/***********************************************************************
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: SPIDER.java
* </p>
*
* The SPIDER algorithm is an instance selection method used to deal with
* the imbalanced problem.
*
* @author Written by Mikel Galar Idoate (Public University of Navarra) 20/06/2010
* @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.SPIDER;
import keel.Algorithms.Preprocess.Basic.*;
import keel.Dataset.Attribute;
import keel.Dataset.Attributes;
import keel.Dataset.Instance;
import org.core.*;
import java.util.StringTokenizer;
public class SPIDER extends Metodo {
/**
* <p>
* The SPIDER algorithm is an instance selection method used to deal with
* the imbalanced problem.
* </p>
*/
/*Own parameters of the algorithm*/
private int k;
private String type;
/**
* <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 SPIDER (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, t;
int nClases;
int claseObt;
boolean safe[];
int nSel = 0;
double conjS[][];
double conjR[][];
int conjN[][];
boolean conjM[][];
int clasesS[];
int nPos = 0;
int nNeg = 0;
int tmp;
int posID, negID;
int amplify[];
int neighbours[] = null;
long tiempo = System.currentTimeMillis();
/*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;
negID = 0;
} else {
posID = 0;
negID = 1;
}
/* Inicialization of the flagged instances vector for a posterior copy
* Inicialization of the amplification vector, counts the number of times
* that an instance of the majority class is amplified */
safe = new boolean[datosTrain.length];
amplify = new int[datosTrain.length]; // number of times to be amplified
for (i=0; i<datosTrain.length; i++)
{
safe[i] = false;
amplify[i] = 1; // default = 1, no amplify
}
/*Getting the number of differents classes*/
nClases = 0;
for (i=0; i<clasesTrain.length; i++)
if (clasesTrain[i] > nClases)
nClases = clasesTrain[i];
nClases++;
/*Body of the algorithm. For each instance in T, search the correspond class conform his mayority
from the nearest neighborhood. Is it is positive, the instance is selected.*/
for (i=0; i<datosTrain.length; i++) {
/*Apply KNN to the instance*/
claseObt = KNN.evaluacionKNN2 (k, datosTrain, realTrain, nominalTrain, nulosTrain, clasesTrain, datosTrain[i], realTrain[i], nominalTrain[i], nulosTrain[i], nClases, distanceEu);
if (claseObt == clasesTrain[i]) //agree with your majority, it is included in the solution set
safe[i] = true;
}
// safe[i] = false = Flagged / = true = No Flagged
if (type.equalsIgnoreCase("weak") || type.equalsIgnoreCase("relabel"))
{
for (i = 0; i < datosTrain.length; i++) {
if (clasesTrain[i] == posID && safe[i] == false) {// minority flagged as noisy
neighbours = new int[k];
claseObt = KNN.evaluacionKNN2 (k, datosTrain, realTrain, nominalTrain, nulosTrain, clasesTrain, datosTrain[i], realTrain[i], nominalTrain[i], nulosTrain[i], nClases, distanceEu, neighbours);
// amplify as many as neighbors of the majority flagged as safe (safe = true)
for (j = 0; j < k; j++)
if (clasesTrain[neighbours[j]] != posID && safe[neighbours[j]] == true)
amplify[i]++;
}
}
if (type.equalsIgnoreCase("relabel"))
{
for (i = 0; i < datosTrain.length; i++) {
if (clasesTrain[i] == posID && safe[i] == false) {// minority flagged as noisy
neighbours = new int[k];
claseObt = KNN.evaluacionKNN2 (k, datosTrain, realTrain, nominalTrain, nulosTrain, clasesTrain, datosTrain[i], realTrain[i], nominalTrain[i], nulosTrain[i], nClases, distanceEu, neighbours);
