Java tutorial
/* * WindowClassificationPerformanceEvaluator.java * Copyright (C) 2009 University of Waikato, Hamilton, New Zealand * @author Albert Bifet (abifet@cs.waikato.ac.nz) * * 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 moa.evaluation; import moa.core.Measurement; import moa.core.ObjectRepository; import moa.options.AbstractOptionHandler; import moa.options.IntOption; import moa.tasks.TaskMonitor; import weka.core.Utils; import weka.core.Instance; /** * Classification evaluator that updates evaluation results using a sliding * window. * * @author Albert Bifet (abifet at cs dot waikato dot ac dot nz) * @version $Revision: 7 $ */ public class WindowClassificationPerformanceEvaluator extends AbstractOptionHandler implements ClassificationPerformanceEvaluator { private static final long serialVersionUID = 1L; public IntOption widthOption = new IntOption("width", 'w', "Size of Window", 1000); protected double TotalweightObserved = 0; protected Estimator weightObserved; protected Estimator weightCorrect; protected Estimator weightCorrectNoChangeClassifier; protected double lastSeenClass; protected Estimator[] columnKappa; protected Estimator[] rowKappa; protected Estimator[] classAccuracy; protected int numClasses; public class Estimator { protected double[] window; protected int posWindow; protected int lenWindow; protected int SizeWindow; protected double sum; public Estimator(int sizeWindow) { window = new double[sizeWindow]; SizeWindow = sizeWindow; posWindow = 0; lenWindow = 0; } public void add(double value) { sum -= window[posWindow]; sum += value; window[posWindow] = value; posWindow++; if (posWindow == SizeWindow) { posWindow = 0; } if (lenWindow < SizeWindow) { lenWindow++; } } public double total() { return sum; } public double length() { return lenWindow; } } /* public void setWindowWidth(int w) { this.width = w; reset(); }*/ @Override public void reset() { reset(this.numClasses); } public void reset(int numClasses) { this.numClasses = numClasses; this.rowKappa = new Estimator[numClasses]; this.columnKappa = new Estimator[numClasses]; this.classAccuracy = new Estimator[numClasses]; for (int i = 0; i < this.numClasses; i++) { this.rowKappa[i] = new Estimator(this.widthOption.getValue()); this.columnKappa[i] = new Estimator(this.widthOption.getValue()); this.classAccuracy[i] = new Estimator(this.widthOption.getValue()); } this.weightCorrect = new Estimator(this.widthOption.getValue()); this.weightCorrectNoChangeClassifier = new Estimator(this.widthOption.getValue()); this.weightObserved = new Estimator(this.widthOption.getValue()); this.TotalweightObserved = 0; this.lastSeenClass = 0; } @Override public void addResult(Instance inst, double[] classVotes) { double weight = inst.weight(); int trueClass = (int) inst.classValue(); if (weight > 0.0) { if (TotalweightObserved == 0) { reset(inst.dataset().numClasses()); } this.TotalweightObserved += weight; this.weightObserved.add(weight); int predictedClass = Utils.maxIndex(classVotes); if (predictedClass == trueClass) { this.weightCorrect.add(weight); } else { this.weightCorrect.add(0); } //Add Kappa statistic information for (int i = 0; i < this.numClasses; i++) { this.rowKappa[i].add(i == predictedClass ? weight : 0); this.columnKappa[i].add(i == trueClass ? weight : 0); } if (this.lastSeenClass == trueClass) { this.weightCorrectNoChangeClassifier.add(weight); } else { this.weightCorrectNoChangeClassifier.add(0); } this.classAccuracy[trueClass].add(predictedClass == trueClass ? weight : 0.0); this.lastSeenClass = trueClass; } } @Override public Measurement[] getPerformanceMeasurements() { return new Measurement[] { new Measurement("classified instances", this.TotalweightObserved), new Measurement("classifications correct (percent)", getFractionCorrectlyClassified() * 100.0), new Measurement("Kappa Statistic (percent)", getKappaStatistic() * 100.0), new Measurement("Kappa Temporal Statistic (percent)", getKappaTemporalStatistic() * 100.0) }; } public double getTotalWeightObserved() { return this.weightObserved.total(); } public double getFractionCorrectlyClassified() { return this.weightObserved.total() > 0.0 ? (double) this.weightCorrect.total() / this.weightObserved.total() : 0.0; } public double getKappaStatistic() { if (this.weightObserved.total() > 0.0) { double p0 = this.weightCorrect.total() / this.weightObserved.total(); double pc = 0; for (int i = 0; i < this.numClasses; i++) { pc += (this.rowKappa[i].total() / this.weightObserved.total()) * (this.columnKappa[i].total() / this.weightObserved.total()); } return (p0 - pc) / (1 - pc); } else { return 0; } } public double getKappaTemporalStatistic() { if (this.weightObserved.total() > 0.0) { double p0 = this.weightCorrect.total() / this.weightObserved.total(); double pc = this.weightCorrectNoChangeClassifier.total() / this.weightObserved.total(); return (p0 - pc) / (1 - pc); } else { return 0; } } public double getFractionIncorrectlyClassified() { return 1.0 - getFractionCorrectlyClassified(); } @Override public void getDescription(StringBuilder sb, int indent) { Measurement.getMeasurementsDescription(getPerformanceMeasurements(), sb, indent); } @Override public void prepareForUseImpl(TaskMonitor monitor, ObjectRepository repository) { } }