| package org.apache.samoa.evaluation; |
| |
| import java.util.Arrays; |
| 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; |
| |
| /** |
| * Classification evaluator that performs basic incremental evaluation. |
| * |
| * @author Richard Kirkby (rkirkby@cs.waikato.ac.nz) |
| * @author Albert Bifet (abifet at cs dot waikato dot ac dot nz) |
| * @version $Revision: 7 $ |
| */ |
| public class BasicClassificationPerformanceEvaluator extends AbstractMOAObject |
| implements ClassificationPerformanceEvaluator { |
| |
| private static final long serialVersionUID = 1L; |
| |
| // the number of decimal places placed for double values in prediction file |
| // the value of 10 is used since some votes can be relatively small |
| public static final int DECIMAL_PLACES = 10; |
| |
| // the vote value to be used when a classifier made no vote for the class at |
| // all |
| public static final int NO_VOTE_FOR_CLASS = 0; |
| |
| // recent vote objects i.e. predicted, true classes and votes for individual |
| // classes |
| protected Vote[] votes; |
| |
| protected double weightObserved; |
| |
| protected double weightCorrect; |
| |
| protected double[] columnKappa; |
| |
| protected double[] rowKappa; |
| |
| protected int numClasses; |
| |
| private double weightCorrectNoChangeClassifier; |
| |
| protected double[] classVotes; |
| |
| private int lastSeenClass; |
| private String instanceIdentifier; |
| |
| private Instance lastSeenInstance; |
| |
| @Override |
| public void reset() { |
| reset(this.numClasses); |
| votes = null; |
| } |
| |
| public void reset(int numClasses) { |
| this.numClasses = numClasses; |
| this.rowKappa = new double[numClasses]; |
| this.columnKappa = new double[numClasses]; |
| for (int i = 0; i < this.numClasses; i++) { |
| this.rowKappa[i] = 0.0; |
| this.columnKappa[i] = 0.0; |
| } |
| this.weightObserved = 0.0; |
| this.weightCorrect = 0.0; |
| this.weightCorrectNoChangeClassifier = 0.0; |
| this.lastSeenClass = 0; |
| votes = null; |
| } |
| |
| @Override |
| public void addResult(Instance inst, double[] classVotes, String instanceIdentifier) { |
| double weight = inst.weight(); |
| int trueClass = (int) inst.classValue(); |
| if (weight > 0.0) { |
| if (this.weightObserved == 0) { |
| reset(inst.numClasses()); |
| } |
| this.weightObserved += weight; |
| int predictedClass = Utils.maxIndex(classVotes); |
| if (predictedClass == trueClass) { |
| this.weightCorrect += weight; |
| } |
| if (rowKappa.length > 0) { |
| this.rowKappa[predictedClass] += weight; |
| } |
| if (columnKappa.length > 0) { |
| this.columnKappa[trueClass] += weight; |
| } |
| } |
| if (this.lastSeenClass == trueClass) { |
| this.weightCorrectNoChangeClassifier += weight; |
| } |
| this.lastSeenClass = trueClass; |
| this.lastSeenInstance = inst; |
| this.instanceIdentifier = instanceIdentifier; |
| this.classVotes = classVotes; |
| } |
| |
| @Override |
| public Measurement[] getPerformanceMeasurements() { |
| return new Measurement[] { new Measurement("classified instances", getTotalWeightObserved()), |
| 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); |
| |
| // initialise votes first time they are supposed to be used |
| if (votes == null) { |
| this.votes = new Vote[classAttributeValues.size() + 3]; |
| votes[0] = new Vote("instance number"); |
| votes[1] = new Vote("true class value"); |
| votes[2] = new Vote("predicted class value"); |
| |
| // create as many objects as the number of classes |
| for (int i = 0; i < classAttributeValues.size(); i++) { |
| votes[3 + i] = new Vote("votes_" + classAttributeValues.get(i)); |
| } |
| } |
| |
| // use/(re-use existing) vote objects |
| votes[0].setValue(this.instanceIdentifier); |
| votes[1].setValue(trueNominalValue); |
| votes[2].setValue(classAttributeValues.get(Utils.maxIndex(classVotes))); |
| for (int i = 0; i < classAttributeValues.size(); i++) { |
| if (i < classVotes.length) { |
| votes[3 + i].setValue(classVotes[i], this.DECIMAL_PLACES); |
| } else { |
| votes[3 + i].setValue(this.NO_VOTE_FOR_CLASS, 0); |
| } |
| } |
| |
| return votes; |
| |
| } |
| |
| public double getTotalWeightObserved() { |
| return this.weightObserved; |
| } |
| |
| public double getFractionCorrectlyClassified() { |
| return this.weightObserved > 0.0 ? this.weightCorrect / this.weightObserved : 0.0; |
| } |
| |
| public double getFractionIncorrectlyClassified() { |
| return 1.0 - getFractionCorrectlyClassified(); |
| } |
| |
| public double getKappaStatistic() { |
| if (this.weightObserved > 0.0) { |
| double p0 = getFractionCorrectlyClassified(); |
| double pc = 0.0; |
| for (int i = 0; i < this.numClasses; i++) { |
| pc += (this.rowKappa[i] / this.weightObserved) * (this.columnKappa[i] / this.weightObserved); |
| } |
| return (p0 - pc) / (1.0 - pc); |
| } else { |
| return 0; |
| } |
| } |
| |
| public double getKappaTemporalStatistic() { |
| if (this.weightObserved > 0.0) { |
| double p0 = this.weightCorrect / this.weightObserved; |
| double pc = this.weightCorrectNoChangeClassifier / this.weightObserved; |
| |
| return (p0 - pc) / (1.0 - pc); |
| } else { |
| return 0; |
| } |
| } |
| |
| @Override |
| public void getDescription(StringBuilder sb, int indent) { |
| Measurement.getMeasurementsDescription(getPerformanceMeasurements(), sb, indent); |
| } |
| } |