for (j = 0; j < k; j++) {
if (clasesTrain[neighbours[j]] != posID && safe[neighbours[j]] == false)
{
clasesTrain[neighbours[j]] = posID;
safe[neighbours[j]] = true;
}
}
}
}
}
}
else
{
for (i = 0; i < datosTrain.length; i++) {
if (clasesTrain[i] == posID && safe[i] == true) {// minority flagged as safe
neighbours = new int[k];
claseObt = KNN.evaluacionKNN2 (k, datosTrain, realTrain, nominalTrain, nulosTrain, clasesTrain, datosTrain[i], realTrain[i], nominalTrain[i], nulosTrain[i], nClases, distanceEu, neighbours);
for (j = 0; j < k; j++)
if (clasesTrain[neighbours[j]] != posID && safe[neighbours[j]] == true)
amplify[i]++;
}
}
for (i = 0; i < datosTrain.length; i++) {
if (clasesTrain[i] == posID && safe[i] == false) {// minority flagged as noisy
claseObt = KNN.evaluacionKNN2 (k + 2, datosTrain, realTrain, nominalTrain, nulosTrain, clasesTrain, datosTrain[i], realTrain[i], nominalTrain[i], nulosTrain[i], nClases, distanceEu);
if (claseObt == clasesTrain[i])
{
neighbours = new int[k];
claseObt = KNN.evaluacionKNN2 (k, datosTrain, realTrain, nominalTrain, nulosTrain, clasesTrain, datosTrain[i], realTrain[i], nominalTrain[i], nulosTrain[i], nClases, distanceEu, neighbours);
// amplify as many as neighbors of the majority flagged as safe (safe = true)
for (j = 0; j < k; j++)
if (clasesTrain[neighbours[j]] != posID && safe[neighbours[j]] == true)
amplify[i]++;
}
else
{
neighbours = new int[k + 2];
claseObt = KNN.evaluacionKNN2 (k + 2, datosTrain, realTrain, nominalTrain, nulosTrain, clasesTrain, datosTrain[i], realTrain[i], nominalTrain[i], nulosTrain[i], nClases, distanceEu, neighbours);
// amplify as many as neighbors of the majority flagged as safe (safe = true)
for (j = 0; j < k + 2; j++)
if (clasesTrain[neighbours[j]] != posID && safe[neighbours[j]] == true)
amplify[i]++;
}
}
}
}
nSel = 0;
for (i = 0; i < datosTrain.length; i++) {
if ((clasesTrain[i] == posID) || (clasesTrain[i] == negID && safe[i] == true))
nSel += amplify[i];
}
/*Building of the S set from the flags*/
conjS = new double[nSel][datosTrain[0].length];
conjR = new double[nSel][datosTrain[0].length];
conjN = new int[nSel][datosTrain[0].length];
conjM = new boolean[nSel][datosTrain[0].length];
clasesS = new int[nSel];
for (i=0, l=0; i<datosTrain.length; i++) {
if ((clasesTrain[i] == posID) || (clasesTrain[i] == negID && safe[i] == true)) { //the instance will be copied to the solution
for (t = 0; t < amplify[i]; t++)
{
for (j=0; j<datosTrain[0].length; j++) {
conjS[l][j] = datosTrain[i][j];
conjR[l][j] = realTrain[i][j];
conjN[l][j] = nominalTrain[i][j];
conjM[l][j] = nulosTrain[i][j];
}
clasesS[l] = clasesTrain[i];
l++;
}
}
}
System.out.println("SPIDER "+ relation + " " + (double)(System.currentTimeMillis()-tiempo)/1000.0 + "s");
OutputIS.escribeSalida(ficheroSalida[0], conjR, conjN, conjM, clasesS, 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 number of neighbors*/
linea = lineasFichero.nextToken();
tokens = new StringTokenizer (linea, "=");
tokens.nextToken();
k = Integer.parseInt(tokens.nextToken().substring(1));
/*Getting the type of distance function*/
linea = lineasFichero.nextToken();
tokens = new StringTokenizer (linea, "=");
tokens.nextToken();
distanceEu = tokens.nextToken().substring(1).equalsIgnoreCase("Euclidean")?true:false;
/*Getting the preprocess type WEAK / RELABEL / STRONG*/
linea = lineasFichero.nextToken();
tokens = new StringTokenizer (linea, "=");
tokens.nextToken();
type = 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
}