/***********************************************************************
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/
**********************************************************************/
/**
* <p>
* File: OSS.java
* </p>
*
* The OSS algorithm is an undersampling method used to deal with the imbalanced
* problem.
*
* @author Written by Salvador Garcia Lopez (University of Granada) 30/03/2006
* @author Modified by Victoria Lopez Morales (University of Granada) 23/07/2010
* @author Modified by Victoria Lopez Morales (University of Granada) 21/09/2010
* @version 0.1
* @since JDK1.5
*
*/
package keel.Algorithms.ImbalancedClassification.Resampling.OSS;
import keel.Algorithms.Preprocess.Basic.*;
import keel.Dataset.Attribute;
import keel.Dataset.Attributes;
import keel.Dataset.Instance;
import org.core.*;
import java.util.StringTokenizer;
import java.util.Arrays;
public class OSS extends Metodo {
/**
* <p>
* The OSS algorithm is an undersampling method used to deal with the imbalanced
* problem.
* </p>
*/
/*Own parameters of the algorithm*/
private long semilla;
private int k;
/**
* <p>
* Constructor of the class. It configures the execution of the algorithm by
* reading the configuration script that indicates the parameters that are
* going to be used.
* </p>
*
* @param ficheroScript Name of the configuration script that indicates the
* parameters that are going to be used during the execution of the algorithm
*/
public OSS (String ficheroScript) {
super (ficheroScript);
}
/**
* <p>
* The main method of the class that includes the operations of the algorithm.
* It includes all the operations that the algorithm has and finishes when it
* writes the output information into files.
* </p>
*/
public void run () {
int i, j, l, m;
int nPos = 0, nNeg = 0;
int posID;
int tmp;
boolean marcas[];
int nSel;
double conjS[][];
int clasesS[];
double conjS2[][];
int clasesS2[];
double minDist, dist;
int pos;
int S[];
int nClases;
int baraje[];
int tamS=0;
int claseObt;
int cont;
int busq;
long tiempo = System.currentTimeMillis();
/*TOMEK LINKS PART*/
/*Count of number of positive and negative examples*/
for (i=0; i<clasesTrain.length; i++) {
if (clasesTrain[i] == 0)
nPos++;
else
nNeg++;
}
if (nPos > nNeg) {
tmp = nPos;
nPos = nNeg;
nNeg = tmp;
posID = 1;
} else {
posID = 0;
}
/*Inicialization of the instance flagged vector of the S set*/
marcas = new boolean[datosTrain.length];
for (i=0; i<datosTrain.length; i++) {
marcas[i] = true;
}
nSel = datosTrain.length;
for (i=0;i<datosTrain.length; i++) {
minDist = Double.POSITIVE_INFINITY;
pos = 0;
for (j=0; j<datosTrain.length; j++) {
if (i != j) {
dist = KNN.distancia (datosTrain[i],datosTrain[j]);
if (dist < minDist) {
minDist = dist;
pos = j;
}
}
}
if (clasesTrain[i] != clasesTrain[pos]) {
if (clasesTrain[i] != posID) {
if (marcas[i]==true)
{
marcas[i] = false;
nSel--;
}
} else {
if (marcas[pos]==true)
{
marcas[pos] = false;
nSel--;
}
}
}
}
/*Construction of the S set from the flags*/
conjS = new double[nSel][datosTrain[0].length];
clasesS = new int[nSel];
for (m=0, l=0; m<datosTrain.length; m++) {
if (marcas[m]) { //the instance will evaluate
for (j=0; j<datosTrain[0].length; j++) {
conjS[l][j] = datosTrain[m][j];
}
clasesS[l] = clasesTrain[m];
l++;
}
}
/*CNN PART*/
/*Inicialization of the candidates set*/
S = new int[conjS.length];
for (i=0; i<S.length; i++)
S[i] = Integer.MAX_VALUE;
/*Inserting an element of mayority class*/
Randomize.setSeed (semilla);
pos = Randomize.Randint (0, clasesS.length-1);
while (clasesS[pos] == posID)
pos = (pos + 1) % clasesS.length;
S[tamS] = pos;
tamS++;
/*Insert all subset of minority class*/
for (i=0; i<clasesS.length; i++) {
if (clasesS[i] == posID) {
S[tamS] = i;
tamS++;
}
}
/*Algorithm body. We resort randomly the instances of T and compare with the rest of S.
