es.jarias.FMC.RunFMC.java Source code

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Here is the source code for es.jarias.FMC.RunFMC.java

Source

/**
    
Copyright 2015 Jacinto Arias  - www.jarias.es
    
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/>.
    
    
    
FMC multilabel classifier. This source code is an experimental
implementation of the method presented in:
    
J. Arias, J.A. Gamez, T.D. Nielsen and J.M. Puerta 
    
A scalable pairwise class interaction framework for multidimensional classification.
    
**/

package es.jarias.FMC;

import java.util.Random;

import mulan.data.InvalidDataFormatException;
import mulan.data.MultiLabelInstances;
import weka.core.Instances;
import weka.core.Utils;

/**
 * 
 * @author jarias
 *
 */
public class RunFMC {

    public static void main(String[] args) {

        try {

            String classifierName = Utils.getOption("classifier", args);

            String discType = Utils.getOption("disc", args);
            String fss = Utils.getOption("fss", args);
            String prune = Utils.getOption("prune", args);

            String arffFilename = Utils.getOption("arff", args);
            String xmlFilename = Utils.getOption("xml", args);
            String outPath = Utils.getOption("outpath", args);

            String cvString = Utils.getOption("cv", args);
            String seedString = Utils.getOption("seed", args);

            // Check parameters:
            if (arffFilename.equals("") || arffFilename.equals("") || arffFilename.equals(""))
                throw new Exception("Please provide valid input and output files.");

            // Set Defaults:
            if (classifierName.equals(""))
                classifierName = "bayes.NaiveBayes";

            if (discType.equals(""))
                discType = "supervised";

            if (fss.equals(""))
                fss = "CFS";

            if (prune.equals(""))
                prune = "full";

            int cv_folds = 10;
            if (!cvString.equals(""))
                cv_folds = Integer.parseInt(cvString);

            int seed = 1990;
            if (!seedString.equals(""))
                seed = Integer.parseInt(seedString);

            MultiLabelInstances original = null;
            try {
                original = new MultiLabelInstances(arffFilename, xmlFilename);
            } catch (InvalidDataFormatException e) {
                System.out.println("Please provide valid multilabel arff+xml mulan files");
                System.exit(-1);
            }

            MultiLabelInstances dataset = original.clone();
            Instances aux = dataset.getDataSet();
            aux.randomize(new Random(seed));

            dataset = dataset.reintegrateModifiedDataSet(aux);

            System.out.println("--------------------------------------------");
            System.out.println("FMC multi-label classifier experiment");
            System.out.println("-Pruning strategy: " + prune);
            System.out.println("-Base Classifier: " + classifierName);
            System.out.println("-Discretization: " + discType);
            System.out.println("-Feature Selection: " + fss);
            System.out.println("-Folds: " + cv_folds);
            System.out.println("-Seed: " + seed);

            // Perform CV or Holdout
            if (cv_folds != 0) {
                for (int fold = 0; fold < cv_folds; fold++) {
                    MultiLabelInstances trainData = original
                            .reintegrateModifiedDataSet(dataset.getDataSet().trainCV(cv_folds, fold));
                    MultiLabelInstances testData = original
                            .reintegrateModifiedDataSet(dataset.getDataSet().testCV(cv_folds, fold));

                    FMC.buildModel(trainData, testData, fold, classifierName, discType, fss, outPath, prune);
                }
            } else {
                double HOLDOUT_PERCENTAGE = 0.6;

                int trainSize = (int) Math.floor(dataset.getNumInstances() * HOLDOUT_PERCENTAGE);
                int testSize = dataset.getNumInstances() - trainSize;

                MultiLabelInstances trainData = dataset
                        .reintegrateModifiedDataSet(new Instances(dataset.getDataSet(), 0, trainSize));
                MultiLabelInstances testData = dataset
                        .reintegrateModifiedDataSet(new Instances(dataset.getDataSet(), trainSize, testSize));

                FMC.buildModel(trainData, testData, 0, classifierName, discType, fss, outPath, prune);
            }
        } catch (Exception e) {
            // TODO Auto-generated catch block
            e.printStackTrace();
        }
    }

}