Java tutorial
// Copyright 2015 Georg-August-Universitt Gttingen, Germany // // 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. package de.ugoe.cs.cpdp.execution; import java.io.File; import java.util.LinkedList; import java.util.List; import java.util.logging.Level; import weka.core.Instances; import de.ugoe.cs.cpdp.ExperimentConfiguration; import de.ugoe.cs.cpdp.dataprocessing.IProcessesingStrategy; import de.ugoe.cs.cpdp.dataselection.IPointWiseDataselectionStrategy; import de.ugoe.cs.cpdp.eval.IEvaluationStrategy; import de.ugoe.cs.cpdp.loader.IVersionLoader; import de.ugoe.cs.cpdp.training.ITrainer; import de.ugoe.cs.cpdp.training.ITrainingStrategy; import de.ugoe.cs.cpdp.training.IWekaCompatibleTrainer; import de.ugoe.cs.cpdp.versions.SoftwareVersion; import de.ugoe.cs.util.console.Console; /** * Class responsible for executing an experiment according to an {@link ExperimentConfiguration}. * The steps of this ClassifierCreationExperiment are as follows: * <ul> * <li>load the data from the provided data path</li> * <li>check if given resultsdir exists, if not create one</li> * <li>execute the following steps for each data set: * <ul> * <li>load the dataset</li> * <li>set testdata == traindata</li> * <li>preprocess the data</li> * <li>postprocess the data</li> * <li>for each configured trainer do the following:</li> * <ul> * <li>if the classifier should be saved, train it with the dataset</li> * <li>save it in the results dir</li> * <li>For each configured evaluator: Do the evaluation and save results</li> * </ul> * </ul> * </ul> * * Note that this class implements {@link IExectuionStrategy}, i.e., each experiment can be started * in its own thread. * * @author Fabian Trautsch */ public class ClassifierCreationExperiment implements IExecutionStrategy { /** * configuration of the experiment */ private final ExperimentConfiguration config; /** * Constructor. Creates a new experiment based on a configuration. * * @param config * configuration of the experiment */ public ClassifierCreationExperiment(ExperimentConfiguration config) { this.config = config; } /** * Executes the experiment with the steps as described in the class comment. * * @see Runnable#run() */ @Override public void run() { final List<SoftwareVersion> versions = new LinkedList<>(); boolean writeHeader = true; for (IVersionLoader loader : config.getLoaders()) { versions.addAll(loader.load()); } File resultsDir = new File(config.getResultsPath()); if (!resultsDir.exists()) { resultsDir.mkdir(); } int versionCount = 1; for (SoftwareVersion testVersion : versions) { // At first: traindata == testdata Instances testdata = testVersion.getInstances(); Instances traindata = new Instances(testdata); List<Double> efforts = testVersion.getEfforts(); // Give the dataset a new name testdata.setRelationName(testVersion.getProject()); for (IProcessesingStrategy processor : config.getPreProcessors()) { Console.traceln(Level.FINE, String.format("[%s] [%02d/%02d] %s: applying preprocessor %s", config.getExperimentName(), versionCount, versions.size(), testVersion.getProject(), processor.getClass().getName())); processor.apply(testdata, traindata); } for (IPointWiseDataselectionStrategy dataselector : config.getPointWiseSelectors()) { Console.traceln(Level.FINE, String.format("[%s] [%02d/%02d] %s: applying pointwise selection %s", config.getExperimentName(), versionCount, versions.size(), testVersion.getProject(), dataselector.getClass().getName())); traindata = dataselector.apply(testdata, traindata); } for (IProcessesingStrategy processor : config.getPostProcessors()) { Console.traceln(Level.FINE, String.format("[%s] [%02d/%02d] %s: applying setwise postprocessor %s", config.getExperimentName(), versionCount, versions.size(), testVersion.getProject(), processor.getClass().getName())); processor.apply(testdata, traindata); } // Trainerlist for evaluation later on List<ITrainer> allTrainers = new LinkedList<>(); for (ITrainingStrategy trainer : config.getTrainers()) { // Add trainer to list for evaluation allTrainers.add(trainer); // Train classifier trainer.apply(traindata); if (config.getSaveClassifier()) { // If classifier should be saved, train him and save him // be careful with typecasting here! IWekaCompatibleTrainer trainerToSave = (IWekaCompatibleTrainer) trainer; // Console.println(trainerToSave.getClassifier().toString()); try { weka.core.SerializationHelper.write(resultsDir.getAbsolutePath() + "/" + trainer.getName() + "-" + testVersion.getProject(), trainerToSave.getClassifier()); } catch (Exception e) { e.printStackTrace(); } } } for (IEvaluationStrategy evaluator : config.getEvaluators()) { Console.traceln(Level.FINE, String.format("[%s] [%02d/%02d] %s: applying evaluator %s", config.getExperimentName(), versionCount, versions.size(), testVersion.getProject(), evaluator.getClass().getName())); if (writeHeader) { evaluator.setParameter(config.getResultsPath() + "/" + config.getExperimentName() + ".csv"); } evaluator.apply(testdata, traindata, allTrainers, efforts, writeHeader, config.getResultStorages()); writeHeader = false; } versionCount++; Console.traceln(Level.INFO, String.format("[%s] [%02d/%02d] %s: finished", config.getExperimentName(), versionCount, versions.size(), testVersion.getProject())); } } }