Java tutorial
/* * EvaluatePrequential.java * Copyright (C) 2007 University of Waikato, Hamilton, New Zealand * @author Richard Kirkby (rkirkby@cs.waikato.ac.nz) * @author Albert Bifet (abifet at cs dot waikato dot ac dot 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 2 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, write to the Free Software * Foundation, Inc., 675 Mass Ave, Cambridge, MA 02139, USA. */ package tr.gov.ulakbim.jDenetX.tasks; import tr.gov.ulakbim.jDenetX.classifiers.Classifier; import tr.gov.ulakbim.jDenetX.core.Measurement; import tr.gov.ulakbim.jDenetX.core.ObjectRepository; import tr.gov.ulakbim.jDenetX.core.TimingUtils; import tr.gov.ulakbim.jDenetX.evaluation.*; import tr.gov.ulakbim.jDenetX.options.ClassOption; import tr.gov.ulakbim.jDenetX.options.FileOption; import tr.gov.ulakbim.jDenetX.options.FloatOption; import tr.gov.ulakbim.jDenetX.options.IntOption; import tr.gov.ulakbim.jDenetX.streams.InstanceStream; import weka.core.Instance; import weka.core.Utils; import java.io.File; import java.io.FileOutputStream; import java.io.PrintStream; public class EvaluatePrequential extends MainTask { @Override public String getPurposeString() { return "Evaluates a classifier on a stream by testing then training with each example in sequence."; } private static final long serialVersionUID = 1L; public ClassOption learnerOption = new ClassOption("learner", 'l', "Classifier to train.", Classifier.class, "NaiveBayes"); public ClassOption streamOption = new ClassOption("stream", 's', "Stream to learn from.", InstanceStream.class, "generators.RandomTreeGenerator"); public ClassOption evaluatorOption = new ClassOption("evaluator", 'e', "Classification performance evaluation method.", ClassificationPerformanceEvaluator.class, "WindowClassificationPerformanceEvaluator"); public IntOption instanceLimitOption = new IntOption("instanceLimit", 'i', "Maximum number of instances to test/train on (-1 = no limit).", 100000000, -1, Integer.MAX_VALUE); public IntOption timeLimitOption = new IntOption("timeLimit", 't', "Maximum number of seconds to test/train for (-1 = no limit).", -1, -1, Integer.MAX_VALUE); public IntOption sampleFrequencyOption = new IntOption("sampleFrequency", 'f', "How many instances between samples of the learning performance.", 100000, 0, Integer.MAX_VALUE); public IntOption maxMemoryOption = new IntOption("maxMemory", 'b', "Maximum size of model (in bytes). -1 = no limit.", -1, -1, Integer.MAX_VALUE); public IntOption memCheckFrequencyOption = new IntOption("memCheckFrequency", 'q', "How many instances between memory bound checks.", 100000, 0, Integer.MAX_VALUE); public FileOption dumpFileOption = new FileOption("dumpFile", 'd', "File to append intermediate csv results to.", null, "csv", true); public FileOption outputPredictionFileOption = new FileOption("outputPredictionFile", 'o', "File to append output predictions to.", null, "pred", true); //New for prequential method public IntOption widthOption = new IntOption("width", 'w', "Size of Window", 1000); public FloatOption alphaOption = new FloatOption("alpha", 'a', "Fading factor or exponential smoothing factor", .01); //End New for prequential methods public Class<?> getTaskResultType() { return LearningCurve.class; } @Override protected Object doMainTask(TaskMonitor monitor, ObjectRepository repository) { Classifier learner = (Classifier) getPreparedClassOption(this.learnerOption); InstanceStream stream = (InstanceStream) getPreparedClassOption(this.streamOption); ClassificationPerformanceEvaluator evaluator = (ClassificationPerformanceEvaluator) getPreparedClassOption( this.evaluatorOption); //New for prequential methods if (evaluator instanceof WindowClassificationPerformanceEvaluator) { ((WindowClassificationPerformanceEvaluator) evaluator).setWindowWidth(widthOption.getValue()); } if (evaluator instanceof EWMAClassificationPerformanceEvaluator) { ((EWMAClassificationPerformanceEvaluator) evaluator).setalpha(alphaOption.getValue()); } if (evaluator instanceof FadingFactorClassificationPerformanceEvaluator) { ((FadingFactorClassificationPerformanceEvaluator) evaluator).setalpha(alphaOption.getValue()); } //End New for prequential methods learner.setModelContext(stream.getHeader()); int