/*
 *    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.
 */

/*
 *    TrainTestExperiment.java
 *    Copyright (C) 2009-2010 Aristotle University of Thessaloniki, Thessaloniki, Greece
 */
package mulan.examples;

import mulan.classifier.transformation.BinaryRelevance;
import mulan.data.MultiLabelInstances;
import mulan.evaluation.Evaluation;
import mulan.evaluation.Evaluator;
import weka.classifiers.Classifier;
import weka.classifiers.bayes.NaiveBayes;
import weka.core.Instances;
import weka.core.Utils;
import weka.filters.Filter;
import weka.filters.unsupervised.instance.RemovePercentage;

/**
 * Class demonstrating a simple train/test evaluation experiment
 *
 * @author Grigorios Tsoumakas
 * @version 2010.12.27
 */
public class TrainTestExperiment {

    public static void main(String[] args) {
        try {
            String path = Utils.getOption("path", args); // e.g. -path dataset/
            String filestem = Utils.getOption("filestem", args); // e.g. -filestem emotions
            String percentage = Utils.getOption("percentage", args); // e.g. -percentage 50 (for 50%)

            System.out.println("Loading the dataset");
            MultiLabelInstances mlDataSet = new MultiLabelInstances(path + filestem + ".arff", path + filestem + ".xml");

            // split the data set into train and test
            Instances dataSet = mlDataSet.getDataSet();
            RemovePercentage rmvp = new RemovePercentage();
            rmvp.setInvertSelection(true);
            rmvp.setPercentage(Double.parseDouble(percentage));
            rmvp.setInputFormat(dataSet);
            Instances trainDataSet = Filter.useFilter(dataSet, rmvp);

            rmvp = new RemovePercentage();
            rmvp.setPercentage(Double.parseDouble(percentage));
            rmvp.setInputFormat(dataSet);
            Instances testDataSet = Filter.useFilter(dataSet, rmvp);

            MultiLabelInstances train = new MultiLabelInstances(trainDataSet, path + filestem + ".xml");
            MultiLabelInstances test = new MultiLabelInstances(testDataSet, path + filestem + ".xml");

            Evaluator eval = new Evaluator();
            Evaluation results;

            Classifier brClassifier = new NaiveBayes();
            BinaryRelevance br = new BinaryRelevance(brClassifier);
            br.setDebug(true);
            br.build(train);
            results = eval.evaluate(br, test);
            System.out.println(results);
        } catch (Exception e) {
            e.printStackTrace();
        }
    }
}