If an instance doesn�t classified correctly, it is inserted in S*/
baraje = new int[conjS.length];
for (i=0; i<conjS.length; i++)
baraje[i] = i;
for (i=0; i<conjS.length; i++) {
pos = Randomize.Randint (i, conjS.length-1);
tmp = baraje[i];
baraje[i] = baraje[pos];
baraje[pos] = tmp;
}
for (i=0; i<conjS.length; i++) {
if (clasesS[i] != posID) { //only for mayority class instances
/*Construction of the S set from the previous vector S*/
conjS2 = new double[tamS][conjS[0].length];
clasesS2 = new int[tamS];
for (j=0; j<tamS; j++) {
for (l=0; l<conjS[0].length; l++)
conjS2[j][l] = conjS[S[j]][l];
clasesS2[j] = clasesS[S[j]];
}
/*Do KNN to the instance*/
claseObt = KNN.evaluacionKNN (k, conjS2, clasesS2, conjS[baraje[i]], 2);
if (claseObt != clasesS[baraje[i]]) {//fail in the class, it is included in S
Arrays.sort(S);
busq = Arrays.binarySearch(S, baraje[i]);
if (busq < 0) {
S[tamS] = baraje[i];
tamS++;
}
}
}
}
/*Construction of the S set from the previous vector S*/
conjS2 = new double[tamS][conjS[0].length];
clasesS2 = new int[tamS];
for (j=0; j<tamS; j++) {
for (l=0; l<conjS[0].length; l++)
conjS2[j][l] = conjS[S[j]][l];
clasesS2[j] = clasesS[S[j]];
}
System.out.println("OSS "+ relation + " " + (double)(System.currentTimeMillis()-tiempo)/1000.0 + "s");
OutputIS.escribeSalida(ficheroSalida[0], conjS2, clasesS2, entradas, salida, nEntradas, relation);
OutputIS.escribeSalida(ficheroSalida[1], test, entradas, salida, nEntradas, relation);
}
/**
* <p>
* Obtains the parameters used in the execution of the algorithm and stores
* them in the private variables of the class
* </p>
*
* @param ficheroScript Name of the configuration script that indicates the
* parameters that are going to be used during the execution of the algorithm
*/
public void leerConfiguracion (String ficheroScript) {
String fichero, linea, token;
StringTokenizer lineasFichero, tokens;
byte line[];
int i, j;
ficheroSalida = new String[2];
fichero = Fichero.leeFichero (ficheroScript);
lineasFichero = new StringTokenizer (fichero,"\n\r");
lineasFichero.nextToken();
linea = lineasFichero.nextToken();
tokens = new StringTokenizer (linea, "=");
tokens.nextToken();
token = tokens.nextToken();
/*Getting the names of the training and test files*/
line = token.getBytes();
for (i=0; line[i]!='\"'; i++);
i++;
for (j=i; line[j]!='\"'; j++);
ficheroTraining = new String (line,i,j-i);
for (i=j+1; line[i]!='\"'; i++);
i++;
for (j=i; line[j]!='\"'; j++);
ficheroTest = new String (line,i,j-i);
/*Getting the path and base name of the results files*/
linea = lineasFichero.nextToken();
tokens = new StringTokenizer (linea, "=");
tokens.nextToken();
token = tokens.nextToken();
/*Getting the names of output files*/
line = token.getBytes();
for (i=0; line[i]!='\"'; i++);
i++;
for (j=i; line[j]!='\"'; j++);
ficheroSalida[0] = new String (line,i,j-i);
for (i=j+1; line[i]!='\"'; i++);
i++;
for (j=i; line[j]!='\"'; j++);
ficheroSalida[1] = new String (line,i,j-i);
/*Getting the seed*/
linea = lineasFichero.nextToken();
tokens = new StringTokenizer (linea, "=");
tokens.nextToken();
semilla = Long.parseLong(tokens.nextToken().substring(1));
