/***********************************************************************
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.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.MOPNAR;
/**
* <p>
* @author Written by Diana Mart�n (dmartin@ceis.cujae.edu.cu)
* @version 1.1
* @since JDK1.6
* </p>
*/
import java.io.FileNotFoundException;
import java.io.IOException;
import java.io.PrintWriter;
import java.util.ArrayList;
import org.core.*;
import keel.Dataset.*;
public class MOPNAR {
/**
* <p>
* It gathers all the parameters, launches the algorithm, and prints out the results
* </p>
*/
private myDataset dataset;
private String rulesFilename;
private String valuesFilename;
private String paretoFilename;
private MOPNARProcess proc;
private ArrayList<AssociationRule> associationRulesPareto;
private String fileTime, fileHora, namedataset;
private int nTrials;
private int numObjectives;
private int H; //control the population size and weight vectors
private int T; //Number of the weight vectors in the neighborhood
private double probDelta; //Probability that parent solutions are selected from the neighborhood
private int nr; //Maximal number of solutions replaced by each child solution
private double pm;
private double af;
private double percentUpdate;
long startTime, totalTime;
private boolean somethingWrong = false; //to check if everything is correct.
/**
* Default constructor
*/
public MOPNAR() {
}
/**
* It reads the data from the input files and parse all the parameters from the parameters array
* @param parameters It contains the input files, output files and parameters
*/
public MOPNAR(parseParameters parameters) {
this.startTime = System.currentTimeMillis();
this.dataset = new myDataset();
try {
System.out.println("\nReading the transaction set: " + parameters.getTransactionsInputFile());
dataset.readDataSet (parameters.getTransactionsInputFile());
}
catch (IOException e) {
System.err.println ("There was a problem while reading the input transaction set: " + e);
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.dataset.hasMissingAttributes();
this.rulesFilename = parameters.getAssociationRulesFile();
this.valuesFilename = parameters.getOutputFile(0);
this.paretoFilename = parameters.getOutputFile(1);
this.fileTime = (parameters.getOutputFile(0)).substring(0,(parameters.getOutputFile(0)).lastIndexOf('/')) + "/time.txt";
this.fileHora = (parameters.getOutputFile(0)).substring(0,(parameters.getOutputFile(0)).lastIndexOf('/')) + "/hora.txt";
long seed = Long.parseLong (parameters.getParameter(0));
this.numObjectives = Integer.parseInt (parameters.getParameter(1));
this.nTrials = Integer.parseInt (parameters.getParameter(2));
this.H = Integer.parseInt (parameters.getParameter(3));
this.T = Integer.parseInt (parameters.getParameter(4));
this.probDelta = Double.parseDouble (parameters.getParameter(5));
this.nr = Integer.parseInt (parameters.getParameter(6));
this.pm = Double.parseDouble (parameters.getParameter(7));
this.af = Double.parseDouble (parameters.getParameter(8));
this.percentUpdate = Double.parseDouble (parameters.getParameter(9));
Randomize.setSeed(seed);
}
/**
* It launches the algorithm
*/
public void execute() {
if (somethingWrong) { //We do not execute the program
System.err.println("An error was found");
System.err.println("Aborting the program");
//We should not use the statement: System.exit(-1);
}
else {
this.proc = new MOPNARProcess(this.dataset,this.numObjectives, this.nTrials, this.H, this.T, this.probDelta , this.nr, this.pm, this.af,this.percentUpdate);
this.proc.run();
this.associationRulesPareto = this.proc.generateRulesPareto();
try {
int r, i;
Gene gen;
AssociationRule a_r;
PrintWriter rules_writer = new PrintWriter(this.rulesFilename);
PrintWriter values_writer = new PrintWriter(this.valuesFilename);
PrintWriter pareto_writer = new PrintWriter(this.paretoFilename);
rules_writer.println("<?xml version=\"1.0\" encoding=\"UTF-8\"?>");
rules_writer.println("<association_rules>");
values_writer.println("<?xml version=\"1.0\" encoding=\"UTF-8\"?>");
values_writer.println("<values>");
pareto_writer.println("<?xml version=\"1.0\" encoding=\"UTF-8\"?>");
pareto_writer.println("<values>");
for (r=0; r < this.associationRulesPareto.size(); r++) {
a_r = this.associationRulesPareto.get(r);
ArrayList<Gene> ant = a_r.getAntecedents();
ArrayList<Gene> cons = a_r.getConsequents();
rules_writer.println("<rule id=\"" + r + "\">");
values_writer.println("<rule id=\"" + r + "\" rule_support=\"" + MOPNARProcess.roundDouble(a_r.getSupport(),2) + "\" antecedent_support=\"" + MOPNARProcess.roundDouble(a_r.getAntSupport(),2) + "\" consequent_support=\"" + MOPNARProcess.roundDouble(a_r.getConsSupport(),2) + "\" confidence=\"" + MOPNARProcess.roundDouble(a_r.getConfidence(),2) +"\" lift=\"" + MOPNARProcess.roundDouble(a_r.getLift(),2) + "\" conviction=\"" + MOPNARProcess.roundDouble(a_r.getConv(),2) + "\" certainFactor=\"" + MOPNARProcess.roundDouble(a_r.getCF(),2) + "\" netConf=\"" + MOPNARProcess.roundDouble(a_r.getNetConf(),2) + "\" yulesQ=\"" + MOPNARProcess.roundDouble(a_r.getYulesQ(),2) + "\" nAttributes=\"" + (a_r.getnAnts()+1) + "\"/>");
rules_writer.println("<antecedents>");
for (i=0; i < ant.size(); i++) {
gen = ant.get(i);
createRule(gen, gen.getAttr(), rules_writer);
}
rules_writer.println("</antecedents>");
rules_writer.println("<consequents>");
for (i=0; i < cons.size(); i++) {
gen = cons.get(i);
createRule(gen, gen.getAttr(), rules_writer);
}
rules_writer.println("</consequents>");
rules_writer.println("</rule>");
}
rules_writer.println("</association_rules>");
values_writer.println("</values>");
this.proc.saveReport(this.associationRulesPareto, values_writer);
rules_writer.close();
values_writer.close();
pareto_writer.print(this.proc.getParetos());
pareto_writer.println("</values>");
pareto_writer.close();
totalTime = System.currentTimeMillis() - startTime;
this.writeTime();
System.out.println("Algorithm Finished");
}
catch (FileNotFoundException e)
{
e.printStackTrace();
}
}
}
public void writeTime() {
long seg, min, hor;
String stringOut = new String("");
stringOut = "" + totalTime / 1000 + " " + this.namedataset + rulesFilename + "\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 + " " + rulesFilename + "\n";
Files.addToFile(this.fileHora, stringOut);
}
private void createRule(Gene g, int id_attr, PrintWriter w) {
w.println("<attribute name=\"" + Attributes.getAttribute(id_attr).getName() + "\" value=\"");
if (! g.getIsPositiveInterval()) w.print("NOT ");
if (this.dataset.getAttributeType(id_attr) == myDataset.NOMINAL) w.print(Attributes.getAttribute(id_attr).getNominalValue ((int)g.getLowerBound()));
else w.print("[" + g.getLowerBound() + ", " + g.getUpperBound() + "]");
w.print("\" />");
}
}