maxInstances = this.instanceLimitOption.getValue(); long instancesProcessed = 0; int maxSeconds = this.timeLimitOption.getValue(); int secondsElapsed = 0; monitor.setCurrentActivity("Evaluating learner...", -1.0); LearningCurve learningCurve = new LearningCurve("learning evaluation instances"); File dumpFile = this.dumpFileOption.getFile(); PrintStream immediateResultStream = null; if (dumpFile != null) { try { if (dumpFile.exists()) { immediateResultStream = new PrintStream(new FileOutputStream(dumpFile, true), true); } else { immediateResultStream = new PrintStream(new FileOutputStream(dumpFile), true); } } catch (Exception ex) { throw new RuntimeException("Unable to open immediate result file: " + dumpFile, ex); } } //File for output predictions File outputPredictionFile = this.outputPredictionFileOption.getFile(); PrintStream outputPredictionResultStream = null; if (outputPredictionFile != null) { try { if (outputPredictionFile.exists()) { outputPredictionResultStream = new PrintStream(new FileOutputStream(outputPredictionFile, true), true); } else { outputPredictionResultStream = new PrintStream(new FileOutputStream(outputPredictionFile), true); } } catch (Exception ex) { throw new RuntimeException("Unable to open prediction result file: " + outputPredictionFile, ex); } } boolean firstDump = true; boolean preciseCPUTiming = TimingUtils.enablePreciseTiming(); long evaluateStartTime = TimingUtils.getNanoCPUTimeOfCurrentThread(); long lastEvaluateStartTime = evaluateStartTime; double RAMHours = 0.0; while (stream.hasMoreInstances() && ((maxInstances < 0) || (instancesProcessed < maxInstances)) && ((maxSeconds < 0) || (secondsElapsed < maxSeconds))) { Instance trainInst = stream.nextInstance(); Instance testInst = (Instance) trainInst.copy(); int trueClass = (int) trainInst.classValue(); testInst.setClassMissing(); double[] prediction = learner.getVotesForInstance(testInst); // Output prediction if (outputPredictionFile != null) { outputPredictionResultStream.println(Utils.maxIndex(prediction) + "," + trueClass); } evaluator.addClassificationAttempt(trueClass, prediction, testInst.weight()); learner.trainOnInstance(trainInst); instancesProcessed++; if (instancesProcessed % this.sampleFrequencyOption.getValue() == 0) { long evaluateTime = TimingUtils.getNanoCPUTimeOfCurrentThread(); double time = TimingUtils.nanoTimeToSeconds(evaluateTime - evaluateStartTime); double timeIncrement = TimingUtils.nanoTimeToSeconds(evaluateTime - lastEvaluateStartTime); double RAMHoursIncrement = learner.measureByteSize() / (1024.0 * 1024.0 * 1024.0); //GBs RAMHoursIncrement *= (timeIncrement / 3600.0); //Hours RAMHours += RAMHoursIncrement; lastEvaluateStartTime = evaluateTime; learningCurve.insertEntry(new LearningEvaluation( new Measurement[] { new Measurement("learning evaluation instances", instancesProcessed), new Measurement("evaluation time (" + (preciseCPUTiming ? "cpu " : "") + "seconds)", time), new Measurement("model cost (RAM-Hours)", RAMHours) }, evaluator, learner)); if (immediateResultStream != null) { if (firstDump) { immediateResultStream.println(learningCurve.headerToString()); firstDump = false; } immediateResultStream.println(learningCurve.entryToString(learningCurve.numEntries() - 1)); immediateResultStream.flush(); } } if (instancesProcessed % INSTANCES_BETWEEN_MONITOR_UPDATES == 0) { if (monitor.taskShouldAbort()) { return null; } long estimatedRemainingInstances = stream.estimatedRemainingInstances(); if (maxInstances > 0) { long maxRemaining = maxInstances - instancesProcessed; if ((estimatedRemainingInstances < 0) || (maxRemaining < estimatedRemainingInstances)) { estimatedRemainingInstances = maxRemaining; } } monitor.setCurrentActivityFractionComplete(estimatedRemainingInstances < 0 ? -1.0 : (double) instancesProcessed / (double) (instancesProcessed + estimatedRemainingInstances)); if (monitor.resultPreviewRequested()) { monitor.setLatestResultPreview(learningCurve.copy()); } secondsElapsed = (int) TimingUtils .nanoTimeToSeconds(TimingUtils.getNanoCPUTimeOfCurrentThread() - evaluateStartTime); } } if (immediateResultStream != null) { immediateResultStream.close(); } if (outputPredictionResultStream != null) { outputPredictionResultStream.close(); } return learningCurve; } }