moa.tasks.EvaluateModelRegression.java Source code

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Here is the source code for moa.tasks.EvaluateModelRegression.java

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/*
 *    EvaluateModel.java
 *    Copyright (C) 2007 University of Waikato, Hamilton, New Zealand
 *    @author Richard Kirkby (rkirkby@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.tasks;

import java.io.File;
import java.io.FileOutputStream;
import java.io.PrintStream;
import moa.classifiers.Classifier;
import moa.classifiers.Regressor;
import moa.core.ObjectRepository;
import moa.evaluation.ClassificationPerformanceEvaluator;
import moa.evaluation.LearningEvaluation;
import moa.evaluation.RegressionPerformanceEvaluator;
import moa.options.ClassOption;
import moa.options.FileOption;
import moa.options.IntOption;
import moa.streams.InstanceStream;
import weka.core.Instance;
import weka.core.Utils;

/**
 * Task for evaluating a static model on a stream.
 *
 * @author Richard Kirkby (rkirkby@cs.waikato.ac.nz)
 * @version $Revision: 7 $
 */
public class EvaluateModelRegression extends RegressionMainTask {

    @Override
    public String getPurposeString() {
        return "Evaluates a static model on a stream.";
    }

    private static final long serialVersionUID = 1L;

    public ClassOption modelOption = new ClassOption("model", 'm', "Classifier to evaluate.", Regressor.class,
            "LearnModelRegression");

    public ClassOption streamOption = new ClassOption("stream", 's', "Stream to evaluate on.", InstanceStream.class,
            "generators.RandomTreeGenerator");

    public ClassOption evaluatorOption = new ClassOption("evaluator", 'e',
            "Classification performance evaluation method.", RegressionPerformanceEvaluator.class,
            "BasicRegressionPerformanceEvaluator");

    public IntOption maxInstancesOption = new IntOption("maxInstances", 'i', "Maximum number of instances to test.",
            1000000, 0, Integer.MAX_VALUE);

    public FileOption outputPredictionFileOption = new FileOption("outputPredictionFile", 'o',
            "File to append output predictions to.", null, "pred", true);

    public EvaluateModelRegression() {
    }

    public EvaluateModelRegression(Classifier model, InstanceStream stream,
            ClassificationPerformanceEvaluator evaluator, int maxInstances) {
        this.modelOption.setCurrentObject(model);
        this.streamOption.setCurrentObject(stream);
        this.evaluatorOption.setCurrentObject(evaluator);
        this.maxInstancesOption.setValue(maxInstances);
    }

    @Override
    public Class<?> getTaskResultType() {
        return LearningEvaluation.class;
    }

    @Override
    public Object doMainTask(TaskMonitor monitor, ObjectRepository repository) {
        Classifier model = (Classifier) getPreparedClassOption(this.modelOption);
        InstanceStream stream = (InstanceStream) getPreparedClassOption(this.streamOption);
        ClassificationPerformanceEvaluator evaluator = (ClassificationPerformanceEvaluator) getPreparedClassOption(
                this.evaluatorOption);
        int maxInstances = this.maxInstancesOption.getValue();
        long instancesProcessed = 0;
        monitor.setCurrentActivity("Evaluating model...", -1.0);

        //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);
            }
        }
        while (stream.hasMoreInstances() && ((maxInstances < 0) || (instancesProcessed < maxInstances))) {
            Instance testInst = (Instance) stream.nextInstance().copy();
            double trueClass = testInst.classValue();
            //testInst.setClassMissing();
            double[] prediction = model.getVotesForInstance(testInst);
            //evaluator.addClassificationAttempt(trueClass, prediction, testInst
            //      .weight());
            if (outputPredictionFile != null) {
                outputPredictionResultStream.println(prediction[0] + "," + trueClass);
            }
            evaluator.addResult(testInst, prediction);
            instancesProcessed++;
            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(new LearningEvaluation(evaluator, model));
                }
            }
        }
        if (outputPredictionResultStream != null) {
            outputPredictionResultStream.close();
        }
        return new LearningEvaluation(evaluator, model);
    }
}