDB = parameters.getOutputFile(0);
this.fileRB = parameters.getOutputFile(1);
this.data = parameters.getTrainingInputFile();
this.fileTime = (parameters.getOutputFile(1)).substring(0,(parameters.getOutputFile(1)).lastIndexOf('/')) + "/time.txt";
this.fileHora = (parameters.getOutputFile(1)).substring(0,(parameters.getOutputFile(1)).lastIndexOf('/')) + "/hora.txt";
//Now we parse the parameters
this.delta = Double.parseDouble(parameters.getParameter(0));
this.min_gain = Double.parseDouble(parameters.getParameter(1));
this.alfa = Double.parseDouble(parameters.getParameter(2));
this.K = Integer.parseInt(parameters.getParameter(3));
}
/**
* 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
this.dataBase = new DataBase(this.train);
this.ruleBase = new RuleBase(this.dataBase, this.train, this.K);
this.prm = new PRM(this.dataBase, this.train, this.ruleBase, this.alfa, this.delta, this.min_gain);
this.prm.generatePR();
this.dataBase.saveFile(this.fileDB);
this.ruleBase.saveFile(this.fileRB);
//Finally we should fill the training and test output files
doOutput(this.val, this.outputTr);
doOutput(this.test, this.outputTst);
totalTime = System.currentTimeMillis() - startTime;
this.writeTime();
System.out.println("Algorithm Finished");
}
}
/**
* It writes the running time of the algorithm
*/
public void writeTime() {
long seg, min, hor;
String stringOut = new String("");
stringOut = "" + totalTime / 1000 + " " + data + "\n";
Files.addToFile(this.fileTime, stringOut);
totalTime /= 1000;
seg = totalTime % 60;
totalTime /= 60;
min = totalTime % 60;
hor = totalTime / 60;
stringOut = "";
if (hor < 10) stringOut = stringOut + "0"+ hor + ":";
else stringOut = stringOut + hor + ":";
if (min < 10) stringOut = stringOut + "0"+ min + ":";
else stringOut = stringOut + min + ":";
if (seg < 10) stringOut = stringOut + "0"+ seg;
else stringOut = stringOut + seg;
stringOut = stringOut + " " + data + "\n";
Files.addToFile(this.fileHora, stringOut);
}
/**
* 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) {
String output = new String("");
output = dataset.copyHeader(); //we insert the header in the output file
//We write the output for each example
for (int i = 0; i < dataset.getnData(); i++) {
//for classification:
output += dataset.getOutputAsString(i) + " " + this.classificationOutput(dataset.getExample(i)) + "\n";
}
Files.writeFile(filename, output);
}
/**
* 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(int[] example) {
String output = new String("?");
/**
Here we should include the algorithm directives to generate the
classification output from the input example
*/
int clas = this.ruleBase.FRM(example);
if (clas >= 0) {
output = train.getOutputValue(clas);
}
return output;
}
}