/*Getting the number of neighbors*/
linea = lineasFichero.nextToken();
tokens = new StringTokenizer (linea, "=");
tokens.nextToken();
k = Integer.parseInt(tokens.nextToken().substring(1));
}
/**
* This function builds the data matrix for reference data and normalizes inputs values
*/
protected void normalizar () throws CheckException {
int i, j, k;
Instance temp;
double caja[];
StringTokenizer tokens;
boolean nulls[];
/*Check if dataset corresponding with a classification problem*/
if (Attributes.getOutputNumAttributes() < 1) {
throw new CheckException ("This dataset haven?t outputs, so it not corresponding to a classification problem.");
} else if (Attributes.getOutputNumAttributes() > 1) {
throw new CheckException ("This dataset have more of one output.");
}
if (Attributes.getOutputAttribute(0).getType() == Attribute.REAL) {
throw new CheckException ("This dataset have an input attribute with floating values, so it not corresponding to a classification problem.");
}
entradas = Attributes.getInputAttributes();
salida = Attributes.getOutputAttribute(0);
nEntradas = Attributes.getInputNumAttributes();
tokens = new StringTokenizer (training.getHeader()," \n\r");
tokens.nextToken();
relation = tokens.nextToken();
datosTrain = new double[training.getNumInstances()][Attributes.getInputNumAttributes()];
clasesTrain = new int[training.getNumInstances()];
caja = new double[1];
nulosTrain = new boolean[training.getNumInstances()][Attributes.getInputNumAttributes()];
nominalTrain = new int[training.getNumInstances()][Attributes.getInputNumAttributes()];
realTrain = new double[training.getNumInstances()][Attributes.getInputNumAttributes()];
for (i=0; i<training.getNumInstances(); i++) {
temp = training.getInstance(i);
nulls = temp.getInputMissingValues();
datosTrain[i] = training.getInstance(i).getAllInputValues();
for (j=0; j<nulls.length; j++)
if (nulls[j]) {
datosTrain[i][j]=0.0;
nulosTrain[i][j] = true;
}
caja = training.getInstance(i).getAllOutputValues();
clasesTrain[i] = (int) caja[0];
for (k = 0; k < datosTrain[i].length; k++) {
if (Attributes.getInputAttribute(k).getType() == Attribute.NOMINAL) {
nominalTrain[i][k] = (int)datosTrain[i][k];
datosTrain[i][k] /= Attributes.getInputAttribute(k).
getNominalValuesList().size() - 1;
} else {
realTrain[i][k] = datosTrain[i][k];
datosTrain[i][k] -= Attributes.getInputAttribute(k).getMinAttribute();
datosTrain[i][k] /= Attributes.getInputAttribute(k).getMaxAttribute() -
Attributes.getInputAttribute(k).getMinAttribute();
if (Double.isNaN(datosTrain[i][k])){
datosTrain[i][k] = realTrain[i][k];
}
}
}
}
datosTest = new double[test.getNumInstances()][Attributes.getInputNumAttributes()];
clasesTest = new int[test.getNumInstances()];
caja = new double[1];
for (i=0; i<test.getNumInstances(); i++) {
temp = test.getInstance(i);
nulls = temp.getInputMissingValues();
datosTest[i] = test.getInstance(i).getAllInputValues();
for (j=0; j<nulls.length; j++)
if (nulls[j]) {
datosTest[i][j]=0.0;
}
caja = test.getInstance(i).getAllOutputValues();
clasesTest[i] = (int) caja[0];
}
} //end-method
}