org.openml.webapplication.fantail.dc.landmarking.RandomTreeBasedLandmarker2.java Source code

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Here is the source code for org.openml.webapplication.fantail.dc.landmarking.RandomTreeBasedLandmarker2.java

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/*
 *  Webapplication - Java library that runs on OpenML servers
 *  Copyright (C) 2014 
 *  @author Jan N. van Rijn (j.n.van.rijn@liacs.leidenuniv.nl)
 *  @author Quan Sun (quan.sun.nz@gmail.com)
 *  
 *  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 org.openml.webapplication.fantail.dc.landmarking;

import java.util.HashMap;
import java.util.Map;
import java.util.Random;

import org.openml.webapplication.fantail.dc.Characterizer;
import org.openml.webapplication.fantail.dc.Randomizable;

import weka.core.Instances;

public class RandomTreeBasedLandmarker2 extends Characterizer implements Randomizable {

    private int m_Seed = 1;
    private int m_NumFolds = 2;
    private int m_K = 1;

    protected final String[] ids = new String[] { "RandomTreeDepth1ErrRate_K_" + m_K,
            "RandomTreeDepth1Kappa_K_" + m_K, "RandomTreeDepth2ErrRate_K_" + m_K, "RandomTreeDepth2Kappa_K_" + m_K,
            "RandomTreeDepth3ErrRate_K_" + m_K, "RandomTreeDepth3Kappa_K_" + m_K };

    public String[] getIDs() {
        return ids;
    }

    @Override
    public void setSeed(int seed) {
        m_Seed = seed;
    }

    public void setK(int k) {
        m_K = k;
    }

    public void setNumFolds(int n) {
        m_NumFolds = n;
    }

    public Map<String, Double> characterize(Instances data) {

        int seed = m_Seed;
        Random r = new Random(seed);

        int numFolds = m_NumFolds;

        double score1 = 0.5;
        double score2 = 0.5;
        // double score3 = 0.5;

        double score3 = 0.5;
        double score4 = 0.5;
        // double score3 = 0.5;

        double score5 = 0.5;
        double score6 = 0.5;

        weka.classifiers.trees.RandomTree cls = new weka.classifiers.trees.RandomTree();
        cls.setSeed(r.nextInt());
        cls.setKValue(m_K);
        // cls.setMaxDepth(1);

        try {
            // ds.buildClassifier(data);
            weka.classifiers.Evaluation eval = new weka.classifiers.Evaluation(data);
            eval.crossValidateModel(cls, data, numFolds, new java.util.Random(1));

            score1 = eval.pctIncorrect();
            score2 = eval.kappa();

        } catch (Exception e) {
            e.printStackTrace();
        }

        //
        cls = new weka.classifiers.trees.RandomTree();
        cls.setSeed(r.nextInt());
        cls.setKValue(m_K);
        // cls.setMaxDepth(2);

        try {

            weka.classifiers.Evaluation eval = new weka.classifiers.Evaluation(data);
            eval.crossValidateModel(cls, data, numFolds, new java.util.Random(1));

            score3 = eval.pctIncorrect();
            score4 = eval.kappa();

        } catch (Exception e) {
            e.printStackTrace();
        }

        //
        cls = new weka.classifiers.trees.RandomTree();
        cls.setSeed(r.nextInt());
        cls.setKValue(m_K);
        // cls.setMaxDepth(3);

        try {

            weka.classifiers.Evaluation eval = new weka.classifiers.Evaluation(data);
            eval.crossValidateModel(cls, data, numFolds, new java.util.Random(1));

            score5 = eval.pctIncorrect();
            score6 = eval.kappa();

        } catch (Exception e) {
            e.printStackTrace();
        }

        //
        cls = new weka.classifiers.trees.RandomTree();
        cls.setSeed(r.nextInt());
        cls.setKValue(m_K);
        // cls.setMaxDepth(4);

        try {

            weka.classifiers.Evaluation eval = new weka.classifiers.Evaluation(data);
            eval.crossValidateModel(cls, data, numFolds, new java.util.Random(1));

        } catch (Exception e) {
            e.printStackTrace();
        }

        //
        cls = new weka.classifiers.trees.RandomTree();
        cls.setSeed(r.nextInt());
        cls.setKValue(m_K);
        // cls.setMaxDepth(5);

        try {
            weka.classifiers.Evaluation eval = new weka.classifiers.Evaluation(data);
            eval.crossValidateModel(cls, data, numFolds, new java.util.Random(1));

        } catch (Exception e) {
            e.printStackTrace();
        }

        Map<String, Double> qualities = new HashMap<String, Double>();
        qualities.put(ids[0], score1);
        qualities.put(ids[1], score2);
        qualities.put(ids[2], score3);
        qualities.put(ids[3], score4);
        qualities.put(ids[4], score5);
        qualities.put(ids[5], score6);
        return qualities;
    }
}