source: trunk/CrossPare/src/de/ugoe/cs/cpdp/execution/ClassifierCreationExperiment.java @ 140

// Copyright 2015 Georg-August-Universität Göttingen, 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()));

        }

    }

}