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197
src/main/java/com/testpbc4/App.java
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197
src/main/java/com/testpbc4/App.java
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package com.testpbc4;
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import weka.core.*;
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import java.util.Random;
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import java.io.File;
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import java.io.FileWriter;
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import java.io.PrintWriter;
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import java.util.Scanner;
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import weka.classifiers.trees.PBC4cip;
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import java.util.ArrayList;
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import java.util.Iterator;
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import weka.core.converters.ConverterUtils.DataSource;
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public class App {
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static int numFolds = 5;
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static String path = "data/";
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static String datasetsFile = path + "datasets.txt";
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static String outputFile = "results_pbc4.txt";
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static boolean debug = true;
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static boolean stratify = false;
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static boolean normalizeData = false;
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public static void printAndExit(String s) {
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System.out.println(s);
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System.exit(0);
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}
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private static Instances getInstances(String fileName) {
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DataSource source = null;
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System.out.println(String.format("Reading file " + path + "%s.arff", fileName));
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try {
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source = new DataSource(String.format(path + "%s.arff", fileName));
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System.out.println("File read.");
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return source.getDataSet();
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} catch (Exception e) {
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printAndExit(
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String.format("*** Error trying to read " + path + "%s.arff file... (%s)", fileName,
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e.getMessage()));
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}
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return null;
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}
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private static void validation(Instances instancesIn, int seed, ArrayList<Double> accuracy,
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ArrayList<Double> timeSpent, ArrayList<Double> complexity) throws Exception {
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PBC4cip pb4 = new PBC4cip();
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Random rand = new Random(seed);
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pb4.setSeed(seed);
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Instances instances;
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Instances training = null;
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Instances test = null;
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long startTime;
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// Normalize normalize = new Normalize();
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// normalize.setInputFormat(instancesIn);
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// if (normalizeData) {
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// instances = Filter.useFilter(instancesIn, normalize);
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// } else {
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instances = instancesIn;
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// }
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instances.randomize(rand);
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if (stratify) {
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instances.stratify(numFolds);
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}
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for (int i = 0; i < numFolds; i++) {
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startTime = System.currentTimeMillis();
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training = instances.trainCV(numFolds, i);
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test = instances.testCV(numFolds, i);
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pb4.buildClassifier(training);
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// let's classify
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int aciertos = 0;
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for (int j = 0; j < test.numInstances(); j++) {
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double v = pb4.classifyInstance(test.instance(j));
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double trueValue = test.instance(j).value(test.classIndex());
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if (trueValue == v)
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aciertos++;
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}
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double acc = aciertos / (double) test.numInstances();
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double tspent = (System.currentTimeMillis() - startTime) / 1000.0;
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if (debug) {
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System.err.println(String.format("%f, %f", acc, tspent));
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}
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accuracy.add(acc);
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timeSpent.add(tspent);
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complexity.add(0.0);
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}
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}
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private static double[] meanAndDeviation(ArrayList<Double> input) {
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double sum = 0.0;
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int i = 0;
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double[] results = new double[4]; // 0 -> mean, 1 -> std
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Iterator it = input.iterator();
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// Compute mean
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while (it.hasNext()) {
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i++;
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sum += (double) it.next();
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}
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results[0] = i != 0 ? sum / (double) i : 0;
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it = input.iterator();
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sum = 0.0;
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while (it.hasNext()) {
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sum += Math.pow((double) it.next() - results[0], 2);
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}
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results[1] = i != 0 ? Math.sqrt(sum / (double) i) : 0;
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return results;
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}
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private static void initializeOutput() {
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try {
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File f = new File(outputFile);
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f.delete();
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} catch (Exception e) {
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printAndExit(
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String.format("*** Error trying to delete the output file %s... (%s)", outputFile, e.getMessage()));
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}
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}
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private static void store(String dataset, double[] accuracy, double[] timeSpent, double[] complexity)
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throws Exception {
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// Append output
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FileWriter file = new FileWriter(outputFile, true);
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PrintWriter linePrint = new PrintWriter(file);
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linePrint.printf("%s; %f; %f; %f; %f; %f; %f%n", dataset, accuracy[0], accuracy[1], timeSpent[0], timeSpent[1],
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complexity[0], complexity[1]);
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linePrint.close();
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}
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public static void main(String[] args) throws Exception {
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Instances instances = null;
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Scanner sc;
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File file = null;
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Random gen;
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double[] accuracyStat = new double[2];
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double[] timeStat = new double[2];
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double[] complexityStat = new double[2];
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int[] seeds = { 57, 31, 1714, 17, 23, 79, 83, 97, 7, 1 };
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String dataset;
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String debugMsg, stratMsg, normMsg;
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ArrayList<Double> timeSpent;
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ArrayList<Double> accuracy;
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ArrayList<Double> complexity;
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debugMsg = debug ? "With debug output" : "";
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stratMsg = stratify ? "stratified" : "without stratification";
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normMsg = normalizeData ? " with normalization" : " without normalization";
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try {
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file = new File(datasetsFile);
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} catch (Exception e) {
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printAndExit(String.format("*** Error trying to read datasets file... (%s)",
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e.getMessage()));
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}
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System.out.println(String.format("%d fold cross validation %s %s %s",
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numFolds, stratMsg, debugMsg, normMsg));
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initializeOutput();
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sc = new Scanner(file);
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while (sc.hasNextLine()) {
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dataset = sc.nextLine();
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if (debug && dataset.equals("balloons")) {
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printAndExit("* Check error output. Debug End.");
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}
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timeSpent = new ArrayList<>();
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accuracy = new ArrayList<>();
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complexity = new ArrayList<>();
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for (int seed : seeds) {
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// Establece la semilla
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gen = new Random(seed);
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// Obtiene los datos
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instances = getInstances(dataset);
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instances.randomize(gen);
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instances.setClassIndex(instances.numAttributes() - 1);
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System.out.println("Instances:");
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System.out.println(instances.toString());
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try {
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validation(instances, seed, accuracy, timeSpent, complexity);
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} catch (Exception e) {
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printAndExit(String.format("*** Error training dataset %s... (%s)", dataset,
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e.getMessage()));
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}
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}
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accuracyStat = meanAndDeviation(accuracy);
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timeStat = meanAndDeviation(timeSpent);
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complexityStat = meanAndDeviation(complexity);
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try {
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store(dataset, accuracyStat, timeStat, complexityStat);
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} catch (Exception e) {
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printAndExit(String.format("*** Error storing results of dataset %s... (%s)",
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dataset, e.getMessage()));
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}
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}
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sc.close();
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printAndExit("* End.");
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}
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}
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