| package org.apache.samoa.evaluation; |
| |
| import java.util.List; |
| |
| import org.apache.samoa.instances.Attribute; |
| |
| /* |
| * #%L |
| * SAMOA |
| * %% |
| * Copyright (C) 2014 - 2015 Apache Software Foundation |
| * %% |
| * Licensed under the Apache License, Version 2.0 (the "License"); |
| * you may not use this file except in compliance with the License. |
| * You may obtain a copy of the License at |
| * |
| * http://www.apache.org/licenses/LICENSE-2.0 |
| * |
| * Unless required by applicable law or agreed to in writing, software |
| * distributed under the License is distributed on an "AS IS" BASIS, |
| * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. |
| * See the License for the specific language governing permissions and |
| * limitations under the License. |
| * #L% |
| */ |
| |
| import org.apache.samoa.instances.Instance; |
| import org.apache.samoa.instances.Utils; |
| import org.apache.samoa.moa.AbstractMOAObject; |
| import org.apache.samoa.moa.core.Measurement; |
| import org.apache.samoa.moa.core.Vote; |
| |
| import com.github.javacliparser.IntOption; |
| |
| /** |
| * 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 AbstractMOAObject 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; |
| |
| private String instanceIdentifier; |
| private Instance lastSeenInstance; |
| protected double[] classVotes; |
| |
| 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, String instanceIndex) { |
| double weight = inst.weight(); |
| int trueClass = (int) inst.classValue(); |
| if (weight > 0.0) { |
| if (TotalweightObserved == 0) { |
| reset(inst.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) |
| }; |
| |
| } |
| |
| /** |
| * This method is used to retrieve predictions and votes (for classification only) |
| * |
| * @return String This returns an array of predictions and votes objects. |
| */ |
| @Override |
| public Vote[] getPredictionVotes() { |
| Attribute classAttribute = this.lastSeenInstance.dataset().classAttribute(); |
| double trueValue = this.lastSeenInstance.classValue(); |
| List<String> classAttributeValues = classAttribute.getAttributeValues(); |
| |
| int trueNominalIndex = (int) trueValue; |
| String trueNominalValue = classAttributeValues.get(trueNominalIndex); |
| |
| Vote[] votes = new Vote[classVotes.length + 3]; |
| votes[0] = new Vote("instance number", |
| this.instanceIdentifier); |
| votes[1] = new Vote("true class value", |
| trueNominalValue); |
| votes[2] = new Vote("predicted class value", |
| classAttributeValues.get(Utils.maxIndex(classVotes))); |
| |
| for (int i = 0; i < classAttributeValues.size(); i++) { |
| if (i < classVotes.length) { |
| votes[2 + i] = new Vote("votes_" + classAttributeValues.get(i), classVotes[i]); |
| } else { |
| votes[2 + i] = new Vote("votes_" + classAttributeValues.get(i), 0); |
| } |
| } |
| return votes; |
| } |
| |
| public double getTotalWeightObserved() { |
| return this.weightObserved.total(); |
| } |
| |
| public double getFractionCorrectlyClassified() { |
| return this.weightObserved.total() > 0.0 ? 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); |
| } |
| |
| } |